From a98448283235395e07e113cc0865e653c4cdfc82 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Mon, 4 Nov 2024 11:01:28 +0000 Subject: [PATCH 01/12] add analysis scripts --- .../analysis/perf_analysis_template.ipynb | 433 ++ .../benchmark_velox/analysis/requirements.txt | 174 + .../workload/benchmark_velox/analysis/run.py | 11 + .../analysis/run_perf_analysis.sh | 120 + .../benchmark_velox/analysis/sparklog.ipynb | 5176 +++++++++++++++++ .../workload/benchmark_velox/initialize.ipynb | 99 +- 6 files changed, 6006 insertions(+), 7 deletions(-) create mode 100644 tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb create mode 100644 tools/workload/benchmark_velox/analysis/requirements.txt create mode 100644 tools/workload/benchmark_velox/analysis/run.py create mode 100755 tools/workload/benchmark_velox/analysis/run_perf_analysis.sh create mode 100644 tools/workload/benchmark_velox/analysis/sparklog.ipynb diff --git a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb new file mode 100644 index 000000000000..ca9c1de27500 --- /dev/null +++ b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "appid=''\n", + "disk=''\n", + "nic=''\n", + "tz=''\n", + "basedir=''\n", + "name=''\n", + "\n", + "compare_appid=''\n", + "compare_basedir=''\n", + "compare_name=''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# start analysis cluster and run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import findspark\n", + "findspark.init()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "def get_py4jzip():\n", + " spark_home=os.environ['SPARK_HOME']\n", + " py4jzip = !ls {spark_home}/python/lib/py4j*.zip\n", + " return py4jzip[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "print(sys.path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "from pyspark.sql import SQLContext\n", + "import time\n", + "import sys\n", + "conf = (SparkConf()\n", + " .set('spark.app.name', f'perf_analysis_{appid}')\n", + " .set('spark.serializer','org.apache.spark.serializer.KryoSerializer')\n", + " .set('spark.executor.instances', '4')\n", + " .set('spark.executor.cores','4')\n", + " .set('spark.executor.memory', '8g')\n", + " .set('spark.driver.memory','20g')\n", + " .set('spark.memory.offHeap.enabled','True')\n", + " .set('spark.memory.offHeap.size','20g')\n", + " .set('spark.executor.memoryOverhead','1g')\n", + " .set('spark.executor.extraJavaOptions',\n", + " '-XX:+UseParallelGC -XX:+UseParallelOldGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps')\n", + " .set('spark.executorEnv.PYTHONPATH',f\"{os.environ['SPARK_HOME']}/python:{get_py4jzip()}:{':'.join(sys.path)}\")\n", + " .set('spark.sql.inMemoryColumnarStorage.compressed','False')\n", + " .set('spark.sql.inMemoryColumnarStorage.batchSize','100000')\n", + " .set('spark.sql.execution.arrow.pyspark.fallback.enabled','True')\n", + " .set('spark.sql.execution.arrow.pyspark.enabled','True')\n", + " .set('spark.sql.execution.arrow.maxRecordsPerBatch','100000')\n", + " .set(\"spark.sql.repl.eagerEval.enabled\", True)\n", + " .set(\"spark.sql.legacy.timeParserPolicy\",\"LEGACY\") \n", + " .set(\"spark.sql.session.timeZone\", tz)\n", + " )\n", + "\n", + "sc = SparkContext(conf=conf,master='yarn')\n", + "sc.setLogLevel(\"ERROR\")\n", + "spark = SQLContext(sc)\n", + "time.sleep(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sparklog" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "%run ~/PAUS/sparklog.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"https_proxy\"] = \"http://10.239.44.250:8080\"\n", + "os.environ[\"http_proxy\"] = \"http://10.239.44.250:8080\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "disk_prefix=[f\"'{dev}'\" for dev in disk.split(',')]\n", + "nic_prefix=[f\"'{dev}'\" for dev in nic.split(',')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(HTML(' 5 App info'))\n", + "display(HTML(' 6 Compare to previous run'))\n", + "display(HTML(' 7 Config compare'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# App info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app=Application_Run(appid, basedir=basedir)\n", + "appals=app.analysis['app']['als']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "appals.get_basic_state()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summary=app.get_summary(disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + "display(summary.style)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "app.generate_trace_view(disk_prefix=disk_prefix,nic_prefix=nic_prefix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "appals.get_app_name()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shuffle_df, dfx=appals.get_shuffle_stat()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "appals.get_app_info(disk_prefix=disk_prefix,nic_prefix=nic_prefix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "appals.show_critical_path_time_breakdown().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare to previous run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if compare_appid:\n", + " compare_app=Application_Run(comapre_appid,basedir=compare_basedir)\n", + " output=app.compare_app(rapp=compare_app,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + " display(HTML(output))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Config compare" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if compare_appid:\n", + " display(comp_spark_conf(app_als, compare_app_als))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# convert to HTML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%javascript\n", + "IPython.notebook.kernel.execute('nb_name = \"' + IPython.notebook.notebook_name + '\"')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# htmlname=nb_name.replace(\"ipynb\",\"html\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !jupyter nbconvert --to html ./{nb_name} --no-input --output html/{htmlname} --template classic" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "197px", + "left": "2188px", + "top": "111px", + "width": "269px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/workload/benchmark_velox/analysis/requirements.txt b/tools/workload/benchmark_velox/analysis/requirements.txt new file mode 100644 index 000000000000..f230f8aa99de --- /dev/null +++ b/tools/workload/benchmark_velox/analysis/requirements.txt @@ -0,0 +1,174 @@ +ansicolors==1.1.8 +anyio==4.4.0 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +asttokens==2.4.1 +attrs==23.2.0 +Automat==20.2.0 +Babel==2.8.0 +bcrypt==3.2.0 +beautifulsoup4==4.12.3 +black==24.4.2 +bleach==6.1.0 +blinker==1.4 +certifi==2020.6.20 +cffi==1.16.0 +chardet==4.0.0 +charset-normalizer==3.4.0 +click==8.0.3 +colorama==0.4.4 +comm==0.2.2 +configobj==5.0.6 +constantly==15.1.0 +contourpy==1.2.1 +cryptography==3.4.8 +cycler==0.12.1 +debugpy==1.8.1 +decorator==5.1.1 +defusedxml==0.7.1 +distro==1.7.0 +entrypoints==0.4 +exceptiongroup==1.2.1 +executing==2.0.1 +fastjsonschema==2.19.1 +findspark==2.0.1 +fire==0.7.0 +fonttools==4.52.4 +fqdn==1.5.1 +gitdb==4.0.11 +GitPython==3.1.43 +greenlet==3.0.3 +httplib2==0.20.2 +hyperlink==21.0.0 +idna==3.10 +importlib-metadata==4.6.4 +incremental==21.3.0 +ipykernel==6.29.4 +ipython==8.24.0 +ipython-genutils==0.2.0 +ipywidgets==8.1.3 +isoduration==20.11.0 +jedi==0.19.1 +jeepney==0.7.1 +Jinja2==3.0.3 +jsonpatch==1.32 +jsonpointer==2.0 +jsonschema==4.22.0 +jsonschema-specifications==2023.12.1 +jupyter_client==7.4.9 +jupyter_contrib_core==0.4.2 +jupyter_contrib_nbextensions==0.7.0 +jupyter_core==5.7.2 +jupyter-events==0.10.0 +jupyter-highlight-selected-word==0.2.0 +jupyter-nbextensions-configurator==0.6.3 +jupyter_server==2.14.0 +jupyter-server-mathjax==0.2.6 +jupyter_server_terminals==0.5.3 +jupyterlab_pygments==0.3.0 +jupyterlab_widgets==3.0.11 +keyring==23.5.0 +kiwisolver==1.4.5 +launchpadlib==1.10.16 +lazr.restfulclient==0.14.4 +lazr.uri==1.0.6 +lxml==5.2.2 +MarkupSafe==2.0.1 +matplotlib==3.5.2 +matplotlib-inline==0.1.7 +metakernel==0.30.2 +mistune==3.0.2 +more-itertools==8.10.0 +mypy-extensions==1.0.0 +nbclassic==1.1.0 +nbclient==0.10.0 +nbconvert==7.16.4 +nbdime==4.0.1 +nbformat==5.10.4 +nest-asyncio==1.6.0 +netifaces==0.11.0 +notebook==6.5.6 +notebook_shim==0.2.4 +NotebookScripter==6.0.0 +numpy==1.26.4 +oauthlib==3.2.0 +overrides==7.7.0 +packaging==24.0 +pandas==1.5.3 +pandasql==0.7.3 +pandocfilters==1.5.1 +papermill==2.6.0 +parso==0.8.4 +pathspec==0.12.1 +pexpect==4.8.0 +pillow==10.3.0 +pip==24.2 +platformdirs==4.2.2 +prometheus_client==0.20.0 +prompt_toolkit==3.0.45 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==16.1.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.1 +pycparser==2.22 +Pygments==2.11.2 +PyHamcrest==2.0.2 +PyHDFS==0.3.1 +PyJWT==2.3.0 +pyOpenSSL==21.0.0 +pyparsing==2.4.7 +pyrsistent==0.18.1 +pyserial==3.5 +pyspark==3.3.1 +python-dateutil==2.9.0.post0 +python-json-logger==2.0.7 +pytz==2022.1 +PyYAML==6.0.2 +pyzmq==24.0.1 +referencing==0.35.1 +requests==2.32.3 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rfc3987==1.3.8 +rpds-py==0.18.1 +seaborn==0.13.2 +SecretStorage==3.3.1 +Send2Trash==1.8.3 +service-identity==18.1.0 +setuptools==75.1.0 +simplejson==3.19.2 +six==1.16.0 +smmap==5.0.1 +sniffio==1.3.1 +soupsieve==2.5 +spylon==0.3.0 +spylon-kernel==0.4.1 +SQLAlchemy==1.4.46 +ssh-import-id==5.11 +stack-data==0.6.3 +tenacity==8.3.0 +termcolor==2.5.0 +terminado==0.18.1 +tinycss2==1.3.0 +tomli==2.0.1 +tornado==6.4 +tqdm==4.66.4 +traitlets==5.14.3 +Twisted==22.1.0 +types-python-dateutil==2.9.0.20240316 +typing_extensions==4.12.0 +tzdata==2024.1 +uri-template==1.3.0 +urllib3==1.26.5 +wadllib==1.3.6 +wcwidth==0.2.13 +webcolors==1.13 +webencodings==0.5.1 +websocket-client==1.8.0 +wheel==0.44.0 +widgetsnbextension==4.0.11 +zipp==1.0.0 +zope.interface==5.4.0 diff --git a/tools/workload/benchmark_velox/analysis/run.py b/tools/workload/benchmark_velox/analysis/run.py new file mode 100644 index 000000000000..06fe712a5e09 --- /dev/null +++ b/tools/workload/benchmark_velox/analysis/run.py @@ -0,0 +1,11 @@ +import fire +import papermill as pm + +def exec(inputnb, outputnb, appid, disk, nic, tz, basedir, name, compare_appid='', compare_basedir='', compare_name=''): + return pm.execute_notebook( + inputnb, + outputnb, + parameters=dict(appid=appid,disk=disk,nic=nic,tz=tz,basedir=basedir,name=name,compare_appid=compare_appid,compare_basedir=compare_basedir,compare_name=compare_name)) + +if __name__ == '__main__': + fire.Fire(exec) diff --git a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh new file mode 100755 index 000000000000..7dcc4ce90c89 --- /dev/null +++ b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh @@ -0,0 +1,120 @@ +#! /bin/bash + +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +SCRIPT_LOCATION=$(dirname $0) +PAUS=$HOME/PAUS + +while [[ $# -gt 0 ]]; do + case $1 in + --ts) + TS="$2" + shift # past argument + shift # past value + ;; + --base-dir) + BASEDIR="$2" + shift # past argument + shift # past value + ;; + --name) + NAME="$2" + shift # past argument + shift # past value + ;; + --appid) + APPID="$2" + shift # past argument + shift # past value + ;; + --disk) + DISK="$2" + shift # past argument + shift # past value + ;; + --nic) + NIC="$2" + shift # past argument + shift # past value + ;; + --tz) + SPARK_TZ="$2" + shift # past argument + shift # past value + ;; + --comp-appid) + COMP_APPID="$2" + shift # past argument + shift # past value + ;; + --comp-base-dir) + COMP_BASEDIR="$2" + shift # past argument + shift # past value + ;; + --comp-name) + COMP_NAME="$2" + shift # past argument + shift # past value + ;; + *) + echo "Error: Unknown argument: $1" + exit 1 + ;; + esac +done + +# Validation: Check if any of the required variables are empty +if [[ -z "${TS+x}" || -z "${BASEDIR+x}" || -z "${NAME+x}" || -z "${APPID+x}" || -z "${DISK+x}" || -z "${NIC+x}" || -z "${SPARK_TZ+x}" ]]; then + echo "Error: One or more required arguments are missing or empty." + exit 1 +fi + +mkdir -p $PAUS +if [ ! -f "$PAUS/perf_analysis_template.ipynb" ]; then + cp $SCRIPT_LOCATION/perf_analysis_template.ipynb $PAUS/ +fi +if [ ! -f "$PAUS/sparklog.ipynb" ]; then + cp $SCRIPT_LOCATION/sparklog.ipynb $PAUS/ +fi + +mkdir -p $PAUS/$BASEDIR +cd $PAUS/$BASEDIR +mkdir -p html + +nb_name0=${TS}_${NAME}_${APPID} +nb_name=${nb_name0}.ipynb + +cp -f $PAUS/perf_analysis_template.ipynb $nb_name +hadoop fs -mkdir -p /history +hadoop fs -cp -f /$BASEDIR/$APPID/app.log /history/$APPID + +EXTRA_ARGS="" +if [ -v COMP_APPID ] +then + if [[ -z "${COMP_BASEDIR+x}" || -z "${COMP_NAME+x}" ]]; then + echo "Missing --comp-base-dir or --comp-name" + exit 1 + fi + hadoop fs -cp -f /$COMP_BASEDIR/$COMP_APPID/app.log /history/$COMP_APPID + EXTRA_ARGS="--compare_appid $COMP_APPID --compare_basedir $COMP_BASEDIR --compare_name $COMP_NAME" +fi + +source ~/paus-env/bin/activate + +python3 $SCRIPT_LOCATION/run.py --inputnb $nb_name --outputnb ${nb_name0}.nbconvert.ipynb --appid $APPID --disk $DISK --nic $NIC --tz $SPARK_TZ --basedir $BASEDIR --name $NAME $EXTRA_ARGS + +jupyter nbconvert --to html --no-input ./${nb_name0}.nbconvert.ipynb --output html/${nb_name0}.html --template classic > /dev/null 2>&1 diff --git a/tools/workload/benchmark_velox/analysis/sparklog.ipynb b/tools/workload/benchmark_velox/analysis/sparklog.ipynb new file mode 100644 index 000000000000..8ceaeb44fe55 --- /dev/null +++ b/tools/workload/benchmark_velox/analysis/sparklog.ipynb @@ -0,0 +1,5176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# initialize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import nested_scopes\n", + "from IPython.core.display import display, HTML\n", + "display(HTML(\"\"))\n", + "display(HTML(''))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pyspark.sql.functions as F\n", + "import json\n", + "import builtins\n", + "from itertools import chain\n", + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lang": "en" + }, + "outputs": [], + "source": [ + "import logging\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.ERROR)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import os\n", + "import pandas\n", + "pandas.set_option('display.max_rows', None)\n", + "\n", + "import matplotlib\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as mtick\n", + "from matplotlib import colors\n", + "from matplotlib import rcParams\n", + "rcParams['font.sans-serif'] = 'Courier New'\n", + "rcParams['font.family'] = 'Courier New'\n", + "rcParams['font.size'] = '12'\n", + "%matplotlib inline\n", + "\n", + "from IPython.display import display,HTML\n", + "import threading\n", + "import collections\n", + "\n", + "from IPython.display import display\n", + "from ipywidgets import IntProgress,Layout\n", + "import time\n", + "import threading\n", + "import gzip\n", + "import pyspark\n", + "import pyspark.sql\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.types import (StructType, StructField, DateType,\n", + " TimestampType, StringType, LongType, IntegerType, DoubleType,FloatType)\n", + "from pyspark.sql.functions import to_date, floor\n", + "from pyspark.ml.feature import StringIndexer, VectorAssembler\n", + "from pyspark.ml import Pipeline\n", + "from pyspark.sql.functions import lit\n", + "import datetime\n", + "import time\n", + "from pyspark.storagelevel import StorageLevel\n", + "from pyspark.sql.window import Window\n", + "from pyspark.sql.functions import rank, col\n", + "from pyspark.ml import Pipeline\n", + "import numpy\n", + "\n", + "import re\n", + "import math\n", + "from functools import reduce\n", + "import json\n", + "\n", + "from pyspark.sql.types import *\n", + "from pyspark.sql import functions as F\n", + "from datetime import date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.types import (StructType, StructField, DateType,\n", + " TimestampType, StringType, LongType, IntegerType, DoubleType,FloatType)\n", + "\n", + "from pyspark.sql.functions import pandas_udf, PandasUDFType\n", + "\n", + "from pyspark.ml.clustering import KMeans\n", + "from pyspark.ml.feature import StringIndexer, VectorAssembler\n", + "\n", + "from pyspark.sql.window import Window\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "from functools import reduce\n", + "import re\n", + "import collections\n", + "from pyspark.ml import Pipeline\n", + "import numpy\n", + "import time\n", + "from pandasql import sqldf\n", + "import html\n", + "\n", + "pandas.options.display.max_rows=50\n", + "pandas.options.display.max_columns=200\n", + "pandas.options.display.float_format = '{:,}'.format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ipywidgets import IntProgress,Layout\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.lines as mlines\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pyhdfs\n", + "\n", + "import socket\n", + "localhost=socket.gethostname()\n", + "local_ip=socket.gethostbyname(localhost)\n", + "\n", + "fs = pyhdfs.HdfsClient(hosts=f'{local_ip}:9870', user_name='sparkuser')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# fs functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def getexecutor_stat(pdir):\n", + " appfolder=fs.list_status(pdir)\n", + " total_rchar=0\n", + " total_wchar=0\n", + " total_read_bytes=0\n", + " total_write_bytes=0\n", + " total_cancelled_write_bytes=0\n", + "\n", + " for t in appfolder:\n", + " if t['type']=='DIRECTORY' and t['pathSuffix']!=\"summary.parquet\":\n", + " cdir=pdir+t['pathSuffix']\n", + " for cntfile in fs.listdir(cdir):\n", + " if cntfile.endswith(\".stat\"):\n", + " with fs.open(cdir+\"/\"+cntfile) as f:\n", + " cnt=f.readlines()\n", + " rchar=0\n", + " wchar=0\n", + " read_bytes=0\n", + " write_bytes=0\n", + " cancelled_write_bytes=0\n", + " for c in cnt:\n", + " c=c.decode('ascii')\n", + " if c.startswith(\"rchar\"):\n", + " v=int(c.split(\" \")[-1])\n", + " rchar=v-rchar\n", + " elif c.startswith(\"wchar\"):\n", + " v=int(c.split(\" \")[-1])\n", + " wchar=v-wchar\n", + " elif c.startswith(\"read_bytes\"):\n", + " v=int(c.split(\" \")[-1])\n", + " read_bytes=v-read_bytes\n", + " elif c.startswith(\"write_bytes\"):\n", + " v=int(c.split(\" \")[-1])\n", + " write_bytes=v-write_bytes\n", + " elif c.startswith(\"cancelled_write_bytes\"):\n", + " v=int(c.split(\" \")[-1])\n", + " cancelled_write_bytes=v-cancelled_write_bytes\n", + " total_rchar+=rchar/1024/1024\n", + " total_wchar+=wchar/1024/1024\n", + " total_read_bytes+=read_bytes/1024/1024\n", + " total_write_bytes+=write_bytes/1024/1024\n", + " total_cancelled_write_bytes+=cancelled_write_bytes/1024/1024\n", + " return (total_rchar,total_wchar,total_read_bytes,total_write_bytes,total_cancelled_write_bytes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):\n", + " from matplotlib import colors\n", + " rng = M - m\n", + " norm = colors.Normalize(m - (rng * low),\n", + " M + (rng * high))\n", + " normed = norm(s.values)\n", + " c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]\n", + " return ['background-color: {:s}'.format(color) for color in c]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": "true" + }, + "source": [ + "# base class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "class SparkLog_Analysis:\n", + " def __init__(self, appid,jobids,clients):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "class Analysis:\n", + " def __init__(self,file):\n", + " self.file=file\n", + " self.starttime=0\n", + " self.df=None\n", + " \n", + " def load_data(self):\n", + " pass\n", + " \n", + " def generate_trace_view_list(self,id=0, **kwargs):\n", + " if self.df==None:\n", + " self.load_data()\n", + " trace_events=[]\n", + " node=kwargs.get('node',\"node\")\n", + " trace_events.append(json.dumps({\"name\": \"process_name\",\"ph\": \"M\",\"pid\":id,\"tid\":0,\"args\":{\"name\":\" \"+node}}))\n", + " return trace_events\n", + " \n", + " def generate_trace_view(self, trace_output, **kwargs):\n", + " traces=[]\n", + " traces.extend(self.generate_trace_view_list(0,**kwargs))\n", + " \n", + " output='''\n", + " {\n", + " \"traceEvents\": [\n", + " \n", + " ''' + \\\n", + " \",\\n\".join(traces)\\\n", + " + '''\n", + " ],\n", + " \"displayTimeUnit\": \"ns\"\n", + " }'''\n", + "\n", + " if(\"home\" in trace_output):\n", + " outputfolder=trace_output\n", + " appidx=trace_output.split(\"/\")[-1]\n", + " else:\n", + " outputfolder='/home/sparkuser/trace_result/'+trace_output+'.json'\n", + " appidx=trace_output\n", + " with open(outputfolder, 'w') as outfile: \n", + " outfile.write(output)\n", + " \n", + " display(HTML(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appidx}.json\"))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# app log analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_his_perf(namelike,currentdir):\n", + " dird=fs.listdir(\"/gluten\")\n", + " apps=[]\n", + " for l in dird:\n", + " if l.startswith(\"2\") and l>(date.today() - timedelta(days=60)).strftime(\"%Y_%m_%d\"):\n", + " for r in fs.listdir(\"/gluten/\"+l):\n", + " if fs.exists(\"/gluten/\"+l+\"/\"+r+\"/app.log\"):\n", + " apps.append(\"/gluten/\"+l+\"/\"+r+\"/app.log\")\n", + " if currentdir not in apps:\n", + " apps.append(currentdir)\n", + " appdf=spark.read.json(apps)\n", + " appdf=appdf.withColumn(\"filename\", F.input_file_name())\n", + " starttime=appdf.where(\"Properties.`spark.app.name` like '\"+namelike+\"%' and Event='SparkListenerJobStart'\").select(\"filename\",F.col('Properties.`spark.app.name`').alias(\"appname\"),F.col('Submission Time').alias(\"starttime\"))\n", + " finishtime=appdf.where(\"Event='SparkListenerJobEnd'\").select(\"filename\",F.col('Completion Time').alias(\"finishtime\"))\n", + " starttime=starttime.groupBy(\"filename\").agg(F.max(\"appname\").alias(\"appname\"),F.min(\"starttime\").alias(\"starttime\"))\n", + " finishtime=finishtime.groupBy(\"filename\").agg(F.max(\"finishtime\").alias(\"finishtime\"))\n", + " elapsedtime=starttime.join(finishtime,\"filename\").orderBy(\"starttime\").select(F.date_format(F.from_unixtime(F.col('starttime')/1000),\"yyyy_MM_dd\").alias(\"test_date\"),(F.col(\"finishtime\")/1000-F.col(\"starttime\")/1000).alias(\"elapsedtime\"))\n", + " epsdf=elapsedtime.toPandas()\n", + " epsdf.plot(x='test_date',y=['elapsedtime'],style=\"-*\",figsize=(30,8))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "from pyspark.sql.functions import udf\n", + "@udf(\"long\")\n", + "def isfinish_udf(s):\n", + " import json\n", + " s=json.loads(s)\n", + " def isfinish(root):\n", + " if \"isFinalPlan=false\" in root['simpleString'] or root['children'] is None:\n", + " return 0\n", + " for c in root[\"children\"]:\n", + " if isfinish(c)==0:\n", + " return 0\n", + " return 1\n", + " if len(s)>0:\n", + " return isfinish(s[0])\n", + " else:\n", + " return 0\n", + " \n", + "@pandas_udf(\"taskid long, start long, dur long, name string\", PandasUDFType.GROUPED_MAP)\n", + "def time_breakdown(pdf):\n", + " ltime=pdf['Launch Time'][0]+2\n", + " pdf['start']=0\n", + " pdf['dur']=0\n", + " outpdf=[]\n", + " ratio=(pdf[\"Finish Time\"][0]-pdf[\"Launch Time\"][0])/pdf[\"Update\"].sum()\n", + " ratio=1 if ratio>1 else ratio\n", + " for idx,l in pdf.iterrows():\n", + " if(l[\"Update\"]*ratio>1):\n", + " outpdf.append([l[\"Task ID\"],ltime,int(l[\"Update\"]*ratio),l[\"mname\"]])\n", + " ltime=ltime+int(l[\"Update\"]*ratio)\n", + " if len(outpdf)>0:\n", + " return pandas.DataFrame(outpdf)\n", + " else:\n", + " return pandas.DataFrame({'taskid': pandas.Series([], dtype='long'),\n", + " 'start': pandas.Series([], dtype='long'),\n", + " 'dur': pandas.Series([], dtype='long'),\n", + " 'name': pandas.Series([], dtype='str'),\n", + " })\n", + " \n", + "class App_Log_Analysis(Analysis):\n", + " def __init__(self, file, jobids):\n", + " Analysis.__init__(self,file)\n", + " self.jobids=[] if jobids is None else [str(l) for l in jobids]\n", + " self.df=None\n", + " self.pids=[]\n", + " \n", + " def load_data(self):\n", + " print(\"load data \", self.file)\n", + " jobids=self.jobids\n", + " df=spark.read.json(self.file)\n", + " \n", + " if 'App ID' in df.columns:\n", + " self.appid=df.where(\"`App ID` is not null\").collect()[0][\"App ID\"]\n", + " else:\n", + " self.appid=\"Application-00000000\"\n", + " \n", + " if df.where(\"Event='org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates'\").count()>0:\n", + " self.dfacc=df.where(\"Event='org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates'\").select(F.col(\"executionId\").alias(\"queryid\"),F.explode(\"accumUpdates\"))\n", + " else:\n", + " self.dfacc = None\n", + " \n", + " if \"sparkPlanInfo\" in df.columns:\n", + " self.queryplans=df.where(\"(Event='org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart' or Event='org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate') \\\n", + " and (sparkPlanInfo.nodeName!='AdaptiveSparkPlan' or sparkPlanInfo.simpleString='AdaptiveSparkPlan isFinalPlan=true') \").select(F.col(\"executionId\").alias(\"queryid\"),'physicalPlanDescription',\"sparkPlanInfo.*\")\n", + " else:\n", + " self.queryplans=None\n", + " \n", + " seen = set()\n", + " \n", + " if self.queryplans is not None:\n", + " self.queryplans=self.queryplans.where(isfinish_udf(F.to_json(\"children\"))==1)\n", + " \n", + " self.allmetrics=[]\n", + " if self.queryplans.count() > 0:\n", + " metrics=self.queryplans.collect()\n", + " def get_metric(root):\n", + " for l in root[\"metrics\"]:\n", + " if l['accumulatorId'] not in seen:\n", + " seen.add(l['accumulatorId'])\n", + " self.allmetrics.append([l['accumulatorId'],l[\"metricType\"],l['name'],root[\"nodeName\"]])\n", + " if root['children'] is not None:\n", + " for c in root[\"children\"]:\n", + " get_metric(c)\n", + " for c in metrics:\n", + " get_metric(c)\n", + " \n", + " amsdf=spark.createDataFrame(self.allmetrics)\n", + " amsdf=amsdf.withColumnRenamed(\"_1\",\"ID\").withColumnRenamed(\"_2\",\"type\").withColumnRenamed(\"_3\",\"Name\").withColumnRenamed(\"_4\",\"nodeName\")\n", + " \n", + " \n", + " if self.dfacc is not None:\n", + " self.dfacc=self.dfacc.select(\"queryid\",(F.col(\"col\")[0]).alias(\"ID\"),(F.col(\"col\")[1]).alias(\"Update\")).join(amsdf,on=[\"ID\"])\n", + " \n", + " if self.queryplans is not None:\n", + " self.metricscollect=[l for l in self.allmetrics if l[1] in ['nsTiming','timing'] and (l[2].startswith(\"time to\") or l[2].startswith(\"time of\") or l[2].startswith(\"scan time\") or l[2].startswith(\"shuffle write time\") or l[2].startswith(\"time to spill\") or l[2].startswith(\"task commit time\")) \n", + " and l[2] not in(\"time to collect batch\", \"time of scan\") ]\n", + " \n", + " #config=df.where(\"event='SparkListenerJobStart' and Properties.`spark.executor.cores` is not null\").select(\"Properties.*\").limit(1).collect()\n", + " config=df.select(\"`Spark Properties`.*\").where(\"`spark.app.id` is not null\").limit(1).collect()\n", + " \n", + " configdic=config[0].asDict()\n", + " self.parallelism=int(configdic['spark.sql.shuffle.partitions']) if 'spark.sql.shuffle.partitions' in configdic else 1\n", + " self.executor_cores=int(configdic['spark.executor.cores']) if 'spark.executor.cores' in configdic else 1\n", + " self.executor_instances=int(configdic['spark.executor.instances']) if 'spark.executor.instances' in configdic else 1\n", + " self.taskcpus= int(configdic['spark.task.cpus'])if 'spark.task.cpus' in configdic else 1\n", + " self.batchsize= int(configdic['spark.gluten.sql.columnar.maxBatchSize'])if 'spark.gluten.sql.columnar.maxBatchSize' in configdic else 4096\n", + " \n", + " self.realexecutors = df.where(~F.isnull(F.col(\"Executor ID\"))).select(\"Executor ID\").distinct().count()\n", + " \n", + " execstart = df.where(\"Event='org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart'\").select(\"executionId\",\"time\")\n", + " execend = df.where(\"Event='org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd'\").select(\"executionId\",\"time\")\n", + " execstart=execstart.withColumnRenamed(\"time\",\"query_starttime\").withColumnRenamed(\"executionId\",\"queryid\")\n", + " execend=execend.withColumnRenamed(\"time\",\"query_endtime\").withColumnRenamed(\"executionId\",\"queryid\")\n", + " exectime = execstart.join(execend,on=[\"queryid\"])\n", + "\n", + " if \"spark.sql.execution.id\" in df.where(\"Event='SparkListenerJobStart'\").select(\"Properties.*\").columns:\n", + " df_jobstart=df.where(\"Event='SparkListenerJobStart'\").select(\"Job ID\",\"Submission Time\",F.col(\"Properties.`spark.sql.execution.id`\").alias(\"queryid\"),\"Stage IDs\")\n", + " else:\n", + " df_jobstart=df.where(\"Event='SparkListenerJobStart'\").select(\"Job ID\",\"Submission Time\",F.lit(0).alias(\"queryid\"),\"Stage IDs\")\n", + " \n", + " df_jobend=df.where(\"Event='SparkListenerJobEnd'\").select(\"`Job ID`\",\"Completion Time\")\n", + " df_job=df_jobstart.join(df_jobend,\"Job ID\")\n", + " df_job=df_job.withColumnRenamed(\"Submission Time\",\"job_start_time\")\n", + " df_job=df_job.withColumnRenamed(\"Completion Time\",\"job_stop_time\")\n", + " self.df_job=df_job\n", + " \n", + " jobstage=df_job.select(\"*\",F.explode(\"Stage IDs\").alias(\"Stage ID\"))\n", + " task=df.where(\"(Event='SparkListenerTaskEnd' or Event='SparkListenerTaskStart') \").select(\"Event\",\"Stage ID\",\"task info.*\",\"task metrics.*\")\n", + " \n", + " self.failed_stages = [str(l['Stage ID']) for l in task.where(\"Failed='true'\").select(\"Stage ID\").distinct().collect()]\n", + " \n", + " self.speculativetask = task.where(\"speculative = 'true'\").count()\n", + " self.speculativekilledtask = task.where(\"speculative = true and killed='true'\").count()\n", + " self.speculativestage = task.where(\"speculative = true and killed='true'\").select(\"`Stage ID`\").distinct().count()\n", + " \n", + " validtsk = task.where(\"Event = 'SparkListenerTaskEnd' and (Failed<>'true' or killed<>'true')\").select(\"`Task ID`\")\n", + " task=task.join(validtsk,on='Task ID',how='inner')\n", + " \n", + " taskjob=task.\\\n", + " select(\"Host\",\"`Event`\",\"`Launch Time`\",\"`Executor ID`\",\"`Task ID`\",\"`Finish Time`\",\n", + " \"`Stage ID`\",\"`Input Metrics`.`Bytes Read`\",\"`Disk Bytes Spilled`\",\"`Memory Bytes Spilled`\",\"`Shuffle Read Metrics`.`Local Bytes Read`\",\"`Shuffle Read Metrics`.`Remote Bytes Read`\",\n", + " \"`Shuffle Write Metrics`.`Shuffle Bytes Written`\",\"`Executor Deserialize Time`\",\"`Shuffle Read Metrics`.`Fetch Wait Time`\",\"`Executor Run Time`\",\"`Shuffle Write Metrics`.`Shuffle Write Time`\",\n", + " \"`Result Serialization Time`\",\"`Getting Result Time`\",\"`JVM GC Time`\",\"`Executor CPU Time`\",\"Accumulables\",\"Peak Execution Memory\",\n", + " F.when(task['Finish Time']==0,task['Launch Time']).otherwise(task['Finish Time']).alias('eventtime')\n", + " ).join(jobstage,\"Stage ID\").where(\"`Finish Time` is null or `Finish Time` <=job_stop_time+5\")\n", + " \n", + " taskjob = taskjob.join(exectime,on=['queryid'],how='left')\n", + " \n", + " self.df=taskjob\n", + " \n", + " if len(jobids)>0:\n", + " self.df=self.df.where('`Job ID` in ({:s})'.format(','.join(jobids)))\n", + " \n", + " queryids=self.df.select(F.col(\"queryid\").astype(IntegerType())).distinct().where(\"queryid is not null\").orderBy(\"queryid\").toPandas()\n", + " \n", + " self.query_num=len(queryids)\n", + " if self.query_num>0:\n", + " queryidx=queryids.reset_index()\n", + " queryidx['index']=queryidx['index']+1\n", + " #tpcds query\n", + " if self.query_num==103:\n", + " queryidx['index']=queryidx['index'].map(tpcds_query_map)\n", + " qidx=spark.createDataFrame(queryidx)\n", + " qidx=qidx.withColumnRenamed(\"index\",\"real_queryid\")\n", + " self.df=self.df.join(qidx,on=\"queryid\",how=\"left\")\n", + " if self.dfacc is not None:\n", + " self.dfacc=self.dfacc.join(qidx,on=\"queryid\",how='left')\n", + "\n", + " if self.queryplans:\n", + " self.queryplans=self.queryplans.join(qidx,\"queryid\",how=\"right\")\n", + " \n", + " self.df=self.df.fillna(0)\n", + " self.df=self.df.withColumn('Executor ID',F.when(F.col(\"Executor ID\")==\"driver\",1).otherwise(F.col(\"Executor ID\")))\n", + " self.df.cache()\n", + " \n", + " \n", + " \n", + " ##############################\n", + " \n", + " dfx=self.df.where(\"Event='SparkListenerTaskEnd'\").select(\"Stage ID\",\"Launch Time\",\"Finish Time\",\"Task ID\")\n", + " dfxpds=dfx.toPandas()\n", + " dfxpds.columns=[l.replace(\" \",\"_\") for l in dfxpds.columns]\n", + " dfxpds_ods=sqldf('''select * from dfxpds order by finish_time desc''')\n", + " criticaltasks=[]\n", + " idx=0\n", + " prefinish=0\n", + " launchtime=dfxpds_ods[\"Launch_Time\"][0]\n", + " criticaltasks.append([dfxpds_ods[\"Task_ID\"][0],launchtime,dfxpds_ods[\"Finish_Time\"][0]])\n", + " total_row=len(dfxpds_ods)\n", + "\n", + " while True:\n", + " while idx=launchtime else cur_finish\n", + " launchtime=dfxpds_ods[\"Launch_Time\"][idx]\n", + " criticaltasks.append([dfxpds_ods[\"Task_ID\"][idx],launchtime,cur_finish])\n", + " self.criticaltasks=criticaltasks\n", + "\n", + " def get_physical_plan(appals,**kwargs):\n", + " if appals.df is None:\n", + " appals.load_data()\n", + " queryid=kwargs.get('queryid',None)\n", + " shownops=kwargs.get(\"shownops\",['ArrowRowToColumnarExec','ColumnarToRow','RowToArrowColumnar',\n", + " 'VeloxNativeColumnarToRowExec','ArrowColumnarToRow','Filter','HashAggregate','Project','SortAggregate','SortMergeJoin','window'])\n", + " \n", + " desensitization=kwargs.get('desensitization',True)\n", + " \n", + " def get_fields(colss):\n", + " lvls=0\n", + " colns=[]\n", + " ks=\"\"\n", + " for c in colss:\n", + " if c==\",\" and lvls==0:\n", + " colns.append(ks)\n", + " ks=\"\"\n", + " continue\n", + " if c==\" \" and ks==\"\":\n", + " continue\n", + " if c==\"(\":\n", + " lvls+=1\n", + " if c==\")\":\n", + " lvls-=1\n", + " ks+=c\n", + " if ks!=\"\":\n", + " colns.append(ks)\n", + " return colns\n", + " \n", + " def get_column_names(s, opname, resultname, prefix, columns, funcs):\n", + " p=re.search(r\" \"+opname+\" \",s[0])\n", + " if p:\n", + " for v in s[1].split(\"\\n\"):\n", + " if v.startswith(resultname):\n", + " cols=re.search(\"\\[([^0-9].+)\\]\",v)\n", + " if cols:\n", + " colss=cols.group(1)\n", + " colns=get_fields(colss)\n", + " if opname+str(len(columns)) not in funcs:\n", + " funcs[opname+str(len(columns))]=[]\n", + " funcs[opname+str(len(columns))].extend(colns)\n", + " for c in colns:\n", + " if \" AS \" in c:\n", + " c=re.sub(\"#\\d+L*\",\"\",c)\n", + " colname=re.search(r\" AS (.+)\",c).group(1)\n", + " if colname not in columns:\n", + " columns[colname]=prefix\n", + " \n", + " plans=appals.queryplans.select('real_queryid','physicalPlanDescription').collect() if queryid is None else appals.queryplans.where(f\"real_queryid='{queryid}'\").select(\"physicalPlanDescription\").collect()\n", + " \n", + " for pr in range(0,len(plans)):\n", + " plan=plans[pr]['physicalPlanDescription']\n", + " nodes={}\n", + " lines=plan.split(\"\\n\")\n", + " for idx in range(0,len(lines)):\n", + " l=lines[idx]\n", + " if l=='+- == Final Plan ==':\n", + " while l!='+- == Initial Plan ==':\n", + " idx+=1\n", + " l=lines[idx]\n", + " if not l.endswith(\")\"):\n", + " break\n", + " idv=re.search(\"\\(\\d+\\)$\",l).group(0)\n", + " nodes[idv]=[l]\n", + " if l==\"== Physical Plan ==\":\n", + " while not lines[idx+1].startswith(\"(\"):\n", + " idx+=1\n", + " l=lines[idx]\n", + " if not l.endswith(\")\"):\n", + " break\n", + " idv=re.search(\"\\(\\d+\\)$\",l).group(0)\n", + " nodes[idv]=[l]\n", + " \n", + " if l.startswith(\"(\"):\n", + " idv=re.search(\"^\\(\\d+\\)\",l).group(0)\n", + " if idv in nodes:\n", + " desc=\"\"\n", + " while l.strip()!=\"\":\n", + " desc+=l+\"\\n\"\n", + " idx+=1\n", + " l=lines[idx]\n", + " desc=re.sub(r\"#\\d+L*\",r\"\",desc)\n", + " desc=re.sub(r\"= [^)]+\",r\"=\",desc)\n", + " desc=re.sub(r\"IN \\([^)]\\)\",r\"IN ()\",desc)\n", + " desc=re.sub(r\"In\\([^)]\\)\",r\"In()\",desc)\n", + " desc=re.sub(r\"EqualTo\\(([^,]+),[^)]+\\)\",r\"EqualTo(\\1,)\",desc)\n", + " desc=re.sub(r\"搜索广告\",r\"xxx\",desc)\n", + " ## add all keyword replace here\n", + " nodes[idv].append(desc)\n", + " tables={}\n", + " columns={}\n", + " functions={}\n", + " for s in nodes.values():\n", + " p=re.search(r\"Scan arrow [^.]*\\.([^ ]+)\",s[0])\n", + " if p:\n", + " tn=p.group(1)\n", + " if not tn in tables:\n", + " tables[tn]=\"table\"\n", + " if desensitization:\n", + " s[0]=s[0].replace(tn,tables[tn])\n", + " s[1]=s[1].replace(tn,tables[tn])\n", + " colsv=[]\n", + " schema=[]\n", + " for v in s[1].split(\"\\n\"):\n", + " if v.startswith(\"ReadSchema\"):\n", + " cols=re.search(\"<(.*)>\",v)\n", + " if cols:\n", + " colss=cols.group(1).split(\",\")\n", + " for c in colss:\n", + " cts=c.split(\":\")\n", + " ct=cts[0]\n", + " if not ct in columns:\n", + " if len(cts)==2:\n", + " cts[1]=cts[1]\n", + " columns[ct]=cts[1]+\"_\"\n", + " else:\n", + " columns[ct]=\"c_\"\n", + " if v.startswith(\"Location\") and desensitization:\n", + " s[1]=s[1].replace(v+\"\\n\",\"\")\n", + " \n", + " get_column_names(s, \"Project\", \"Output\", \"proj_\", columns, functions)\n", + " get_column_names(s, \"HashAggregate\", \"Results\", \"shagg_\", columns, functions)\n", + " get_column_names(s, \"SortAggregate\", \"Results\", \"stagg_\", columns, functions)\n", + " get_column_names(s, \"ColumnarConditionProject\", \"Arguments\", \"cproj_\", columns, functions)\n", + " get_column_names(s, \"ColumnarHashAggregate\", \"Results\", \"cshagg_\", columns, functions)\n", + " get_column_names(s, \"Window\", \"Arguments\", \"window_\", columns, functions)\n", + "\n", + " keys=[]\n", + " ckeys=list(columns.keys())\n", + " for l in range(0,len(ckeys)):\n", + " k1=ckeys[l]\n", + " for k in range(0,len(keys)):\n", + " if keys[k] in k1:\n", + " keys.insert(k,k1)\n", + " break\n", + " else:\n", + " keys.append(k1)\n", + " \n", + " for s in nodes.values():\n", + " s[1]=html.escape(s[1])\n", + " if desensitization:\n", + " for c in keys:\n", + " v=columns[c]\n", + " if v.startswith(\"array\") or v.startswith(\"map\") or v.startswith(\"struct\"):\n", + " s[1]=re.sub(c, ''+html.escape(v)+\"\",s[1])\n", + " else:\n", + " s[1]=re.sub(c, \"\"+html.escape(v)+\"\",s[1])\n", + "\n", + "\n", + " htmls=['''''']\n", + " qid=pr+1 if queryid is None else queryid\n", + " htmls.append(f\"\")\n", + " for l in nodes.values():\n", + " if shownops is not None:\n", + " for k in shownops:\n", + " if \" \"+k+\" \" in l[0]:\n", + " break\n", + " else:\n", + " continue\n", + " htmls.append(\"\")\n", + " htmls.append('\")\n", + " htmls.append('\")\n", + " htmls.append(\"\")\n", + " htmls.append(\"
{qid}
')\n", + " htmls.append(l[0].replace(\" \",\"_\")\n", + " .replace(\"ColumnarToRow\",\"ColumnarToRow\")\n", + " .replace(\"RowToArrowColumnar\",\"RowToArrowColumnar\")\n", + " .replace(\"ArrowColumnarToRow\",\"ArrowColumnarToRow\")\n", + " .replace(\"ArrowRowToColumnar\",\"ArrowRowToColumnar\")\n", + " .replace(\"VeloxNativeColumnarToRowExec\",\"VeloxNativeColumnarToRowExec\")\n", + " )\n", + " htmls.append(\"
')\n", + " ls=l[1].split(\"\\n\")\n", + " lsx=[]\n", + " for t in ls:\n", + " cols=re.search(\"\\[([^0-9].+)\\]\",t)\n", + " if cols:\n", + " colss=cols.group(1)\n", + " colns=get_fields(colss)\n", + " t=re.sub(\"\\[([^0-9].+)\\]\",\"\",t)\n", + " t+=\"[\"+';'.join(colns)+\"]\" \n", + " if \":\" in t:\n", + " lsx.append(re.sub(r'^([^:]+:)',r'\\1',t))\n", + " else:\n", + " lsx.append(t)\n", + " htmls.append(\"
\".join(lsx))\n", + " htmls.append(\"
\")\n", + " display(HTML(\"\\n\".join(htmls)))\n", + " \n", + " for k, v in functions.items():\n", + " functions[k]=[l for l in v if \"(\" in l]\n", + " for f in functions.values():\n", + " for idx in range(0,len(f)):\n", + " for c in keys:\n", + " v=columns[c]\n", + " if v.startswith(\"array\") or v.startswith(\"map\") or v.startswith(\"struct\"):\n", + " f[idx]=re.sub(c, ''+html.escape(v)+\"\",f[idx])\n", + " else:\n", + " f[idx]=re.sub(c, \"\"+html.escape(v)+\"\",f[idx])\n", + " funchtml=\"\"\n", + " for k,v in functions.items():\n", + " if shownops is not None:\n", + " for ks in shownops:\n", + " if \" \"+ks+\" \" in k:\n", + " break\n", + " else:\n", + " continue\n", + " funchtml+=\"\"\n", + " funchtml+=\"
\"+k+''\n", + " for f in v:\n", + " funchtml+='\"\n", + " funchtml+=\"
'+f+\"
\" \n", + " display(HTML(funchtml))\n", + " \n", + " return plans\n", + " \n", + " def get_physical_allnodes(appals,**kwargs):\n", + " if appals.df is None:\n", + " appals.load_data()\n", + " queryid=None\n", + " \n", + " plans=appals.queryplans.select('real_queryid','physicalPlanDescription').collect() if queryid is None else appals.queryplans.where(f\"real_queryid='{queryid}'\").select(\"physicalPlanDescription\").collect()\n", + " \n", + " allnodes={}\n", + " for pr in range(0,len(plans)):\n", + " plan=plans[pr]['physicalPlanDescription']\n", + " allnodes[pr]={}\n", + " nodes=allnodes[pr]\n", + " if plan is None:\n", + " continue\n", + " lines=plan.split(\"\\n\")\n", + " for idx in range(0,len(lines)):\n", + " l=lines[idx]\n", + " if l=='+- == Final Plan ==':\n", + " while l!='+- == Initial Plan ==':\n", + " idx+=1\n", + " l=lines[idx]\n", + " if not l.endswith(\")\"):\n", + " break\n", + " idv=re.search(\"\\(\\d+\\)$\",l).group(0)\n", + " nodes[idv]=[l]\n", + " if l.startswith(\"(\"):\n", + " idv=re.search(\"^\\(\\d+\\)\",l).group(0)\n", + " if idv in nodes:\n", + " desc=\"\"\n", + " while l!=\"\":\n", + " desc+=l+\"\\n\"\n", + " idx+=1\n", + " l=lines[idx]\n", + " nodes[idv].append(desc)\n", + " return allnodes\n", + " \n", + " \n", + " def get_basic_state(appals):\n", + " if appals.df is None:\n", + " appals.load_data()\n", + " display(HTML(f\"http://{localhost}:18080/history/{appals.appid}\"))\n", + " \n", + " errorcolor=\"#000000\" if appals.executor_instances == appals.realexecutors else \"#c0392b\"\n", + " \n", + " qtime=appals.get_query_time(plot=False)\n", + " sums=qtime.sum()\n", + " \n", + " total_rchar,total_wchar,total_read_bytes,total_write_bytes,total_cancelled_write_bytes = getexecutor_stat(appals.file[:-len(\"app.log\")])\n", + " \n", + " if len(appals.failed_stages)>0:\n", + " failure=\"
\".join([\"query: \" + str(l[\"real_queryid\"])+\"|stage: \" + str(l[\"Stage ID\"]) for l in appals.df.where(\"`Stage ID` in (\"+\",\".join(appals.failed_stages)+\")\").select(\"real_queryid\",\"Stage ID\").distinct().collect()])\n", + " else:\n", + " failure=\"\"\n", + " \n", + " stats={\"appid\":appals.appid,\n", + " \"executor.instances\":appals.executor_instances,\n", + " \"executor.cores\":appals.executor_cores,\n", + " \"shuffle.partitions\":appals.parallelism,\n", + " \"batch size\":appals.batchsize,\n", + " \"real executors\":appals.realexecutors,\n", + " \"Failed Tasks\":failure,\n", + " \"Speculative Tasks\":appals.speculativetask,\n", + " \"Speculative Killed Tasks\":appals.speculativekilledtask,\n", + " \"Speculative Stage\":appals.speculativestage,\n", + " \"runtime\":round(sums['runtime'],2),\n", + " \"disk spilled\":round(sums['disk spilled'],2),\n", + " \"memspilled\":round(sums['memspilled'],2),\n", + " \"local_read\":round(sums['local_read'],2),\n", + " \"remote_read\":round(sums['remote_read'],2),\n", + " \"shuffle_write\":round(sums['shuffle_write'],2),\n", + " \"task run time\":round(sums['run_time'],2),\n", + " \"ser_time\":round(sums['ser_time'],2),\n", + " \"f_wait_time\":round(sums['f_wait_time'],2),\n", + " \"gc_time\":round(sums['gc_time'],2),\n", + " \"input read\":round(sums['input read'],2),\n", + " \"acc_task_time\":round(sums['acc_task_time'],2),\n", + " \"file read size\":round(total_rchar,2),\n", + " \"file write size\":round(total_wchar,2),\n", + " \"disk read size\":round(total_read_bytes,2),\n", + " \"disk write size\":round(total_write_bytes,2),\n", + " \"disk cancel size\":round(total_cancelled_write_bytes,2)\n", + " }\n", + " \n", + " display(HTML(f'''\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
appid{appals.appid}
executor.instances{appals.executor_instances}
executor.cores{appals.executor_cores}
shuffle.partitions{(appals.parallelism)}
batch size{(appals.batchsize):,}
real executors{(appals.realexecutors)}
Failed Tasks{(failure)}
Speculative Tasks{(appals.speculativetask)}
Speculative Killed Tasks{(appals.speculativekilledtask)}
Speculative Stage{(appals.speculativestage)}
runtime{round(sums['runtime'],2):,}
disk spilled{round(sums['disk spilled'],2):,}
memspilled{round(sums['memspilled'],2):,}
local_read{round(sums['local_read'],2):,}
remote_read{round(sums['remote_read'],2):,}
shuffle_write{round(sums['shuffle_write'],2):,}
task run time{round(sums['run_time'],2):,}
ser_time{round(sums['ser_time'],2):,}
f_wait_time{round(sums['f_wait_time'],2):,}
gc_time{round(sums['gc_time'],2):,}
input read{round(sums['input read'],2):,}
acc_task_time{round(sums['acc_task_time'],2):,}
file read size{round(total_rchar,2):,}
file write size{round(total_wchar,2):,}
disk read size{round(total_read_bytes,2):,}
disk write size{round(total_write_bytes,2):,}
disk cancel size{round(total_cancelled_write_bytes,2):,}
\n", + "\n", + " '''))\n", + " return stats\n", + " \n", + " \n", + " def generate_trace_view_list_exec(self,id=0,**kwargs):\n", + " Analysis.generate_trace_view_list(self,**kwargs)\n", + " showcpu=kwargs.get('showcpu',False)\n", + " shownodes=kwargs.get(\"shownodes\",None)\n", + " \n", + " showdf=self.df.where(F.col(\"Host\").isin(shownodes)) if shownodes else self.df\n", + " \n", + " events=showdf.toPandas()\n", + " coretrack={}\n", + " trace_events=[]\n", + " starttime=self.starttime\n", + " taskend=[]\n", + " trace={\"traceEvents\":[]}\n", + " exec_hosts={}\n", + " hostsdf=showdf.select(\"Host\").distinct().orderBy(\"Host\")\n", + " hostid=100000\n", + " ended_event=[]\n", + " \n", + " for i,l in hostsdf.toPandas().iterrows():\n", + " exec_hosts[l['Host']]=hostid\n", + " hostid=hostid+100000\n", + "\n", + " for idx,l in events.iterrows():\n", + " if l['Event']=='SparkListenerTaskStart':\n", + " hostid=exec_hosts[l['Host']]\n", + "\n", + " tsk=l['Task ID']\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " self.pids.append(pid)\n", + " stime=l['Launch Time']\n", + " #the task's starttime and finishtime is the same, ignore it.\n", + " if tsk in ended_event:\n", + " continue\n", + " if not pid in coretrack:\n", + " tids={}\n", + " trace_events.append({\n", + " \"name\": \"process_name\",\n", + " \"ph\": \"M\",\n", + " \"pid\":pid,\n", + " \"tid\":0,\n", + " \"args\":{\"name\":\"{:s}.{:s}\".format(l['Host'],l['Executor ID'])}\n", + " })\n", + "\n", + " else:\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==-1:\n", + " tids[t]=[tsk,stime]\n", + " break\n", + " else:\n", + " t=len(tids)\n", + " tids[t]=[tsk,stime]\n", + " #print(\"task {:d} tid is {:s}.{:d}\".format(tsk,pid,t))\n", + " coretrack[pid]=tids\n", + "\n", + " if l['Event']=='SparkListenerTaskEnd':\n", + " sevt={}\n", + " eevt={}\n", + " hostid=exec_hosts[l['Host']]\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " tsk=l['Task ID']\n", + " fintime=l['Finish Time']\n", + "\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==tsk:\n", + " tids[t]=[-1,-1]\n", + " break\n", + " else:\n", + " ended_event.append(tsk)\n", + " continue\n", + " for ps in reversed([key for key in tids.keys()]) :\n", + " if tids[ps][1]-fintime<0 and tids[ps][1]-fintime>=-2:\n", + " fintime=tids[ps][1]\n", + " tids[t]=tids[ps]\n", + " tids[ps]=[-1,-1]\n", + " break\n", + " if starttime==0:\n", + " starttime=l['Launch Time']\n", + " print(f'applog start time: {starttime}')\n", + "\n", + " sstime=l['Launch Time']-starttime\n", + "\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':sstime,\n", + " 'dur':fintime-l['Launch Time'],\n", + " 'pid':pid,\n", + " \"ph\":'X',\n", + " 'name':\"stg{:d}\".format(l['Stage ID']),\n", + " 'args':{\"job id\": l['job id'],\n", + " \"stage id\": l['Stage ID'],\n", + " \"tskid\":tsk,\n", + " \"input\":builtins.round(l[\"Bytes Read\"]/1024/1024,2),\n", + " \"spill\":builtins.round(l[\"Memory Bytes Spilled\"]/1024/1024,2),\n", + " \"Shuffle Read Metrics\": \"\",\n", + " \"|---Local Read\": builtins.round(l[\"Local Bytes Read\"]/1024/1024,2),\n", + " \"|---Remote Read\":builtins.round(l[\"Remote Bytes Read\"]/1024/1024,2),\n", + " \"Shuffle Write Metrics\": \"\",\n", + " \"|---Write\":builtins.round(l['Shuffle Bytes Written']/1024/1024,2)\n", + " }\n", + " })\n", + "\n", + " des_time=l['Executor Deserialize Time']\n", + " read_time=l['Fetch Wait Time']\n", + " exec_time=l['Executor Run Time']\n", + " write_time=math.floor(l['Shuffle Write Time']/1000000)\n", + " ser_time=l['Result Serialization Time']\n", + " getrst_time=l['Getting Result Time']\n", + " durtime=fintime-sstime-starttime;\n", + "\n", + " times=[0,des_time,read_time,exec_time,write_time,ser_time,getrst_time]\n", + " time_names=['sched delay','deserialize time','read time','executor time','write time','serialize time','result time']\n", + " evttime=reduce((lambda x, y: x + y),times)\n", + " if evttime>durtime:\n", + " times=[math.floor(l*1.0*durtime/evttime) for l in times]\n", + " else:\n", + " times[0]=durtime-evttime\n", + "\n", + " esstime=sstime\n", + " for idx in range(0,len(times)):\n", + " if times[idx]>0:\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':esstime,\n", + " 'dur':times[idx], \n", + " 'pid':pid,\n", + " 'ph':'X',\n", + " 'name':time_names[idx]})\n", + " if idx==3:\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':esstime,\n", + " 'dur':l['JVM GC Time'],\n", + " 'pid':pid,\n", + " 'ph':'X',\n", + " 'name':'GC Time'})\n", + " if showcpu:\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':esstime,\n", + " 'pid':pid,\n", + " 'ph':'C',\n", + " 'name':'cpu% {:d}'.format(pid+int(t)),\n", + " 'args':{'value':l['Executor CPU Time']/1000000.0/times[idx]}})\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':esstime+times[idx],\n", + " 'pid':pid,\n", + " 'ph':'C',\n", + " 'name':'cpu% {:d}'.format(pid+int(t)),\n", + " 'args':{'value':0}})\n", + " esstime=esstime+times[idx]\n", + " self.starttime=starttime\n", + " return [json.dumps(l) for l in trace_events]\n", + "\n", + " def generate_trace_view_list(self,id=0,**kwargs):\n", + " Analysis.generate_trace_view_list(self,**kwargs)\n", + " showcpu=kwargs.get('showcpu',False)\n", + " shownodes=kwargs.get(\"shownodes\",None)\n", + " \n", + " showdf=self.df.where(F.col(\"Host\").isin(shownodes)) if shownodes else self.df\n", + " \n", + " showdf=showdf.orderBy([\"eventtime\", \"Finish Time\"], ascending=[1, 0])\n", + " \n", + " events=showdf.drop(\"Accumulables\").toPandas()\n", + " coretrack={}\n", + " trace_events=[]\n", + " starttime=self.starttime\n", + " taskend=[]\n", + " trace={\"traceEvents\":[]}\n", + " exec_hosts={}\n", + " hostsdf=showdf.select(\"Host\").distinct().orderBy(\"Host\")\n", + " hostid=100000\n", + " ended_event=[]\n", + " \n", + " for i,l in hostsdf.toPandas().iterrows():\n", + " exec_hosts[l['Host']]=hostid\n", + " hostid=hostid+100000\n", + "\n", + " tskmap={}\n", + " for idx,l in events.iterrows():\n", + " if l['Event']=='SparkListenerTaskStart':\n", + " hostid=exec_hosts[l['Host']]\n", + "\n", + " tsk=l['Task ID']\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " self.pids.append(pid)\n", + " stime=l['Launch Time']\n", + " #the task's starttime and finishtime is the same, ignore it.\n", + " if tsk in ended_event:\n", + " continue\n", + " if not pid in coretrack:\n", + " tids={}\n", + " trace_events.append({\n", + " \"name\": \"process_name\",\n", + " \"ph\": \"M\",\n", + " \"pid\":pid,\n", + " \"tid\":0,\n", + " \"args\":{\"name\":\"{:s}.{:s}\".format(l['Host'],l['Executor ID'])}\n", + " })\n", + "\n", + " else:\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==-1:\n", + " tids[t]=[tsk,stime]\n", + " break\n", + " else:\n", + " t=len(tids)\n", + " tids[t]=[tsk,stime]\n", + " #print(f\"task {tsk} tid is {pid}.{t}\")\n", + " coretrack[pid]=tids\n", + "\n", + " if l['Event']=='SparkListenerTaskEnd':\n", + " sevt={}\n", + " eevt={}\n", + " hostid=exec_hosts[l['Host']]\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " tsk=l['Task ID']\n", + " fintime=l['Finish Time']\n", + " \n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==tsk:\n", + " tids[t]=[-1,-1]\n", + " break\n", + " else:\n", + " ended_event.append(tsk)\n", + " continue\n", + " for ps in reversed([key for key in tids.keys()]) :\n", + " if tids[ps][1]-fintime<0 and tids[ps][1]-fintime>=-2:\n", + " fintime=tids[ps][1]\n", + " tids[t]=tids[ps]\n", + " tids[ps]=[-1,-1]\n", + " break\n", + " if starttime==0:\n", + " starttime=l['Launch Time']\n", + " print(f'applog start time: {starttime}')\n", + "\n", + " sstime=l['Launch Time']-starttime\n", + "\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':sstime,\n", + " 'dur':fintime-l['Launch Time'],\n", + " 'pid':pid,\n", + " \"ph\":'X',\n", + " 'name':\"stg{:d}\".format(l['Stage ID']),\n", + " 'args':{\"job id\": l['Job ID'],\n", + " \"stage id\": l['Stage ID'],\n", + " \"tskid\":tsk,\n", + " \"input\":builtins.round(l[\"Bytes Read\"]/1024/1024,2),\n", + " \"spill\":builtins.round(l[\"Memory Bytes Spilled\"]/1024/1024,2),\n", + " \"Shuffle Read Metrics\": \"\",\n", + " \"|---Local Read\": builtins.round(l[\"Local Bytes Read\"]/1024/1024,2),\n", + " \"|---Remote Read\":builtins.round(l[\"Remote Bytes Read\"]/1024/1024,2),\n", + " \"Shuffle Write Metrics\": \"\",\n", + " \"|---Write\":builtins.round(l['Shuffle Bytes Written']/1024/1024,2)\n", + " }\n", + " })\n", + " tskmap[tsk]={'pid':pid,'tid':pid+int(t)}\n", + "\n", + " self.starttime=starttime\n", + " self.tskmap=tskmap\n", + " output=[json.dumps(l) for l in trace_events]\n", + " \n", + " df=self.df\n", + " \n", + " if showcpu and len(self.metricscollect)>0:\n", + " metricscollect=self.metricscollect\n", + " metrics_explode=df.where(\"Event='SparkListenerTaskEnd'\").withColumn(\"metrics\",F.explode(\"Accumulables\"))\n", + " m1092=metrics_explode.select(F.col(\"Executor ID\"),F.col(\"`Stage ID`\"),\"`Task ID`\",F.col(\"`Finish Time`\"),F.col(\"`Launch Time`\"),(F.col(\"`Finish Time`\")-F.col(\"`Launch Time`\")).alias(\"elapsedtime\"),\"metrics.*\").where(F.col(\"ID\").isin([l[0] for l in metricscollect]))\n", + " metric_name_df = spark.createDataFrame(metricscollect)\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_1\",\"ID\")\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_2\",\"unit\")\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_3\",\"mname\")\n", + "\n", + " met_df=m1092.join(metric_name_df,on=\"ID\")\n", + " met_df=met_df.withColumn(\"Update\",F.when(F.col(\"unit\")=='nsTiming',F.col(\"Update\")/1000000).otherwise(F.col(\"Update\")+0))\n", + " met_df=met_df.where(\"Update>1\")\n", + "\n", + " metdfx=met_df.groupBy(\"Task ID\",\"elapsedtime\").agg(F.sum(\"Update\").alias(\"totalCnt\"))\n", + " taskratio=metdfx.withColumn(\"ratio\",F.when(F.col(\"totalCnt\") 'time to collect batch' and mname <> 'time of scan'\")\n", + "\n", + " met_df=m1092.join(metric_name_df,on=\"ID\")\n", + " met_df=met_df.withColumn(\"Update\",F.when(F.col(\"unit\")=='nsTiming',F.col(\"Update\")/1000000).otherwise(F.col(\"Update\")+0))\n", + " \n", + " #pandas UDF doesn't work. hang\n", + " #tmbk=met_df.groupBy('Task ID').apply(time_breakdown)\n", + " \n", + " w=Window.partitionBy('Task ID')\n", + " met_df1=met_df.withColumn(\"sum_update\",F.sum(\"Update\").over(w))\n", + " met_df2=met_df1.withColumn(\"ratio\",(F.col(\"Finish Time\")-F.col(\"Launch Time\")-2)/F.col(\"sum_update\"))\n", + " met_df3=met_df2.withColumn(\"ratio\",F.when(F.col(\"ratio\")>1,1).otherwise(F.col(\"ratio\")))\n", + " met_df4=met_df3.withColumn(\"update_ratio\",F.floor(F.col(\"ratio\")*F.col(\"Update\")))\n", + " met_df5=met_df4.where(F.col(\"update_ratio\")>2)\n", + " w = (Window.partitionBy('Task ID').orderBy(F.desc(\"update_ratio\")).rowsBetween(Window.unboundedPreceding, Window.currentRow))\n", + " met_df6=met_df5.withColumn('ltime_dur', F.sum('update_ratio').over(w))\n", + " met_df8=met_df6.withColumn(\"ltime\",F.col(\"ltime_dur\")+F.col(\"Launch Time\")-F.col(\"update_ratio\"))\n", + "\n", + " tmbk=met_df8.withColumn(\"taskid\",F.col(\"Task ID\")).withColumn(\"start\",F.col(\"ltime\")+F.lit(1)).withColumn(\"dur\",F.col(\"update_ratio\")-F.lit(1)).withColumn(\"name\",F.col(\"mname\"))\n", + " \n", + " \n", + " traces.extend(tmbk.select(\n", + " F.lit(38).alias(\"tid\"),\n", + " (F.col(\"start\")-F.lit(self.starttime)).alias(\"ts\"),\n", + " (F.col(\"dur\")).alias(\"dur\"),\n", + " F.lit(pid).alias(\"pid\"),\n", + " F.lit(\"X\").alias(\"ph\"),\n", + " F.col(\"name\").alias(\"name\")).toJSON().collect())\n", + " traces.append(json.dumps({\n", + " \"name\": \"process_name\",\n", + " \"ph\": \"M\",\n", + " \"pid\":pid,\n", + " \"tid\":0,\n", + " \"args\":{\"name\":\"critical path\"}\n", + " }))\n", + " return traces \n", + " \n", + " def show_Stage_histogram(apps,stageid,bincount):\n", + " if apps.df is None:\n", + " apps.load_data()\n", + " \n", + " inputsize = apps.df.where(\"`Stage ID`={:d}\".format(stageid)).select(\"Stage ID\",\"Executor ID\", \"Task ID\", F.explode(\"Accumulables\")) \\\n", + " .select(\"Stage ID\",\"Executor ID\", \"Task ID\",\"col.*\") \\\n", + " .where(\"Name='input size in bytes' or Name='size of files read'\") \\\n", + " .groupBy(\"Task ID\") \\\n", + " .agg((F.sum(\"Update\")).alias(\"input read\"))\n", + "\n", + "\n", + " stage37=apps.df.where(\"`Stage ID`={:d} and event='SparkListenerTaskEnd'\".format(stageid) )\\\n", + " .join(inputsize,on=[\"Task ID\"],how=\"left\")\\\n", + " .fillna(0) \\\n", + " .select(F.col('Host'), \n", + " F.round((F.col('Finish Time')/1000-F.col('Launch Time')/1000),2).alias('elapsedtime'),\n", + " F.round((F.col('`input read`')+F.col('`Bytes Read`')+F.col('`Local Bytes Read`')+F.col('`Remote Bytes Read`'))/1024/1024,2).alias('input'))\n", + " stage37=stage37.cache()\n", + " hist_elapsedtime=stage37.select('elapsedtime').rdd.flatMap(lambda x: x).histogram(15)\n", + " hist_input=stage37.select('input').rdd.flatMap(lambda x: x).histogram(15)\n", + " fig, axs = plt.subplots(figsize=(30, 5),nrows=1, ncols=2)\n", + " ax=axs[0]\n", + " binSides, binCounts = hist_elapsedtime\n", + " binSides=[builtins.round(l,2) for l in binSides]\n", + "\n", + " N = len(binCounts)\n", + " ind = numpy.arange(N)\n", + " width = 0.5\n", + "\n", + " rects1 = ax.bar(ind+0.5, binCounts, width, color='b')\n", + "\n", + " ax.set_ylabel('Frequencies')\n", + " ax.set_title('stage{:d} elapsed time breakdown'.format(stageid))\n", + " ax.set_xticks(numpy.arange(N+1))\n", + " ax.set_xticklabels(binSides)\n", + "\n", + " ax=axs[1]\n", + " binSides, binCounts = hist_input\n", + " binSides=[builtins.round(l,2) for l in binSides]\n", + "\n", + " N = len(binCounts)\n", + " ind = numpy.arange(N)\n", + " width = 0.5\n", + " rects1 = ax.bar(ind+0.5, binCounts, width, color='b')\n", + "\n", + " ax.set_ylabel('Frequencies')\n", + " ax.set_title('stage{:d} input data breakdown'.format(stageid))\n", + " ax.set_xticks(numpy.arange(N+1))\n", + " ax.set_xticklabels(binSides)\n", + "\n", + " out=stage37\n", + " outpds=out.toPandas()\n", + "\n", + " fig, axs = plt.subplots(nrows=1, ncols=3, sharey=False,figsize=(30,8),gridspec_kw = {'width_ratios':[1, 1, 1]})\n", + " plt.subplots_adjust(wspace=0.01)\n", + "\n", + " groups= outpds.groupby('Host')\n", + " for name, group in groups:\n", + " axs[0].plot(group.input, group.elapsedtime, marker='o', linestyle='', ms=5, label=name)\n", + " axs[0].set_xlabel('input size (MB)')\n", + " axs[0].set_ylabel('elapsed time (s)')\n", + "\n", + " axs[0].legend()\n", + "\n", + " axs[0].get_shared_y_axes().join(axs[0], axs[1])\n", + "\n", + " sns.violinplot(y='elapsedtime', x='Host', data=outpds,palette=['g'],ax=axs[1])\n", + "\n", + " sns.violinplot(y='input', x='Host', data=outpds,palette=['g'],ax=axs[2])\n", + "\n", + " #ax.xaxis.set_major_formatter(mtick.FormatStrFormatter(''))\n", + " #ax.yaxis.set_major_formatter(mtick.FormatStrFormatter(''))\n", + "\n", + " if False:\n", + " out=stage37\n", + " vecAssembler = VectorAssembler(inputCols=[\"input\",'elapsedtime'], outputCol=\"features\").setHandleInvalid(\"skip\")\n", + " new_df = vecAssembler.transform(out)\n", + " kmeans = KMeans(k=2, seed=1) # 2 clusters here\n", + " model = kmeans.fit(new_df.select('features'))\n", + " transformed = model.transform(new_df)\n", + "\n", + "\n", + " outpds=transformed.select('Host','elapsedtime','input','prediction').toPandas()\n", + "\n", + " fig, axs = plt.subplots(nrows=1, ncols=2, sharey=False,figsize=(30,8),gridspec_kw = {'width_ratios':[1, 1]})\n", + " plt.subplots_adjust(wspace=0.01)\n", + "\n", + " groups= outpds.groupby('prediction')\n", + " for name, group in groups:\n", + " axs[0].plot(group.input, group.elapsedtime, marker='o', linestyle='', ms=5, label=name)\n", + " axs[0].legend()\n", + "\n", + " bars=transformed.where('prediction=1').groupBy(\"Host\").count().toPandas()\n", + "\n", + " axs[1].bar(bars['Host'], bars['count'], 0.4, color='coral')\n", + " axs[1].set_title('cluster=1')\n", + "\n", + " plt.show()\n", + " \n", + " def show_Stages_hist(apps,**kwargs):\n", + " if apps.df is None:\n", + " apps.load_data()\n", + " \n", + " bincount=kwargs.get(\"bincount\",15)\n", + " threshold=kwargs.get(\"threshold\",0.9)\n", + " \n", + " query=kwargs.get(\"queryid\",None)\n", + " if query and type(query)==int:\n", + " query = [query,]\n", + " df=apps.df.where(F.col(\"real_queryid\").isin(query)) if query else apps.df\n", + " \n", + " totaltime=df.where(\"event='SparkListenerTaskEnd'\" ).agg(F.sum(F.col('Finish Time')-F.col('Launch Time')).alias('total_time')).collect()[0]['total_time']\n", + " stage_time=df.where(\"event='SparkListenerTaskEnd'\" ).groupBy('`Stage ID`').agg(F.sum(F.col('Finish Time')-F.col('Launch Time')).alias('total_time')).orderBy('total_time', ascending=False).toPandas()\n", + " stage_time['acc_total'] = stage_time['total_time'].cumsum()/totaltime\n", + " stage_time=stage_time.reset_index()\n", + " fig, ax = plt.subplots(figsize=(30, 5))\n", + "\n", + " rects1 = ax.plot(stage_time['index'],stage_time['acc_total'],'b.-')\n", + " ax.set_xticks(stage_time['index'])\n", + " ax.set_xticklabels(stage_time['Stage ID'])\n", + " ax.set_xlabel('stage')\n", + " ax.grid(which='major', axis='x')\n", + " plt.show()\n", + " shownstage=[]\n", + " for x in stage_time.index:\n", + " if stage_time['acc_total'][x]<=threshold:\n", + " shownstage.append(stage_time['Stage ID'][x])\n", + " else:\n", + " shownstage.append(stage_time['Stage ID'][x])\n", + " break\n", + " for row in shownstage:\n", + " apps.show_Stage_histogram(row,bincount) \n", + " \n", + " def get_hottest_stages(apps,**kwargs):\n", + " if apps.df is None:\n", + " apps.load_data()\n", + " \n", + " bincount=kwargs.get(\"bincount\",15)\n", + " threshold=kwargs.get(\"threshold\",0.9)\n", + " plot=kwargs.get(\"plot\",True)\n", + " \n", + " query=kwargs.get(\"queryid\",None)\n", + " if query and type(query)==int:\n", + " query = [query,]\n", + " df=apps.df.where(F.col(\"real_queryid\").isin(query)) if query else apps.df.where(\"queryid is not NULL\")\n", + "\n", + " stage_time=df.where(\"event='SparkListenerTaskEnd'\" ).groupBy('`Stage ID`','Job ID','real_queryid').agg(\n", + " F.sum(F.col('Finish Time')-F.col('Launch Time')).alias('total_time'),\n", + " F.stddev(F.col('Finish Time')/1000-F.col('Launch Time')/1000).alias('stdev_time'),\n", + " F.count(\"*\").alias(\"cnt\"),\n", + " F.first('queryid').astype(IntegerType()).alias('queryid')\n", + " )\\\n", + " .select('`Stage ID`','Job ID','real_queryid','queryid',\n", + " (F.col(\"total_time\")/1000/(F.when(F.col(\"cnt\")>F.lit(apps.executor_instances*apps.executor_cores/apps.taskcpus),F.lit(apps.executor_instances*apps.executor_cores/apps.taskcpus)).otherwise(F.col(\"cnt\")))).alias(\"total_time\"),\n", + " F.col(\"stdev_time\")\n", + " ).orderBy('total_time', ascending=False).toPandas()\n", + "\n", + " totaltime=stage_time['total_time'].sum()\n", + " stage_time['acc_total'] = stage_time['total_time'].cumsum()/totaltime\n", + " stage_time['total'] = stage_time['total_time']/totaltime\n", + " stage_time=stage_time.reset_index()\n", + "\n", + " shownstage=stage_time.loc[stage_time['acc_total'] <=threshold]\n", + " shownstage['stg']=shownstage['real_queryid'].astype(str)+'_'+shownstage['Job ID'].astype(str)+'_'+shownstage['Stage ID'].astype(str)\n", + " if plot:\n", + " shownstage.plot.bar(x=\"stg\",y=\"total\",figsize=(30,8))\n", + "\n", + "\n", + "\n", + " norm = matplotlib.colors.Normalize(vmin=0, vmax=max(stage_time.queryid))\n", + " cmap = matplotlib.cm.get_cmap('brg')\n", + " def setbkcolor(x):\n", + " rgba=cmap(norm(x['queryid']))\n", + " return ['background-color:rgba({:d},{:d},{:d},1); color:white'.format(int(rgba[0]*255),int(rgba[1]*255),int(rgba[2]*255))]*9\n", + "\n", + " if plot:\n", + " display(stage_time.style.apply(setbkcolor,axis=1).format({\"total_time\":lambda x: '{:,.2f}'.format(x),\"acc_total\":lambda x: '{:,.2%}'.format(x),\"total\":lambda x: '{:,.2%}'.format(x)}))\n", + " \n", + " return stage_time\n", + "\n", + " def scatter_elapsetime_input(apps,stageid):\n", + " if apps.df is None:\n", + " apps.load_data()\n", + " stage37=apps.df.where(\"`Stage ID`={:d} and event='SparkListenerTaskEnd'\".format(stageid) ).select(F.round((F.col('Finish Time')/1000-F.col('Launch Time')/1000),2).alias('elapsedtime'),F.round((F.col('`Bytes Read`')+F.col('`Local Bytes Read`')+F.col('`Remote Bytes Read`'))/1024/1024,2).alias('input')).toPandas()\n", + " stage37.plot.scatter('input','elapsedtime',figsize=(30, 5))\n", + "\n", + " def get_critical_path_stages(self): \n", + " df=self.df.where(\"Event='SparkListenerTaskEnd'\")\n", + " criticaltasks=self.criticaltasks\n", + " cripds=pandas.DataFrame(criticaltasks)\n", + " cripds.columns=['task_id',\"launch\",\"finish\"]\n", + " cridf=spark.createDataFrame(cripds)\n", + " df_ctsk=df.join(cridf,on=[F.col(\"task_id\")==F.col(\"Task ID\")],how=\"inner\")\n", + " df_ctsk=df_ctsk.withColumn(\"elapsed\",(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000)\n", + " return df_ctsk.where(\"elapsed>10\").orderBy(F.desc(\"elapsed\")).select(\"real_queryid\",F.round(\"elapsed\",2).alias(\"elapsed\"),\"Host\",\"executor ID\",\"Stage ID\",\"Task ID\",F.round(F.col(\"Bytes Read\")/1000000,0).alias(\"file read\"),F.round((F.col(\"Local Bytes Read\")+F.col(\"Remote Bytes Read\"))/1000000,0).alias(\"shuffle read\")).toPandas()\n", + " \n", + " def show_time_metric(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + " shownodes=kwargs.get(\"shownodes\",None)\n", + " query=kwargs.get(\"queryid\",None)\n", + " plot=kwargs.get(\"plot\",True)\n", + " taskids=kwargs.get(\"taskids\",None)\n", + " \n", + " if query and type(query)==int:\n", + " query = [query,]\n", + " \n", + " showexecutor=kwargs.get(\"showexecutor\",True) if not taskids else False\n", + " queryid = query[0] if query else 0\n", + " \n", + " df=self.df.where(F.col(\"Host\").isin(shownodes)) if shownodes else self.df\n", + " df=df.where(F.col(\"real_queryid\").isin(query)) if query else df.where(\"queryid is not NULL\")\n", + "\n", + " df=df.where(F.col(\"Task ID\").isin(taskids)) if taskids else df\n", + "\n", + " exec_cores=1 if taskids else self.executor_cores\n", + " execs=1 if taskids else self.executor_instances\n", + "\n", + " metricscollect=self.metricscollect\n", + "\n", + " metrics_explode=df.where(\"Event='SparkListenerTaskEnd'\").withColumn(\"metrics\",F.explode(\"Accumulables\"))\n", + " m1092=metrics_explode.select(F.col(\"Executor ID\"),F.col(\"`Stage ID`\"),\"`Task ID`\",F.col(\"`Finish Time`\"),F.col(\"`Launch Time`\"),(F.col(\"`Finish Time`\")-F.col(\"`Launch Time`\")).alias(\"elapsedtime\"),\"metrics.*\").where(F.col(\"ID\").isin([l[0] for l in metricscollect]))\n", + " metric_name_df = spark.createDataFrame(metricscollect)\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_1\",\"ID\")\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_2\",\"unit\")\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_3\",\"mname\")\n", + " metric_name_df=metric_name_df.withColumnRenamed(\"_4\",\"node\")\n", + "\n", + " runtime=metrics_explode.agg(F.round(F.max(\"Finish Time\")/1000-F.min(\"Launch Time\")/1000,2).alias(\"runtime\")).collect()[0][\"runtime\"]\n", + "\n", + " met_df=m1092.join(metric_name_df,on=\"ID\")\n", + " met_df=met_df.withColumn(\"Update\",F.when(F.col(\"unit\")=='nsTiming',F.col(\"Update\")/1000000).otherwise(F.col(\"Update\")+0))\n", + " outpdf=met_df.groupBy(\"`Executor ID`\",\"mname\").sum(\"Update\").orderBy(\"Executor ID\").toPandas()\n", + "\n", + " met_time_cnt=df.where(\"Event='SparkListenerTaskEnd'\")\n", + " exectime=met_time_cnt.groupBy(\"Executor ID\").agg((F.max(\"Finish Time\")-F.min(\"Launch Time\")).alias(\"totaltime\"),F.sum(F.col(\"`Finish Time`\")-F.col(\"`Launch Time`\")).alias(\"tasktime\"))\n", + "\n", + " totaltime_query=met_time_cnt.groupBy(\"real_queryid\").agg((F.max(\"Finish Time\")-F.min(\"Launch Time\")).alias(\"totaltime\")).agg(F.sum(\"totaltime\").alias(\"totaltime\")).collect()\n", + " totaltime_query=totaltime_query[0][\"totaltime\"]\n", + " \n", + " pdf=exectime.toPandas()\n", + " exeids=set(outpdf['Executor ID'])\n", + " outpdfs=[outpdf[outpdf[\"Executor ID\"]==l] for l in exeids]\n", + " tasktime=pdf.set_index(\"Executor ID\").to_dict()['tasktime']\n", + "\n", + " def comb(l,r):\n", + " execid=list(r['Executor ID'])[0]\n", + " lp=r[['mname','sum(Update)']]\n", + " lp.columns=[\"mname\",\"val_\"+execid]\n", + " idle=totaltime_query*exec_cores-tasktime[execid]\n", + " nocount=tasktime[execid]-sum(lp[\"val_\"+execid])\n", + " if idle<0:\n", + " idle=0\n", + " if nocount<0:\n", + " nocount=0\n", + " lp=lp.append([{\"mname\":\"idle\",\"val_\"+execid:idle}])\n", + " lp=lp.append([{\"mname\":\"not_counted\",\"val_\"+execid:nocount}])\n", + " if l is not None:\n", + " return pandas.merge(lp, l,on=[\"mname\"],how='outer')\n", + " else:\n", + " return lp\n", + "\n", + " rstpdf=None\n", + " for l in outpdfs[0:]:\n", + " rstpdf=comb(rstpdf,l)\n", + " \n", + " for l in [l for l in rstpdf.columns if l!=\"mname\"]:\n", + " rstpdf[l]=rstpdf[l]/1000/exec_cores\n", + " \n", + " rstpdf=rstpdf.sort_values(by=\"val_\"+list(exeids)[0],axis=0,ascending=False)\n", + " if showexecutor and plot:\n", + " rstpdf.set_index(\"mname\").T.plot.bar(stacked=True,figsize=(30,8))\n", + " pdf_sum=pandas.DataFrame(rstpdf.set_index(\"mname\").T.sum())\n", + " totaltime=totaltime_query/1000\n", + " pdf_sum[0]=pdf_sum[0]/(execs)\n", + " pdf_sum[0][\"idle\"]=(totaltime_query-sum(tasktime.values())/execs/exec_cores)/1000\n", + " pdf_sum=pdf_sum.sort_values(by=0,axis=0,ascending=False)\n", + " pdf_sum=pdf_sum.T\n", + " pdf_sum.columns=[\"{:>2.0f}%_{:s}\".format(pdf_sum[l][0]/totaltime*100,l) for l in pdf_sum.columns]\n", + " matplotlib.rcParams['font.sans-serif'] = \"monospace\"\n", + " matplotlib.rcParams['font.family'] = \"monospace\"\n", + " import matplotlib.font_manager as font_manager\n", + " if plot:\n", + " ax=pdf_sum.plot.bar(stacked=True,figsize=(30,8))\n", + " font = font_manager.FontProperties(family='monospace',\n", + " style='normal', size=14)\n", + " ax.legend(prop=font,loc=4)\n", + " plt.title(\"{:s} q{:d} executors={:d} cores_per_executor={:d} parallelism={:d} sumtime={:.0f} runtime={:.0f}\".format(self.file.split(\"/\")[2],queryid,self.executor_instances,self.executor_cores,self.parallelism,totaltime,runtime),fontdict={'fontsize':24})\n", + " return pdf_sum\n", + "\n", + " def show_critical_path_time_breakdown(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + " return self.show_time_metric(taskids=[l[0].item() for l in self.criticaltasks])\n", + " \n", + " def get_spark_config(self):\n", + " df=spark.read.json(self.file)\n", + " self.appid=df.where(\"`App ID` is not null\").collect()[0][\"App ID\"]\n", + " pandas.set_option('display.max_rows', None)\n", + " pandas.set_option('display.max_columns', None)\n", + " pandas.set_option('display.max_colwidth', 100000)\n", + " return df.select(\"Properties.*\").where(\"`spark.app.id` is not null\").limit(1).toPandas().T\n", + " \n", + " def get_app_name(self):\n", + " cfg=self.get_spark_config()\n", + " display(HTML(\"\" + cfg.loc[cfg.index=='spark.app.name'][0][0]+\"\"))\n", + " \n", + " \n", + " def get_query_time(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + " queryid=kwargs.get(\"queryid\",None)\n", + " showtable=kwargs.get(\"showtable\",True)\n", + " plot=kwargs.get(\"plot\",True)\n", + " \n", + " if queryid and type(queryid)==int:\n", + " queryid = [queryid,]\n", + " \n", + " df=self.df.where(F.col(\"real_queryid\").isin(queryid)) if queryid else self.df.where(\"queryid is not NULL\")\n", + " \n", + " \n", + " stages=df.select(\"real_queryid\",\"Stage ID\").distinct().orderBy(\"Stage ID\").groupBy(\"real_queryid\").agg(F.collect_list(\"Stage ID\").alias(\"stages\")).orderBy(\"real_queryid\")\n", + " runtimeacc=df.where(\"Event='SparkListenerTaskEnd'\") \\\n", + " .groupBy(\"real_queryid\") \\\n", + " .agg(F.round(F.sum(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000/self.executor_instances/self.executor_cores*self.taskcpus,2).alias(\"acc_task_time\"))\n", + " inputsize = df.select(\"real_queryid\",\"Stage ID\",\"Executor ID\", \"Task ID\", F.explode(\"Accumulables\")) \\\n", + " .select(\"real_queryid\",\"Stage ID\",\"Executor ID\", \"Task ID\",\"col.*\") \\\n", + " .where(\"Name='input size in bytes' or Name='size of files read'\") \\\n", + " .groupBy(\"real_queryid\") \\\n", + " .agg(F.round(F.sum(\"Update\")/1024/1024/1024,2).alias(\"input read\")).orderBy(\"real_queryid\")\n", + " if self.dfacc is not None:\n", + " inputsizev1 = self.dfacc.where(\"Name='size of files read'\").groupBy(\"real_queryid\").agg(F.round(F.sum(\"Update\")/1024/1024/1024,2).alias(\"input read v1\")).orderBy(\"real_queryid\")\n", + " inputsize=inputsize.join(inputsizev1,on=\"real_queryid\",how=\"outer\")\n", + " inputsize=inputsize.withColumn(\"input read\",F.coalesce(F.col(\"input read\"),F.col(\"input read v1\"))).drop(\"input read v1\")\n", + " \n", + " outputrows = df.select(\"real_queryid\",\"Stage ID\",\"Stage ID\",F.explode(\"Accumulables\"))\\\n", + " .select(\"real_queryid\",\"Stage ID\",\"Stage ID\",\"col.*\")\\\n", + " .where(\"Name='number of output rows'\")\\\n", + " .groupBy(\"real_queryid\")\\\n", + " .agg(F.round(F.sum(\"Update\")/1000000000,2).alias(\"output rows\"))\n", + " \n", + " stages=runtimeacc.join(stages,on=\"real_queryid\",how=\"left\")\n", + " stages=inputsize.join(stages,on=\"real_queryid\",how=\"left\")\n", + " stages=stages.join(outputrows,on='real_queryid',how=\"left\")\n", + " \n", + " out=df.groupBy(\"real_queryid\").agg(\n", + " F.round(F.max(\"query_endtime\")/1000-F.min(\"query_starttime\")/1000,2).alias(\"runtime\"),\n", + " F.round(F.sum(\"Disk Bytes Spilled\")/1024/1024/1024,2).alias(\"disk spilled\"),\n", + " F.round(F.sum(\"Memory Bytes Spilled\")/1024/1024/1024,2).alias(\"memspilled\"),\n", + " F.round(F.sum(\"Local Bytes Read\")/1024/1024/1024,2).alias(\"local_read\"),\n", + " F.round(F.sum(\"Remote Bytes Read\")/1024/1024/1024,2).alias(\"remote_read\"),\n", + " F.round(F.sum(\"Shuffle Bytes Written\")/1024/1024/1024,2).alias(\"shuffle_write\"),\n", + " F.round(F.sum(\"Executor Deserialize Time\")/1000/self.parallelism,2).alias(\"deser_time\"),\n", + " F.round(F.sum(\"Executor Run Time\")/1000/self.parallelism,2).alias(\"run_time\"),\n", + " F.round(F.sum(\"Result Serialization Time\")/1000/self.parallelism,2).alias(\"ser_time\"),\n", + " F.round(F.sum(\"Fetch Wait Time\")/1000/self.parallelism,2).alias(\"f_wait_time\"),\n", + " F.round(F.sum(\"JVM GC Time\")/1000/self.parallelism,2).alias(\"gc_time\"),\n", + " F.round(F.max(\"Peak Execution Memory\")/1000000000*self.executor_instances*self.executor_cores,2).alias(\"peak_mem\"),\n", + " F.max(\"queryid\").alias(\"queryid\")\n", + " ).join(stages,\"real_queryid\",how=\"left\").orderBy(\"real_queryid\").toPandas().set_index(\"real_queryid\")\n", + " out[\"executors\"]=self.executor_instances\n", + " out[\"core/exec\"]=self.executor_cores\n", + " out[\"task.cpus\"]=self.taskcpus\n", + " out['parallelism']=self.parallelism\n", + " \n", + " if not showtable:\n", + " return out\n", + "\n", + " def highlight_greater(x):\n", + " m1 = x['acc_task_time'] / x['runtime'] * 100\n", + " m2 = x['run_time'] / x['runtime'] * 100\n", + " m3 = x['f_wait_time'] / x['runtime'] * 100\n", + " \n", + "\n", + " df1 = pandas.DataFrame('', index=x.index, columns=x.columns)\n", + "\n", + " df1['acc_task_time'] = m1.apply(lambda x: 'background-image: linear-gradient(to right,#5fba7d {:f}%,white {:f}%)'.format(x,x))\n", + " df1['run_time'] = m2.apply(lambda x: 'background-image: linear-gradient(to right,#5fba7d {:f}%,white {:f}%)'.format(x,x))\n", + " df1['f_wait_time'] = m3.apply(lambda x: 'background-image: linear-gradient(to right,#d65f5f {:f}%,white {:f}%)'.format(x,x))\n", + " return df1\n", + "\n", + "\n", + " cm = sns.light_palette(\"green\", as_cmap=True)\n", + " if plot:\n", + " display(out.style.apply(highlight_greater, axis=None).background_gradient(cmap=cm,subset=['input read', 'shuffle_write']))\n", + " \n", + " return out\n", + " \n", + " def get_query_time_metric(self):\n", + " if self.df is None:\n", + " self.load_data()\n", + " querids=self.df.select(\"queryid\").distinct().collect()\n", + " for idx,q in enumerate([l[\"queryid\"] for l in querids]):\n", + " self.show_time_metric(query=[q,],showexecutor=False)\n", + " \n", + " def getOperatorCount(self):\n", + " if self.df is None:\n", + " self.load_data()\n", + " df=spark.read.json(self.file)\n", + " queryids=self.df.select(F.col(\"queryid\").astype(LongType()),F.col(\"real_queryid\")).distinct().orderBy(\"real_queryid\")\n", + " queryplans=self.queryplans.collect()\n", + " list_queryid=[l.real_queryid for l in queryids.collect()]\n", + "\n", + " def get_child(execid,node):\n", + " #wholestagetransformer not counted\n", + " if node['nodeName'] is not None and not node['nodeName'].startswith(\"WholeStageCodegenTransformer\"):\n", + " if node[\"nodeName\"] not in qps:\n", + " qps[node[\"nodeName\"]]={l:0 for l in list_queryid}\n", + " qps[node[\"nodeName\"]][execid]=qps[node[\"nodeName\"]][execid]+1\n", + " if node[\"children\"] is not None:\n", + " for c in node[\"children\"]:\n", + " get_child(execid,c)\n", + "\n", + " qps={}\n", + " for c in queryplans:\n", + " get_child(c['real_queryid'],c)\n", + "\n", + " return pandas.DataFrame(qps).T.sort_index(axis=0) \n", + " \n", + " def get_query_plan(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " queryid=kwargs.get(\"queryid\",None)\n", + " stageid=kwargs.get(\"stageid\",None)\n", + " \n", + " outputstage=kwargs.get(\"outputstage\",None)\n", + " \n", + " show_plan_only=kwargs.get(\"show_plan_only\",False)\n", + " show_simple_string=kwargs.get(\"show_simple_string\",False)\n", + "\n", + " plot=kwargs.get(\"plot\",True)\n", + " \n", + " colors=[\"#{:02x}{:02x}{:02x}\".format(int(l[0]*255),int(l[1]*255),int(l[2]*255)) for l in matplotlib.cm.get_cmap('tab20').colors]\n", + " \n", + " if queryid is not None:\n", + " if type(queryid)==int or type(queryid)==str:\n", + " queryid = [queryid,]\n", + " shown_stageid = [l[\"Stage ID\"] for l in self.df.where(F.col(\"real_queryid\").isin(queryid)).select(\"Stage ID\").distinct().collect()]\n", + " if stageid is not None:\n", + " if type(stageid)==int:\n", + " shown_stageid = [stageid,]\n", + " elif type(stageid)==list:\n", + " shown_stageid = stageid\n", + " queryid = [l[\"real_queryid\"] for l in self.df.where(F.col(\"`Stage ID`\").isin(shown_stageid)).select(\"real_queryid\").limit(1).collect()]\n", + "\n", + "\n", + " queryplans=[]\n", + " queryplans = self.queryplans.where(F.col(\"real_queryid\").isin(queryid)).orderBy(\"real_queryid\").collect() if queryid else self.queryplans.orderBy(\"real_queryid\").collect()\n", + " dfmetric=self.df.where(\"Event='SparkListenerTaskEnd'\").select(\"queryid\",\"real_queryid\",\"Stage ID\",\"Job ID\",F.explode(\"Accumulables\").alias(\"metric\")).select(\"*\",\"metric.*\").select(\"Stage ID\",\"ID\",\"Update\").groupBy(\"ID\",\"Stage ID\").agg(F.round(F.sum(\"Update\"),1).alias(\"value\"),F.round(F.stddev(\"Update\"),1).alias(\"stdev\")).collect()\n", + " accid2stageid={l.ID:(l[\"Stage ID\"],l[\"value\"],l[\"stdev\"]) for l in dfmetric}\n", + "\n", + " stagetime=self.df.where((F.col(\"real_queryid\").isin(queryid))).where(F.col(\"Event\")=='SparkListenerTaskEnd').groupBy(\"Stage ID\").agg(\n", + " F.round(F.sum(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000/self.executor_instances/self.executor_cores*self.taskcpus,1).alias(\"elapsed time\"),\n", + " F.round(F.stddev(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000,1).alias(\"time stdev\"),\n", + " F.count(F.col(\"Task ID\")).alias(\"partitions\")\n", + " ).orderBy(F.desc(\"elapsed time\")).collect()\n", + "\n", + " apptotaltime=reduce(lambda x,y: x+y['elapsed time'], stagetime,0)\n", + " if apptotaltime==0:\n", + " display(HTML(\"Error, totaltime is 0 \"))\n", + " apptotaltime=1\n", + " return \"\"\n", + "\n", + " stagemap={l[\"Stage ID\"]:l[\"elapsed time\"] for l in stagetime}\n", + " stage_time_stdev_map={l[\"Stage ID\"]:l[\"time stdev\"] for l in stagetime}\n", + " stagepartmap={l[\"Stage ID\"]:l[\"partitions\"] for l in stagetime}\n", + "\n", + " keystage=[]\n", + " keystagetime=[]\n", + " subtotal=0\n", + " for s in stagetime:\n", + " subtotal=subtotal+s['elapsed time']\n", + " keystage.append(s['Stage ID'])\n", + " keystagetime.append(s['elapsed time'])\n", + " if subtotal/apptotaltime>0.9:\n", + " break\n", + " keystagetime=[\"{:02x}{:02x}\".format(int(255*l/keystagetime[0]),255-int(255*l/keystagetime[0])) for l in keystagetime if keystagetime[0]>0]\n", + " keystagemap=dict(zip(keystage,keystagetime))\n", + " outstr=[]\n", + " def print_plan(real_queryid,level,node,parent_stageid):\n", + " stageid = accid2stageid[int(node[\"metrics\"][0][\"accumulatorId\"])][0] if node[\"metrics\"] is not None and len(node[\"metrics\"])>0 and node[\"metrics\"][0][\"accumulatorId\"] in accid2stageid else parent_stageid\n", + "\n", + " if stageid in shown_stageid:\n", + " fontcolor=f\"color:#{keystagemap[stageid]}00;font-weight:bold\" if stageid in keystagemap else \"color:#000000\"\n", + " stagetime=0 if stageid not in stagemap else stagemap[stageid]\n", + " stageParts=0 if stageid not in stagepartmap else stagepartmap[stageid]\n", + "\n", + " input_rowcntstr=\"\"\n", + " output_rowcntstr=\"\"\n", + " timename={}\n", + " input_columnarbatch=\"\"\n", + " output_columnarbatch=\"\"\n", + " output_row_batch=\"\"\n", + " other_metric_name={}\n", + "\n", + " outputrows=0\n", + " outputbatches=0\n", + " if node[\"metrics\"] is not None:\n", + " for m in node[\"metrics\"]:\n", + "\n", + " if m[\"accumulatorId\"] not in accid2stageid:\n", + " continue\n", + " \n", + " if m[\"name\"].endswith(\"block wall nanos\") or m['name'].endswith(\"cpu nanos\"):\n", + " continue\n", + " \n", + " \n", + " value=accid2stageid[m[\"accumulatorId\"]][1]\n", + " stdev_value=accid2stageid[m[\"accumulatorId\"]][2]\n", + " stdev_value=0 if stdev_value is None else stdev_value\n", + " if m[\"metricType\"] in ['nsTiming','timing']:\n", + " totaltime=value/1000 if m[\"metricType\"] == 'timing' else value/1000000000\n", + " stdev_value=stdev_value/1000 if m[\"metricType\"] == 'timing' else stdev_value/1000000000\n", + " \n", + " timeratio= 0 if stagetime==0 else totaltime/self.executor_instances/self.executor_cores*self.taskcpus/stagetime*100\n", + " timeratio_query = totaltime/self.executor_instances/self.executor_cores*self.taskcpus/apptotaltime*100\n", + " if timeratio > 10 or timeratio_query>10:\n", + " timename[m[\"name\"]]=\"{:.2f}s ({:.1f}%, {:.1f}%, {:.2f})\".format(totaltime,timeratio, totaltime/self.executor_instances/self.executor_cores*self.taskcpus/apptotaltime*100,stdev_value)\n", + " else:\n", + " timename[m[\"name\"]]=\"{:.2f}s ({:.1f}%, {:.1f}%, {:.2f})\".format(totaltime,timeratio, totaltime/self.executor_instances/self.executor_cores*self.taskcpus/apptotaltime*100,stdev_value)\n", + " elif m[\"name\"] in [\"number of output rows\",\"number of final output rows\"]:\n", + " output_rowcntstr=\"{:,.1f}\".format(value/1000/1000)+\" M\"\n", + " outputrows=value\n", + " elif m[\"name\"] in [\"number of output columnar batches\",\"number of output batches\",\"output_batches\", \"number of output vectors\",\"number of final output vectors\", \"records read\"]: \n", + " # records reads is the output of shuffle\n", + " output_columnarbatch=\"{:,d}\".format(int(value))\n", + " outputbatches=value\n", + " elif m[\"name\"]==\"number of input rows\":\n", + " input_rowcntstr=\"{:,.1f}\".format(value/1000/1000)+\" M\"\n", + " elif m[\"name\"] in [\"number of input batches\",\"input_batches\",\"number of input vectors\"]:\n", + " input_columnarbatch=\"{:,d}\".format(int(value))\n", + " else:\n", + " if value>1000000000:\n", + " other_metric_name[m[\"name\"]]=\"{:,.1f} G ({:,.1f})\".format(value/1000000000,stdev_value/1000000000)\n", + " elif value>1000000:\n", + " other_metric_name[m[\"name\"]]=\"{:,.1f} M ({:,.1f})\".format(value/1000000,stdev_value/1000000)\n", + " elif value>1000:\n", + " other_metric_name[m[\"name\"]]=\"{:,.1f} K ({:,.1f})\".format(value/1000,stdev_value/1000)\n", + " else:\n", + " other_metric_name[m[\"name\"]]=\"{:,d} ({:,.1f})\".format(int(value),stdev_value)\n", + "\n", + "\n", + " if outputrows>0 and outputbatches>0:\n", + " output_row_batch=\"{:,d}\".format(int(outputrows/outputbatches))\n", + "\n", + "\n", + " fontcolor=f\"color:#{keystagemap[stageid]}00;font-weight:bold\" if stageid in keystage else \"color:#000000\"\n", + " stagetime=0 if stageid not in stagemap else stagemap[stageid]\n", + " stage_time_stdev=0 if stageid not in stage_time_stdev_map else stage_time_stdev_map[stageid]\n", + " \n", + " nodenamestr=node[\"nodeName\"]\n", + " if nodenamestr is None:\n", + " nodenamestr=\"\"\n", + " if nodenamestr in ['ColumnarToRow','RowToArrowColumnar','ArrowColumnarToRow','ArrowRowToColumnarExec','GlutenColumnarToRowExec','GlutenRowToArrowColumnar']:\n", + " nodename=''+nodenamestr+''\n", + " else:\n", + " nodename=nodenamestr\n", + " if outputstage is not None:\n", + " outputstage.append({\"queryid\":real_queryid,\"stageid\":stageid,\"stagetime\":stagetime,\"stageParts\":stageParts,\"nodename\":nodenamestr,\"output_rowcnt\":outputrows,\"nodename_level\":\" \".join([\"|_\" for l in range(0,level)]) + \" \" + nodenamestr})\n", + " if not show_plan_only:\n", + " nodestr= \" \".join([\"|_\" for l in range(0,level)]) + \" \" + nodename\n", + " if show_simple_string :\n", + " simstr=node['simpleString']\n", + " nodestr = nodestr + \"
\\n\" + simstr \n", + " \n", + " timenametable='\\n'\n", + " \n", + " timenameSort=list(timename)\n", + " \n", + " for nameidx in sorted(timename):\n", + " timenametable+=f\"\"\n", + " timenametable+=\"
{nameidx}{timename[nameidx]}
\\n\"\n", + " \n", + " \n", + " othertable='\\n'\n", + " for nameidx in sorted(other_metric_name):\n", + " othertable+=f\"\"\n", + " othertable+=\"
{nameidx}{other_metric_name[nameidx]}
\\n\"\n", + " \n", + " outstr.append(f\"{stageid}\"+\n", + " f\" {stagetime}({stage_time_stdev}) \"+\n", + " f\" {stageParts} \"+\n", + " f\"\" + nodestr + f\"\"+\n", + " f\" {input_rowcntstr} \"+\n", + " f\" {input_columnarbatch} \"+\n", + " f\" {output_rowcntstr} \"+\n", + " f\" {output_columnarbatch} \"+\n", + " f\" {output_row_batch} \"+\n", + " f\" {timenametable} \"+\n", + " f\" {othertable} \"+\n", + " \"\")\n", + " else:\n", + " outstr.append(f\"{stageid}\"+\n", + " f\" {stagetime} \"+\n", + " f\" {stageParts} \"+\n", + " f\"\" + \" \".join([\"|_\" for l in range(0,level)]) + \" \" + nodename + f\"\"+\n", + " f\" {output_rowcntstr} \")\n", + " \n", + " if node[\"children\"] is not None:\n", + " for c in node[\"children\"]:\n", + " print_plan(real_queryid, level+1,c,stageid)\n", + "\n", + " for c in queryplans:\n", + " outstr.append(\"\"+str(c['real_queryid'])+\"\")\n", + " if not show_plan_only:\n", + " outstr.append('''\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " ''')\n", + " else:\n", + " outstr.append('''\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " ''')\n", + "\n", + " print_plan(c['real_queryid'],0,c,0)\n", + " outstr.append(\"
stage idstage timepartionsoperatorinput rowsinput batchesoutput rowsoutput batchesoutput rows/batchtime metric nametime(%stage,%total,stdev)other metric namevalue(stdev)
stage idstage timepartionsoperatoroutput rows
\")\n", + " if plot:\n", + " display(HTML(\" \".join(outstr)))\n", + " return \" \".join(outstr)\n", + " \n", + " def get_metric_output_rowcnt(self, **kwargs):\n", + " return self.get_metric_rowcnt(\"number of output rows\",**kwargs)\n", + " \n", + " def get_metric_input_rowcnt(self, **kwargs):\n", + " return self.get_metric_rowcnt(\"number of input rows\",**kwargs)\n", + " \n", + " def get_metric_rowcnt(self,rowname, **kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " queryid=kwargs.get(\"queryid\",None)\n", + " stageid=kwargs.get(\"stageid\",None)\n", + " show_task=kwargs.get(\"show_task\",False)\n", + " \n", + " if queryid and type(queryid)==int:\n", + " queryid = [queryid,]\n", + " \n", + " if stageid and type(stageid)==int:\n", + " stageid = [stageid,]\n", + " \n", + " queryplans = self.queryplans.where(F.col(\"real_queryid\").isin(queryid)).orderBy(\"real_queryid\").collect() if queryid else self.queryplans.orderBy(\"real_queryid\").collect()\n", + " qps=[]\n", + "\n", + " rownames=rowname if type(rowname)==list else [rowname,]\n", + " def get_child(execid,node):\n", + " if node['metrics'] is not None:\n", + " outputrows=[x for x in node[\"metrics\"] if \"name\" in x and x[\"name\"] in rownames]\n", + " if len(outputrows)>0:\n", + " qps.append([node[\"nodeName\"],execid,outputrows[0]['accumulatorId']])\n", + " if node[\"children\"] is not None:\n", + " for c in node[\"children\"]:\n", + " get_child(execid,c)\n", + " for c in queryplans:\n", + " get_child(c['real_queryid'],c)\n", + "\n", + " if len(qps)==0:\n", + " print(\"Metric \",rowname,\" is not found. \")\n", + " return None\n", + " stagetime=self.df.where(\"Event='SparkListenerTaskEnd'\").groupBy(\"Stage ID\").agg(F.round(F.sum(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000/self.executor_instances/self.executor_cores*self.taskcpus,2).alias(\"stage time\"))\n", + " dfmetric=self.df.where(\"Event='SparkListenerTaskEnd'\").select(\"queryid\",\"real_queryid\",\"Stage ID\",\"Job ID\",F.explode(\"Accumulables\").alias(\"metric\")).select(\"*\",\"metric.*\").drop(\"metric\")\n", + " numrowmetric=spark.createDataFrame(qps)\n", + " numrowmetric=numrowmetric.withColumnRenamed(\"_1\",\"metric\").withColumnRenamed(\"_2\",\"real_queryid\").withColumnRenamed(\"_3\",\"metricid\")\n", + " dfmetric_rowcnt=dfmetric.join(numrowmetric.drop(\"real_queryid\"),on=[F.col(\"metricid\")==F.col(\"ID\")],how=\"right\")\n", + " if show_task:\n", + " stagemetric=dfmetric_rowcnt.join(stagetime,\"Stage ID\")\n", + " else:\n", + " stagemetric=dfmetric_rowcnt.groupBy(\"queryid\",\"real_queryid\",\"Job ID\",\"Stage ID\",\"metricid\").agg(F.round(F.sum(\"Update\")/1000000,2).alias(\"total_row\"),F.max(\"metric\").alias(\"nodename\")).join(stagetime,\"Stage ID\")\n", + "\n", + " if queryid:\n", + " if stageid:\n", + " return stagemetric.where(F.col(\"real_queryid\").isin(queryid) & F.col(\"Stage ID\").isin(stageid)).orderBy(\"Stage ID\")\n", + " else:\n", + " return stagemetric.where(F.col(\"real_queryid\").isin(queryid)).orderBy(\"Stage ID\")\n", + " else:\n", + " noderow=stagemetric.groupBy(\"real_queryid\",\"nodename\").agg(F.round(F.sum(\"total_row\"),2).alias(\"total_row\")).orderBy(\"nodename\").collect()\n", + " out={}\n", + " qids=set([r.real_queryid for r in noderow])\n", + " for r in noderow:\n", + " if r.nodename not in out:\n", + " out[r.nodename]={c:0 for c in qids}\n", + " out[r.nodename][r.real_queryid]=r.total_row\n", + " return pandas.DataFrame(out).T.sort_index(axis=0)\n", + " \n", + " def get_query_info(self,queryid):\n", + " display(HTML(\" time stat info \",))\n", + " tmp=self.get_query_time(queryid=queryid)\n", + " display(HTML(\" stage stat info \",))\n", + " display(self.get_stage_stat(queryid=queryid))\n", + " display(HTML(\" query plan \",))\n", + " self.get_query_plan(queryid=queryid)\n", + " display(HTML(\" stage hist info \",))\n", + " self.show_Stages_hist(queryid=queryid)\n", + " display(HTML(\" time info \",))\n", + " display(self.show_time_metric(queryid=queryid))\n", + " display(HTML(\" operator and rowcount \",))\n", + " display(self.get_metric_input_rowcnt(queryid=queryid))\n", + " display(self.get_metric_output_rowcnt(queryid=queryid))\n", + " \n", + " def get_app_info(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " display(HTML(f\" {self.appid} \",))\n", + " display(HTML(f\"http://{localhost}:18080/history/{self.appid}\"))\n", + " display(HTML(\" query time \",))\n", + " tmp=self.get_query_time(**kwargs)\n", + " display(HTML(\" operator count \",))\n", + " pdf=self.getOperatorCount()\n", + " display(pdf.style.apply(background_gradient,\n", + " cmap='OrRd',\n", + " m=pdf.min().min(),\n", + " M=pdf.max().max(),\n", + " low=0,\n", + " high=1))\n", + " \n", + " display(HTML(\" operator input row count \",))\n", + " pdf=self.get_metric_input_rowcnt(**kwargs)\n", + " if pdf is not None:\n", + " display(pdf.style.apply(background_gradient,\n", + " cmap='OrRd',\n", + " m=pdf.min().min(),\n", + " M=pdf.max().max(),\n", + " low=0,\n", + " high=1))\n", + " display(HTML(\" operator output row count \",))\n", + " pdf=self.get_metric_output_rowcnt(**kwargs)\n", + " if pdf is not None:\n", + " display(pdf.style.apply(background_gradient,\n", + " cmap='OrRd',\n", + " m=pdf.min().min(),\n", + " M=pdf.max().max(),\n", + " low=0,\n", + " high=1))\n", + " self.show_time_metric(**kwargs)\n", + " \n", + " def get_stage_stat(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " queryid=kwargs.get(\"queryid\",None)\n", + "\n", + " if queryid and type(queryid)==int:\n", + " queryid = [queryid,]\n", + " \n", + " df=self.df.where(F.col(\"real_queryid\").isin(queryid)).where(F.col(\"Event\")=='SparkListenerTaskEnd')\n", + " \n", + " inputsize = df.select(\"real_queryid\",\"Stage ID\",\"Executor ID\", \"Task ID\", F.explode(\"Accumulables\")) \\\n", + " .select(\"real_queryid\",\"Stage ID\",\"Executor ID\", \"Task ID\",\"col.*\") \\\n", + " .where(\"Name='input size in bytes' or Name='size of files read'\") \\\n", + " .groupBy(\"Stage ID\") \\\n", + " .agg(F.round(F.sum(\"Update\")/1024/1024/1024,2).alias(\"input read\"))\n", + " \n", + " return df.groupBy(\"Job ID\",\"Stage ID\").agg(\n", + " F.round(F.sum(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000/self.executor_instances/self.executor_cores*self.taskcpus,1).alias(\"elapsed time\"),\n", + " F.round(F.sum(F.col(\"Disk Bytes Spilled\"))/1024/1024/1024,1).alias(\"disk spilled\"),\n", + " F.round(F.sum(F.col(\"Memory Bytes Spilled\"))/1024/1024/1024,1).alias(\"mem spilled\"),\n", + " F.round(F.sum(F.col(\"Local Bytes Read\"))/1024/1024/1024,1).alias(\"local read\"),\n", + " F.round(F.sum(F.col(\"Remote Bytes Read\"))/1024/1024/1024,1).alias(\"remote read\"),\n", + " F.round(F.sum(F.col(\"Shuffle Bytes Written\"))/1024/1024/1024,1).alias(\"shuffle write\"),\n", + " F.round(F.sum(F.col(\"Executor Deserialize Time\"))/1000,1).alias(\"deseri time\"),\n", + " F.round(F.sum(F.col(\"Fetch Wait Time\"))/1000,1).alias(\"fetch wait time\"),\n", + " F.round(F.sum(F.col(\"Shuffle Write Time\"))/1000000000,1).alias(\"shuffle write time\"),\n", + " F.round(F.sum(F.col(\"Result Serialization Time\"))/1000,1).alias(\"seri time\"),\n", + " F.round(F.sum(F.col(\"Getting Result Time\"))/1000,1).alias(\"get result time\"),\n", + " F.round(F.sum(F.col(\"JVM GC Time\"))/1000,1).alias(\"gc time\"),\n", + " F.round(F.sum(F.col(\"Executor CPU Time\"))/1000000000,1).alias(\"exe cpu time\") \n", + " ).join(inputsize,on=[\"Stage ID\"],how=\"left\").orderBy(\"Stage ID\").toPandas()\n", + " \n", + " def get_metrics_by_node(self,node_name):\n", + " if self.df is None:\n", + " self.load_data()\n", + " \n", + " if type(node_name)==str:\n", + " node_name=[node_name]\n", + " metrics=self.queryplans.collect()\n", + " coalesce=[]\n", + " metricsid=[0]\n", + " def get_metric(root):\n", + " if root['nodeName'] in node_name:\n", + " metricsid[0]=metricsid[0]+1\n", + " for l in root[\"metrics\"]:\n", + " coalesce.append([l['accumulatorId'],l[\"metricType\"],l['name'],root[\"nodeName\"],metricsid[0]])\n", + " if root[\"children\"] is not None:\n", + " for c in root[\"children\"]:\n", + " get_metric(c)\n", + " for c in metrics:\n", + " get_metric(c)\n", + "\n", + " df=self.df.select(\"queryid\",\"real_queryid\",'Stage ID','Task ID','Job ID',F.explode(\"Accumulables\"))\n", + " df=df.select(\"*\",\"col.*\")\n", + " metricdf=spark.createDataFrame(coalesce)\n", + " metricdf=metricdf.withColumnRenamed(\"_1\",\"ID\").withColumnRenamed(\"_2\",\"Unit\").withColumnRenamed(\"_3\",\"metricName\").withColumnRenamed(\"_4\",\"nodeName\").withColumnRenamed(\"_5\",\"nodeID\")\n", + " df=df.join(metricdf,on=[\"ID\"],how=\"right\")\n", + " shufflemetric=set(l[2] for l in coalesce)\n", + " metricdfs=[df.where(F.col(\"Name\")==l).groupBy(\"real_queryid\",\"nodeID\",\"Stage ID\").agg(F.stddev(\"Update\").alias(l+\"_stddev\"),F.mean(\"Update\").alias(l+\"_mean\"),F.mean(\"Update\").alias(l) if l.startswith(\"avg\") else F.sum(\"Update\").alias(l)) for l in shufflemetric]\n", + " \n", + " stagetimedf=self.df.where(\"Event='SparkListenerTaskEnd'\").groupBy(\"Stage ID\").agg(F.count(\"*\").alias(\"partnum\"),F.round(F.sum(F.col(\"Finish Time\")-F.col(\"Launch Time\"))/1000,2).alias(\"ElapsedTime\"))\n", + " \n", + " nodemetric=reduce(lambda x,y: x.join(y, on=['nodeID',\"Stage ID\",\"real_queryid\"],how=\"full\"),metricdfs)\n", + " return nodemetric.join(stagetimedf,on=\"Stage ID\")\n", + " \n", + " \n", + " def get_coalesce_batch_row_cnt(self,**kwargs):\n", + " stagesum=self.get_metrics_by_node(\"CoalesceBatches\")\n", + " \n", + " pandas.options.display.float_format = '{:,}'.format\n", + " \n", + " stagesum=stagesum.withColumnRenamed(\"number of output rows\",\"rows\")\n", + " \n", + " coalescedf = stagesum.orderBy(\"real_queryid\",'Stage ID').where(\"rows>4000\").toPandas()\n", + " \n", + " coalescedf[\"row/input_batch\"] = coalescedf[\"rows\"]/coalescedf[\"input_batches\"]\n", + " coalescedf[\"row/out_batch\"] = coalescedf[\"rows\"]/coalescedf[\"output_batches\"]\n", + " coalescedf['stage']=coalescedf[\"real_queryid\"].astype(str)+\"_\"+coalescedf['Stage ID'].astype(str)\n", + " \n", + " ax=coalescedf.plot(y=[\"row/input_batch\",\"row/out_batch\"],figsize=(30,8),style=\"-*\")\n", + " coalescedf.plot(ax=ax,y=['rows'],secondary_y=['rows'],style=\"k_\")\n", + " self.print_real_queryid(ax,coalescedf)\n", + " \n", + " return coalescedf\n", + " \n", + " def print_real_queryid(self,ax,dataset):\n", + " ax.axes.get_xaxis().set_ticks([])\n", + "\n", + " ymin, ymax = ax.get_ybound()\n", + "\n", + " real_queryid=list(dataset['real_queryid'])\n", + " s=real_queryid[0]\n", + " lastx=0\n", + " for idx,v in enumerate(real_queryid):\n", + " if v!=s:\n", + " xmin = xmax = idx-1+0.5\n", + " l = mlines.Line2D([xmin,xmax], [ymin,ymax],color=\"green\")\n", + " ax.add_line(l)\n", + " ax.text(lastx+(xmin-lastx)/2-0.25,ymin-(ymax-ymin)/20,f\"{s}\",size=20)\n", + " s=v\n", + " lastx=xmin\n", + "\n", + " def get_shuffle_stat(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + " \n", + " shufflesize=kwargs.get(\"shuffle_size\",1000000)\n", + " queryid=kwargs.get(\"queryid\",None)\n", + " if queryid is not None:\n", + " if type(queryid) is str or type(queryid) is int:\n", + " queryid=[queryid,]\n", + "\n", + " exchangedf=self.get_metrics_by_node([\"ColumnarExchange\",\"ColumnarExchangeAdaptor\"])\n", + " exchangedf.cache()\n", + " exchangedf.count()\n", + " mapdf=exchangedf.where(\"`time to split` is not null\").select(\"nodeID\",F.col(\"Stage ID\").alias(\"map_stageid\"),\"real_queryid\",F.floor(F.col(\"time to split\")/F.col(\"time to split_mean\")).alias(\"map_partnum\"),\"time to compress\",\"time to split\",\"shuffle write time\",\"time to spill\",'shuffle records written','data size','shuffle bytes written','shuffle bytes written_mean','shuffle bytes written_stddev','shuffle bytes spilled','number of input rows','number of input batches')\n", + " reducerdf=exchangedf.where(\"`time to split` is null\").select(\"nodeID\",F.col(\"Stage ID\").alias(\"reducer_stageid\"),\"real_queryid\",'local blocks read','local bytes read',F.floor(F.col(\"records read\")/F.col(\"records read_mean\")).alias(\"reducer_partnum\"),(F.col('avg read batch num rows')/10).alias(\"avg read batch num rows\"),'remote bytes read','records read','remote blocks read',(F.col(\"number of output rows\")/F.col(\"records read\")).alias(\"avg rows per split recordbatch\"))\n", + " shuffledf=mapdf.join(reducerdf,on=[\"nodeID\",\"real_queryid\"],how=\"full\")\n", + " if queryid is not None:\n", + " shuffledf=shuffledf.where(F.col(\"real_queryid\").isin(queryid))\n", + " shuffle_pdf=shuffledf.where(\"`shuffle bytes written`>1000000\").orderBy(\"real_queryid\",\"map_stageid\",\"nodeID\").toPandas()\n", + " shuffle_pdf[\"shuffle bytes written\"]=shuffle_pdf[\"shuffle bytes written\"]/1000000000\n", + " shuffle_pdf[\"data size\"]=shuffle_pdf[\"data size\"]/1000000000\n", + " shuffle_pdf[\"shuffle bytes written_mean\"]=shuffle_pdf[\"shuffle bytes written_mean\"]/1000000\n", + " shuffle_pdf[\"shuffle bytes written_stddev\"]=shuffle_pdf[\"shuffle bytes written_stddev\"]/1000000\n", + " ax=shuffle_pdf.plot(y=[\"avg read batch num rows\",'avg rows per split recordbatch'],figsize=(30,8),style=\"-*\",title=\"average batch size after split\")\n", + " self.print_real_queryid(ax,shuffle_pdf)\n", + " shuffle_pdf[\"split_ratio\"]=shuffle_pdf[\"records read\"]/shuffle_pdf['number of input batches']\n", + " ax=shuffle_pdf.plot(y=[\"split_ratio\",\"records read\"],secondary_y=[\"records read\"],figsize=(30,8),style=\"-*\",title=\"Split Ratio\")\n", + " self.print_real_queryid(ax,shuffle_pdf)\n", + " shuffle_pdf[\"compress_ratio\"]=shuffle_pdf[\"data size\"]/shuffle_pdf['shuffle bytes written']\n", + " ax=shuffle_pdf.plot(y=[\"shuffle bytes written\",\"compress_ratio\"],secondary_y=[\"compress_ratio\"],figsize=(30,8),style=\"-*\",title=\"compress ratio\")\n", + " self.print_real_queryid(ax,shuffle_pdf)\n", + " shufflewritepdf=shuffle_pdf\n", + " ax=shufflewritepdf.plot.bar(y=[\"shuffle write time\",\"time to spill\",\"time to compress\",\"time to split\"],stacked=True,figsize=(30,8),title=\"split time + shuffle write time vs. shuffle bytes written\")\n", + " ax=shufflewritepdf.plot(ax=ax,y=[\"shuffle bytes written\"],secondary_y=[\"shuffle bytes written\"],style=\"-*\")\n", + " self.print_real_queryid(ax,shufflewritepdf)\n", + " shuffle_pdf['avg input batch size']=shuffle_pdf[\"number of input rows\"]/shuffle_pdf[\"number of input batches\"]\n", + " ax=shuffle_pdf.plot(y=[\"avg input batch size\"],figsize=(30,8),style=\"b-*\",title=\"average input batch size\")\n", + " ax=shuffle_pdf.plot.bar(ax=ax,y=['number of input rows'],secondary_y=True)\n", + " self.print_real_queryid(ax,shuffle_pdf)\n", + " \n", + " metrics=self.queryplans.collect()\n", + " coalesce=[]\n", + " metricsid=[0]\n", + " def get_metric(root):\n", + " if root['nodeName'] in [\"ColumnarExchange\",\"ColumnarExchangeAdaptor\"]:\n", + " metricsid[0]=metricsid[0]+1\n", + " for l in root[\"metrics\"]:\n", + " coalesce.append([l['accumulatorId'],l[\"metricType\"],l['name'],root[\"nodeName\"],metricsid[0],root[\"simpleString\"]])\n", + " if root[\"children\"] is not None:\n", + " for c in root[\"children\"]:\n", + " get_metric(c)\n", + " for c in metrics:\n", + " get_metric(c)\n", + "\n", + " tps={}\n", + " for r in coalesce:\n", + " rx=re.search(r\"\\[OUTPUT\\] List\\((.*)\\)\",r[5])\n", + " if rx:\n", + " if r[4] not in tps:\n", + " tps[r[4]]={}\n", + " fds=rx.group(1).split(\", \")\n", + " for f in fds:\n", + " if f.endswith(\"Type\"):\n", + " tp=re.search(r\":(.+Type)\",f).group(1)\n", + " if tp not in tps[r[4]]:\n", + " tps[r[4]][tp]=1\n", + " else:\n", + " tps[r[4]][tp]+=1\n", + " if len(tps)>0:\n", + " typedf=pandas.DataFrame(tps).T.reset_index()\n", + " typedf=typedf.fillna(0)\n", + " shuffle_pdf=pandas.merge(shuffle_pdf,typedf,left_on=\"nodeID\",right_on=\"index\")\n", + " shufflewritepdf=shuffle_pdf\n", + " ax=shufflewritepdf.plot.bar(y=[\"number of input rows\"],stacked=True,figsize=(30,8),title=\"rows vs. shuffle data type\")\n", + " ax=shufflewritepdf.plot(ax=ax,y=list(typedf.columns[1:]),secondary_y=list(typedf.columns[1:]),style=\"-o\")\n", + " self.print_real_queryid(ax,shufflewritepdf)\n", + " ax=shufflewritepdf.plot.bar(y=[\"time to split\"],stacked=True,figsize=(30,8),title=\"split time vs. shuffle data type\")\n", + " ax=shufflewritepdf.plot(ax=ax,y=list(typedf.columns[1:]),secondary_y=list(typedf.columns[1:]),style=\"-o\")\n", + " self.print_real_queryid(ax,shufflewritepdf)\n", + "\n", + " \n", + " \n", + " shufflewritepdf.plot(x=\"shuffle bytes written\",y=[\"shuffle write time\",\"time to split\"],figsize=(30,8),style=\"*\")\n", + " shufflewritepdf[\"avg shuffle batch size after split\"]=shufflewritepdf[\"shuffle bytes written\"]*1000000/shufflewritepdf['records read']\n", + " shufflewritepdf[\"avg raw batch size after split\"]=shufflewritepdf[\"data size\"]*1000000/shufflewritepdf['records read']\n", + " ax=shufflewritepdf.plot(y=[\"avg shuffle batch size after split\",\"avg raw batch size after split\",\"shuffle bytes written\"],secondary_y=[\"shuffle bytes written\"],figsize=(30,8),style=\"-*\",title=\"avg batch KB after split\")\n", + " self.print_real_queryid(ax,shufflewritepdf)\n", + " shufflewritepdf[\"avg batch# per splitted partition\"]=shufflewritepdf['records read']/(shufflewritepdf['local blocks read']+shufflewritepdf['remote blocks read'])\n", + " ax=shufflewritepdf.plot(y=[\"avg batch# per splitted partition\",'records read'],secondary_y=['records read'],figsize=(30,8),style=\"-*\",title=\"avg batch# per splitted partition\")\n", + " self.print_real_queryid(ax,shufflewritepdf)\n", + " fig, ax = plt.subplots(figsize=(30,8))\n", + " ax.set_title('shuffle wite bytes with stddev')\n", + " ax.errorbar(x=shuffle_pdf.index,y=shuffle_pdf['shuffle bytes written_mean'], yerr=shuffle_pdf['shuffle bytes written_stddev'], linestyle='None', marker='o')\n", + " self.print_real_queryid(ax,shuffle_pdf)\n", + " shuffle_pdf['record batch per mapper per reducer']=shuffle_pdf['records read']/(shuffle_pdf[\"map_partnum\"]*shuffle_pdf['reducer_partnum'])\n", + " ax=shuffle_pdf.plot(y=[\"record batch per mapper per reducer\"],figsize=(30,8),style=\"b-*\",title=\"record batch per mapper per reducer\")\n", + " self.print_real_queryid(ax,shuffle_pdf)\n", + " \n", + " inputsize = self.df.select(\"Stage ID\",\"Executor ID\", \"Task ID\", F.explode(\"Accumulables\")) \\\n", + " .select(\"Stage ID\",\"Executor ID\", \"Task ID\",\"col.*\") \\\n", + " .where(\"Name='input size in bytes' or Name='size of files read'\") \\\n", + " .groupBy(\"Task ID\") \\\n", + " .agg((F.sum(\"Update\")).alias(\"input read\"))\n", + " stageinput=self.df.where(\"event='SparkListenerTaskEnd'\" )\\\n", + " .join(inputsize,on=[\"Task ID\"],how=\"left\")\\\n", + " .fillna(0) \\\n", + " .select(F.col('Host'), F.col(\"real_queryid\"),F.col('Stage ID'),F.col('Task ID'),\n", + " F.round((F.col('Finish Time')/1000-F.col('Launch Time')/1000),2).alias('elapsedtime'),\n", + " F.round((F.col('`input read`')+F.col('`Bytes Read`')+F.col('`Local Bytes Read`')+F.col('`Remote Bytes Read`'))/1024/1024,2).alias('input'))\n", + " baisstage=stageinput.groupBy(\"real_queryid\",\"Stage ID\").agg(F.mean(\"elapsedtime\").alias(\"elapsed\"),F.mean(\"input\").alias(\"input\"),\n", + " (F.stddev(\"elapsedtime\")).alias(\"elapsedtime_err\"),\n", + " (F.stddev(\"input\")).alias(\"input_err\"),\n", + " (F.max(\"elapsedtime\")-F.mean(\"elapsedtime\")).alias(\"elapsed_max\"),\n", + " (F.mean(\"elapsedtime\")-F.min(\"elapsedtime\")).alias(\"elapsed_min\"),\n", + " (F.max(\"input\")-F.mean(\"input\")).alias(\"input_max\"),\n", + " (F.mean(\"input\")-F.min(\"input\")).alias(\"input_min\")).orderBy(\"real_queryid\",\"Stage ID\")\n", + " dfx=baisstage.toPandas()\n", + " fig, ax = plt.subplots(figsize=(30,8))\n", + " ax.set_title('input size')\n", + " ax.errorbar(x=dfx.index,y=dfx['input'], yerr=dfx['input_err'], fmt='ok', ecolor='red', lw=3)\n", + " ax.errorbar(x=dfx.index,y=dfx['input'],yerr=[dfx['input_min'],dfx['input_max']],\n", + " fmt='.k', ecolor='gray', lw=1)\n", + " self.print_real_queryid(ax,dfx)\n", + " \n", + " fig, ax = plt.subplots(figsize=(30,8))\n", + " ax.set_title('stage time')\n", + "\n", + " ax.errorbar(x=dfx.index,y=dfx['elapsed'], yerr=dfx['elapsedtime_err'], fmt='ok', ecolor='red', lw=5)\n", + " ax.errorbar(x=dfx.index,y=dfx['elapsed'],yerr=[dfx['elapsed_min'],dfx['elapsed_max']],\n", + " fmt='.k', ecolor='gray', lw=1)\n", + "\n", + " self.print_real_queryid(ax,dfx)\n", + " return (shuffle_pdf,dfx)\n", + " \n", + " def get_stages_w_odd_partitions(appals,**kwargs):\n", + " if appals.df is None:\n", + " appals.load_data()\n", + " return appals.df.where(\"Event='SparkListenerTaskEnd'\")\\\n", + " .groupBy(\"Stage ID\",\"real_queryid\")\\\n", + " .agg((F.sum(F.col('Finish Time')-F.col('Launch Time'))/1000).alias(\"elapsed time\"),\n", + " F.count('*').alias('partitions'))\\\n", + " .where(F.col(\"partitions\")%(appals.executor_cores*appals.executor_instances/appals.taskcpus)!=0)\\\n", + " .orderBy(F.desc(\"elapsed time\")).toPandas()\n", + " \n", + " def get_scaned_column_v1(appals):\n", + " def get_scans(node):\n", + " if node['nodeName'].startswith(\"Scan arrow\"):\n", + " scans.append(node)\n", + " for c in node['children']:\n", + " get_scans(c)\n", + "\n", + " alltable=[]\n", + " for qid in range(1,23):\n", + " scans=[]\n", + " plans=appals.queryplans.where(\"real_queryid=\"+str(qid)).collect()\n", + " get_scans(plans[0])\n", + " for s in scans:\n", + " alltable.append([qid,\",\".join([l.split(\":\")[0] for l in re.split(r'[<>]',s['metadata']['ReadSchema'])[1].split(\",\")])])\n", + " return alltable\n", + " \n", + " def get_scaned_column_v2(appals):\n", + " def get_scans(node):\n", + " if node['nodeName'].startswith(\"ColumnarBatchScan\"):\n", + " scans.append(node)\n", + " for c in node['children']:\n", + " get_scans(c)\n", + "\n", + " alltable=[]\n", + " for qid in range(1,23):\n", + " scans=[]\n", + " plans=appals.queryplans.where(\"real_queryid=\"+str(qid)).collect()\n", + " get_scans(plans[0])\n", + " for s in scans:\n", + " alltable.append([qid,\",\".join([l.split(\"#\")[0] for l in re.split(r\"[\\[\\]]\",s['simpleString'])[1].split(\",\")])])\n", + " return alltable\n", + " \n", + " def compare_query(appals,queryid,appbaseals):\n", + " print(f\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~Query{queryid}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n", + " appals.show_critical_path_time_breakdown(queryid=22)\n", + " s1=appals.get_stage_stat(queryid=queryid)\n", + " s2=appbaseals.get_stage_stat(queryid=queryid)\n", + " ls=s1[['Stage ID','elapsed time']]\n", + " ls.columns=['l sid','l time']\n", + " rs=s2[['Stage ID','elapsed time']]\n", + " rs.columns=['r sid','r time']\n", + " js=ls.join(rs)\n", + " js['gap']=js['r time'] - js['l time']\n", + " js['gap']=js['gap'].round(2)\n", + " display(js)\n", + " display(s1)\n", + " display(s2)\n", + " stagesmap={}\n", + " for x in range(0,min(len(s1),len(s2))):\n", + " stagesmap[s1['Stage ID'][x]]=s2['Stage ID'][x]\n", + " totaltime=sum(s1['elapsed time'])\n", + " acctime=0\n", + " s1time=s1.sort_values(\"elapsed time\",ascending=False,ignore_index=True)\n", + " ldfx=appals.get_metric_output_rowcnt(queryid=queryid)\n", + " rdfx=appbaseals.get_metric_output_rowcnt(queryid=queryid)\n", + "\n", + " for x in range(0,len(s1time)):\n", + " sid1=int(s1time['Stage ID'][x])\n", + " sid2=int(stagesmap[sid1])\n", + " print(f\"============================================================\")\n", + " display(ldfx[ldfx['Stage ID']==sid1])\n", + " display(rdfx[ldfx['Stage ID']==sid2])\n", + " print(f\" Gazelle Query {queryid} Stage {sid1}\")\n", + " xf=appals.get_query_plan(stageid=sid1,show_simple_string=True)\n", + " print(f\" Photon Query {queryid} Stage {sid2}\")\n", + " xf=appbaseals.get_query_plan(stageid=sid2,show_simple_string=True)\n", + " acctime+=s1time['elapsed time'][x]\n", + " if acctime/totaltime>=0.9:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "notlist=['resource.executor.cores',\n", + " 'spark.app.id',\n", + " 'spark.app.initial.file.urls',\n", + " 'spark.app.name',\n", + " 'spark.app.startTime',\n", + " 'spark.driver.port',\n", + " 'spark.job.description',\n", + " 'spark.jobGroup.id',\n", + " 'spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n", + " 'spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n", + " 'spark.rdd.scope',\n", + " 'spark.sql.execution.id',\n", + " '__fetch_continuous_blocks_in_batch_enabled',\n", + " 'spark.driver.appUIAddress'\n", + " 'spark.driver.appUIAddress',\n", + " 'spark.driver.host',\n", + " 'spark.driver.appUIAddress',\n", + " 'spark.driver.extraClassPath',\n", + " 'spark.eventLog.dir',\n", + " 'spark.executorEnv.CC',\n", + " 'spark.executorEnv.LD_LIBRARY_PATH',\n", + " 'spark.executorEnv.LD_PRELOAD',\n", + " 'spark.executorEnv.LIBARROW_DIR',\n", + " 'spark.files',\n", + " 'spark.history.fs.logDirectory',\n", + " 'spark.sql.warehouse.dir',\n", + " 'spark.yarn.appMasterEnv.LD_PRELOAD',\n", + " 'spark.yarn.dist.files'\n", + "]\n", + "def comp_spark_conf(app0,app1): \n", + " pdf_sparkconf_0=app0.get_spark_config()\n", + " pdf_sparkconf_1=app1.get_spark_config()\n", + " pdfc=pdf_sparkconf_0.join(pdf_sparkconf_1,lsuffix=app0.appid[-8:],rsuffix=app1.appid[-8:])\n", + " pdfc[\"0\"+app0.appid[-8:]]=pdfc[\"0\"+app0.appid[-8:]].str.lower()\n", + " pdfc[\"0\"+app1.appid[-8:]]=pdfc[\"0\"+app1.appid[-8:]].str.lower()\n", + " \n", + " pdfc['comp']=(pdfc[\"0\"+app0.appid[-8:]]==pdfc[\"0\"+app1.appid[-8:]])\n", + " return pdfc.loc[(pdfc['comp']==False) & (~pdfc.index.isin(notlist))]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node log analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "@pandas_udf(\"host string, id string,taskid int, time double\", PandasUDFType.GROUPED_MAP)\n", + "def collect_udf_time(pdf):\n", + " proxy_handler = request.ProxyHandler({})\n", + " opener = request.build_opener(proxy_handler)\n", + "\n", + " rst=[]\n", + " for idx,l in pdf.iterrows():\n", + " ip=\"10.1.2.19\"+l['Host'][-1:]\n", + " execid=\"{:06d}\".format(int(l['Executor ID'])+1)\n", + " appid=l['appid']\n", + " url = f'http://{ip}:8042/node/containerlogs/container_{appid}_01_{execid}/sparkuser/stderr/?start=0'\n", + " # open the website with the opener\n", + " req = opener.open(url)\n", + " data = req.read().decode('utf8')\n", + " cnt=data.split(\"\\n\")\n", + " cnt_udf=[l.split(\" \") for l in cnt if l.startswith('start UDF') or l.startswith('stop UDF')]\n", + " unf_pdf=pandas.DataFrame(cnt_udf)\n", + " srst=unf_pdf.loc[:,[0,4,6]]\n", + " srst.columns=['id','taskid','time']\n", + " srst['host']=l['Host']\n", + " srst['taskid']=srst['taskid'].astype(int)\n", + " srst['time']=srst['time'].apply(lambda f: float(re.search('\\d+\\.\\d+',f).group(0)))\n", + " rst.append(srst)\n", + " return pandas.concat(rst)\n", + "\n", + "\n", + "class App_Log_Analysis_Node_log(App_Log_Analysis):\n", + " def __init__(self, appid,jobids):\n", + " App_Log_Analysis.__init__(self, appid,jobids)\n", + " \n", + " def generate_trace_view_list(self,id=0, **kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " showcpu=kwargs['showcpu'] if 'showcpu' in kwargs else False\n", + " \n", + " appid=self.appid\n", + " events=self.df.toPandas()\n", + " coretrack={}\n", + " trace_events=[]\n", + " starttime=0\n", + " taskend=[]\n", + " trace={\"traceEvents\":[]}\n", + " exec_hosts={}\n", + " hostsdf=self.df.select(\"Host\").distinct().orderBy(\"Host\")\n", + " hostid=100000\n", + " ended_event=[]\n", + "\n", + " for i,l in hostsdf.toPandas().iterrows():\n", + " exec_hosts[l['Host']]=hostid\n", + " hostid=hostid+100000\n", + "\n", + " tskmap={}\n", + " for idx,l in events.iterrows():\n", + " if l['Event']=='SparkListenerTaskStart':\n", + " hostid=exec_hosts[l['Host']]\n", + "\n", + " tsk=l['Task ID']\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " stime=l['Launch Time']\n", + " #the task's starttime and finishtime is the same, ignore it.\n", + " if tsk in ended_event:\n", + " continue\n", + " if not pid in coretrack:\n", + " tids={}\n", + " trace_events.append({\n", + " \"name\": \"process_name\",\n", + " \"ph\": \"M\",\n", + " \"pid\":pid,\n", + " \"tid\":0,\n", + " \"args\":{\"name\":\"{:s}.{:s}\".format(l['Host'],l['Executor ID'])}\n", + " })\n", + "\n", + " else:\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==-1:\n", + " tids[t]=[tsk,stime]\n", + " break\n", + " else:\n", + " t=len(tids)\n", + " tids[t]=[tsk,stime]\n", + " #print(\"task {:d} tid is {:s}.{:d}\".format(tsk,pid,t))\n", + " coretrack[pid]=tids\n", + "\n", + " if l['Event']=='SparkListenerTaskEnd':\n", + " sevt={}\n", + " eevt={}\n", + " hostid=exec_hosts[l['Host']]\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " tsk=l['Task ID']\n", + " fintime=l['Finish Time']\n", + "\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==tsk:\n", + " tids[t]=[-1,-1]\n", + " break\n", + " else:\n", + " ended_event.append(tsk)\n", + " continue\n", + " for ps in reversed([key for key in tids.keys()]) :\n", + " if tids[ps][1]-fintime<0 and tids[ps][1]-fintime>=-2:\n", + " fintime=tids[ps][1]\n", + " tids[t]=tids[ps]\n", + " tids[ps]=[-1,-1]\n", + " break\n", + " if starttime==0:\n", + " starttime=l['Launch Time']\n", + "\n", + " sstime=l['Launch Time']-starttime\n", + "\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':sstime,\n", + " 'dur':fintime-l['Launch Time'],\n", + " 'pid':pid,\n", + " \"ph\":'X',\n", + " 'name':\"stg{:d}\".format(l['Stage ID']),\n", + " 'args':{\"job id\": l['job id'],\n", + " \"stage id\": l['Stage ID'],\n", + " \"tskid\":tsk,\n", + " \"input\":builtins.round(l[\"Bytes Read\"]/1024/1024,2),\n", + " \"spill\":builtins.round(l[\"Memory Bytes Spilled\"]/1024/1024,2),\n", + " \"Shuffle Read Metrics\": \"\",\n", + " \"|---Local Read\": builtins.round(l[\"Local Bytes Read\"]/1024/1024,2),\n", + " \"|---Remote Read\":builtins.round(l[\"Remote Bytes Read\"]/1024/1024,2),\n", + " \"Shuffle Write Metrics\": \"\",\n", + " \"|---Write\":builtins.round(l['Shuffle Bytes Written']/1024/1024,2)\n", + " }\n", + " })\n", + " tskmap[tsk]={'pid':pid,'tid':pid+int(t)}\n", + "\n", + " self.starttime=starttime\n", + " self.tskmap=tskmap\n", + "\n", + " hostdf=self.df.select('Host','Executor ID',F.lit(appid[len('application_'):]).alias('appid')).distinct().orderBy('Host')\n", + " rst=hostdf.groupBy('Host').apply(collect_udf_time)\n", + " rst.cache()\n", + " start_df=rst.where(\"id='start'\").select(F.col('taskid').alias('start_taskid'),F.col('time').alias(\"starttime\"))\n", + " stop_df=rst.where(\"id='stop'\").select('taskid',F.col('time').alias(\"stop_time\"))\n", + " df=start_df.join(stop_df, on=[start_df.start_taskid==stop_df.taskid,stop_df['stop_time']>=start_df['starttime']],how='left').groupBy('taskid','starttime').agg(F.min('stop_time').alias('stop_time'))\n", + " pdf=df.toPandas() \n", + " for idx,l in pdf.iterrows():\n", + " trace_events.append({\n", + " 'tid':self.tskmap[l['taskid']]['tid'],\n", + " 'ts':l['starttime']*1000-self.starttime,\n", + " 'dur':(l['stop_time']-l['starttime'])*1000, \n", + " 'pid':self.tskmap[l['taskid']]['pid'],\n", + " 'ph':'X',\n", + " 'name':'udf'})\n", + " \n", + " return [json.dumps(l) for l in trace_events]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "class App_Log_Analysis_Node_log(App_Log_Analysis):\n", + " def __init__(self, appid,jobids):\n", + " App_Log_Analysis.__init__(self, appid,jobids)\n", + " \n", + " def generate_trace_view_list(self,id=0, **kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " showcpu=kwargs['showcpu'] if 'showcpu' in kwargs else False\n", + " \n", + " appid=self.appid\n", + " events=self.df.toPandas()\n", + " coretrack={}\n", + " trace_events=[]\n", + " starttime=0\n", + " taskend=[]\n", + " trace={\"traceEvents\":[]}\n", + " exec_hosts={}\n", + " hostsdf=self.df.select(\"Host\").distinct().orderBy(\"Host\")\n", + " hostid=100000\n", + " ended_event=[]\n", + "\n", + " for i,l in hostsdf.toPandas().iterrows():\n", + " exec_hosts[l['Host']]=hostid\n", + " hostid=hostid+100000\n", + "\n", + " tskmap={}\n", + " for idx,l in events.iterrows():\n", + " if l['Event']=='SparkListenerTaskStart':\n", + " hostid=exec_hosts[l['Host']]\n", + "\n", + " tsk=l['Task ID']\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " stime=l['Launch Time']\n", + " #the task's starttime and finishtime is the same, ignore it.\n", + " if tsk in ended_event:\n", + " continue\n", + " if not pid in coretrack:\n", + " tids={}\n", + " trace_events.append({\n", + " \"name\": \"process_name\",\n", + " \"ph\": \"M\",\n", + " \"pid\":pid,\n", + " \"tid\":0,\n", + " \"args\":{\"name\":\"{:s}.{:s}\".format(l['Host'],l['Executor ID'])}\n", + " })\n", + "\n", + " else:\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==-1:\n", + " tids[t]=[tsk,stime]\n", + " break\n", + " else:\n", + " t=len(tids)\n", + " tids[t]=[tsk,stime]\n", + " #print(\"task {:d} tid is {:s}.{:d}\".format(tsk,pid,t))\n", + " coretrack[pid]=tids\n", + "\n", + " if l['Event']=='SparkListenerTaskEnd':\n", + " sevt={}\n", + " eevt={}\n", + " hostid=exec_hosts[l['Host']]\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " tsk=l['Task ID']\n", + " fintime=l['Finish Time']\n", + "\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==tsk:\n", + " tids[t]=[-1,-1]\n", + " break\n", + " else:\n", + " ended_event.append(tsk)\n", + " continue\n", + " for ps in reversed([key for key in tids.keys()]) :\n", + " if tids[ps][1]-fintime<0 and tids[ps][1]-fintime>=-2:\n", + " fintime=tids[ps][1]\n", + " tids[t]=tids[ps]\n", + " tids[ps]=[-1,-1]\n", + " break\n", + " if starttime==0:\n", + " starttime=l['Launch Time']\n", + "\n", + " sstime=l['Launch Time']-starttime\n", + "\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':sstime,\n", + " 'dur':fintime-l['Launch Time'],\n", + " 'pid':pid,\n", + " \"ph\":'X',\n", + " 'name':\"stg{:d}\".format(l['Stage ID']),\n", + " 'args':{\"job id\": l['job id'],\n", + " \"stage id\": l['Stage ID'],\n", + " \"tskid\":tsk,\n", + " \"input\":builtins.round(l[\"Bytes Read\"]/1024/1024,2),\n", + " \"spill\":builtins.round(l[\"Memory Bytes Spilled\"]/1024/1024,2),\n", + " \"Shuffle Read Metrics\": \"\",\n", + " \"|---Local Read\": builtins.round(l[\"Local Bytes Read\"]/1024/1024,2),\n", + " \"|---Remote Read\":builtins.round(l[\"Remote Bytes Read\"]/1024/1024,2),\n", + " \"Shuffle Write Metrics\": \"\",\n", + " \"|---Write\":builtins.round(l['Shuffle Bytes Written']/1024/1024,2)\n", + " }\n", + " })\n", + " tskmap[tsk]={'pid':pid,'tid':pid+int(t)}\n", + "\n", + " self.starttime=starttime\n", + " self.tskmap=tskmap\n", + "\n", + " hostdf=self.df.select('Host','Executor ID',F.lit(appid[len('application_'):]).alias('appid')).distinct().orderBy('Host')\n", + " rst=hostdf.groupBy('Host').apply(collect_udf_time)\n", + " rst.cache()\n", + " start_df=rst.where(\"id='start'\").select(F.col('taskid').alias('start_taskid'),F.col('time').alias(\"starttime\"))\n", + " stop_df=rst.where(\"id='stop'\").select('taskid',F.col('time').alias(\"stop_time\"))\n", + " df=start_df.join(stop_df, on=[start_df.start_taskid==stop_df.taskid,stop_df['stop_time']>=start_df['starttime']],how='left').groupBy('taskid','starttime').agg(F.min('stop_time').alias('stop_time'))\n", + " pdf=df.toPandas() \n", + " for idx,l in pdf.iterrows():\n", + " trace_events.append({\n", + " 'tid':self.tskmap[l['taskid']]['tid'],\n", + " 'ts':l['starttime']*1000-self.starttime,\n", + " 'dur':(l['stop_time']-l['starttime'])*1000, \n", + " 'pid':self.tskmap[l['taskid']]['pid'],\n", + " 'ph':'X',\n", + " 'name':'udf'})\n", + " \n", + " return [json.dumps(l) for l in trace_events]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "class App_Log_Analysis_Node_Log_Uni(App_Log_Analysis):\n", + " def __init__(self, file,jobids):\n", + " App_Log_Analysis.__init__(self, file,jobids)\n", + " \n", + " def generate_trace_view_list(self,id=0, **kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " showcpu=False\n", + " \n", + " shownodes=kwargs.get(\"shownodes\",None)\n", + "\n", + " showdf=self.df #self.df.where(F.col(\"Host\").isin(shownodes)) if shownodes else self.df\n", + "\n", + " events=showdf.drop(\"Accumulables\",\"Stage IDs\").orderBy(\"Launch Time\",\"Finish Time\").toPandas()\n", + " coretrack={}\n", + " trace_events=[]\n", + " starttime=0\n", + " taskend=[]\n", + " trace={\"traceEvents\":[]}\n", + " exec_hosts={}\n", + " hostsdf=showdf.select(\"Host\").distinct().orderBy(\"Host\")\n", + " hostid=100000\n", + " ended_event=[]\n", + "\n", + " applog=os.path.splitext(self.file)[0]+\".stdout\"\n", + " logdfs=[]\n", + " if fs.exists(applog):\n", + " logdata=sc.textFile(os.path.splitext(self.file)[0]+\".stdout\",84)\n", + " logdf=logdata.mapPartitions(splits).toDF()\n", + " logdfs.append(logdf)\n", + "\n", + " p=os.path.split(self.file)\n", + " for c in shownodes:\n", + " f=p[0]+\"/\"+c+\"/xgbtck.txt\"\n", + " if fs.exists(f):\n", + " logdata=sc.textFile(f,84)\n", + " logdf=logdata.mapPartitions(splits).toDF()\n", + " logdfs.append(logdf)\n", + " logdf=reduce(lambda l,r: l.concat(r),logdfs)\n", + " logdf=logdf.cache()\n", + " logdf.count()\n", + "\n", + " firstrow=logdf.limit(1).collect()\n", + "\n", + " for c in logdf.columns:\n", + " if firstrow[0][c]!=\"xgbtck\":\n", + " logdf=logdf.drop(c)\n", + " else:\n", + " break\n", + "\n", + " usefulc=[\"xgbtck\",\"event\",\"ts\",\"elapsed\",\"threadid\",\"taskid\"]\n", + " for i in range(0,len(usefulc)):\n", + " logdf=logdf.withColumnRenamed(logdf.columns[i],usefulc[i])\n", + "\n", + " logdf=logdf.where(F.col(\"event\").isin(['load_library','data_load','data_convert']))\n", + " \n", + " task_thread=logdf.where(\"event='data_convert'\").select(F.col(\"taskid\").astype(IntegerType()),F.col(\"threadid\").astype(IntegerType())).distinct().toPandas().set_index('taskid').to_dict('index')\n", + " #task_thread={}\n", + "\n", + " for i,l in hostsdf.toPandas().iterrows():\n", + " exec_hosts[l['Host']]=hostid\n", + " hostid=hostid+100000\n", + "\n", + " tskmap={}\n", + " for idx,l in events.iterrows():\n", + " if l['Event']=='SparkListenerTaskStart':\n", + " hostid=exec_hosts[l['Host']]\n", + "\n", + " tsk=l['Task ID']\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " stime=l['Launch Time']\n", + " #the task's starttime and finishtime is the same, ignore it.\n", + " if tsk in ended_event:\n", + " continue\n", + " if not pid in coretrack:\n", + " tids={}\n", + " trace_events.append({\n", + " \"name\": \"process_name\",\n", + " \"ph\": \"M\",\n", + " \"pid\":pid,\n", + " \"tid\":0,\n", + " \"args\":{\"name\":\"{:s}.{:s}\".format(l['Host'],l['Executor ID'])}\n", + " })\n", + "\n", + " else:\n", + " tids=coretrack[pid]\n", + "\n", + " tidarr=[tsk,stime]\n", + "\n", + " for t in tids.keys():\n", + " if tids[t][0]==-1:\n", + " tids[t]=tidarr\n", + " break\n", + " else:\n", + " t=len(tids)\n", + " tids[t]=tidarr\n", + " #print(\"task {:d} tid is {:s}.{:d}\".format(tsk,pid,t))\n", + " coretrack[pid]=tids\n", + "\n", + " if l['Event']=='SparkListenerTaskEnd':\n", + " sevt={}\n", + " eevt={}\n", + " hostid=exec_hosts[l['Host']]\n", + " pid=int(l['Executor ID'])*100+hostid\n", + " tsk=l['Task ID']\n", + " fintime=l['Finish Time']\n", + "\n", + " tids=coretrack[pid]\n", + " for t in tids.keys():\n", + " if tids[t][0]==tsk:\n", + " tids[t]=[-1,-1]\n", + " break\n", + " else:\n", + " ended_event.append(tsk)\n", + " continue\n", + " for ps in reversed([key for key in tids.keys()]):\n", + " if (tids[ps][1]-fintime<0 and tids[ps][1]-fintime>=-2) or \\\n", + " (tsk in task_thread and tids[ps][0] in task_thread and task_thread[tsk][\"threadid\"]==task_thread[tids[ps][0]][\"threadid\"]):\n", + " fintime=tids[ps][1]\n", + " tids[t]=tids[ps]\n", + " tids[ps]=[-1,-1]\n", + " break\n", + " if starttime==0:\n", + " starttime=l['Launch Time']\n", + "\n", + " sstime=l['Launch Time']-starttime\n", + "\n", + " trace_events.append({\n", + " 'tid':pid+int(t),\n", + " 'ts':sstime,\n", + " 'dur':fintime-l['Launch Time'],\n", + " 'pid':pid,\n", + " \"ph\":'X',\n", + " 'name':\"stg{:d}\".format(l['Stage ID']),\n", + " 'args':{\"job id\": l['Job ID'],\n", + " \"stage id\": l['Stage ID'],\n", + " \"tskid\":tsk,\n", + " \"input\":builtins.round(l[\"Bytes Read\"]/1024/1024,2),\n", + " \"spill\":builtins.round(l[\"Memory Bytes Spilled\"]/1024/1024,2),\n", + " \"Shuffle Read Metrics\": \"\",\n", + " \"|---Local Read\": builtins.round(l[\"Local Bytes Read\"]/1024/1024,2),\n", + " \"|---Remote Read\":builtins.round(l[\"Remote Bytes Read\"]/1024/1024,2),\n", + " \"Shuffle Write Metrics\": \"\",\n", + " \"|---Write\":builtins.round(l['Shuffle Bytes Written']/1024/1024,2)\n", + " }\n", + " })\n", + " tskmap[tsk]={'pid':pid,'tid':pid+int(t)}\n", + "\n", + " self.starttime=starttime\n", + " self.tskmap=tskmap\n", + "\n", + " tskmapdf = spark.createDataFrame(pandas.DataFrame(self.tskmap).T.reset_index())\n", + " logdf=logdf.withColumn(\"ts\",F.col(\"ts\").astype(LongType()))\n", + " logdf=logdf.withColumn(\"taskid\",F.col(\"taskid\").astype(LongType()))\n", + " logdf=logdf.withColumnRenamed(\"event\",'type')\n", + " mgd=logdf.join(tskmapdf,on=(F.col('taskid')==F.col(\"index\")),how=\"right\")\n", + " rstdf=mgd.select(F.col('tid').alias(\"tid\"),\n", + " (F.round(F.col('ts')-F.lit(self.starttime),3)).alias(\"ts\"),\n", + " F.round(F.col(\"elapsed\"),3).alias(\"dur\"),\n", + " F.lit(F.col('pid')).alias(\"pid\"),\n", + " F.lit(\"X\").alias(\"ph\"),\n", + " F.col(\"type\").alias(\"name\")\n", + " ).where(F.col(\"ts\").isNotNull()).orderBy('ts')\n", + "\n", + " # logdf=logdf.withColumn(\"type\",F.substring_index(\"event\",\"_\",1))\n", + " # window= Window.partitionBy(logdf['taskid']).orderBy(\"type\",\"ts\")\n", + " # logdfx=logdf.select(\"taskid\",\"event\",\"type\",\"ts\",F.lag('ts',1).over(window).alias(\"last\"),F.lag('rownum',1).over(window).alias(\"rownum\")).orderBy(\"taskid\",\"ts\").where(\"event like '%end'\")\n", + "\n", + "\n", + " output=[json.dumps(l) for l in trace_events]\n", + " output.extend(rstdf.toJSON().collect())\n", + "\n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# perf trace analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "def split_trace(x):\n", + " fi=[]\n", + " for l in x:\n", + " rst1=re.search(r\"^(\\d+\\.\\d+).*sched:(sched_switch):.+:(\\d+) \\[\\d+\\] (\\S+) ==> .+:(\\d+) \"\"\",l)\n", + " rst2=re.search(r\"(\\d+\\.\\d+) \\( +(\\d+\\.\\d+) +ms\\):[^/]+/(\\d+) (recvfrom|sendto)\\(fd: \\d+<\\S+:\\[\\d+\\]>, \\S+: 0x[a-f0-9]+, \\S+: (\\d+)\",l)\n", + " rst3=re.search(r\"(\\d+\\.\\d+) \\( +\\): [^/]+/(\\d+) (recvfrom|sendto)\\(fd: \\d+<\\S+:\\[\\d+\\]>, \\S+: 0x[a-f0-9]+, \\S+: (\\d+)\",l)\n", + " rst4=re.search(r\"(\\d+\\.\\d+) \\( *(\\d+\\.\\d+) ms\\): [^/]+/(\\d+) ... \\[continued\\]: (sendto|recvfrom|poll)\",l)\n", + " rst5=re.search(r\"(\\d+\\.\\d+) \\( +(\\d+\\.\\d+) +ms\\): [^/]+/(\\d+) (poll)\",l)\n", + " rst6=re.search(r\"(\\d+\\.\\d+) \\( +\\): [^/]+/(\\d+) (poll)\",l)\n", + "\n", + " rstx=re.search(r\"(\\d+\\.\\d+)*sched:(sched_switch):.*prev_pid=(\\d+).*prev_state=(\\S+) ==> .*next_pid=(\\d+)\"\"\",l)\n", + " if not rst1:\n", + " rst1=rstx\n", + " \n", + " if rst1:\n", + " fi.append((rst1.group(1),rst1.group(2),rst1.group(3),rst1.group(4),rst1.group(5))) #time, switch, src, status, dst\n", + " elif rst2:\n", + " fi.append((rst2.group(1),rst2.group(4),rst2.group(3),rst2.group(2),rst2.group(5))) #time, sed/rcv, pid, ms, size \n", + " elif rst3:\n", + " fi.append((rst3.group(1),rst3.group(3),rst3.group(2),0, rst3.group(4))) #time, sed/rcv, pid, 0, size\n", + " elif rst4:\n", + " fi.append((rst4.group(1),rst4.group(4),rst4.group(3),rst4.group(2), 0)) #time, sed/rcv, pid, ms, 0\n", + " elif rst5:\n", + " fi.append((rst5.group(1),rst5.group(4),rst5.group(3),rst5.group(2), 0)) #time, sed/rcv, pid, ms, 0\n", + " elif rst6:\n", + " fi.append((rst6.group(1),rst6.group(3),rst6.group(2),0, 0)) #time, sed/rcv, pid, ms0, 0\n", + " elif not re.match(r\"^ +?\",l):\n", + " fi.append((0,l,'','',''))\n", + " return iter(fi)\n", + " \n", + "\n", + "\n", + "class Perf_trace_analysis(Analysis):\n", + " def __init__(self,sar_file):\n", + " Analysis.__init__(self,sar_file)\n", + " self.starttime=None\n", + " \n", + " def load_data(self):\n", + " sardata=sc.textFile(self.file)\n", + " sardf=sardata.mapPartitions(split_trace).toDF()\n", + " display(sardf.where(\"_1=0\").limit(5).collect())\n", + " sardf=sardf.withColumn(\"_1\",F.col(\"_1\").astype(DoubleType()))\n", + " sardf=sardf.where(\"_1>0\")\n", + " starttime=sardf.agg(F.min(\"_1\")).collect()[0][0]\n", + " if self.starttime is None:\n", + " self.starttime=(float(starttime))\n", + " else:\n", + " paths=os.path.split(self.file)\n", + " if fs.exists(paths[0]+\"/uptime.txt\"):\n", + " with fs.open(paths[0]+\"/uptime.txt\") as f:\n", + " strf=f.read().decode('ascii')\n", + " print(\"input starttime:\",self.starttime,\"uptime:\",float(strf)*1000,\"record starttime:\",starttime)\n", + " self.starttime=self.starttime-float(strf)*1000\n", + " else:\n", + " print(\"uptime.txt isn't found, wrong\")\n", + " return\n", + " \n", + " self.df=sardf\n", + " return sardf\n", + "\n", + " def generate_sched_view_list(self,id=0,**kwargs):\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " starttime=starttime+kwargs.get(\"sched_time_offset\",0)\n", + " print(\"offset time\",starttime)\n", + " \n", + " swdf=sardf.where(\"_2='sched_switch'\")\n", + " \n", + " cputhreshold=kwargs.get(\"cpu_threshold\",0.1)\n", + " sched_cnt = kwargs.get(\"sched_cnt\",10)\n", + " \n", + " pidstat_tids=kwargs.get(\"pidstat_tids\",None)\n", + " pidstat_tids_txt=kwargs.get(\"pidstat_tids_txt\",\"sched_threads.txt\")\n", + " \n", + " if pidstat_tids:\n", + " if type(pidstat_tids) is list:\n", + " tids=pidstat_tids\n", + " else:\n", + " tids=[re.split(r'\\s+',t) for t in pidstat_tids.split(\"\\n\")]\n", + " tids=[t[3] for t in tids if len(t)>4]\n", + " else:\n", + " paths=os.path.split(self.file)\n", + " if fs.exists(paths[0]+\"/\"+pidstat_tids_txt):\n", + " with fs.open(paths[0]+\"/\"+pidstat_tids_txt) as f:\n", + " tids=[l.strip() for l in f.read().decode('ascii').split(\"\\n\") if len(l)>0] \n", + " else:\n", + " print(\"Wrong, no pidstat_tids args and no sched_threads.txt file\")\n", + " return []\n", + " tidcnt=swdf.where(F.col(\"_5\").isin(tids)).groupBy(\"_5\").count()\n", + " tidm10=tidcnt.where(\"count>{:d}\".format(sched_cnt)).select(\"_5\").collect()\n", + " rtids=[t[0] for t in tidm10]\n", + " rtiddf=swdf.where(F.col(\"_5\").isin(rtids) | F.col(\"_3\").isin(rtids))\n", + " rtiddf=rtiddf.withColumn(\"_1\",F.col(\"_1\").astype(DoubleType())-starttime)\n", + " rtiddf=rtiddf.withColumn(\"_3\",F.col(\"_3\").astype(IntegerType()))\n", + " rtiddf=rtiddf.withColumn(\"_5\",F.col(\"_5\").astype(IntegerType()))\n", + " rtiddf=rtiddf.withColumn(\"_1\",F.round(F.col(\"_1\"),3))\n", + " rtidcol=rtiddf.collect()\n", + " tidmap={}\n", + " tidtotal={}\n", + " for t in rtids:\n", + " tidmap[int(t)]=0\n", + " tidtotal[int(t)]=0\n", + " trace_events=[]\n", + " mintime=rtidcol[0][\"_1\"]\n", + " maxtime=0\n", + " for r in rtidcol:\n", + " if r[\"_3\"] in tidtotal:\n", + " tidtotal[r[\"_3\"]]=tidtotal[r[\"_3\"]]+r[\"_1\"]-tidmap[r[\"_3\"]]\n", + " tidmap[r[\"_3\"]]=r[\"_1\"]\n", + " maxtime=r[\"_1\"]\n", + " if r[\"_5\"] in tidmap:\n", + " tidmap[r[\"_5\"]]=r[\"_1\"]\n", + " for r in rtidcol:\n", + " if r[\"_3\"] in tidmap and tidtotal[r[\"_3\"]]/(maxtime-mintime)>cputhreshold:\n", + " trace_events.append({\n", + " 'tid':r[\"_3\"],\n", + " 'ts':tidmap[r[\"_3\"]],\n", + " 'pid':id,\n", + " 'ph':'X',\n", + " 'dur':round(r[\"_1\"]-tidmap[r[\"_3\"]],3),\n", + " 'name':r[\"_4\"]\n", + " })\n", + "\n", + " tidmap[r[\"_3\"]]=r[\"_1\"]\n", + " if r[\"_5\"] in tidmap:\n", + " tidmap[r[\"_5\"]]=r[\"_1\"]\n", + " return [json.dumps(l) for l in trace_events]\n", + "\n", + " def generate_nic_view_list(self,id=0,**kwargs):\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " starttime=starttime+kwargs.get(\"sched_time_offset\",0)\n", + " print(\"offset time\",starttime)\n", + " \n", + " nicdf=sardf.where(\"_2<>'sched_switch'\")\n", + " cntdf=nicdf.where(\"_2='continued'\")\n", + " cntdf=cntdf.select(\"_1\",\"_3\",\"_4\").withColumnRenamed(\"_4\",\"cnt_4\")\n", + " nicdf=nicdf.join(cntdf,on=[\"_1\",\"_3\"],how=\"leftouter\")\n", + " nicdf=nicdf.where(\"_2<>'continued'\")\n", + " nicdf=nicdf.select(F.col(\"_1\"),F.col(\"_2\"),F.col(\"_3\"),F.when(F.col(\"cnt_4\").isNull(), F.col(\"_4\")).otherwise(F.col(\"cnt_4\")).alias(\"_4\"),F.col(\"_5\"))\n", + " nicdf=nicdf.withColumn(\"_1\",F.col(\"_1\").astype(DoubleType())-starttime)\n", + " nicdf=nicdf.withColumn(\"_3\",F.col(\"_3\").astype(IntegerType()))\n", + " nicdf=nicdf.withColumn(\"_5\",F.col(\"_5\").astype(IntegerType()))\n", + " nicdf=nicdf.withColumn(\"_1\",F.col(\"_1\").astype(IntegerType()))\n", + " nicdf=nicdf.withColumn(\"_4\",F.col(\"_4\").astype(DoubleType()))\n", + " nicdf=nicdf.withColumn(\"_4\",F.col(\"_4\").astype(LongType()))\n", + " return nicdf.select(\n", + " F.col(\"_3\").alias('tid'),\n", + " (F.col(\"_1\")).alias('ts'),\n", + " F.lit(0).alias('pid'),\n", + " F.lit('X').alias('ph'),\n", + " F.col(\"_4\").alias('dur'),\n", + " F.col(\"_2\").alias('name'),\n", + " F.struct(\n", + " F.col(\"_5\").alias(\"size\")\n", + " ).alias('args')\n", + " ).toJSON().collect()\n", + " \n", + " def generate_trace_view_list(self,id=0,**kwargs):\n", + " trace_events=Analysis.generate_trace_view_list(self,id,**kwargs)\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " \n", + " events=self.generate_sched_view_list(id,**kwargs)\n", + " events.extend(self.generate_nic_view_list(id,**kwargs))\n", + " events.extend(trace_events)\n", + " \n", + "# events.extend(nicdf.where(\"_5>1000 and _2='sendto'\").select(\n", + "# F.lit(0).alias('tid'),\n", + "# F.col(\"_1\").alias('ts'),\n", + "# F.lit(0).alias('pid'),\n", + "# F.lit('i').alias('ph'),\n", + "# F.col(\"_2\").alias('name'),\n", + "# F.lit(\"g\").alias(\"s\")\n", + "# ).toJSON().collect())\n", + "\n", + "\n", + " return events\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sar analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "def splits(x):\n", + " fi=[]\n", + " for l in x:\n", + " li=re.split(r'\\s+',l)\n", + " for j in range(len(li),118):\n", + " li.append('')\n", + " fi.append(li)\n", + " return iter(fi)\n", + "\n", + "class Sar_analysis(Analysis):\n", + " def __init__(self,sar_file):\n", + " Analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " sardata=sc.textFile(self.file)\n", + " sardf=sardata.mapPartitions(splits).toDF()\n", + " sardf=sardf.where(\"_1<>'Average:'\")\n", + " \n", + " colstart=1;\n", + " ampm=sardf.where(\"_2='AM' or _2='PM'\").count()\n", + " if ampm==0:\n", + " for i in range(len(sardf.columns),1,-1):\n", + " sardf=sardf.withColumnRenamed(f'_{i}',f'_{i+1}')\n", + " self.timeformat='yyyy-MM-dd HH:mm:ss '\n", + " sardf=sardf.withColumn('_2',F.lit(''))\n", + " #print('no PM/AM')\n", + " colstart=1\n", + " else:\n", + " self.timeformat='yyyy-MM-dd hh:mm:ss a'\n", + " colstart=2\n", + " #print('with PM/AM')\n", + " \n", + " f=fs.open(self.file)\n", + " t=f.readline()\n", + " t=f.readline()\n", + " while len(t)==1:\n", + " t=f.readline()\n", + " cols=t.decode('ascii')\n", + " li=re.split(r'\\s+',cols)\n", + " ci=3;\n", + " for c in li[colstart:]:\n", + " sardf=sardf.withColumnRenamed(f\"_{ci}\",c)\n", + " ci=ci+1\n", + " \n", + " sardf=sardf.where(F.col(li[-2])!=li[-2]).where(F.col(\"_1\")!=F.lit(\"Linux\")) \n", + " \n", + " sardf.cache()\n", + " self.df=sardf\n", + " \n", + " self.sarversion=\"\"\n", + " paths=os.path.split(self.file)\n", + " if fs.exists(paths[0]+\"/sarv.txt\"):\n", + " with fs.open(paths[0]+\"/sarv.txt\") as f:\n", + " allcnt = f.read().decode('ascii')\n", + " #print(allcnt)\n", + " self.sarversion=allcnt.split(\"\\n\")[0].split(\" \")[2]\n", + " \n", + " return sardf\n", + "\n", + " def col_df(self,cond,colname,args,slaver_id=0, thread_id=0):\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " cpudf=sardf.where(cond)\n", + " #cpudf.select(F.date_format(F.from_unixtime(F.lit(starttime/1000)), 'yyyy-MM-dd HH:mm:ss').alias('starttime'),'_1').show(1)\n", + "\n", + " cpudf=cpudf.withColumn('time',F.unix_timestamp(F.concat_ws(' ',F.date_format(F.from_unixtime(F.lit(starttime/1000)), 'yyyy-MM-dd'),F.col('_1'),F.col('_2')),self.timeformat))\n", + "\n", + " cols=cpudf.columns\n", + " \n", + " cpudf=cpudf.groupBy('time').agg(\n", + " F.sum(F.when(F.col(cols[1]).rlike('^\\d+(\\.\\d+)*$'),F.col(cols[1]).astype(FloatType())).otherwise(0)).alias(cols[1]),\n", + " F.sum(F.when(F.col(cols[2]).rlike('^\\d+(\\.\\d+)*$'),F.col(cols[2]).astype(FloatType())).otherwise(0)).alias(cols[2]),\n", + " *[F.sum(F.col(c)).alias(c) for c in cols[3:] if not c.startswith(\"_\") and c!=\"\" and c!=\"time\"]\n", + " )\n", + " \n", + " traces=cpudf.orderBy(F.col(\"time\")).select(\n", + " F.lit(thread_id).alias('tid'),\n", + " (F.expr(\"time*1000\")-F.lit(self.starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(slaver_id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.lit(colname).alias('name'),\n", + " args(cpudf).alias('args')\n", + " ).toJSON().collect()\n", + " return traces\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_events=Analysis.generate_trace_view_list(self,id, **kwargs)\n", + " return trace_events\n", + "\n", + " def get_stat(self,**kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + " \n", + "class Sar_cpu_analysis(Sar_analysis):\n", + " def __init__(self,sar_file):\n", + " Sar_analysis.__init__(self,sar_file)\n", + " \n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_events=Sar_analysis.generate_trace_view_list(self,id, **kwargs)\n", + " \n", + " self.df=self.df.withColumn(\"%iowait\",F.when(F.col(\"%iowait\")>100,F.lit(100)).otherwise(F.col(\"%iowait\")))\n", + " \n", + " trace_events.extend(self.col_df(\"CPU='all'\", \"all cpu%\", lambda l: F.struct(\n", + " F.floor(F.col('%user').astype(FloatType())).alias('user'),\n", + " F.floor(F.col('%system').astype(FloatType())).alias('system'),\n", + " F.floor(F.col('%iowait').astype(FloatType())).alias('iowait')\n", + " ), id, 0))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":0,\"args\":{\"sort_index \":0}}))\n", + " \n", + " return trace_events \n", + " def get_stat(sar_cpu,**kwargs):\n", + " Sar_analysis.get_stat(sar_cpu)\n", + " \n", + " cpuutil=sar_cpu.df.where(\"CPU='all'\").groupBy(\"_1\").agg(*[F.mean(F.col(l).astype(FloatType())).alias(l) for l in [\"%user\",\"%system\",\"%iowait\"]]).orderBy(\"_1\")\n", + " cnt=cpuutil.count()\n", + " user_morethan_90=cpuutil.where(\"`%user`>0.9\").count()\n", + " kernel_morethan_10=cpuutil.where(\"`%system`>0.1\").count()\n", + " iowait_morethan_10=cpuutil.where(\"`%iowait`>0.1\").count()\n", + " out=[['%user>90%',user_morethan_90/cnt],['%kernel>10%',kernel_morethan_10/cnt],[\"%iowait>10%\",iowait_morethan_10/cnt]]\n", + " avgutil=cpuutil.agg(*[F.mean(l).alias(l) for l in [\"%user\",\"%system\",\"%iowait\"]]).collect()\n", + " out.extend([[\"avg \" + l,avgutil[0][l]] for l in [\"%user\",\"%system\",\"%iowait\"]])\n", + " pdout=pandas.DataFrame(out).set_index(0)\n", + " pdout.columns=[sar_cpu.file.split(\"/\")[-2]]\n", + " return pdout\n", + " \n", + "class Sar_mem_analysis(Sar_analysis):\n", + " def __init__(self,sar_file):\n", + " Sar_analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " Sar_analysis.load_data(self)\n", + " sarv=[int(l) for l in self.sarversion.split(\".\")]\n", + " if sarv[0]>=12 and sarv[1]>=2:\n", + " self.df=self.df.withColumn(\"kbrealused\",F.col(\"kbmemused\"))\n", + " else:\n", + " # sar 10.1.5, sar 11.6.1\n", + " self.df=self.df.withColumn(\"kbrealused\",F.col(\"kbmemused\")-F.col(\"kbcached\")-F.col(\"kbbuffers\"))\n", + " \n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_events=Sar_analysis.generate_trace_view_list(self,id, **kwargs)\n", + " \n", + " \n", + " trace_events.extend(self.col_df(F.col('kbmemfree').rlike('^\\d+$'),\"mem % \", lambda l: F.struct(F.floor(l['kbcached']*l['%memused']/l['kbmemused']).alias('cached'), # kbcached / (kbmemfree+kbmemused)\n", + " F.floor(l['kbbuffers']*l['%memused']/l['kbmemused']).alias('buffered'),# kbbuffers / (kbmemfree+kbmemused)\n", + " F.floor(l['kbrealused']*l['%memused']/l['kbmemused']).alias('used')), # (%memused- kbcached-kbbuffers )/ (kbmemfree+kbmemused)\n", + " id,1))\n", + " #trace_events.extend(self.col_df(self.df._3.rlike('^\\d+$'),\"mem cmt % \", lambda l: F.struct(F.floor(l._8*F.lit(100)/(l._3+l._4)).alias('commit/phy'),\n", + " # F.floor(l._10-l._8*F.lit(100)/(l._3+l._4)).alias('commit/all')), id))\n", + " trace_events.extend(self.col_df(F.col('kbmemfree').rlike('^\\d+$'),\"pagecache % \", lambda l: F.struct(F.floor((l['kbcached']-l['kbdirty'])*l['%memused']/l['kbmemused']).alias('clean'), \n", + " F.floor(l['kbdirty']*l['%memused']/l['kbmemused']).alias('dirty')),\n", + " id,2))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":1,\"args\":{\"sort_index \":1}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":2,\"args\":{\"sort_index \":2}}))\n", + " return trace_events \n", + " def get_stat(sar_mem,**kwargs):\n", + " Sar_analysis.get_stat(sar_mem)\n", + " \n", + " memutil=sar_mem.df.where(F.col('kbmemfree').rlike('^\\d+$')).select(F.floor(F.col('kbcached').astype(FloatType())*F.lit(100)*F.col('%memused')/F.col('kbmemused')).alias('cached'), \n", + " F.floor(F.col('kbbuffers').astype(FloatType())*F.lit(100)*F.col('%memused')/F.col('kbmemused')).alias('buffered'),\n", + " F.floor(F.col('kbrealused').astype(FloatType())*F.lit(100)*F.col('%memused')/F.col('kbmemused')).alias('used'),\n", + " F.floor(F.col('kbdirty').astype(FloatType())*F.lit(100)*F.col('%memused')/F.col('kbmemused')).alias('dirty'))\n", + " memsum=memutil.summary().toPandas()\n", + " memsum=memsum.set_index(\"summary\")\n", + " out=[\n", + " [[l + ' mean',float(memsum[l][\"mean\"])],\n", + " [l + ' 75%',float(memsum[l][\"75%\"])],\n", + " [l + ' max',float(memsum[l][\"max\"])]] for l in [\"cached\",\"used\",\"dirty\"]]\n", + " out=[*out[0],*out[1]]\n", + " pdout=pandas.DataFrame(out).set_index(0)\n", + " pdout.columns=[sar_mem.file.split(\"/\")[-2]]\n", + " return pdout\n", + " \n", + "class Sar_PageCache_analysis(Sar_analysis):\n", + " def __init__(self,sar_file):\n", + " Sar_analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " Sar_analysis.load_data(self)\n", + " \n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_events=Sar_analysis.generate_trace_view_list(self,id, **kwargs)\n", + " \n", + " \n", + " trace_events.extend(self.col_df(F.col('pgpgin/s').rlike('^\\d'),\"page inout\", lambda l: F.struct(\n", + " F.floor(l['pgpgin/s']/1024).alias('in'),\n", + " F.floor(l['pgpgout/s']/1024).alias('out')),\n", + " id,11))\n", + " trace_events.extend(self.col_df(F.col('pgpgin/s').rlike('^\\d'),\"faults\", lambda l: F.struct(F.floor((l['majflt/s'])).alias('major'), \n", + " F.floor(l['fault/s']-l['majflt/s']).alias('minor')),\n", + " id,12))\n", + " trace_events.extend(self.col_df(F.col('pgpgin/s').rlike('^\\d'),\"page free\", lambda l: F.struct(F.floor((l['pgfree/s']*4/1024)).alias('free')),\n", + " id,13))\n", + " trace_events.extend(self.col_df(F.col('pgpgin/s').rlike('^\\d'),\"scan\", lambda l: F.struct(F.floor((l['pgscank/s'])*4/1024).alias('kernel'), \n", + " F.floor(l['pgscand/s']*4/1024).alias('app')),\n", + " id,14))\n", + " trace_events.extend(self.col_df(F.col('pgpgin/s').rlike('^\\d'),\"vmeff\", lambda l: F.struct(F.floor((l['%vmeff'])).alias('steal')),\n", + " id,15))\n", + " \n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":11,\"args\":{\"sort_index \":11}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":12,\"args\":{\"sort_index \":12}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":13,\"args\":{\"sort_index \":13}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":14,\"args\":{\"sort_index \":14}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":15,\"args\":{\"sort_index \":15}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":16,\"args\":{\"sort_index \":16}}))\n", + " return trace_events \n", + " def get_stat(sar_mem,**kwargs):\n", + " Sar_analysis.get_stat(sar_mem)\n", + " \n", + " memutil=sar_mem.df.where(F.col('pgpgin/s').rlike('^\\d')).select(F.floor(F.col('pgpgin/s').astype(FloatType())/1024).alias('pgin'), \n", + " F.floor(F.col('pgpgout/s').astype(FloatType())/1024).alias('pgout'),\n", + " F.floor(F.col('fault/s').astype(FloatType())-F.col('majflt/s').astype(FloatType())).alias('fault')\n", + " )\n", + " memsum=memutil.summary().toPandas()\n", + " memsum=memsum.set_index(\"summary\")\n", + " out=[\n", + " [[l + ' mean',float(memsum[l][\"mean\"])],\n", + " [l + ' 75%',float(memsum[l][\"75%\"])],\n", + " [l + ' max',float(memsum[l][\"max\"])]] for l in [\"pgin\",\"pgout\",\"fault\"]]\n", + " out=[*out[0],*out[1],*out[2]]\n", + " pdout=pandas.DataFrame(out).set_index(0)\n", + " pdout.columns=[sar_mem.file.split(\"/\")[-2]]\n", + " return pdout\n", + " \n", + " \n", + "class Sar_disk_analysis(Sar_analysis):\n", + " def __init__(self,sar_file):\n", + " Sar_analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " Sar_analysis.load_data(self)\n", + " \n", + " self.df=self.df.withColumn(\"%util\",F.col(\"%util\").astype(IntegerType()))\n", + " used_disk=self.df.groupBy(\"DEV\").agg(F.max(F.col(\"%util\")).alias(\"max_util\"),F.mean(\"%util\").alias(\"avg_util\")).where(F.col(\"max_util\")>10).collect()\n", + " self.df=self.df.where(F.col(\"DEV\").isin([l['DEV'] for l in used_disk]))\n", + " #print(\"used disks with its max util% and avg util% are: \")\n", + " #display([(l['DEV'],l[\"max_util\"],l[\"avg_util\"]) for l in used_disk])\n", + " \n", + " if \"rd_sec/s\" in self.df.columns:\n", + " self.df=self.df.withColumn(\"rkB/s\",F.expr('cast(`rd_sec/s` as float)*512/1024'))\n", + " if \"wr_sec/s\" in self.df.columns:\n", + " self.df=self.df.withColumn(\"wkB/s\",F.expr('cast(`wr_sec/s` as float)*512/1024'))\n", + " \n", + " if \"areq-sz\" in self.df.columns:\n", + " self.df=self.df.withColumnRenamed(\"areq-sz\",\"avgrq-sz\")\n", + " if \"aqu-sz\" in self.df.columns:\n", + " self.df=self.df.withColumnRenamed(\"aqu-sz\",\"avgqu-sz\")\n", + " \n", + " if \"rkB/s\" in self.df.columns:\n", + " self.df=self.df.withColumn(\"rkB/s\",F.expr('cast(`rkB/s` as float)/1024'))\n", + " if \"wkB/s\" in self.df.columns:\n", + " self.df=self.df.withColumn(\"wkB/s\",F.expr('cast(`wkB/s` as float)/1024'))\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_events=Sar_analysis.generate_trace_view_list(self,id, **kwargs)\n", + "\n", + " disk_prefix=kwargs.get('disk_prefix',\"\")\n", + " \n", + " if type(disk_prefix)==str:\n", + " diskfilter = \"DEV like '\"+disk_prefix+\"%'\"\n", + " elif type(disk_prefix)==list:\n", + " diskfilter = \"DEV in (\"+\",\".join(disk_prefix)+\")\"\n", + " else:\n", + " diskfilter = \"DEV like '%'\"\n", + "\n", + " print(diskfilter)\n", + " devcnt=self.df.where(diskfilter).select(\"DEV\").distinct().count()\n", + " \n", + " trace_events.extend(self.col_df(diskfilter, \"disk b/w\", lambda l: F.struct(\n", + " F.floor(F.col(\"rKB/s\")).alias('read'),\n", + " F.floor(F.col(\"wKB/s\")).alias('write')),id, 3))\n", + " trace_events.extend(self.col_df(diskfilter, \"disk%\", lambda l: F.struct(\n", + " (F.col(\"%util\")/F.lit(devcnt)).alias('%util')),id, 4))\n", + " trace_events.extend(self.col_df(diskfilter, \"req size\", lambda l: F.struct(\n", + " (F.col(\"avgrq-sz\")/F.lit(devcnt)).alias('avgrq-sz')),id, 5))\n", + " trace_events.extend(self.col_df(diskfilter, \"queue size\", lambda l: F.struct(\n", + " (F.col(\"avgqu-sz\")/F.lit(512*devcnt/1024)).alias('avgqu-sz')),id, 6))\n", + " trace_events.extend(self.col_df(diskfilter, \"await\", lambda l: F.struct(\n", + " (F.col(\"await\")/F.lit(devcnt)).alias('await')),id,7))\n", + " \n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":3,\"args\":{\"sort_index \":3}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":4,\"args\":{\"sort_index \":4}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":5,\"args\":{\"sort_index \":5}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":6,\"args\":{\"sort_index \":6}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":7,\"args\":{\"sort_index \":7}}))\n", + " return trace_events \n", + "\n", + " def get_stat(sar_disk,**kwargs):\n", + " Sar_analysis.get_stat(sar_disk)\n", + " disk_prefix=kwargs.get('disk_prefix',\"\")\n", + " \n", + " if type(disk_prefix)==str:\n", + " diskfilter = \"DEV like '\"+disk_prefix+\"%'\"\n", + " elif type(disk_prefix)==list:\n", + " diskfilter = \"DEV in (\"+\",\".join(disk_prefix)+\")\"\n", + " else:\n", + " diskfilter = \"DEV like '%'\"\n", + "\n", + " diskutil=sar_disk.df.where(diskfilter).groupBy(\"_1\").agg(F.mean(F.col(\"%util\").astype(FloatType())).alias(\"%util\")).orderBy(\"_1\")\n", + " totalcnt=diskutil.count()\n", + " time_morethan_90=diskutil.where(F.col(\"%util\")>90).count()/totalcnt\n", + " avgutil=diskutil.agg(F.mean(\"%util\")).collect()\n", + " out=[[\"avg disk util\",avgutil[0][\"avg(%util)\"]],\n", + " [\"time more than 90%\", time_morethan_90]]\n", + " diskbw=sar_disk.df.where(diskfilter).groupBy(\"_1\").agg(F.sum(F.col(\"rKB/s\")).alias(\"rd_bw\"),F.sum(F.col(\"wKB/s\")).alias(\"wr_bw\"))\n", + " bw=diskbw.agg(F.sum(\"rd_bw\").alias(\"total read\"),F.sum(\"wr_bw\").alias(\"total write\"),F.mean(\"rd_bw\").alias(\"read bw\"),F.mean(\"wr_bw\").alias(\"write bw\"),F.max(\"rd_bw\").alias(\"max read\"),F.max(\"wr_bw\").alias(\"max write\")).collect()\n", + " maxread=bw[0][\"max read\"]\n", + " maxwrite=bw[0][\"max write\"]\n", + " rdstat, wrstat = diskbw.stat.approxQuantile(['rd_bw','wr_bw'],[0.75,0.95,0.99],0.0)\n", + " time_rd_morethan_95 = diskbw.where(F.col(\"rd_bw\")>rdstat[1]).count()/totalcnt\n", + " time_wr_morethan_95 = diskbw.where(F.col(\"wr_bw\")>rdstat[1]).count()/totalcnt\n", + " out.append(['total read (G)' , bw[0][\"total read\"]/1024])\n", + " out.append(['total write (G)', bw[0][\"total write\"]/1024])\n", + " out.append(['avg read bw (MB/s)', bw[0][\"read bw\"]])\n", + " out.append(['avg write bw (MB/s)', bw[0][\"write bw\"]])\n", + " out.append(['read bw %75', rdstat[0]])\n", + " out.append(['read bw %95', rdstat[1]])\n", + " out.append(['read bw max', rdstat[2]])\n", + " out.append(['time_rd_morethan_95', time_rd_morethan_95])\n", + " out.append(['write bw %75', wrstat[0]])\n", + " out.append(['write bw %95', wrstat[1]])\n", + " out.append(['write bw max', wrstat[2]])\n", + " out.append(['time_wr_morethan_95', time_wr_morethan_95])\n", + " pdout=pandas.DataFrame(out).set_index(0)\n", + " pdout.columns=[sar_disk.file.split(\"/\")[-2]]\n", + " return pdout\n", + " \n", + "class Sar_nic_analysis(Sar_analysis):\n", + " def __init__(self,sar_file):\n", + " Sar_analysis.__init__(self,sar_file)\n", + " \n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_events=Sar_analysis.generate_trace_view_list(self,id, **kwargs)\n", + " \n", + " nicfilter=\"\"\n", + " if 'nic_prefix' in kwargs.keys():\n", + " nicfilter= \"IFACE in (\" + \",\".join(kwargs.get('nic_prefix',[\"'eth3'\",\"'enp24s0f1'\"])) + \")\"\n", + " else:\n", + " nicfilter= \"IFACE != 'lo'\"\n", + " \n", + " trace_events.extend(self.col_df(nicfilter, \"eth \", lambda l: F.struct(F.floor(F.expr('cast(`rxkB/s` as float)/1024')).alias('rxmb/s'),F.floor(F.expr('cast(`txkB/s` as float)/1024')).alias('txmb/s')), id, 8))\n", + " trace_events.extend(self.col_df(\"_3 like 'ib%'\", \"ib \", lambda l: F.struct(F.floor(F.expr('cast(`rxkB/s` as float)/1024')).alias('rxmb/s'),F.floor(F.expr('cast(`txkB/s` as float)/1024')).alias('txmb/s')), id, 9))\n", + " trace_events.extend(self.col_df(\"_3 = 'lo'\", \"lo \", lambda l: F.struct(F.floor(F.expr('cast (`rxkB/s` as float)/1024')).alias('rxmb/s'),F.floor(F.expr('cast (`txkB/s` as float)/1024')).alias('txmb/s')), id, 10))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":8,\"args\":{\"sort_index \":8}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":9,\"args\":{\"sort_index \":9}}))\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":10,\"args\":{\"sort_index \":10}}))\n", + " return trace_events \n", + " \n", + " def get_stat(sar_nic,**kwargs):\n", + " Sar_analysis.get_stat(sar_nic)\n", + " nicfilter=\"\"\n", + " \n", + " if 'nic_prefix' in kwargs.keys():\n", + " nicfilter= \"IFACE in (\" + \",\".join(kwargs.get('nic_prefix',[\"'eth3'\",\"'enp24s0f1'\"])) + \")\"\n", + " else:\n", + " nicfilter= \"IFACE != 'lo'\"\n", + " \n", + " nicbw=sar_nic.df.where(nicfilter).groupBy(\"_1\").agg(F.sum(F.col(\"rxkB/s\").astype(FloatType())/1024).alias(\"rx MB/s\")).orderBy(\"_1\")\n", + " if nicbw.count()==0:\n", + " out=[[\"rx MB/s 75%\",0],[\"rx MB/s 95%\",0],[\"rx MB/s 99%\",0]]\n", + " else:\n", + " out=nicbw.stat.approxQuantile(['rx MB/s'],[0.75,0.95,0.99],0.0)[0]\n", + " out=[[\"rx MB/s 75%\",out[0]],[\"rx MB/s 95%\",out[1]],[\"rx MB/s 99%\",out[2]]]\n", + " pdout=pandas.DataFrame(out).set_index(0)\n", + " pdout.columns=[sar_nic.file.split(\"/\")[-2]]\n", + " return pdout" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# PID State analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class Pidstat_analysis(Analysis):\n", + " def __init__(self,sar_file):\n", + " Analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " sardata=sc.textFile(self.file)\n", + " sardf=sardata.mapPartitions(splits).toDF()\n", + " sardf=sardf.where(\"_1<>'Average:'\")\n", + " \n", + " headers=sardf.where(\"_4='TID' or _5='TID'\").limit(1).collect()\n", + " r=headers[0].asDict()\n", + " findtime=False\n", + " for i,v in r.items():\n", + " if(v==\"Time\"):\n", + " findtime=True\n", + " if not findtime:\n", + " r[\"_1\"]=\"Time\"\n", + " for i,v in r.items():\n", + " if(v!=\"\"):\n", + " sardf=sardf.withColumnRenamed(i,v)\n", + " sardf=sardf.where(\"TGID='0' or TGID='-'\") \n", + "\n", + " self.df=sardf\n", + " return sardf\n", + "\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_list=Analysis.generate_trace_view_list(self,id,**kwargs)\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " \n", + " sardf=sardf.withColumn(\"%CPU\",F.col(\"%CPU\").astype(FloatType()))\n", + " sardf=sardf.withColumn(\"Time\",F.col(\"Time\").astype(LongType()))\n", + " sardf=sardf.withColumn(\"TID\",F.col(\"TID\").astype(LongType()))\n", + " hotthreads=sardf.where(\"`%CPU`>30\").groupBy(\"TID\").count().collect()\n", + " hts=[(r[0],r[1]) for r in hotthreads]\n", + " htc=[r[1] for r in hotthreads]\n", + " if len(htc)==0:\n", + " return trace_list\n", + " maxcnt=max(htc)\n", + " hts=[r[0] for r in hts if r[1]>maxcnt/2]\n", + " tdfs=list(map(lambda x: sardf.withColumnRenamed(\"TID\",\"TID_\"+str(x)).withColumnRenamed(\"%CPU\",\"CPU_\"+str(x)).where(F.col(\"TID\")==x).select(\"Time\",\"TID_\"+str(x),\"CPU_\"+str(x)),hts))\n", + " finaldf=reduce(lambda x,y: x.join(y,on=[\"Time\"]),tdfs)\n", + " othersdf=sardf.where(\"TID not in (\"+\",\".join(map(lambda x: str(x),hts))+\")\").groupBy(\"Time\").agg(F.sum(\"%CPU\").alias(\"CPU_Other\"))\n", + " finaldf=finaldf.join(othersdf,on=[\"Time\"])\n", + " finaldf=finaldf.orderBy(\"Time\")\n", + " hts.append(\"Other\")\n", + " stt=[F.col(\"CPU_\"+str(x)).alias(str(x)) for x in hts]\n", + " args=F.struct(*stt)\n", + " \n", + " trace_list.extend(finaldf.select(\n", + " F.lit(6).alias('tid'),\n", + " (F.expr(\"Time*1000\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.lit(\"pidstat\").alias('name'),\n", + " args.alias('args')\n", + " ).toJSON().collect())\n", + " return trace_list\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Perf Trace Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class Perfstat_analysis(Analysis):\n", + " def __init__(self,sar_file):\n", + " Analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " sardata=sc.textFile(self.file)\n", + " sardf=sardata.mapPartitions(splits).toDF()\n", + " \n", + " paths=os.path.split(self.file)\n", + " if fs.exists(paths[0]+\"/perfstarttime\"):\n", + " with fs.open(paths[0]+\"/perfstarttime\") as f:\n", + " strf=f.read().decode('ascii')\n", + " else:\n", + " print(\"error, perfstarttime not found\")\n", + " return\n", + " \n", + " strf=strf[len(\"# started on \"):].strip()\n", + " starttime=datetime.strptime(strf, \"%a %b %d %H:%M:%S %Y\").timestamp()*1000\n", + " sardf=sardf.where(\"_1<>'#'\")\n", + " sardf=sardf.withColumn(\"ts\",F.col(\"_2\").astype(DoubleType())*1000+F.lit(starttime)).where(\"ts is not null\").select(\"ts\",\"_3\",\"_4\")\n", + " sardf=sardf.withColumn('_3', F.regexp_replace('_3', ',', '').astype(LongType()))\n", + " sardf=sardf.cache()\n", + " self.df=sardf\n", + " return sardf\n", + "\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_list=Analysis.generate_trace_view_list(self,id,**kwargs)\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " \n", + " stringIndexer = StringIndexer(inputCol=\"_4\", outputCol=\"syscall_idx\")\n", + " model = stringIndexer.fit(sardf)\n", + " sardf=model.transform(sardf)\n", + " \n", + "# cnts=sardf.select(\"_4\").distinct().collect()\n", + "# cnts=[l['_4'] for l in cnts]\n", + "# cntmap={ cnts[i]:i for i in range(0, len(cnts) ) }\n", + "# mapexpr=F.create_map([F.lit(x) for x in chain(*cntmap.items())])\n", + "# sardf.select(mapexpr.getItem(F.col(\"_4\")))\n", + " \n", + " sardf=sardf.withColumn(\"syscall_idx\",F.col(\"syscall_idx\").astype(IntegerType()))\n", + " \n", + " trace_list.extend(sardf.select(\n", + " (F.lit(100)+F.col(\"syscall_idx\")).alias('tid'),\n", + " (F.col(\"ts\")-F.lit(starttime)).astype(LongType()).alias('ts'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.col(\"_4\").alias('name'),\n", + " F.struct(F.col(\"_3\").alias(\"cnt\")).alias('args')\n", + " ).toJSON().collect())\n", + " return trace_list\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# GPU analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class gpu_analysis(Analysis):\n", + " def __init__(self,gpu_file):\n", + " Analysis.__init__(self,gpu_file)\n", + " \n", + " def load_data(self):\n", + " df_pf=spark.read.format(\"com.databricks.spark.csv\").option(\"header\",\"true\").option(\"mode\", \"DROPMALFORMED\").option(\"delimiter\", \",\").load(self.file)\n", + " df_pf2=df_pf.withColumn('timestamp',F.unix_timestamp(F.col('timestamp'),'yyyy/MM/dd HH:mm:ss')*1000+(F.split(F.col('timestamp'),'\\.')[1]).astype(IntegerType()))\n", + " df_pf2=df_pf2.withColumnRenamed(' utilization.gpu [%]','gpu_util')\n", + " df_pf2=df_pf2.withColumnRenamed(' utilization.memory [%]','mem_util')\n", + " df_pf2=df_pf2.withColumnRenamed(' memory.used [MiB]','mem_used')\n", + " df_pf2=df_pf2.withColumnRenamed(' index','index')\n", + " df_pf2=df_pf2.withColumn('gpu_util', (F.split('gpu_util',' ')[1]).astype(IntegerType()))\n", + " df_pf2=df_pf2.withColumn('mem_util', (F.split('mem_util',' ')[1]).astype(IntegerType()))\n", + " df_pf2=df_pf2.withColumn('mem_used', (F.split('mem_used',' ')[1]).astype(IntegerType()))\n", + " df_pf.cache()\n", + " self.df=df_pf2\n", + " return df_pf2\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " Analysis.generate_trace_view_list(self,id)\n", + " \n", + " df_pf2=self.df\n", + " starttime=self.starttime\n", + " trace_events=[]\n", + " \n", + " trace_events.extend(df_pf2.orderBy(df_pf2['timestamp']).select(\n", + " F.col('index').alias('tid'),\n", + " (F.expr(\"timestamp\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.concat(F.lit('gpu_util_'),F.col('index')).alias('name'),\n", + " F.struct(F.col('gpu_util').alias('gpu')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " trace_events.extend(df_pf2.orderBy(df_pf2['timestamp']).select(\n", + " F.col('index').alias('tid'),\n", + " (F.expr(\"timestamp\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(int(id)+1).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.concat(F.lit('mem_util_'),F.col('index')).alias('name'),\n", + " F.struct((F.col('mem_used')/F.lit(32768)).alias('mem')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " return trace_events" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "def splits_dmon(x):\n", + " fi=[]\n", + " for l in x:\n", + " l=l.strip()\n", + " if l.startswith('20'):\n", + " li=re.split(r'\\s+',l)\n", + " if len(li)==11:\n", + " fi.append(li)\n", + " return iter(fi)\n", + "\n", + "class gpu_dmon_analysis(Analysis):\n", + " def __init__(self,gpu_file):\n", + " Analysis.__init__(self,gpu_file)\n", + " \n", + " def load_data(self):\n", + " df_pf=sc.textFile(self.file)\n", + " df_pf=df_pf.mapPartitions(splits_dmon).toDF()\n", + " \n", + " df_pf2=df_pf.withColumn('_1',F.unix_timestamp(F.concat_ws(' ',F.col('_1'),F.col('_2')),'yyyyMMdd HH:mm:ss')*1000)\n", + " for c in range(3,12):\n", + " df_pf2=df_pf2.withColumn(f'_{c}',F.col(f'_{c}').astype(IntegerType()))\n", + "\n", + " df_pf.cache()\n", + " self.df=df_pf2\n", + " return df_pf2\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " Analysis.generate_trace_view_list(self,id)\n", + "\n", + " df_pf2=self.df\n", + " starttime=self.starttime\n", + " trace_events=[]\n", + " \n", + " trace_events.extend(df_pf2.orderBy(df_pf2['_1']).select(\n", + " F.col('_3').alias('tid'),\n", + " (F.expr(\"_1\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.concat(F.lit('gpu_util_'),F.col('_3')).alias('name'),\n", + " F.struct(F.col('_4').alias('gpu')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " trace_events.extend(df_pf2.orderBy(df_pf2['_1']).select(\n", + " F.col('_3').alias('tid'),\n", + " (F.expr(\"_1\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(id+1).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.concat(F.lit('mem_util_'),F.col('_3')).alias('name'),\n", + " F.struct(F.col('_5').alias('mem')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " trace_events.extend(df_pf2.orderBy(df_pf2['_1']).select(\n", + " F.col('_3').alias('tid'),\n", + " (F.expr(\"_1\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(id+2).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.concat(F.lit('gpu_freq_'),F.col('_3')).alias('name'),\n", + " F.struct(F.col('_9').alias('gpu_freq')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " trace_events.extend(df_pf2.orderBy(df_pf2['_1']).select(\n", + " F.col('_3').alias('tid'),\n", + " (F.expr(\"_1\")-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(id+3).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.concat(F.lit('pcie_'),F.col('_3')).alias('name'),\n", + " F.struct(F.col('_10').alias('tx'),F.col('_11').alias('rx')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " return trace_events\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# DASK analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "def split_dask(x):\n", + " fi=[]\n", + " for l in x:\n", + " print(l)\n", + " li=[]\n", + " if l.startswith('('):\n", + " lx=re.split(r'[()]',l)\n", + " lv=lx[1]\n", + " p=re.search(r\"'(.*)-([0-9a-f]+)', *(\\d+)\",lv)\n", + " if not p:\n", + " print(\"dask log first field doesn't match (.*)-[0-9a-f]+', *(\\d+)\")\n", + " return\n", + " li.append(p.group(1))\n", + " li.extend(lx[2].split(\",\")[1:])\n", + " li.append(p.group(3))\n", + " else:\n", + " li=l.split(',')\n", + " p=re.search(r\"(.*)-([0-9a-f]+-[0-9a-f]+-[0-9a-f]+-[0-9a-f]+-[0-9a-f]+)$\",li[0])\n", + " if not p:\n", + " p=re.search(r\"(.*)-([0-9a-f]+)$\",li[0])\n", + " \n", + " li[0]=p.group(1)\n", + " li.append(p.group(2))\n", + " fi.append(li)\n", + " return iter(fi)\n", + "\n", + "class dask_analysis(Analysis):\n", + " def __init__(self,dask_file):\n", + " Analysis.__init__(self,dask_file)\n", + "\n", + " def load_data(self):\n", + " rdds=sc.textFile(self.file)\n", + " df_pf=rdds.mapPartitions(split_dask).toDF()\n", + " df_pf=df_pf.withColumnRenamed('_1','_c0')\n", + " df_pf=df_pf.withColumnRenamed('_2','_c1')\n", + " df_pf=df_pf.withColumnRenamed('_3','_c2')\n", + " df_pf=df_pf.withColumnRenamed('_4','_c3')\n", + " df_pf=df_pf.withColumnRenamed('_5','_id')\n", + " \n", + " df_pf=df_pf.withColumn('_c1',F.split(F.col('_c1'),\":\")[2])\n", + " df_pf=df_pf.withColumn('_c3',df_pf._c3.astype(DoubleType())*1000) \n", + " df_pf=df_pf.withColumn('_c2',df_pf._c2.astype(DoubleType())*1000)\n", + " \n", + " df_pf.cache()\n", + " self.df=df_pf\n", + " self.starttime=df_pf.agg(F.min(\"_c2\")).collect()[0]['min(_c2)']\n", + " return df_pf\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " Analysis.generate_trace_view_list(self,id)\n", + " \n", + " df_pf=self.df\n", + "\n", + " window = Window.partitionBy(\"_c1\").orderBy(\"_c3\")\n", + " df_pf=df_pf.withColumn(\"last_tsk_done\", F.lag('_c3', 1, None).over(window))\n", + " df_pf=df_pf.withColumn('last_tsk_done',F.coalesce('last_tsk_done','_c2'))\n", + " df_pf=df_pf.withColumn('last_tsk_done',F.when(F.col('_c2')>F.col('last_tsk_done'),F.col('_c2')).otherwise(F.col('last_tsk_done')) )\n", + " \n", + " trace_events=[]\n", + " \n", + " trace_events.extend(df_pf.select(\n", + " F.col('_c1').alias('tid'),\n", + " (F.col('last_tsk_done')-F.lit(self.starttime)).astype(IntegerType()).alias('ts'),\n", + " F.expr('_c3 - last_tsk_done ').alias('dur'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('X').alias('ph'),\n", + " F.col('_c0').alias('name'),\n", + " F.struct(F.col('_id').alias('uuid')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " return trace_events" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class dask_analysis_log(dask_analysis):\n", + " def __init__(self,dask_file,logs):\n", + " Analysis.__init__(self,dask_file)\n", + "\n", + " def load_data(self):\n", + " rdds=sc.textFile(self.file)\n", + " df_pf=rdds.mapPartitions(split_dask).toDF()\n", + " df_pf=df_pf.withColumnRenamed('_1','_c0')\n", + " df_pf=df_pf.withColumnRenamed('_2','_c1')\n", + " df_pf=df_pf.withColumnRenamed('_3','_c2')\n", + " df_pf=df_pf.withColumnRenamed('_4','_c3')\n", + " df_pf=df_pf.withColumnRenamed('_5','_id')\n", + " \n", + " df_pf=df_pf.withColumn('_c1',F.split(F.col('_c1'),\":\")[2])\n", + " df_pf=df_pf.withColumn('_c3',df_pf._c3.astype(DoubleType())*1000) \n", + " df_pf=df_pf.withColumn('_c2',df_pf._c2.astype(DoubleType())*1000)\n", + " \n", + " df_pf.cache()\n", + " self.df=df_pf\n", + " self.starttime=df_pf.agg(F.min(\"_c2\")).collect()[0]['min(_c2)']\n", + " return df_pf\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " Analysis.generate_trace_view_list(self,id)\n", + " \n", + " df_pf=self.df\n", + "\n", + " window = Window.partitionBy(\"_c1\").orderBy(\"_c3\")\n", + " df_pf=df_pf.withColumn(\"last_tsk_done\", F.lag('_c3', 1, None).over(window))\n", + " df_pf=df_pf.withColumn('last_tsk_done',F.coalesce('last_tsk_done','_c2'))\n", + " df_pf=df_pf.withColumn('last_tsk_done',F.when(F.col('_c2')>F.col('last_tsk_done'),F.col('_c2')).otherwise(F.col('last_tsk_done')) )\n", + " \n", + " trace_events=[]\n", + " \n", + " trace_events.extend(df_pf.select(\n", + " F.col('_c1').alias('tid'),\n", + " (F.col('last_tsk_done')-F.lit(self.starttime)).astype(IntegerType()).alias('ts'),\n", + " F.expr('_c3 - last_tsk_done ').alias('dur'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('X').alias('ph'),\n", + " F.col('_c0').alias('name'),\n", + " F.struct(F.col('_id').alias('uuid')).alias('args')\n", + " ).toJSON().collect())\n", + "\n", + " return trace_events" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# instantevent analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "## format: _2 = Name; _3 = time\n", + "\n", + "class InstantEvent_analysis(Analysis):\n", + " def __init__(self,sar_file):\n", + " Analysis.__init__(self,sar_file)\n", + " \n", + " def load_data(self):\n", + " sardata=sc.textFile(self.file)\n", + " sardf=sardata.mapPartitions(splits).toDF()\n", + " self.df=sardf\n", + " return sardf\n", + "\n", + "\n", + " def generate_trace_view_list(self,id=0,**kwargs):\n", + " Analysis.generate_trace_view_list(self,id)\n", + " sardf=self.df\n", + " starttime=self.starttime\n", + " return sardf.select(F.lit(0).alias('tid'),\n", + " (F.col(\"_3\").astype(DoubleType())*1000-F.lit(starttime)).astype(IntegerType()).alias('ts'),\n", + " F.lit(0).alias('pid'),\n", + " F.lit('i').alias('ph'),\n", + " F.col(\"_2\").alias('name'),\n", + " F.lit(\"g\").alias(\"s\")\n", + " ).toJSON().collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# HBM_Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class HBM_analysis(Analysis):\n", + " def __init__(self,file):\n", + " Analysis.__init__(self,file)\n", + " \n", + " def load_data(self):\n", + " df=spark.read.option(\"delimiter\", \", \").option(\"header\", \"true\").csv(self.file)\n", + " self.df=df.withColumn(\"ts\", F.unix_timestamp(df.timestamp)).withColumn(\"size\", df.size.cast(LongType())).withColumn(\"free\", df.free.cast(LongType()))\n", + " return self.df\n", + "\n", + " def generate_trace_view_list(self,id,**kwargs):\n", + " trace_list=Analysis.generate_trace_view_list(self,id,**kwargs)\n", + " hbmdf=self.df\n", + " starttime=self.starttime\n", + " \n", + " trace_list.extend(hbmdf.select(\n", + " F.lit(0).alias('tid'),\n", + " (F.col(\"ts\") * F.lit(1000)-F.lit(starttime)).astype(LongType()).alias('ts'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.lit(\"hbm\").alias('name'),\n", + " F.struct((F.col(\"size\")-F.col(\"free\")).alias('hbmused'), F.col(\"free\").alias('hbmfree')).alias('args')\n", + " ).toJSON().collect())\n", + " \n", + " trace_list.extend(hbmdf.select(\n", + " F.lit(0).alias('tid'),\n", + " (F.col(\"ts\") * F.lit(1000)-F.lit(starttime)).astype(LongType()).alias('ts'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.lit(\"hbm %\").alias('name'),\n", + " F.struct(((F.lit(1) - F.col(\"free\") / F.col(\"size\")) * F.lit(100)).alias('%hbmused')).alias('args')\n", + " ).toJSON().collect())\n", + " return trace_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Run base" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class Run:\n", + " def __init__(self,samples):\n", + " self.samples=samples\n", + " \n", + " def generate_trace_view(self,appid,**kwargs):\n", + " traces=[]\n", + " \n", + " for idx, s in enumerate(self.samples):\n", + " traces.extend(s.generate_trace_view_list(idx,**kwargs)) \n", + " output='''\n", + " {\n", + " \"traceEvents\": [\n", + " \n", + " ''' + \\\n", + " \",\\n\".join(traces)\\\n", + " + '''\n", + " ]\n", + " }'''\n", + "\n", + " with open('/home/sparkuser/trace_result/'+appid+'.json', 'w') as outfile: \n", + " outfile.write(output)\n", + "\n", + " print(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appid}.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Dask Application Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class Dask_Application_Run:\n", + " def __init__(self, appid):\n", + " self.appid=appid\n", + " self.filedir=\"/tmp/dgx-2Log/\"+self.appid+\"/\"\n", + " \n", + " self.analysis={\n", + " 'dask':{'als':dask_analysis(self.filedir+\"cluster.log\"),'pid':8000},\n", + " 'sar_cpu':{'als':Sar_cpu_analysis(self.filedir + \"/\"+\"sar_cpu.sar\"),'pid':10*0+0},\n", + " 'sar_disk':{'als':Sar_disk_analysis(self.filedir + \"/\"+\"sar_disk.sar\"),'pid':10*0+1},\n", + " 'sar_mem':{'als':Sar_mem_analysis(self.filedir + \"/\"+\"sar_mem.sar\"),'pid':10*0+2},\n", + " 'sar_nic':{'als':Sar_nic_analysis(self.filedir + \"/\"+\"sar_nic.sar\"),'pid':10*0+3},\n", + " 'emon':{'als':Emon_Analysis(self.filedir + \"/\"+\"emon.rst\"),'pid':10*0+4},\n", + " 'gpu':{'als':gpu_analysis(self.filedir + \"/gpu.txt\"),'pid':10*0+5},\n", + " }\n", + " \n", + " \n", + " def generate_trace_view(self,showsar=True,showemon=False,showgpu=True,**kwargs):\n", + " traces=[]\n", + " daskals=self.analysis['dask']['als']\n", + " traces.extend(daskals.generate_trace_view_list(self.analysis['dask']['pid'],**kwargs))\n", + " if showsar:\n", + " sarals=self.analysis['sar_cpu']['als']\n", + " sarals.starttime=daskals.starttime\n", + " traces.extend(sarals.generate_trace_view_list(self.analysis['sar_cpu']['pid'],**kwargs))\n", + " sarals=self.analysis['sar_disk']['als']\n", + " sarals.starttime=daskals.starttime\n", + " traces.extend(sarals.generate_trace_view_list(self.analysis['sar_disk']['pid'],**kwargs))\n", + " sarals=self.analysis['sar_mem']['als']\n", + " sarals.starttime=daskals.starttime\n", + " traces.extend(sarals.generate_trace_view_list(self.analysis['sar_mem']['pid'],**kwargs))\n", + " sarals=self.analysis['sar_nic']['als']\n", + " sarals.starttime=daskals.starttime\n", + " traces.extend(sarals.generate_trace_view_list(self.analysis['sar_nic']['pid'],**kwargs))\n", + " if showemon:\n", + " emonals=self.analysis['emon']['als']\n", + " emonals.starttime=daskals.starttime\n", + " traces.extend(emonals.generate_trace_view_list(self.analysis['emon']['pid'],**kwargs))\n", + " if showgpu:\n", + " gpuals=self.analysis['gpu']['als']\n", + " gpuals.starttime=daskals.starttime\n", + " traces.extend(gpuals.generate_trace_view_list(self.analysis['gpu']['pid'],**kwargs))\n", + " \n", + " output='''\n", + " {\n", + " \"traceEvents\": [\n", + " \n", + " ''' + \\\n", + " \",\\n\".join(traces)\\\n", + " + '''\n", + " ]\n", + " }'''\n", + "\n", + " with open('/home/sparkuser/trace_result/'+self.appid+'.json', 'w') as outfile: \n", + " outfile.write(output)\n", + "\n", + " print(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appid}.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class Dask_Application_Run2:\n", + " def __init__(self, appid):\n", + " self.appid=appid\n", + " \n", + " self.filedir=\"/tmp/dgx-2Log/\"+self.appid+\"/\"\n", + " self.dask=self.load_dask()\n", + " self.sar=self.load_sar()\n", + " self.gpu=self.load_gpu()\n", + " \n", + " \n", + " def load_dask(self):\n", + " return dask_analysis(self.filedir+\"cluster.log\")\n", + " \n", + " def load_sar(self):\n", + " return Sar_analysis(self.filedir+\"sar_data.sar\")\n", + " \n", + " def load_emon(self):\n", + " return Emon_Analysis(self.filedir+\"emon.rst\")\n", + " \n", + " def load_gpu(self):\n", + " return gpu_dmon_analysis(self.filedir+\"gpu_dmon.txt\")\n", + " \n", + " def generate_dask_trace_view(self):\n", + " return self.dask.generate_dask_trace_view(8000)\n", + " \n", + " def generate_sar_trace_view(self):\n", + " return self.sar.generate_sar_trace_view(0)\n", + " \n", + " def generate_gpu_trace_view(self):\n", + " return self.gpu.generate_gpu_trace_view(1)\n", + "\n", + " def generate_emon_trace_view(self,collected_cores):\n", + " return self.emon.generate_emon_trace_view(5,collected_cores)\n", + " \n", + " def generate_trace_view(self,showsar=True,showemon=False,showgpu=True):\n", + " traces=[]\n", + " traces.extend(self.generate_dask_trace_view())\n", + " if showsar:\n", + " self.sar.starttime=self.dask.starttime\n", + " traces.extend(self.generate_sar_trace_view())\n", + " if showemon:\n", + " traces.extend(self.generate_emon_trace_view(collected_cores))\n", + " if showgpu:\n", + " self.gpu.starttime=self.dask.starttime\n", + " traces.extend(self.generate_gpu_trace_view())\n", + " \n", + " output='''\n", + " {\n", + " \"traceEvents\": [\n", + " \n", + " ''' + \\\n", + " \",\\n\".join(traces)\\\n", + " + '''\n", + " ]\n", + " }'''\n", + "\n", + " with open('/home/sparkuser/trace_result/'+self.appid+'.json', 'w') as outfile: \n", + " outfile.write(output)\n", + "\n", + " print(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appid}.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Application RUN STD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "class Application_Run_STD:\n", + " def __init__(self, appid):\n", + " self.appid=appid\n", + " self.filedir=\"/tmp/dgx-2Log/\"+self.appid+\"/\"\n", + " \n", + " self.analysis={\n", + " 'sar':{'als':Sar_analysis(self.filedir+\"sar_data.sar\"),'pid':0},\n", + " 'emon':{'als':Emon_Analysis(self.filedir+\"emon.rst\"),'pid':1},\n", + " 'gpu':{'als':gpu_analysis(self.filedir+\"gpu.txt\"),'pid':100},\n", + " }\n", + " \n", + " \n", + " def generate_trace_view(self,showsar=True,showemon=False,showgpu=True,**kwargs):\n", + " traces=[]\n", + " starttime=time.time()*1000\n", + " if showsar:\n", + " sarals=self.analysis['sar']['als']\n", + " sarals.starttime=starttime\n", + " traces.extend(sarals.generate_trace_view_list(self.analysis['sar']['pid'],**kwargs))\n", + " if showemon:\n", + " emonals=self.analysis['emon']['als']\n", + " emonals.starttime=starttime\n", + " traces.extend(emonals.generate_trace_view_list(self.analysis['emon']['pid'],**kwargs))\n", + " if showgpu:\n", + " gpuals=self.analysis['gpu']['als']\n", + " gpuals.starttime=starttime\n", + " traces.extend(gpuals.generate_trace_view_list(self.analysis['gpu']['pid'],**kwargs))\n", + " \n", + " output='''\n", + " {\n", + " \"traceEvents\": [\n", + " \n", + " ''' + \\\n", + " \",\\n\".join(traces)\\\n", + " + '''\n", + " ]\n", + " }'''\n", + "\n", + " with open('/home/sparkuser/trace_result/'+self.appid+'.json', 'w') as outfile: \n", + " outfile.write(output)\n", + "\n", + " print(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appid}.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# application Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "hidden": true + }, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "\n", + "class Application_Run:\n", + " def __init__(self, appid,**kwargs):\n", + " self.appid=appid\n", + " \n", + " basedir=kwargs.get(\"basedir\",\"skylake\")\n", + " self.filedir=\"/\"+basedir+\"/\"+self.appid+\"/\"\n", + " self.basedir=basedir\n", + " \n", + " slaves=fs.list_status(\"/\"+basedir+\"/\"+appid)\n", + " slaves=[f['pathSuffix'] for f in slaves if f['type']=='DIRECTORY' and f['pathSuffix']!=\"summary.parquet\"]\n", + " \n", + " jobids=kwargs.get(\"jobids\",None)\n", + " \n", + " self.clients=slaves\n", + " \n", + " sarclnt={}\n", + " for idx,l in enumerate(self.clients):\n", + " sarclnt[l]={'sar_cpu':{'als':Sar_cpu_analysis(self.filedir + l + \"/\"+\"sar_cpu.sar\"),'pid':idx},\n", + " 'sar_disk':{'als':Sar_disk_analysis(self.filedir + l + \"/\"+\"sar_disk.sar\"),'pid':idx},\n", + " 'sar_mem':{'als':Sar_mem_analysis(self.filedir + l + \"/\"+\"sar_mem.sar\"),'pid':idx},\n", + " 'sar_nic':{'als':Sar_nic_analysis(self.filedir + l + \"/\"+\"sar_nic.sar\"),'pid':idx}\n", + " }\n", + " if fs.exists(self.filedir + l + \"/sar_page.sar\"):\n", + " sarclnt[l]['sar_page']={'als':Sar_PageCache_analysis(self.filedir + l + \"/\"+\"sar_page.sar\"),'pid':idx}\n", + " \n", + " if fs.exists(self.filedir + l + \"/pidstat.out\"):\n", + " sarclnt[l]['sar_pid']={'als':Pidstat_analysis(self.filedir + l + \"/pidstat.out\"),'pid':idx}\n", + " if fs.exists(self.filedir + l + \"/sched.txt\"):\n", + " sarclnt[l]['sar_perf']={'als':Perf_trace_analysis(self.filedir + l + \"/sched.txt\"),'pid':100+idx}\n", + " if fs.exists(self.filedir + l + \"/perfstat.txt\"):\n", + " sarclnt[l]['perfstat']={'als':Perfstat_analysis(self.filedir + l + \"/perfstat.txt\"),'pid':300+idx}\n", + " if fs.exists(self.filedir + l + \"/gpu.txt\"):\n", + " sarclnt[l]['gpu']={'als':gpu_analysis(self.filedir + l + \"/gpu.txt\"),'pid':400+idx}\n", + " \n", + " \n", + " self.analysis={\n", + " \"sar\": sarclnt\n", + " }\n", + " \n", + " if fs.exists(self.filedir+\"app.log\"):\n", + " self.analysis['app']={'als':App_Log_Analysis(self.filedir+\"app.log\",jobids)}\n", + " \n", + " if fs.exists(self.filedir+\"instevent.out\"):\n", + " self.analysis['instant']={'als':InstantEvent_analysis(self.filedir+\"instevent.out\")}\n", + " \n", + " self.starttime=0\n", + " if fs.exists(self.filedir+\"starttime\"):\n", + " with fs.open(self.filedir+\"starttime\") as f:\n", + " st = f.read().decode('ascii')\n", + " self.starttime=int(st)\n", + " \n", + " def generate_trace_view(self,showsar=True,showgpu=True,showhbm=False,**kwargs):\n", + " traces=[]\n", + " shownodes=kwargs.get(\"shownodes\",self.clients)\n", + " for l in shownodes:\n", + " if l not in self.clients:\n", + " print(l,\"is not in clients\",self.clients)\n", + " return\n", + " self.clients=shownodes\n", + " \n", + " xgbtcks=kwargs.get('xgbtcks',(\"calltrain\",'enter','begin','end'))\n", + " \n", + " if \"app\" in self.analysis:\n", + " appals=self.analysis['app']['als']\n", + " appals.starttime=self.starttime\n", + " traces.extend(appals.generate_trace_view_list(self.analysis['app'],**kwargs))\n", + " self.starttime=appals.starttime\n", + " \n", + " if 'instant' in self.analysis:\n", + " als=self.analysis['instant']['als']\n", + " als.starttime=self.starttime\n", + " traces.extend(als.generate_trace_view_list(**kwargs))\n", + " \n", + " counttime=kwargs.get(\"counttime\",False)\n", + " \n", + " pidmap={}\n", + " if showsar:\n", + " for l in self.clients:\n", + " for alskey, sarals in self.analysis[\"sar\"][l].items():\n", + " t1 = time.time()\n", + " sarals['als'].starttime=self.starttime\n", + " traces.extend(sarals['als'].generate_trace_view_list(sarals['pid'],node=l, **kwargs))\n", + " if counttime:\n", + " print(l,alskey,\" spend time: \", time.time()-t1)\n", + " \n", + " if showhbm:\n", + " for l in self.clients:\n", + " t1 = time.time()\n", + " hbm_analysis=HBM_analysis(self.filedir + l + \"/numactl.csv\")\n", + " hbm_analysis.starttime=self.starttime\n", + " traces.extend(hbm_analysis.generate_trace_view_list(0,**kwargs))\n", + " if counttime:\n", + " print(l, \" hbm process spend time: \", time.time()-t1)\n", + " \n", + " for idx,l in enumerate(self.clients):\n", + " traces.append(json.dumps({\"name\": \"process_sort_index\",\"ph\": \"M\",\"pid\":idx,\"tid\":0,\"args\":{\"sort_index \":idx}}))\n", + " traces.append(json.dumps({\"name\": \"process_sort_index\",\"ph\": \"M\",\"pid\":idx+100,\"tid\":0,\"args\":{\"sort_index \":idx+100}}))\n", + " traces.append(json.dumps({\"name\": \"process_sort_index\",\"ph\": \"M\",\"pid\":idx+200,\"tid\":0,\"args\":{\"sort_index \":idx+200}}))\n", + " \n", + " if \"app\" in self.analysis:\n", + " for pid in self.analysis['app']['als'].pids:\n", + " traces.append(json.dumps({\"name\": \"process_sort_index\",\"ph\": \"M\",\"pid\":pid+200,\"tid\":0,\"args\":{\"sort_index \":pid+200}}))\n", + "\n", + " allcnt=\"\"\n", + " for c in self.clients:\n", + " paths=self.filedir+c\n", + " if fs.exists(paths+\"/xgbtck.txt\"):\n", + " with fs.open(paths+\"/xgbtck.txt\") as f:\n", + " tmp = f.read().decode('ascii')\n", + " allcnt=allcnt+tmp\n", + " allcnt=allcnt.strip().split(\"\\n\")\n", + " if len(allcnt) > 1:\n", + " allcnt=[l.split(\" \") for l in allcnt]\n", + " cnts=pandas.DataFrame([[l[0],l[1],l[2],l[3]] for l in allcnt if len(l)>1 and l[1] in xgbtcks])\n", + " if len(cnts) > 0:\n", + " cnts.columns=['xgbtck','name','rank','time']\n", + " cntgs=cnts.groupby(\"name\").agg({\"time\":\"min\"})\n", + " cntgs=cntgs.reset_index()\n", + " cntgs.columns=['name','ts']\n", + " cntgs['ph']=\"i\"\n", + " cntgs['ts']=pandas.to_numeric(cntgs['ts'])-self.starttime\n", + " cntgs['pid']=0\n", + " cntgs['tid']=0\n", + " cntgs['s']='g'\n", + " traces.extend([json.dumps(l) for l in cntgs.to_dict(orient='records')])\n", + " \n", + " output='''\n", + " {\n", + " \"traceEvents\": [\n", + " \n", + " ''' + \\\n", + " \",\\n\".join(traces)\\\n", + " + '''\n", + " ],\n", + " \"displayTimeUnit\": \"ns\"\n", + " }'''\n", + "\n", + " with open('/home/sparkuser/trace_result/'+self.appid+'.json', 'w') as outfile: \n", + " outfile.write(output)\n", + "\n", + " display(HTML(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{self.appid}.json\"))\n", + " \n", + " def get_sar_stat(app_ww44,**kwargs):\n", + " disk_prefix=kwargs.get(\"disk_prefix\",\"dev259\")\n", + " nic_prefix = kwargs.get(\"nic_prefix\",[\"'eth3'\",\"'enp24s0f1'\"])\n", + " cpustat=[app_ww44.analysis[\"sar\"][l]['sar_cpu']['als'].get_stat() for l in app_ww44.clients]\n", + " cpustat=reduce(lambda l,r:l.join(r),cpustat)\n", + " diskstat=[app_ww44.analysis[\"sar\"][l]['sar_disk']['als'].get_stat(disk_prefix=disk_prefix) for l in app_ww44.clients]\n", + " diskstat=reduce(lambda l,r:l.join(r),diskstat)\n", + " memstat=[app_ww44.analysis[\"sar\"][l]['sar_mem']['als'].get_stat() for l in app_ww44.clients]\n", + " memstat=reduce(lambda l,r:l.join(r),memstat)\n", + " nicstat=[app_ww44.analysis[\"sar\"][l]['sar_nic']['als'].get_stat(nic_prefix=nic_prefix) for l in app_ww44.clients]\n", + " nicstat=reduce(lambda l,r:l.join(r),nicstat)\n", + " pagestat=[app_ww44.analysis[\"sar\"][l]['sar_page']['als'].get_stat() for l in app_ww44.clients]\n", + " pagestat=reduce(lambda l,r:l.join(r),pagestat)\n", + " pandas.options.display.float_format = '{:,.2f}'.format\n", + " return pandas.concat([cpustat,diskstat,memstat,nicstat,pagestat])\n", + " \n", + " def get_summary(app, **kwargs):\n", + " output=[]\n", + " \n", + " appals=app.analysis[\"app\"][\"als\"]\n", + " \n", + " out=appals.get_query_time(plot=False)\n", + " \n", + " lrun=app.appid\n", + " \n", + " cmpcolumns=['runtime','disk spilled','shuffle_write','f_wait_time','input read','acc_task_time','output rows']\n", + " outcut=out[cmpcolumns]\n", + " \n", + " pdsout=pandas.DataFrame(outcut.sum(),columns=[lrun])\n", + " pdstime=pdsout\n", + "\n", + " node=\"\"\n", + " for l in fs.list_status(app.filedir):\n", + " if l['type']==\"DIRECTORY\" and l['pathSuffix']!=\"summary.parquet\":\n", + " node=l['pathSuffix']\n", + " break\n", + "\n", + " print(\"sar metric\")\n", + " sardf=app.get_sar_stat(**kwargs)\n", + " \n", + " def get_sar_agg(sardf):\n", + " aggs=[]\n", + " for x in sardf.index:\n", + " if \"total\" in x:\n", + " aggs.append(sardf.loc[x].sum())\n", + " elif \"max\" in x:\n", + " aggs.append(sardf.loc[x].max())\n", + " else:\n", + " aggs.append(sardf.loc[x].mean())\n", + "\n", + " sardf['agg']=aggs\n", + " return sardf\n", + " sardf=get_sar_agg(sardf)\n", + "\n", + " sarsum=sardf[[\"agg\"]]\n", + "\n", + " sarsum.columns=[lrun]\n", + " \n", + " summary=pandas.concat([pdstime,sarsum])\n", + " \n", + " df_sum=spark.createDataFrame(summary.T.reset_index())\n", + " for c in df_sum.columns:\n", + " df_sum=df_sum.withColumnRenamed(c,c.replace(\" \",\"_\").replace(\"(\",\"\").replace(\")\",\"\"))\n", + " df_sum.write.mode(\"overwrite\").parquet(app.filedir+\"summary.parquet\")\n", + " \n", + " return summary\n", + " \n", + " def compare_app(app2,**kwargs):\n", + " output=[]\n", + " \n", + " lbasedir=kwargs.get(\"basedir\",app2.basedir)\n", + " r_appid=kwargs.get(\"r_appid\",app2.appid)\n", + " \n", + " app=kwargs.get(\"rapp\",Application_Run(r_appid,basedir=lbasedir))\n", + "\n", + " show_queryplan_diff=kwargs.get(\"show_queryplan_diff\",True)\n", + " \n", + " queryids=kwargs.get(\"queryids\",None)\n", + " \n", + " appals=app.analysis[\"app\"][\"als\"]\n", + " appals2=app2.analysis[\"app\"][\"als\"]\n", + "\n", + " out=appals.get_query_time(plot=False)\n", + " out2=appals2.get_query_time(plot=False)\n", + "\n", + " lrun=app.appid\n", + " rrun=app2.appid\n", + " cmpcolumns=['runtime','shuffle_write','f_wait_time','input read','acc_task_time','output rows']\n", + " outcut=out[cmpcolumns]\n", + " out2cut=out2[cmpcolumns]\n", + " cmp=outcut.join(out2cut,lsuffix='_'+lrun,rsuffix='_'+rrun)\n", + "\n", + " pdsout=pandas.DataFrame(outcut.sum(),columns=[lrun])\n", + " pdsout2=pandas.DataFrame(out2cut.sum(),columns=[rrun])\n", + " pdstime=pdsout.join(pdsout2)\n", + "\n", + " print(\"sar metric\")\n", + " sardf=app.get_sar_stat(**kwargs)\n", + " sardf2=app2.get_sar_stat(**kwargs)\n", + " \n", + " def get_sar_agg(sardf):\n", + " aggs=[]\n", + " for x in sardf.index:\n", + " if \"total\" in x:\n", + " aggs.append(sardf.loc[x].sum())\n", + " elif \"max\" in x:\n", + " aggs.append(sardf.loc[x].max())\n", + " else:\n", + " aggs.append(sardf.loc[x].mean())\n", + "\n", + " sardf['agg']=aggs\n", + " return sardf\n", + " sardf=get_sar_agg(sardf)\n", + " sardf2=get_sar_agg(sardf2)\n", + " #in case we compare two clusters\n", + " sardf2.columns=sardf.columns\n", + "\n", + " sarcolumns=sardf.columns\n", + " sarcmp=sardf.join(sardf2,lsuffix='_'+lrun,rsuffix='_'+rrun)\n", + " sarsum=sarcmp[[\"agg_\"+lrun,\"agg_\"+rrun]]\n", + "\n", + " sarsum.columns=[lrun,rrun]\n", + " \n", + " summary=pandas.concat([pdstime,sarsum])\n", + " \n", + " summary[\"diff\"]=numpy.where(summary[rrun] > 0, summary[lrun]/summary[rrun]-1, 0)\n", + " \n", + " \n", + " def highlight_diff(x):\n", + " styles=[]\n", + " mx=x.max()\n", + " mn=x.min()\n", + " mx=max(mx,-mn,0.2)\n", + " for j in x.index:\n", + " m1=(x[j])/mx*100 if x[j]!=None else 0\n", + " if m1>0:\n", + " styles.append(f'width: 400px ; background-image: linear-gradient(to right, transparent 50%, #5fba7d 50%, #5fba7d {50+m1/2}%, transparent {50+m1/2}%)')\n", + " else:\n", + " styles.append(f'width: 400px ;background-image: linear-gradient(to left, transparent 50%, #f1a863 50%, #f1a863 {50-m1/2}%, transparent {50-m1/2}%)')\n", + " return styles\n", + "\n", + " output.append(summary.style.apply(highlight_diff,subset=['diff']).format({lrun:\"{:,.2f}\",rrun:\"{:,.2f}\",'diff':\"{:,.2%}\"}).render())\n", + "\n", + " cmp_plot=cmp\n", + " cmp_plot['diff']=cmp_plot['runtime_'+lrun]-cmp_plot['runtime_'+rrun]\n", + "\n", + " pltx=cmp_plot.sort_values(by='diff',axis=0).plot.bar(y=['runtime_'+lrun,'runtime_'+rrun],figsize=(30,8))\n", + " better_num=sqldf('''select count(*) from cmp_plot where diff>0''')['count(*)'][0]\n", + " pltx.text(0.1, 0.8,'{:d} queries are better'.format(better_num), ha='center', va='center', transform=pltx.transAxes)\n", + "\n", + " df1 = pandas.DataFrame('', index=cmp.index, columns=cmpcolumns)\n", + " for l in cmpcolumns:\n", + " for j in cmp.index:\n", + " df1[l][j]=[cmp[l+\"_\"+lrun][j],cmp[l+\"_\"+rrun][j],cmp[l+\"_\"+lrun][j]/cmp[l+\"_\"+rrun][j]-1]\n", + "\n", + " def highlight_greater(x,columns):\n", + " df1 = pandas.DataFrame('', index=x.index, columns=x.columns)\n", + " for l in columns:\n", + " m={}\n", + " for j in x.index:\n", + " m[j] = (x[l][j][1] / x[l][j][0])*100 if x[l][j][0]!=0 else 100\n", + " mx=max(m.values())-100\n", + " mn=100-min(m.values())\n", + " mx=max(mx,mn)\n", + " for j in x.index:\n", + " m1=-(100-m[j])/mx*100 if x[l][j][0]!=0 else 0\n", + " if m1>0:\n", + " df1[l][j] = f'background-image: linear-gradient(to right, transparent 50%, #5fba7d 50%, #5fba7d {50+m1/2}%, transparent {50+m1/2}%)'\n", + " else:\n", + " df1[l][j] = f'background-image: linear-gradient(to left, transparent 50%, #f1a863 50%, #f1a863 {50-m1/2}%, transparent {50-m1/2}%)'\n", + "\n", + " return df1\n", + "\n", + " def display_compare(df,columns):\n", + " output.append(df.style.set_properties(**{'width': '300px','border-style':'solid','border-width':'1px'}).apply(lambda x: highlight_greater(x,columns), axis=None).format(lambda x: '''\n", + "
{:,.2f}
\n", + "
{:,.2f}
\n", + "
{:,.2f}%
\n", + " '''.format(x[0],x[1],x[2]*100)).render())\n", + " display_compare(df1,cmpcolumns)\n", + "\n", + " df3 = pandas.DataFrame('', index=sarcmp.index, columns=sarcolumns)\n", + " for l in sarcolumns:\n", + " for j in df3.index:\n", + " df3[l][j]=[sarcmp[l+\"_\"+lrun][j],sarcmp[l+\"_\"+rrun][j],sarcmp[l+\"_\"+lrun][j]/sarcmp[l+\"_\"+rrun][j]-1]\n", + " display_compare(df3,sarcolumns)\n", + "\n", + " print(\"time breakdown\")\n", + " ################################ time breakdown ##################################################################################################\n", + " timel=appals.show_time_metric(plot=False)\n", + " timer=appals2.show_time_metric(plot=False)\n", + " timer.columns=[l.replace(\"scan time\",\"time_batchscan\") for l in timer.columns]\n", + " timel.columns=[l.replace(\"scan time\",\"time_batchscan\") for l in timel.columns]\n", + " rcols=timer.columns\n", + " lcols=[]\n", + " for c in [l.split(\"%\")[1][1:] for l in rcols]:\n", + " for t in timel.columns:\n", + " if t.endswith(c):\n", + " lcols.append(t)\n", + " for t in timel.columns:\n", + " if t not in lcols:\n", + " lcols.append(t)\n", + " timel_adj=timel[lcols]\n", + "\n", + " fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True,figsize=(30,8),gridspec_kw = {'width_ratios':[1, 1]})\n", + " plt.subplots_adjust(wspace=0.01)\n", + " ax=timel_adj.plot.bar(ax=axs[0],stacked=True)\n", + " list_values=timel_adj.loc[0].values\n", + " for rect, value in zip(ax.patches, list_values):\n", + " h = rect.get_height() /2.\n", + " w = rect.get_width() /2.\n", + " x, y = rect.get_xy()\n", + " ax.text(x+w, y+h,\"{:,.2f}\".format(value),horizontalalignment='center',verticalalignment='center',color=\"white\")\n", + " ax=timer.plot.bar(ax=axs[1],stacked=True)\n", + " list_values=timer.loc[0].values\n", + " for rect, value in zip(ax.patches, list_values):\n", + " h = rect.get_height() /2.\n", + " w = rect.get_width() /2.\n", + " x, y = rect.get_xy()\n", + " ax.text(x+w, y+h,\"{:,.2f}\".format(value),horizontalalignment='center',verticalalignment='center',color=\"white\")\n", + "\n", + "################################ critical time breakdown ##################################################################################################\n", + " timel=appals.show_time_metric(plot=False,taskids=[l[0].item() for l in appals.criticaltasks])\n", + " timer=appals2.show_time_metric(plot=False,taskids=[l[0].item() for l in appals2.criticaltasks])\n", + " timer.columns=[l.replace(\"scan time\",\"time_batchscan\") for l in timer.columns]\n", + " timel.columns=[l.replace(\"scan time\",\"time_batchscan\") for l in timel.columns]\n", + " rcols=timer.columns\n", + " lcols=[]\n", + " for c in [l.split(\"%\")[1][1:] for l in rcols]:\n", + " for t in timel.columns:\n", + " if t.endswith(c):\n", + " lcols.append(t)\n", + " for t in timel.columns:\n", + " if t not in lcols:\n", + " lcols.append(t)\n", + " timel_adj=timel[lcols]\n", + "\n", + " fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True,figsize=(30,8),gridspec_kw = {'width_ratios':[1, 1]})\n", + " plt.subplots_adjust(wspace=0.01)\n", + " ax=timel_adj.plot.bar(ax=axs[0],stacked=True)\n", + " list_values=timel_adj.loc[0].values\n", + " for rect, value in zip(ax.patches, list_values):\n", + " h = rect.get_height() /2.\n", + " w = rect.get_width() /2.\n", + " x, y = rect.get_xy()\n", + " ax.text(x+w, y+h,\"{:,.2f}\".format(value),horizontalalignment='center',verticalalignment='center',color=\"white\")\n", + " ax=timer.plot.bar(ax=axs[1],stacked=True)\n", + " list_values=timer.loc[0].values\n", + " for rect, value in zip(ax.patches, list_values):\n", + " h = rect.get_height() /2.\n", + " w = rect.get_width() /2.\n", + " x, y = rect.get_xy()\n", + " ax.text(x+w, y+h,\"{:,.2f}\".format(value),horizontalalignment='center',verticalalignment='center',color=\"white\")\n", + "\n", + "\n", + " ################################ hot stage ##########################################################################################################\n", + "\n", + " hotstagel=appals.get_hottest_stages(plot=False)\n", + " hotstager=appals2.get_hottest_stages(plot=False)\n", + " hotstagel.style.format(lambda x: '''{:,.2f}'''.format(x))\n", + "\n", + " norm = matplotlib.colors.Normalize(vmin=0, vmax=max(hotstager.queryid))\n", + " cmap = matplotlib.cm.get_cmap('brg')\n", + " def setbkcolor(x):\n", + " rgba=cmap(norm(x['queryid']))\n", + " return ['background-color:rgba({:d},{:d},{:d},1); color:white'.format(int(rgba[0]*255),int(rgba[1]*255),int(rgba[2]*255))]*9\n", + "\n", + " output.append(\"
\" + hotstagel.style.apply(setbkcolor,axis=1).format({\"total_time\":lambda x: '{:,.2f}'.format(x),\"stdev_time\":lambda x: '{:,.2f}'.format(x),\"acc_total\":lambda x: '{:,.2%}'.format(x),\"total\":lambda x: '{:,.2%}'.format(x)}).render()+\n", + " \"\" + hotstager.style.apply(setbkcolor,axis=1).format({\"total_time\":lambda x: '{:,.2f}'.format(x),\"stdev_time\":lambda x: '{:,.2f}'.format(x),\"acc_total\":lambda x: '{:,.2%}'.format(x),\"total\":lambda x: '{:,.2%}'.format(x)}).render()+ \"
\")\n", + "\n", + " if not show_queryplan_diff:\n", + " return \"\\n\".join(output)\n", + " \n", + " print(\"hot stage\")\n", + "\n", + " loperators=appals.getOperatorCount()\n", + " roperators=appals2.getOperatorCount()\n", + " loperators_rowcnt=appals.get_metric_output_rowcnt()\n", + " roperators_rowcnt=appals2.get_metric_output_rowcnt()\n", + " \n", + " def show_query_diff(queryid, always_show=True):\n", + " lops=pandas.DataFrame(loperators[queryid])\n", + " lops.columns=['calls_l']\n", + " lops=lops.loc[lops['calls_l'] >0]\n", + "\n", + " rops=pandas.DataFrame(roperators[queryid])\n", + " rops.columns=[\"calls_r\"]\n", + " rops=rops.loc[rops['calls_r'] >0]\n", + " lops_row=pandas.DataFrame(loperators_rowcnt[queryid])\n", + " lops_row.columns=[\"rows_l\"]\n", + " lops_row=lops_row.loc[lops_row['rows_l'] >0]\n", + "\n", + " rops_row=pandas.DataFrame(roperators_rowcnt[queryid])\n", + " rops_row.columns=[\"rows_r\"]\n", + " rops_row=rops_row.loc[rops_row['rows_r'] >0]\n", + "\n", + " opscmp=pandas.merge(pandas.merge(pandas.merge(lops,rops,how=\"outer\",left_index=True,right_index=True),lops_row,how=\"outer\",left_index=True,right_index=True),rops_row,how=\"outer\",left_index=True,right_index=True)\n", + " opscmp=opscmp.fillna(\"\")\n", + " \n", + " def set_bk_color_opscmp(x):\n", + " calls_l= 0 if x['calls_l']==\"\" else x['calls_l']\n", + " calls_r= 0 if x['calls_r']==\"\" else x['calls_r']\n", + " rows_l= 0 if x['rows_l']==\"\" else x['rows_l']\n", + " rows_r= 0 if x['rows_r']==\"\" else x['rows_r']\n", + "\n", + " if calls_l > calls_r or rows_l > rows_r:\n", + " return ['background-color:#eb6b34']*4\n", + " if calls_l < calls_r or rows_l < rows_r:\n", + " return ['background-color:#8ad158']*4\n", + " return ['color:#dbd4d0']*4\n", + "\n", + " if always_show or not (opscmp[\"rows_l\"].equals(opscmp[\"rows_r\"]) and opscmp[\"calls_l\"].equals(opscmp[\"calls_r\"])):\n", + " print(f\"query {queryid} queryplan diff \")\n", + " if not always_show:\n", + " output.append(f\"

query{queryid} is different

\")\n", + " output.append(opscmp.style.apply(set_bk_color_opscmp,axis=1).render())\n", + "\n", + " planl=appals.get_query_plan(queryid=queryid,show_plan_only=True,plot=False)\n", + " planr=appals2.get_query_plan(queryid=queryid,show_plan_only=True,plot=False)\n", + " output.append(\"
\"+planl+\"\"+planr+\"
\")\n", + "\n", + " outputx=df1['output rows']\n", + " runtimex = df1['runtime']\n", + " for x in outputx.index:\n", + " if runtimex[x][0]/runtimex[x][1]<0.95 or runtimex[x][0]/runtimex[x][1]>1.05:\n", + " output.append(f\"

query{x} is different,{lrun} time: {df1['runtime'][x][0]}, {rrun} time: {df1['runtime'][x][1]}

\")\n", + " if queryids is not None and x not in queryids:\n", + " print(\"query plan skipped\")\n", + " continue\n", + " try:\n", + " show_query_diff(x, True)\n", + " except:\n", + " print(\" query diff error\")\n", + " else:\n", + " try:\n", + " show_query_diff(x, False)\n", + " except:\n", + " print(\" query diff error\")\n", + " \n", + " return \"\\n\".join(output)\n", + " \n", + "\n", + " \n", + " def show_queryplan_diff(app2, queryid,**kwargs):\n", + " lbasedir=kwargs.get(\"basedir\",app2.basedir)\n", + " r_appid=kwargs.get(\"r_appid\",app2.appid)\n", + " \n", + " app=kwargs.get(\"rapp\",Application_Run(r_appid,basedir=lbasedir))\n", + "\n", + " appals=app.analysis[\"app\"][\"als\"]\n", + " appals2=app2.analysis[\"app\"][\"als\"]\n", + "\n", + " hotstagel=appals.get_hottest_stages(plot=False)\n", + " hotstager=appals2.get_hottest_stages(plot=False)\n", + " hotstagel.style.format(lambda x: '''{:,.2f}'''.format(x))\n", + "\n", + " loperators=appals.getOperatorCount()\n", + " roperators=appals2.getOperatorCount()\n", + " loperators_rowcnt=appals.get_metric_output_rowcnt()\n", + " roperators_rowcnt=appals2.get_metric_output_rowcnt()\n", + "\n", + " lrun=app.appid\n", + " rrun=app2.appid\n", + "\n", + " output=[]\n", + "\n", + " def show_query_diff(queryid):\n", + " lops=pandas.DataFrame(loperators[queryid])\n", + " lops.columns=['calls_l']\n", + " lops=lops.loc[lops['calls_l'] >0]\n", + "\n", + " rops=pandas.DataFrame(roperators[queryid])\n", + " rops.columns=[\"calls_r\"]\n", + " rops=rops.loc[rops['calls_r'] >0]\n", + " lops_row=pandas.DataFrame(loperators_rowcnt[queryid])\n", + " lops_row.columns=[\"rows_l\"]\n", + " lops_row=lops_row.loc[lops_row['rows_l'] >0]\n", + "\n", + " rops_row=pandas.DataFrame(roperators_rowcnt[queryid])\n", + " rops_row.columns=[\"rows_r\"]\n", + " rops_row=rops_row.loc[rops_row['rows_r'] >0]\n", + "\n", + " opscmp=pandas.merge(pandas.merge(pandas.merge(lops,rops,how=\"outer\",left_index=True,right_index=True),lops_row,how=\"outer\",left_index=True,right_index=True),rops_row,how=\"outer\",left_index=True,right_index=True)\n", + " opscmp=opscmp.fillna(\"\")\n", + "\n", + " def set_bk_color_opscmp(x):\n", + " calls_l= 0 if x['calls_l']==\"\" else x['calls_l']\n", + " calls_r= 0 if x['calls_r']==\"\" else x['calls_r']\n", + " rows_l= 0 if x['rows_l']==\"\" else x['rows_l']\n", + " rows_r= 0 if x['rows_r']==\"\" else x['rows_r']\n", + "\n", + " if calls_l > calls_r or rows_l > rows_r:\n", + " return ['background-color:#eb6b34']*4\n", + " if calls_l < calls_r or rows_l < rows_r:\n", + " return ['background-color:#8ad158']*4\n", + " return ['color:#dbd4d0']*4\n", + "\n", + " output.append(opscmp.style.apply(set_bk_color_opscmp,axis=1).render())\n", + "\n", + " planl=appals.get_query_plan(queryid=queryid,show_plan_only=True,plot=False)\n", + " planr=appals2.get_query_plan(queryid=queryid,show_plan_only=True,plot=False)\n", + " output.append(\"
\"+planl+\"\"+planr+\"
\")\n", + "\n", + " x=queryid\n", + " print(\"query \",x,\" queryplan diff \")\n", + " #output.append(f\"

query{x} is different,{lrun} time: {df1['runtime'][x][0]}, {rrun} time: {df1['runtime'][x][1]}

\")\n", + " show_query_diff(x)\n", + " display(HTML(\"\\n\".join(output)))\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MISC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def reduce_metric(pdrst,slave_id,metric,core,agg_func):\n", + " pdrst['rst']=pdrst.apply(lambda x:x['app_id'].get_reduce_metric(slave_id,metric,core,agg_func), axis=1)\n", + " for l in agg_func:\n", + " pdrst[get_alias_name(metric,l)]=pdrst.apply(lambda x:x['rst'].iloc[0][get_alias_name(metric,l)],axis=1)\n", + " return pdrst.drop(columns=['rst'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TPCDS query map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "m='''1\tq01\n", + " 2\tq02\n", + " 3\tq03\n", + " 4\tq04\n", + " 5\tq05\n", + " 6\tq06\n", + " 7\tq07\n", + " 8\tq08\n", + " 9\tq09\n", + " 10\tq10\n", + " 11\tq11\n", + " 12\tq12\n", + " 13\tq13\n", + " 14\tq14a\n", + " 15\tq14b\n", + " 16\tq15\n", + " 17\tq16\n", + " 18\tq17\n", + " 19\tq18\n", + " 20\tq19\n", + " 21\tq20\n", + " 22\tq21\n", + " 23\tq22\n", + " 24\tq23a\n", + " 25\tq23b\n", + " 26\tq24a\n", + " 27\tq24b\n", + " 28\tq25\n", + " 29\tq26\n", + " 30\tq27\n", + " 31\tq28\n", + " 32\tq29\n", + " 33\tq30\n", + " 34\tq31\n", + " 35\tq32\n", + " 36\tq33\n", + " 37\tq34\n", + " 38\tq35\n", + " 39\tq36\n", + " 40\tq37\n", + " 41\tq38\n", + " 42\tq39a\n", + " 43\tq39b\n", + " 44\tq40\n", + " 45\tq41\n", + " 46\tq42\n", + " 47\tq43\n", + " 48\tq44\n", + " 49\tq45\n", + " 50\tq46\n", + " 51\tq47\n", + " 52\tq48\n", + " 53\tq49\n", + " 54\tq50\n", + " 55\tq51\n", + " 56\tq52\n", + " 57\tq53\n", + " 58\tq54\n", + " 59\tq55\n", + " 60\tq56\n", + " 61\tq57\n", + " 62\tq58\n", + " 63\tq59\n", + " 64\tq60\n", + " 65\tq61\n", + " 66\tq62\n", + " 67\tq63\n", + " 68\tq64\n", + " 69\tq65\n", + " 70\tq66\n", + " 71\tq67\n", + " 72\tq68\n", + " 73\tq69\n", + " 74\tq70\n", + " 75\tq71\n", + " 76\tq72\n", + " 77\tq73\n", + " 78\tq74\n", + " 79\tq75\n", + " 80\tq76\n", + " 81\tq77\n", + " 82\tq78\n", + " 83\tq79\n", + " 84\tq80\n", + " 85\tq81\n", + " 86\tq82\n", + " 87\tq83\n", + " 88\tq84\n", + " 89\tq85\n", + " 90\tq86\n", + " 91\tq87\n", + " 92\tq88\n", + " 93\tq89\n", + " 94\tq90\n", + " 95\tq91\n", + " 96\tq92\n", + " 97\tq93\n", + " 98\tq94\n", + " 99\tq95\n", + " 100\tq96\n", + " 101\tq97\n", + " 102\tq98\n", + " 103\tq99'''.split(\"\\n\")\n", + "tpcds_query_map=[l.strip().split(\"\\t\") for l in m]\n", + "tpcds_query_map={int(l[0]):l[1] for l in tpcds_query_map}" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "298.281px", + "left": "1205px", + "top": "421.125px", + "width": "332px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tools/workload/benchmark_velox/initialize.ipynb b/tools/workload/benchmark_velox/initialize.ipynb index cbbc27686951..f280d60fdac0 100644 --- a/tools/workload/benchmark_velox/initialize.ipynb +++ b/tools/workload/benchmark_velox/initialize.ipynb @@ -2761,7 +2761,7 @@ "heading_collapsed": true }, "source": [ - "# Install Trace-Viewer (optional)" + "# Set up perf analysis tools (optional)" ] }, { @@ -2770,7 +2770,21 @@ "hidden": true }, "source": [ - "Clone the master branch\n", + "We have a set of perf analysis scripts under $GLUTEN_HOME/tools/workload/benchmark_velox/analysis. You can follow below steps to deploy the scripts on the same cluster and use them for performance analysis after each run." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install and deploy Trace-Viewer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clone the master branch of project catapult:\n", "```\n", "cd ~\n", "git clone https://github.com/catapult-project/catapult.git -b master\n", @@ -2783,11 +2797,11 @@ "hidden": true }, "source": [ - "Trace-Viewer requires python version 2.7. Create a virtualenv for python2.7\n", + "Trace-Viewer requires python version 2.7. Create a virtualenv for python2.7:\n", "```\n", "sudo apt install -y python2.7\n", - "virtualenv -p /usr/bin/python2.7 py27\n", - "source py27/bin/activate\n", + "virtualenv -p /usr/bin/python2.7 py27-env\n", + "source py27-env/bin/activate\n", "```" ] }, @@ -2797,7 +2811,7 @@ "hidden": true }, "source": [ - "Apply patch\n", + "Apply patch:\n", "\n", "```\n", "cd catapult\n", @@ -2832,13 +2846,84 @@ "hidden": true }, "source": [ - "Start the service\n", + "Start the service:\n", "\n", "```\n", "mkdir -p ~/trace_result\n", "cd ~/catapult && nohup ./bin/run_dev_server --no-install-hooks -d ~/trace_result -p1088 &\n", "```" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploy perf analysis scripts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a virtualenv to run the perf analaysis scripts:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "```bash\n", + "cd ~\n", + "virtualenv -p python3 -v paus-env\n", + "source paus-env/bin/activate\n", + "python3 -m pip install -r ~/gluten/tools/workload/benchmark_velox/analysis/requirements.txt\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We will put all perf analysis notebooks under `$HOME/PAUS`. Create the directory and start the notebook:\n", + "\n", + "```bash\n", + "mkdir -p ~/PAUS\n", + "cd ~/PAUS\n", + "nohup jupyter notebook --ip=0.0.0.0 --port=8889 &\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Package the virtual environment so that it can be distributed to other nodes:\n", + "```bash\n", + "cd ~\n", + "tar -czf paus-env.tar.gz paus-env\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Distribute to the worker nodes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for l in clients:\n", + " !scp ~/paus-env.tar.gz {l}:~/\n", + " !ssh {l} tar -zxf paus-env.tar.gz" + ] } ], "metadata": { From 19397316297a96a35740e6408a78ee3a9e58700e Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Wed, 27 Nov 2024 07:55:26 +0000 Subject: [PATCH 02/12] update --- .../benchmark_velox/analysis/sparklog.ipynb | 185 ++++++++---------- .../native_sql_initialize.ipynb | 40 ++-- 2 files changed, 111 insertions(+), 114 deletions(-) diff --git a/tools/workload/benchmark_velox/analysis/sparklog.ipynb b/tools/workload/benchmark_velox/analysis/sparklog.ipynb index 8ceaeb44fe55..8a74c97d4961 100644 --- a/tools/workload/benchmark_velox/analysis/sparklog.ipynb +++ b/tools/workload/benchmark_velox/analysis/sparklog.ipynb @@ -14,24 +14,11 @@ "outputs": [], "source": [ "from __future__ import nested_scopes\n", - "from IPython.core.display import display, HTML\n", - "display(HTML(\"\"))\n", + "from IPython.display import display, HTML\n", + "display(HTML(''))\n", "display(HTML(''))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pyspark.sql.functions as F\n", - "import json\n", - "import builtins\n", - "from itertools import chain\n", - "import seaborn as sns\n" - ] - }, { "cell_type": "code", "execution_count": null, @@ -54,15 +41,29 @@ "metadata": {}, "outputs": [], "source": [ - "import re\n", "import os\n", + "import datetime\n", + "from datetime import date\n", + "import time\n", + "import threading\n", + "import gzip\n", + "import json\n", + "import math\n", + "import re\n", + "import html\n", + "import builtins\n", + "\n", + "import collections\n", + "import numpy\n", "import pandas\n", - "pandas.set_option('display.max_rows', None)\n", + "pandas.options.display.max_rows=50\n", + "pandas.options.display.max_columns=200\n", + "pandas.options.display.float_format = '{:,}'.format\n", "\n", "import matplotlib\n", - "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mtick\n", + "import matplotlib.lines as mlines\n", "from matplotlib import colors\n", "from matplotlib import rcParams\n", "rcParams['font.sans-serif'] = 'Courier New'\n", @@ -70,90 +71,24 @@ "rcParams['font.size'] = '12'\n", "%matplotlib inline\n", "\n", - "from IPython.display import display,HTML\n", - "import threading\n", - "import collections\n", - "\n", - "from IPython.display import display\n", "from ipywidgets import IntProgress,Layout\n", - "import time\n", - "import threading\n", - "import gzip\n", + "\n", "import pyspark\n", "import pyspark.sql\n", + "import pyspark.sql.functions as F\n", "from pyspark.sql import SparkSession\n", - "from pyspark.sql.types import (StructType, StructField, DateType,\n", - " TimestampType, StringType, LongType, IntegerType, DoubleType,FloatType)\n", - "from pyspark.sql.functions import to_date, floor\n", - "from pyspark.ml.feature import StringIndexer, VectorAssembler\n", - "from pyspark.ml import Pipeline\n", - "from pyspark.sql.functions import lit\n", - "import datetime\n", - "import time\n", - "from pyspark.storagelevel import StorageLevel\n", + "from pyspark.sql.functions import to_date, floor, lit, rank, col, pandas_udf, PandasUDFType\n", "from pyspark.sql.window import Window\n", - "from pyspark.sql.functions import rank, col\n", - "from pyspark.ml import Pipeline\n", - "import numpy\n", - "\n", - "import re\n", - "import math\n", - "from functools import reduce\n", - "import json\n", - "\n", "from pyspark.sql.types import *\n", - "from pyspark.sql import functions as F\n", - "from datetime import date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyspark.sql.types import (StructType, StructField, DateType,\n", - " TimestampType, StringType, LongType, IntegerType, DoubleType,FloatType)\n", - "\n", - "from pyspark.sql.functions import pandas_udf, PandasUDFType\n", - "\n", - "from pyspark.ml.clustering import KMeans\n", + "from pyspark.ml import Pipeline\n", "from pyspark.ml.feature import StringIndexer, VectorAssembler\n", + "from pyspark.ml.clustering import KMeans\n", + "from pyspark.storagelevel import StorageLevel\n", "\n", - "from pyspark.sql.window import Window\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", + "import seaborn as sns\n", "from functools import reduce\n", - "import re\n", - "import collections\n", - "from pyspark.ml import Pipeline\n", - "import numpy\n", - "import time\n", "from pandasql import sqldf\n", - "import html\n", - "\n", - "pandas.options.display.max_rows=50\n", - "pandas.options.display.max_columns=200\n", - "pandas.options.display.float_format = '{:,}'.format" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ipywidgets import IntProgress,Layout\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.lines as mlines\n" + "from itertools import chain" ] }, { @@ -163,7 +98,6 @@ "outputs": [], "source": [ "import pyhdfs\n", - "\n", "import socket\n", "localhost=socket.gethostname()\n", "local_ip=socket.gethostbyname(localhost)\n", @@ -4167,6 +4101,15 @@ "# Dask Application Run" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -4231,9 +4174,48 @@ " with open('/home/sparkuser/trace_result/'+self.appid+'.json', 'w') as outfile: \n", " outfile.write(output)\n", "\n", - " print(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appid}.json\")" + " print(\"http://sr219:1088/tracing_examples/trace_viewer.html#/tracing/test_data/\"+self.appid+\".json\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "datetime.fromtimestamp(1546439400)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -4953,7 +4935,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# MISC" ] @@ -4961,7 +4945,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "def reduce_metric(pdrst,slave_id,metric,core,agg_func):\n", @@ -4973,7 +4959,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# TPCDS query map" ] @@ -4982,7 +4970,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index ee6bf443f6b4..e345a9a0bd57 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -69,6 +69,20 @@ "# System Settings" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "home = os.path.realpath(str(Path.home()))\n", + "cwd = os.getcwd()\n", + "print(f'home: {home}')\n", + "print(f'cwd: {cwd}')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -264,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "code_folding": [] }, @@ -274,23 +288,23 @@ "import os\n", "import sys\n", "\n", - "from pathlib import Path\n", - "home = str(Path.home())\n", - "\n", "def upload_profile(server, base_dir, appid):\n", " local_profile_dir = os.path.join(home, 'profile')\n", " !mkdir -p {local_profile_dir}\n", - " !cd {local_profile_dir}; rm -f {appid}.tar.gz; tar zcvf {appid}.tar.gz {appid} >/dev/null 2>&1\n", + " !(cd {local_profile_dir}; rm -f {appid}.tar.gz; tar zcvf {appid}.tar.gz {appid}) >/dev/null 2>&1\n", " \n", " server_local_dir=os.path.join('PAUS', base_dir)\n", " server_local_profile_dir=os.path.join(server_local_dir, 'profile')\n", " server_hdfs_dir=f'/{base_dir}/'\n", "\n", " !ssh {server} \"mkdir -p {server_local_profile_dir}\"\n", - " !ssh {server} \"cd {server_local_profile_dir}; rm {appid}.tar.gz; rm -r {appid} >/dev/null 2>&1\"\n", + " !ssh {server} \"cd {server_local_profile_dir} && rm {appid}.tar.gz >/dev/null 2>&1\"\n", + " !ssh {server} \"cd {server_local_profile_dir} && rm -r {appid} >/dev/null 2>&1\"\n", " !scp {local_profile_dir}/{appid}.tar.gz {server}:{server_local_profile_dir}/\n", " !ssh {server} \"cd {server_local_profile_dir} && tar zxf {appid}.tar.gz\"\n", - " !ssh {server} \"hdfs dfs -mkdir -p {server_hdfs_dir}; hdfs dfs -rm -r {server_hdfs_dir}{appid}; hdfs dfs -put {server_local_profile_dir}/{appid} {server_hdfs_dir}\"\n", + " !ssh {server} \"hdfs dfs -mkdir -p {server_hdfs_dir}\"\n", + " !ssh {server} \"hdfs dfs -rm -r {server_hdfs_dir}{appid} >/dev/null 2>&1\"\n", + " !ssh {server} \"hdfs dfs -put {server_local_profile_dir}/{appid} {server_hdfs_dir}\"\n", " !ssh {server} \"cd {server_local_profile_dir}; rm {appid}.tar.gz; rm -r {appid}\"\n", "\n", "def killsar(clients):\n", @@ -797,15 +811,9 @@ " stopmonitor(clients, self.sc, self.appid, **kw)\n", " if self.server:\n", " output_nb = f'{self.nb_name[:-6]}-{self.appid}.ipynb'\n", - " if output_nb.startswith(home):\n", - " output_nb_name = os.path.relpath(output_nb, home)\n", - " else:\n", - " output_nb_name = output_nb\n", - " output_nb_dir = os.path.dirname(output_nb_name)\n", - " server_nb_dir = os.path.join('PAUS', self.base_dir, output_nb_dir)\n", - " !ssh {self.server} \"mkdir -p {server_nb_dir}\"\n", - " !scp {output_nb} {self.server}:{server_nb_dir}\n", - " self.finished_nb = f\"http://{self.server}:8888/tree/{self.base_dir}/{output_nb_name}\"\n", + " if output_nb.startswith(cwd):\n", + " output_nb = os.path.relpath(output_nb, home)\n", + " self.finished_nb = f\"http://{localhost}:8888/tree/{output_nb}\"\n", " self.stopped = True\n", "\n", " def run_perf_analysis(self, disk_dev, nic_dev):\n", From 032cf090663f430f7c58517318b2e0c438b5e4d5 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Wed, 27 Nov 2024 08:49:43 +0000 Subject: [PATCH 03/12] add sample trace_view --- tools/workload/benchmark_velox/README.md | 20 + .../sample/trace_result_tpch_q1.json | 517 ++++++++++++++++++ 2 files changed, 537 insertions(+) create mode 100644 tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json diff --git a/tools/workload/benchmark_velox/README.md b/tools/workload/benchmark_velox/README.md index 5f080077f606..120bed8d9c99 100644 --- a/tools/workload/benchmark_velox/README.md +++ b/tools/workload/benchmark_velox/README.md @@ -36,3 +36,23 @@ We also provide a script [run_tpc_workload.sh](./run_tpc_workload.sh). This scri ## Analyzing Performance Results You can check the **Show Performance** section in the output notebook after execution. It shows the cpu% per query, and draws charts for the cpu%, memory throughput, disk throughput/util%, network throughput and pagefaults. + +## Set up Performance Analysis Tools + +Please check the **Set up perf analysis tools (optional)** section in [initialize.ipynb](./initialize.ipynb) to set up the environment required for running performance analysis scripts. Once the setup is complete, update the following variables in your YAML file (as documented in [params.yaml.template](./params.yaml.template)) before running TPC-H/TPC-DS Benchmarks: + +- server: Hostname or IP to server for perf analysis. Able to connect via ssh. Can be localhost if you deploy the perf analysis scripts on the local cluster. +- base_dir: Specify the directory on perf analysis server. Usually a codename for this run. +- analyze_perf: Whether to upload profile to perf analysis server and run perf analysis scripts. Only takes effect if server is set. In this case set to `True` +- proxy: Proxy used to connect to server for perf analysis. Only needed if the perf analysis server is accessed via proxy. + +After the workload completes, the tool generates a notebook, executes it automatically, and saves the output notebook in the `$HOME/PAUS/base_dir` directory with a suffix of `[APPLICATION ID].nbconvert.ipynb`. Additionally, the output notebook is converted into an HTML format for improved readability, with the same filename, and stored in the `html` sub-folder. + +The notebook also produces a trace-viewer JSON file to analyze workload statistics. This includes SAR metrics and stage/task-level breakdowns. Using this tool, users can compare statistics across stages and queries, identify performance bottlenecks, and target specific stages for optimization. + +If you have set up and launched Catapult trace-viewer server (refer to the **Set up perf analysis tools (optional)** section in [initialize.ipynb](./initialize.ipynb)), you can explore a sample trace-viewer JSON file. To do so: + +1. Copy the sample file [trace_result_tpch_q1.json](./sample/trace_result_tpch_q1.json) to the `$HOME/trace_result` directory +2. Open the following link in your browser to view the results: http://[your-host-ip]:1088/tracing_examples/trace_viewer.html#/tracing/test_data/trace_result_tpch_q1.json + +This visualization helps to better understand performance metrics and optimize accordingly. diff --git a/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json b/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json new file mode 100644 index 000000000000..4099df0c4ada --- /dev/null +++ b/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json @@ -0,0 +1,517 @@ + + { + "traceEvents": [ + + {"name": "process_name", "ph": "M", "pid": 100300, "tid": 0, "args": {"name": "sr217.3"}}, +{"tid": 100300, "ts": -29221, "dur": 1658, "pid": 100300, "ph": "X", "name": "stg0", "args": {"job id": 0, "stage id": 0, "tskid": 0, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"name": "process_name", "ph": "M", "pid": 100200, "tid": 0, "args": {"name": "sr217.2"}}, +{"tid": 100200, "ts": -25482, "dur": 1673, "pid": 100200, "ph": "X", "name": "stg1", "args": {"job id": 1, "stage id": 1, "tskid": 1, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -23725, "dur": 44, "pid": 100300, "ph": "X", "name": "stg2", "args": {"job id": 2, "stage id": 2, "tskid": 2, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"name": "process_name", "ph": "M", "pid": 100400, "tid": 0, "args": {"name": "sr217.4"}}, +{"tid": 100400, "ts": -23602, "dur": 1568, "pid": 100400, "ph": "X", "name": "stg3", "args": {"job id": 3, "stage id": 3, "tskid": 3, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100400, "ts": -21959, "dur": 45, "pid": 100400, "ph": "X", "name": "stg4", "args": {"job id": 4, "stage id": 4, "tskid": 4, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -21844, "dur": 33, "pid": 100300, "ph": "X", "name": "stg5", "args": {"job id": 5, "stage id": 5, "tskid": 5, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"name": "process_name", "ph": "M", "pid": 100100, "tid": 0, "args": {"name": "sr217.1"}}, +{"tid": 100100, "ts": -21745, "dur": 1580, "pid": 100100, "ph": "X", "name": "stg6", "args": {"job id": 6, "stage id": 6, "tskid": 6, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100400, "ts": -20094, "dur": 34, "pid": 100400, "ph": "X", "name": "stg7", "args": {"job id": 7, "stage id": 7, "tskid": 7, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100303, "ts": -18980, "dur": 13476, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 20, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100302, "ts": -18981, "dur": 13530, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 16, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -18985, "dur": 13603, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 8, "input": 315.73, "spill": 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"args": {"sort_index ": 100500}}, +{"name": "process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, +{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}} + ], + "displayTimeUnit": "ns" + } \ No newline at end of file From 663d4ffa5ee62c05ed20e1bb3571bf5577bbe539 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Mon, 2 Dec 2024 10:02:50 +0000 Subject: [PATCH 04/12] add perf stat --- .../analysis/perf_analysis_template.ipynb | 15 +- .../workload/benchmark_velox/analysis/run.py | 15 ++ .../analysis/run_perf_analysis.sh | 10 + .../benchmark_velox/analysis/sparklog.ipynb | 179 ++++++++++++++---- .../native_sql_initialize.ipynb | 9 +- 5 files changed, 173 insertions(+), 55 deletions(-) diff --git a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb index ca9c1de27500..973474a08a0d 100644 --- a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb +++ b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb @@ -23,6 +23,7 @@ "tz=''\n", "basedir=''\n", "name=''\n", + "proxy=''\n", "\n", "compare_appid=''\n", "compare_basedir=''\n", @@ -59,16 +60,6 @@ " return py4jzip[0]" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "print(sys.path)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -149,8 +140,8 @@ "metadata": {}, "outputs": [], "source": [ - "os.environ[\"https_proxy\"] = \"http://10.239.44.250:8080\"\n", - "os.environ[\"http_proxy\"] = \"http://10.239.44.250:8080\"" + "os.environ[\"https_proxy\"] = proxy\n", + "os.environ[\"http_proxy\"] = proxy" ] }, { diff --git a/tools/workload/benchmark_velox/analysis/run.py b/tools/workload/benchmark_velox/analysis/run.py index 06fe712a5e09..ba8f008d3553 100644 --- a/tools/workload/benchmark_velox/analysis/run.py +++ b/tools/workload/benchmark_velox/analysis/run.py @@ -1,3 +1,18 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import fire import papermill as pm diff --git a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh index 7dcc4ce90c89..42b391ffa3f4 100755 --- a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh +++ b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh @@ -55,6 +55,11 @@ while [[ $# -gt 0 ]]; do shift # past argument shift # past value ;; + --proxy) + PROXY="$2" + shift # past argument + shift # past value + ;; --comp-appid) COMP_APPID="$2" shift # past argument @@ -113,6 +118,11 @@ then EXTRA_ARGS="--compare_appid $COMP_APPID --compare_basedir $COMP_BASEDIR --compare_name $COMP_NAME" fi +if [ -n "${PROXY}" ] +then + EXTRA_ARGS=$EXTRA_ARGS" --proxy $PROXY" +fi + source ~/paus-env/bin/activate python3 $SCRIPT_LOCATION/run.py --inputnb $nb_name --outputnb ${nb_name0}.nbconvert.ipynb --appid $APPID --disk $DISK --nic $NIC --tz $SPARK_TZ --basedir $BASEDIR --name $NAME $EXTRA_ARGS diff --git a/tools/workload/benchmark_velox/analysis/sparklog.ipynb b/tools/workload/benchmark_velox/analysis/sparklog.ipynb index 8a74c97d4961..d2396c2cd24c 100644 --- a/tools/workload/benchmark_velox/analysis/sparklog.ipynb +++ b/tools/workload/benchmark_velox/analysis/sparklog.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# initialize" ] @@ -10,7 +12,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "from __future__ import nested_scopes\n", @@ -23,6 +27,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "hidden": true, "lang": "en" }, "outputs": [], @@ -38,7 +43,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "import os\n", @@ -77,7 +84,7 @@ "import pyspark.sql\n", "import pyspark.sql.functions as F\n", "from pyspark.sql import SparkSession\n", - "from pyspark.sql.functions import to_date, floor, lit, rank, col, pandas_udf, PandasUDFType\n", + "from pyspark.sql.functions import to_date, floor, lit, rank, col, lag, when, pandas_udf, PandasUDFType, avg, sum as _sum\n", "from pyspark.sql.window import Window\n", "from pyspark.sql.types import *\n", "from pyspark.ml import Pipeline\n", @@ -94,7 +101,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "import pyhdfs\n", @@ -107,7 +116,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# fs functions" ] @@ -115,7 +126,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "def getexecutor_stat(pdir):\n", @@ -166,7 +179,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):\n", @@ -182,7 +197,8 @@ { "cell_type": "markdown", "metadata": { - "collapsed": "true" + "collapsed": "true", + "heading_collapsed": true }, "source": [ "# base class" @@ -192,7 +208,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -205,7 +222,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -256,7 +274,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# app log analysis" ] @@ -264,7 +284,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "def get_his_perf(namelike,currentdir):\n", @@ -292,7 +314,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -2387,7 +2410,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "notlist=['resource.executor.cores',\n", @@ -2432,7 +2457,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "## Node log analysis" ] @@ -2441,7 +2468,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -2604,7 +2632,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -2741,7 +2770,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -2923,7 +2953,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# perf trace analysis" ] @@ -2932,7 +2964,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -3119,7 +3152,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# Sar analysis" ] @@ -3128,7 +3163,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [] + "code_folding": [], + "hidden": true }, "outputs": [], "source": [ @@ -3585,7 +3621,7 @@ "heading_collapsed": true }, "source": [ - "# Perf Trace Analysis" + "# Perf stat Analysis" ] }, { @@ -3613,6 +3649,20 @@ " print(\"error, perfstarttime not found\")\n", " return\n", " \n", + " tsc_freq_file = os.path.join(paths[0], 'tsc_freq')\n", + " if fs.exists(tsc_freq_file):\n", + " self.tsc_freq = int(spark.read.text(tsc_freq_file).collect()[0][0])\n", + " else:\n", + " print(f'{tsc_freq_file} not exists')\n", + " return\n", + " \n", + " totalcores_file = os.path.join(paths[0], 'totalcores')\n", + " if fs.exists(totalcores_file):\n", + " self.totalcores = int(spark.read.text(totalcores_file).collect()[0][0])\n", + " else:\n", + " print(f'{totalcores_file} not exists')\n", + " return\n", + " \n", " strf=strf[len(\"# started on \"):].strip()\n", " starttime=datetime.strptime(strf, \"%a %b %d %H:%M:%S %Y\").timestamp()*1000\n", " sardf=sardf.where(\"_1<>'#'\")\n", @@ -3649,7 +3699,50 @@ " F.struct(F.col(\"_3\").alias(\"cnt\")).alias('args')\n", " ).toJSON().collect())\n", " return trace_list\n", - " \n" + " \n", + " def get_stat(self, **kwargs):\n", + " if self.df is None:\n", + " self.load_data()\n", + "\n", + " raw_data = spark.read.text(self.file)\n", + "\n", + " # Filter out non-data lines and split the data into columns\n", + " filtered_data = raw_data.filter(\n", + " ~raw_data.value.startswith('#') & raw_data.value.rlike(r\"^\\s*\\d\")\n", + " )\n", + "\n", + " split_data = filtered_data.rdd.map(lambda row: row[0].split()).map(\n", + " lambda parts: (float(parts[0]), int(parts[1].replace(\",\", \"\")), parts[2], '' if len(parts) == 3 else parts[4])\n", + " )\n", + "\n", + " schema = [\"time\", \"counts\", \"events\", \"ipc\"]\n", + " df = split_data.toDF(schema)\n", + "\n", + " events_df = df.filter(col('ipc') == '')\n", + " ipc_df = df.filter(col('ipc') != '')\n", + "\n", + " instructions = ipc_df.select(_sum(col(\"counts\"))).collect()[0][0] / 1e9\n", + " avg_ipc = ipc_df.select(avg(col(\"ipc\"))).collect()[0][0]\n", + "\n", + " df_ccu_ref_tsc = events_df.select(col('time'), col('counts')).filter(col('events') == 'cpu_clk_unhalted.ref_tsc').withColumnRenamed('counts', 'cpu_clk_unhalted_ref_tsc')\n", + " df_ccu_thread = events_df.select(col('time'), col('counts')).filter(col('events') == 'cpu_clk_unhalted.thread').withColumnRenamed('counts', 'cpu_clk_unhalted_thread')\n", + "\n", + " window_spec = Window.orderBy(\"time\")\n", + " df_ccu_ref_tsc = df_ccu_ref_tsc.withColumn(\"prev_time\", lag(\"time\").over(window_spec))\n", + " df_ccu_ref_tsc = df_ccu_ref_tsc.withColumn(\"prev_time\", when(col(\"prev_time\").isNull(), 0).otherwise(col(\"prev_time\")))\n", + " df_ccu_ref_tsc = df_ccu_ref_tsc.withColumn(\"tsc\", (col(\"time\") - col(\"prev_time\")) * self.tsc_freq)\n", + "\n", + " joined_df = df_ccu_ref_tsc.join(df_ccu_thread, on=[\"time\"], how=\"inner\")\n", + " cpu_freq_df = joined_df.withColumn(\"freq\", joined_df.cpu_clk_unhalted_thread / joined_df.cpu_clk_unhalted_ref_tsc * self.tsc_freq / 1e9)\n", + " cpu_freq = cpu_freq_df.select(avg(col('freq'))).collect()[0][0]\n", + "\n", + " cpu_util_df = df_ccu_ref_tsc.withColumn(\"cpu%\", col(\"cpu_clk_unhalted_ref_tsc\") / col(\"tsc\") / self.totalcores * 100)\n", + " cpu_util = cpu_util_df.select(avg(col('cpu%'))).collect()[0][0]\n", + "\n", + " out = [['ipc', avg_ipc], ['instructions', instructions], ['cpu_freq', cpu_freq], ['cpu%', cpu_util]]\n", + " pdout=pandas.DataFrame(out).set_index(0)\n", + " \n", + " return pdout" ] }, { @@ -4355,7 +4448,7 @@ "heading_collapsed": true }, "source": [ - "# application Run" + "# Application Run" ] }, { @@ -4367,10 +4460,6 @@ }, "outputs": [], "source": [ - "\n", - "\n", - "\n", - "\n", "class Application_Run:\n", " def __init__(self, appid,**kwargs):\n", " self.appid=appid\n", @@ -4513,22 +4602,26 @@ "\n", " display(HTML(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{self.appid}.json\"))\n", " \n", - " def get_sar_stat(app_ww44,**kwargs):\n", + " def get_sar_stat(self,**kwargs):\n", " disk_prefix=kwargs.get(\"disk_prefix\",\"dev259\")\n", " nic_prefix = kwargs.get(\"nic_prefix\",[\"'eth3'\",\"'enp24s0f1'\"])\n", - " cpustat=[app_ww44.analysis[\"sar\"][l]['sar_cpu']['als'].get_stat() for l in app_ww44.clients]\n", + " cpustat=[self.analysis[\"sar\"][l]['sar_cpu']['als'].get_stat() for l in self.clients]\n", " cpustat=reduce(lambda l,r:l.join(r),cpustat)\n", - " diskstat=[app_ww44.analysis[\"sar\"][l]['sar_disk']['als'].get_stat(disk_prefix=disk_prefix) for l in app_ww44.clients]\n", + " diskstat=[self.analysis[\"sar\"][l]['sar_disk']['als'].get_stat(disk_prefix=disk_prefix) for l in self.clients]\n", " diskstat=reduce(lambda l,r:l.join(r),diskstat)\n", - " memstat=[app_ww44.analysis[\"sar\"][l]['sar_mem']['als'].get_stat() for l in app_ww44.clients]\n", + " memstat=[self.analysis[\"sar\"][l]['sar_mem']['als'].get_stat() for l in self.clients]\n", " memstat=reduce(lambda l,r:l.join(r),memstat)\n", - " nicstat=[app_ww44.analysis[\"sar\"][l]['sar_nic']['als'].get_stat(nic_prefix=nic_prefix) for l in app_ww44.clients]\n", + " nicstat=[self.analysis[\"sar\"][l]['sar_nic']['als'].get_stat(nic_prefix=nic_prefix) for l in self.clients]\n", " nicstat=reduce(lambda l,r:l.join(r),nicstat)\n", - " pagestat=[app_ww44.analysis[\"sar\"][l]['sar_page']['als'].get_stat() for l in app_ww44.clients]\n", + " pagestat=[self.analysis[\"sar\"][l]['sar_page']['als'].get_stat() for l in self.clients]\n", " pagestat=reduce(lambda l,r:l.join(r),pagestat)\n", " pandas.options.display.float_format = '{:,.2f}'.format\n", " return pandas.concat([cpustat,diskstat,memstat,nicstat,pagestat])\n", " \n", + " def get_perf_stat(self, **kwargs):\n", + " perfstat=[self.analysis[\"sar\"][l]['perfstat']['als'].get_stat() for l in self.clients]\n", + " return reduce(lambda l,r: l.join(r), perfstat)\n", + " \n", " def get_summary(app, **kwargs):\n", " output=[]\n", " \n", @@ -4571,7 +4664,13 @@ "\n", " sarsum.columns=[lrun]\n", " \n", - " summary=pandas.concat([pdstime,sarsum])\n", + " # perf stat\n", + " print(\"perf stat metric\")\n", + " perf_stat = app.get_perf_stat(**kwargs)\n", + " perf_stat = get_sar_agg(perf_stat)[['agg']]\n", + " perf_stat.columns=[lrun]\n", + " \n", + " summary=pandas.concat([pdstime,sarsum,perf_stat])\n", " \n", " df_sum=spark.createDataFrame(summary.T.reset_index())\n", " for c in df_sum.columns:\n", @@ -5122,9 +5221,9 @@ "title_sidebar": "Contents", "toc_cell": false, "toc_position": { - "height": "298.281px", - "left": "1205px", - "top": "421.125px", + "height": "298.275px", + "left": "1180px", + "top": "317.125px", "width": "332px" }, "toc_section_display": true, diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index e345a9a0bd57..1d41e6c85a05 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "code_folding": [] }, @@ -345,13 +345,16 @@ " !ssh {l} mkdir -p {prof_client}\n", " !ssh {l} \"sar -o {prof_client}/sar.bin -r -u -d -B -n DEV 1 >/dev/null 2>&1 &\"\n", " !ssh root@{l} \"jps | grep CoarseGrainedExecutorBackend | cut -d' ' -f 1 | xargs -I % bash -c '(cat /proc/%/status >> {prof_client}/%.stat; cat /proc/%/io >> {prof_client}/%.stat)'\"\n", + " !ssh root@{l} \"perf stat -e 'instructions,cycles,cpu_clk_unhalted.thread,cpu_clk_unhalted.ref_tsc' -a -I 500 -o {prof_client}/perfstat.txt >/dev/null 2>&1 & \"\n", + " !ssh {l} \"cat /sys/devices/system/cpu/cpu0/tsc_freq_khz | xargs -I% echo %000 > {prof_client}/tsc_freq 2>/dev/null &\"\n", + " !ssh {l} \"lscpu | grep '^CPU(s):' | cut -d ':' -f 2 | tr -d ' ' > {prof_client}/totalcores 2>/dev/null &\"\n", " if kwargs.get(\"collect_pid\",False):\n", " !ssh {l} \"jps | grep CoarseGrainedExecutorBackend | head -n 1 | cut -d' ' -f 1 | xargs -I % pidstat -h -t -p % 1 > {prof_client}/pidstat.out 2>/dev/null &\"\n", " !ssh root@{l} 'cat /proc/uptime | cut -d\" \" -f 1 | xargs -I ^ date -d \"- ^ seconds\" +%s.%N' > $prof/$l/uptime.txt\n", " if kwargs.get(\"collect_sched\",False):\n", " !ssh root@{l} 'perf trace -e \"sched:sched_switch\" -C 8-15 -o {prof_client}/sched.txt -T -- sleep 10000 >/dev/null 2>/dev/null &'\n", " if perfsyscalls is not None:\n", - " !ssh root@{l} \"perf stat -e 'syscalls:sys_exit_poll,syscalls:sys_exit_epoll_wait' -a -I 1000 -o {prof_client}/perfstat.txt >/dev/null 2>&1 & \"\n", + " !ssh root@{l} \"perf stat -e 'syscalls:sys_exit_poll,syscalls:sys_exit_epoll_wait' -a -I 1000 -o {prof_client}/perfsyscalls.txt >/dev/null 2>&1 & \"\n", " if kwargs.get(\"collect_hbm\",False):\n", " hbm_nodes = kwargs.get(\"hbm_nodes\")\n", " if hbm_nodes is not None:\n", @@ -384,7 +387,7 @@ " !ssh {l} \"sar -f {prof_client}/sar.bin -r > {prof_client}/sar_mem.sar;sar -f {prof_client}/sar.bin -u > {prof_client}/sar_cpu.sar;sar -f {prof_client}/sar.bin -d -p > {prof_client}/sar_disk.sar;sar -f {prof_client}/sar.bin -n DEV > {prof_client}/sar_nic.sar;sar -f {prof_client}/sar.bin -B > {prof_client}/sar_page.sar;\" \n", " !ssh root@{l} \"jps | grep CoarseGrainedExecutorBackend | cut -d' ' -f 1 | xargs -I % bash -c '(cat /proc/%/status >> {prof_client}/%.stat; cat /proc/%/io >> {prof_client}/%.stat)'\"\n", " !ssh {l} \"sar -V \" > {prof_client}/sarv.txt\n", - " !test -f {prof_client}/perfstat.txt && head -n 1 {prof_client}/perfstat.txt > {prof_client}/perfstarttime\n", + " !ssh {l} \"test -f {prof_client}/perfstat.txt && head -n 1 {prof_client}/perfstat.txt > {prof_client}/perfstarttime\"\n", " if l!= socket.gethostname():\n", " !scp -r {l}:{prof_client} {prof}/ > /dev/null 2>&1\n", " \n", From 02fdacf8d3222cbe9e7793364885dabb94887af8 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Mon, 2 Dec 2024 15:40:23 +0000 Subject: [PATCH 05/12] add samples --- tools/workload/benchmark_velox/README.md | 15 +- .../benchmark_velox/analysis/sparklog.ipynb | 7 +- .../native_sql_initialize.ipynb | 4 +- .../benchmark_velox/sample/Trace-viewer.png | Bin 0 -> 154395 bytes .../benchmark_velox/sample/tpch_q1.html | 14376 ++++++++++++++++ .../sample/tpch_q1.nbconvert.ipynb | 2550 +++ 6 files changed, 16945 insertions(+), 7 deletions(-) create mode 100644 tools/workload/benchmark_velox/sample/Trace-viewer.png create mode 100644 tools/workload/benchmark_velox/sample/tpch_q1.html create mode 100644 tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb diff --git a/tools/workload/benchmark_velox/README.md b/tools/workload/benchmark_velox/README.md index 120bed8d9c99..b1e7a2f795b8 100644 --- a/tools/workload/benchmark_velox/README.md +++ b/tools/workload/benchmark_velox/README.md @@ -48,11 +48,18 @@ Please check the **Set up perf analysis tools (optional)** section in [initializ After the workload completes, the tool generates a notebook, executes it automatically, and saves the output notebook in the `$HOME/PAUS/base_dir` directory with a suffix of `[APPLICATION ID].nbconvert.ipynb`. Additionally, the output notebook is converted into an HTML format for improved readability, with the same filename, and stored in the `html` sub-folder. +A sample generated notebook for TPCH Q1 and its corresponding HTML file are available for reference: +- Notebook: [tpch_q1.nbconvert.ipynb](./sample/tpch_q1.nbconvert.ipynb) +- HTML file: [tpch_q1.html](./sample/tpch_q1.html) + The notebook also produces a trace-viewer JSON file to analyze workload statistics. This includes SAR metrics and stage/task-level breakdowns. Using this tool, users can compare statistics across stages and queries, identify performance bottlenecks, and target specific stages for optimization. -If you have set up and launched Catapult trace-viewer server (refer to the **Set up perf analysis tools (optional)** section in [initialize.ipynb](./initialize.ipynb)), you can explore a sample trace-viewer JSON file. To do so: +You can explore the sample trace-viewer JSON file using the Google Chrome browser. To do so: + +1. Download the sample file [trace_result_tpch_q1.json](./sample/trace_result_tpch_q1.json) +2. Launch Google Chrome. In the address bar, enter "chrome://tracing/". +3. Use the "Load" button to upload the sample JSON file. -1. Copy the sample file [trace_result_tpch_q1.json](./sample/trace_result_tpch_q1.json) to the `$HOME/trace_result` directory -2. Open the following link in your browser to view the results: http://[your-host-ip]:1088/tracing_examples/trace_viewer.html#/tracing/test_data/trace_result_tpch_q1.json +This will allow you to check the sample trace data interactively. -This visualization helps to better understand performance metrics and optimize accordingly. +![trace-result-tpch-q1](./sample/Trace-viewer.png) diff --git a/tools/workload/benchmark_velox/analysis/sparklog.ipynb b/tools/workload/benchmark_velox/analysis/sparklog.ipynb index d2396c2cd24c..876b4f1f2c28 100644 --- a/tools/workload/benchmark_velox/analysis/sparklog.ipynb +++ b/tools/workload/benchmark_velox/analysis/sparklog.ipynb @@ -2204,13 +2204,18 @@ "\n", " exchangedf=self.get_metrics_by_node([\"ColumnarExchange\",\"ColumnarExchangeAdaptor\"])\n", " exchangedf.cache()\n", - " exchangedf.count()\n", + " if exchangedf.count() == 0:\n", + " return (None, None)\n", + "\n", " mapdf=exchangedf.where(\"`time to split` is not null\").select(\"nodeID\",F.col(\"Stage ID\").alias(\"map_stageid\"),\"real_queryid\",F.floor(F.col(\"time to split\")/F.col(\"time to split_mean\")).alias(\"map_partnum\"),\"time to compress\",\"time to split\",\"shuffle write time\",\"time to spill\",'shuffle records written','data size','shuffle bytes written','shuffle bytes written_mean','shuffle bytes written_stddev','shuffle bytes spilled','number of input rows','number of input batches')\n", " reducerdf=exchangedf.where(\"`time to split` is null\").select(\"nodeID\",F.col(\"Stage ID\").alias(\"reducer_stageid\"),\"real_queryid\",'local blocks read','local bytes read',F.floor(F.col(\"records read\")/F.col(\"records read_mean\")).alias(\"reducer_partnum\"),(F.col('avg read batch num rows')/10).alias(\"avg read batch num rows\"),'remote bytes read','records read','remote blocks read',(F.col(\"number of output rows\")/F.col(\"records read\")).alias(\"avg rows per split recordbatch\"))\n", " shuffledf=mapdf.join(reducerdf,on=[\"nodeID\",\"real_queryid\"],how=\"full\")\n", " if queryid is not None:\n", " shuffledf=shuffledf.where(F.col(\"real_queryid\").isin(queryid))\n", " shuffle_pdf=shuffledf.where(\"`shuffle bytes written`>1000000\").orderBy(\"real_queryid\",\"map_stageid\",\"nodeID\").toPandas()\n", + " if shuffle_pdf.shape[0] == 0:\n", + " return (shuffledf, None)\n", + "\n", " shuffle_pdf[\"shuffle bytes written\"]=shuffle_pdf[\"shuffle bytes written\"]/1000000000\n", " shuffle_pdf[\"data size\"]=shuffle_pdf[\"data size\"]/1000000000\n", " shuffle_pdf[\"shuffle bytes written_mean\"]=shuffle_pdf[\"shuffle bytes written_mean\"]/1000000\n", diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index 1d41e6c85a05..1cdab3a47e02 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -819,7 +819,7 @@ " self.finished_nb = f\"http://{localhost}:8888/tree/{output_nb}\"\n", " self.stopped = True\n", "\n", - " def run_perf_analysis(self, disk_dev, nic_dev):\n", + " def run_perf_analysis(self, disk_dev, nic_dev, proxy):\n", " if not self.server:\n", " return\n", "\n", @@ -832,7 +832,7 @@ " disk=','.join(disk_dev)\n", " nic=','.join(nic_dev)\n", "\n", - " command =' '.join(['bash', run_script, '--ts', ts, '--base-dir', self.base_dir, '--name', name, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt()])\n", + " command =' '.join(['bash', run_script, '--ts', ts, '--base-dir', self.base_dir, '--name', name, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt(), '--proxy', proxy if proxy != '' else \"''\"])\n", " print(command)\n", "\n", " # Block if running on local cluster.\n", diff --git a/tools/workload/benchmark_velox/sample/Trace-viewer.png b/tools/workload/benchmark_velox/sample/Trace-viewer.png new file mode 100644 index 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+2024_12_02_152940_tpch_gluten_application_1733153225851_0001.nbconvert + + + + + + + + + + +
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Parameters

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start analysis cluster and run

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Setting default log level to "WARN".
+To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
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24/12/02 15:29:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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24/12/02 15:29:48 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
+24/12/02 15:29:48 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
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/home/sparkuser/spark/python/pyspark/sql/context.py:112: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.
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Sparklog

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Content

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App info

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load data  /sr213/application_1733153225851_0001/app.log
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[Stage 39:===>                                                  (13 + 16) / 200]
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[Stage 39:======>                                               (24 + 16) / 200]

[Stage 39:=========>                                            (37 + 16) / 200]
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[Stage 39:================>                                     (60 + 16) / 200]

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[Stage 39:=================================>                   (126 + 16) / 200]

[Stage 39:=============================================>       (171 + 16) / 200]
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http://sr213:18080/history/application_1733153225851_0001
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appidapplication_1733153225851_0001
executor.instances4
executor.cores4
shuffle.partitions32
batch size4,096
real executors4
Failed Tasks
Speculative Tasks0
Speculative Killed Tasks0
Speculative Stage0
runtime17.8
disk spilled0.0
memspilled0.0
local_read0.0
remote_read0.0
shuffle_write0.0
task run time6.73
ser_time0.0
f_wait_time0.0
gc_time0.04
input read22.54
acc_task_time14.14
file read size5,944.5
file write size21.74
disk read size4.95
disk write size12.38
disk cancel size0.0
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{'appid': 'application_1733153225851_0001',
+ 'executor.instances': 4,
+ 'executor.cores': 4,
+ 'shuffle.partitions': 32,
+ 'batch size': 4096,
+ 'real executors': 4,
+ 'Failed Tasks': '',
+ 'Speculative Tasks': 0,
+ 'Speculative Killed Tasks': 0,
+ 'Speculative Stage': 0,
+ 'runtime': 17.8,
+ 'disk spilled': 0.0,
+ 'memspilled': 0.0,
+ 'local_read': 0.0,
+ 'remote_read': 0.0,
+ 'shuffle_write': 0.0,
+ 'task run time': 6.73,
+ 'ser_time': 0.0,
+ 'f_wait_time': 0.0,
+ 'gc_time': 0.04,
+ 'input read': 22.54,
+ 'acc_task_time': 14.14,
+ 'file read size': 5944.5,
+ 'file write size': 21.74,
+ 'disk read size': 4.95,
+ 'disk write size': 12.38,
+ 'disk cancel size': 0.0}
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[Stage 91:============> (174 + 4) / 200][Stage 92:=======>      (105 + 0) / 200]
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sar metric
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perf stat metric
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[Stage 258:>                                                        (0 + 1) / 1]
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 application_1733153225851_0001
runtime17.800000
disk spilled0.000000
shuffle_write0.000000
f_wait_time0.000000
input read22.540000
acc_task_time14.140000
output rows1.180000
%user>90%0.931034
%kernel>10%0.965517
%iowait>10%0.620690
avg %user41.233793
avg %system4.608621
avg %iowait0.623793
avg disk util33.448276
time more than 90%0.000000
total read (G)5.432129
total write (G)0.994891
avg read bw (MB/s)191.810354
avg write bw (MB/s)35.129954
read bw %75402.394531
read bw %95479.738281
read bw max480.722656
time_rd_morethan_950.034483
write bw %751.074219
write bw %9535.035156
write bw max945.855469
time_wr_morethan_950.034483
cached mean97.137931
cached 75%152.000000
cached max188.000000
used mean573.379310
used 75%593.000000
used max597.000000
rx MB/s 75%0.000000
rx MB/s 95%0.000000
rx MB/s 99%0.000000
pgin mean191.965517
pgin 75%402.000000
pgin max480.000000
pgout mean35.103448
pgout 75%1.000000
pgout max947.000000
fault mean117681.655172
fault 75%193074.000000
fault max287177.000000
ipc1.144464
instructions962.586404
cpu_freq3.225843
cpu%7.480452
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[Stage 288:>                                                      (0 + 12) / 12]
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[Stage 288:==============>                                         (3 + 9) / 12]

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DEV in ('nvme0n1')
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http://sr213:1088/tracing_examples/trace_viewer.html#/tracing/test_data/application_1733153225851_0001.json
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gluten tpch_power 663d4f
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application_1733153225851_0001
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http://sr213:18080/history/application_1733153225851_0001
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query time
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 runtimedisk spilledmemspilledlocal_readremote_readshuffle_writedeser_timerun_timeser_timef_wait_timegc_timepeak_memqueryidinput readacc_task_timestagesoutput rowsexecutorscore/exectask.cpusparallelism
real_queryid                     
117.8000000.0000000.0000000.0000000.0000000.0000000.3300006.7300000.0000000.0000000.0400001.340000822.54000014.140000[ 8 10 12 15]1.18000044132
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 01
AQEShuffleRead02
AdaptiveSparkPlan01
ColumnarExchange02
FilterExecTransformer01
FlushableHashAggregateExecTransformer01
InputAdapter02
InputIteratorTransformer02
ProjectExecTransformer02
RegularHashAggregateExecTransformer01
Scan parquet 01
ShuffleQueryStage02
SortExecTransformer01
VeloxColumnarToRow01
VeloxResizeBatches02
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operator input row count
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 1
ColumnarExchange0.000000
VeloxResizeBatches0.000000
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operator output row count
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 1
ColumnarExchange0.000000
FlushableHashAggregateExecTransformer0.000000
InputIteratorTransformer0.000000
ProjectExecTransformer591.600000
RegularHashAggregateExecTransformer0.000000
Scan parquet 591.600000
SortExecTransformer0.000000
VeloxColumnarToRow0.000000
VeloxResizeBatches0.000000
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0
40%_time of scan and filter6.95
35%_time of project6.02
21%_not_counted3.55
3%_idle0.54
0%_time of input iterator0.06
0%_time of aggregation0.03
0%_time to append / split batches0.00
0%_time of rowConstruction0.00
0%_time to split0.00
0%_time to deserialize0.00
0%_time of sort0.00
0%_time of extraction0.00
0%_shuffle write time0.00
0%_time to compress0.00
0%_time to spill0.00
0%_time to decompress0.00
0%_time to convert0.00
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+ + diff --git a/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb b/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb new file mode 100644 index 000000000000..26430241e69f --- /dev/null +++ b/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb @@ -0,0 +1,2550 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e2615af", + "metadata": { + "papermill": { + "duration": 0.003759, + "end_time": "2024-12-02T15:29:45.316600", + "exception": false, + "start_time": "2024-12-02T15:29:45.312841", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3007d85d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:29:45.324819Z", + "iopub.status.busy": "2024-12-02T15:29:45.324405Z", + "iopub.status.idle": "2024-12-02T15:29:45.330628Z", + "shell.execute_reply": "2024-12-02T15:29:45.330233Z" + }, + "papermill": { + "duration": 0.011609, + "end_time": "2024-12-02T15:29:45.331772", + "exception": false, + "start_time": "2024-12-02T15:29:45.320163", + "status": "completed" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "appid=''\n", + "disk=''\n", + "nic=''\n", + "tz=''\n", + "basedir=''\n", + "name=''\n", + "proxy=''\n", + "\n", + "compare_appid=''\n", + "compare_basedir=''\n", + "compare_name=''" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f8ea15a6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:29:45.339630Z", + "iopub.status.busy": "2024-12-02T15:29:45.339237Z", + "iopub.status.idle": "2024-12-02T15:29:45.341862Z", + "shell.execute_reply": "2024-12-02T15:29:45.341478Z" + }, + "papermill": { + "duration": 0.007763, + "end_time": "2024-12-02T15:29:45.342990", + "exception": false, + "start_time": "2024-12-02T15:29:45.335227", + "status": "completed" + }, + "tags": [ + "injected-parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters\n", + "appid = \"application_1733153225851_0001\"\n", + "disk = \"nvme0n1\"\n", + "nic = \"enp61s0f0\"\n", + "tz = \"Etc/GMT+0\"\n", + "basedir = \"sr213\"\n", + "name = \"tpch_gluten\"\n", + "compare_appid = \"\"\n", + "compare_basedir = \"\"\n", + "compare_name = \"\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "f5b83da6", + "metadata": { + "papermill": { + "duration": 0.003418, + "end_time": "2024-12-02T15:29:45.350391", + "exception": false, + "start_time": "2024-12-02T15:29:45.346973", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# start analysis cluster and run" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f1ed544f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:29:45.358265Z", + "iopub.status.busy": "2024-12-02T15:29:45.357941Z", + "iopub.status.idle": "2024-12-02T15:29:45.360578Z", + "shell.execute_reply": "2024-12-02T15:29:45.360205Z" + }, + "papermill": { + "duration": 0.007969, + "end_time": "2024-12-02T15:29:45.361868", + "exception": false, + "start_time": "2024-12-02T15:29:45.353899", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import findspark\n", + "findspark.init()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ace428dd", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:29:45.369886Z", + "iopub.status.busy": "2024-12-02T15:29:45.369520Z", + "iopub.status.idle": "2024-12-02T15:29:45.372438Z", + "shell.execute_reply": "2024-12-02T15:29:45.372052Z" + }, + "papermill": { + "duration": 0.008252, + "end_time": "2024-12-02T15:29:45.373616", + "exception": false, + "start_time": "2024-12-02T15:29:45.365364", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "def get_py4jzip():\n", + " spark_home=os.environ['SPARK_HOME']\n", + " py4jzip = !ls {spark_home}/python/lib/py4j*.zip\n", + " return py4jzip[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d25fd5b9", + "metadata": { + "code_folding": [], + "execution": { + "iopub.execute_input": "2024-12-02T15:29:45.381704Z", + "iopub.status.busy": "2024-12-02T15:29:45.381384Z", + "iopub.status.idle": "2024-12-02T15:30:13.999878Z", + "shell.execute_reply": "2024-12-02T15:30:13.999216Z" + }, + "papermill": { + "duration": 28.624314, + "end_time": "2024-12-02T15:30:14.001501", + "exception": false, + "start_time": "2024-12-02T15:29:45.377187", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/12/02 15:29:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/12/02 15:29:48 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.\n", + "24/12/02 15:29:48 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sparkuser/spark/python/pyspark/sql/context.py:112: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "from pyspark.sql import SQLContext\n", + "import time\n", + "import sys\n", + "conf = (SparkConf()\n", + " .set('spark.app.name', f'perf_analysis_{appid}')\n", + " .set('spark.serializer','org.apache.spark.serializer.KryoSerializer')\n", + " .set('spark.executor.instances', '4')\n", + " .set('spark.executor.cores','4')\n", + " .set('spark.executor.memory', '8g')\n", + " .set('spark.driver.memory','20g')\n", + " .set('spark.memory.offHeap.enabled','True')\n", + " .set('spark.memory.offHeap.size','20g')\n", + " .set('spark.executor.memoryOverhead','1g')\n", + " .set('spark.executor.extraJavaOptions',\n", + " '-XX:+UseParallelGC -XX:+UseParallelOldGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps')\n", + " .set('spark.executorEnv.PYTHONPATH',f\"{os.environ['SPARK_HOME']}/python:{get_py4jzip()}:{':'.join(sys.path)}\")\n", + " .set('spark.sql.inMemoryColumnarStorage.compressed','False')\n", + " .set('spark.sql.inMemoryColumnarStorage.batchSize','100000')\n", + " .set('spark.sql.execution.arrow.pyspark.fallback.enabled','True')\n", + " .set('spark.sql.execution.arrow.pyspark.enabled','True')\n", + " .set('spark.sql.execution.arrow.maxRecordsPerBatch','100000')\n", + " .set(\"spark.sql.repl.eagerEval.enabled\", True)\n", + " .set(\"spark.sql.legacy.timeParserPolicy\",\"LEGACY\") \n", + " .set(\"spark.sql.session.timeZone\", tz)\n", + " )\n", + "\n", + "sc = SparkContext(conf=conf,master='yarn')\n", + "sc.setLogLevel(\"ERROR\")\n", + "spark = SQLContext(sc)\n", + "time.sleep(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5aed17d6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:30:14.010905Z", + "iopub.status.busy": "2024-12-02T15:30:14.010558Z", + "iopub.status.idle": "2024-12-02T15:30:14.016526Z", + "shell.execute_reply": "2024-12-02T15:30:14.016117Z" + }, + "papermill": { + "duration": 0.012011, + "end_time": "2024-12-02T15:30:14.017774", + "exception": false, + "start_time": "2024-12-02T15:30:14.005763", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "3247c23f", + "metadata": { + "papermill": { + "duration": 0.003742, + "end_time": "2024-12-02T15:30:14.025349", + "exception": false, + "start_time": "2024-12-02T15:30:14.021607", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Sparklog" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6126b5d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:30:14.034277Z", + "iopub.status.busy": "2024-12-02T15:30:14.033993Z", + "iopub.status.idle": "2024-12-02T15:30:16.344751Z", + "shell.execute_reply": "2024-12-02T15:30:16.344056Z" + }, + "papermill": { + "duration": 2.317118, + "end_time": "2024-12-02T15:30:16.346569", + "exception": false, + "start_time": "2024-12-02T15:30:14.029451", + "status": "completed" + }, + "scrolled": false, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%run ~/PAUS/sparklog.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "71b8be80", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:30:16.388630Z", + "iopub.status.busy": "2024-12-02T15:30:16.388161Z", + "iopub.status.idle": "2024-12-02T15:30:16.391534Z", + "shell.execute_reply": "2024-12-02T15:30:16.390964Z" + }, + "papermill": { + "duration": 0.041493, + "end_time": "2024-12-02T15:30:16.392813", + "exception": false, + "start_time": "2024-12-02T15:30:16.351320", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "os.environ[\"https_proxy\"] = proxy\n", + "os.environ[\"http_proxy\"] = proxy" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7689c4d6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:30:16.402338Z", + "iopub.status.busy": "2024-12-02T15:30:16.402063Z", + "iopub.status.idle": "2024-12-02T15:30:16.405000Z", + "shell.execute_reply": "2024-12-02T15:30:16.404449Z" + }, + "papermill": { + "duration": 0.009284, + "end_time": "2024-12-02T15:30:16.406279", + "exception": false, + "start_time": "2024-12-02T15:30:16.396995", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "de3d224d", + "metadata": { + "execution": { 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "appals.show_critical_path_time_breakdown().T" + ] + }, + { + "cell_type": "markdown", + "id": "bde3cd7c", + "metadata": { + "papermill": { + "duration": 0.012768, + "end_time": "2024-12-02T15:31:54.340986", + "exception": false, + "start_time": "2024-12-02T15:31:54.328218", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Compare to previous run" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "888c7084", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:31:54.367369Z", + "iopub.status.busy": "2024-12-02T15:31:54.367111Z", + "iopub.status.idle": "2024-12-02T15:31:54.370128Z", + "shell.execute_reply": "2024-12-02T15:31:54.369683Z" + }, + "papermill": { + "duration": 0.017843, + "end_time": "2024-12-02T15:31:54.371339", + "exception": false, + "start_time": "2024-12-02T15:31:54.353496", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if compare_appid:\n", + " compare_app=Application_Run(comapre_appid,basedir=compare_basedir)\n", + " output=app.compare_app(rapp=compare_app,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + " display(HTML(output))" + ] + }, + { + "cell_type": "markdown", + "id": "bb199e20", + "metadata": { + "papermill": { + "duration": 0.012114, + "end_time": "2024-12-02T15:31:54.396409", + "exception": false, + "start_time": "2024-12-02T15:31:54.384295", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Config compare" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ae5a681e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:31:54.422666Z", + "iopub.status.busy": "2024-12-02T15:31:54.422386Z", + "iopub.status.idle": "2024-12-02T15:31:54.424818Z", + "shell.execute_reply": "2024-12-02T15:31:54.424396Z" + }, + "papermill": { + "duration": 0.01699, + "end_time": "2024-12-02T15:31:54.426008", + "exception": false, + "start_time": "2024-12-02T15:31:54.409018", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if compare_appid:\n", + " display(comp_spark_conf(app_als, compare_app_als))" + ] + }, + { + "cell_type": "markdown", + "id": "6118d3af", + "metadata": { + "papermill": { + "duration": 0.012438, + "end_time": "2024-12-02T15:31:54.451132", + "exception": false, + "start_time": "2024-12-02T15:31:54.438694", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# convert to HTML" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "902ebd2c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:31:54.477825Z", + "iopub.status.busy": "2024-12-02T15:31:54.477584Z", + "iopub.status.idle": "2024-12-02T15:31:54.480969Z", + "shell.execute_reply": "2024-12-02T15:31:54.480577Z" + }, + "papermill": { + "duration": 0.018124, + "end_time": "2024-12-02T15:31:54.482237", + "exception": false, + "start_time": "2024-12-02T15:31:54.464113", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "IPython.notebook.kernel.execute('nb_name = \"' + IPython.notebook.notebook_name + '\"')\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%javascript\n", + "IPython.notebook.kernel.execute('nb_name = \"' + IPython.notebook.notebook_name + '\"')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1f55d7ac", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:31:54.509184Z", + "iopub.status.busy": "2024-12-02T15:31:54.508952Z", + "iopub.status.idle": "2024-12-02T15:31:54.511234Z", + "shell.execute_reply": "2024-12-02T15:31:54.510830Z" + }, + "papermill": { + "duration": 0.016976, + "end_time": "2024-12-02T15:31:54.512431", + "exception": false, + "start_time": "2024-12-02T15:31:54.495455", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# htmlname=nb_name.replace(\"ipynb\",\"html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "45249902", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-02T15:31:54.539435Z", + "iopub.status.busy": "2024-12-02T15:31:54.539198Z", + "iopub.status.idle": "2024-12-02T15:31:54.541484Z", + "shell.execute_reply": "2024-12-02T15:31:54.541077Z" + }, + "papermill": { + "duration": 0.01708, + "end_time": "2024-12-02T15:31:54.542686", + "exception": false, + "start_time": "2024-12-02T15:31:54.525606", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# !jupyter nbconvert --to html ./{nb_name} --no-input --output html/{htmlname} --template classic" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + }, + "papermill": { + "default_parameters": {}, + "duration": 132.778369, + "end_time": "2024-12-02T15:31:57.173789", + "environment_variables": {}, + "exception": null, + "input_path": "2024_12_02_152940_tpch_gluten_application_1733153225851_0001.ipynb", + "output_path": "2024_12_02_152940_tpch_gluten_application_1733153225851_0001.nbconvert.ipynb", + "parameters": { + "appid": "application_1733153225851_0001", + "basedir": "sr213", + "compare_appid": "", + "compare_basedir": "", + "compare_name": "", + "disk": "nvme0n1", + "name": "tpch_gluten", + "nic": "enp61s0f0", + "tz": "Etc/GMT+0" + }, + "start_time": "2024-12-02T15:29:44.395420", + "version": "2.6.0" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "197px", + "left": "2188px", + "top": "111px", + "width": "269px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 9a83994e2dbbd5c97b7c72565924dd0b68022f51 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Mon, 2 Dec 2024 15:52:10 +0000 Subject: [PATCH 06/12] install tsc_freq_khz --- .../workload/benchmark_velox/initialize.ipynb | 139 +++++++++--------- .../benchmark_velox/tpc_workload.ipynb | 2 +- 2 files changed, 70 insertions(+), 71 deletions(-) diff --git a/tools/workload/benchmark_velox/initialize.ipynb b/tools/workload/benchmark_velox/initialize.ipynb index f280d60fdac0..ab17c5f00840 100644 --- a/tools/workload/benchmark_velox/initialize.ipynb +++ b/tools/workload/benchmark_velox/initialize.ipynb @@ -2,34 +2,28 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "heading_collapsed": true - }, + "metadata": {}, "source": [ "# System Setup" ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "**1. Install system dependencies and python packages. Prepare the environment.**" ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "First, install all dependencies and python packages as `root`. Run commands and make sure the installations are successful.\n", "\n", "```bash\n", "apt update\n", "\n", - "apt install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk maven vim pip sysstat gcc-9 libjemalloc-dev nvme-cli curl zip unzip bison flex\n", + "apt install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk maven vim pip sysstat gcc-9 libjemalloc-dev nvme-cli curl zip unzip bison flex linux-tools-common linux-tools-generic linux-tools-`uname -r`\n", "\n", "python3 -m pip install notebook==6.5.2\n", "python3 -m pip install jupyter_server==1.23.4\n", @@ -45,9 +39,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "***Required for Ubuntu***\n", "\n", @@ -73,18 +65,14 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "**2. Format and mount disks**" ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Create a python virtual environment to finish the system setup process:\n", "\n", @@ -101,18 +89,14 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Run script [init_disks.py](./init_disks.py) to format and mount disks. **Be careful when choosing the disks to format.** If you see errors like `device or resource busy`, perhaps the partition has been mounted, you should unmount it first. If you still see this error, reboot the system and try again." ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Exit `venv`:\n", "```bash\n", @@ -122,18 +106,14 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "**3. Create user `sparkuser`**" ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Create user `sparkuser` without password and with sudo priviledge. It's recommended to use one of the disks as the home directory instead of the system drive.\n", "\n", @@ -151,9 +131,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Generate ssh keys for `sparkuser`\n", "\n", @@ -172,9 +150,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Generate ssh keys for `root`, and enable no password ssh from `sparkuser`\n", "\n", @@ -188,9 +164,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Login to `sparkuser` and run the first-time ssh to the `root`\n", "\n", @@ -207,9 +181,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "***Required for Ubuntu***\n", "\n", @@ -222,18 +194,14 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "**4. Configure jupyter notebook**" ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "As `sparkuser`, install python packages\n", "\n", @@ -252,9 +220,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Configure jupyter notebook. Setup password when it prompts\n", "\n", @@ -341,9 +307,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Clone Gluten\n", "\n", @@ -355,9 +319,7 @@ }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "Start jupyter notebook\n", "\n", @@ -546,7 +508,7 @@ "source": [ "for l in clients:\n", " !ssh root@{l} apt update > /dev/null 2>&1\n", - " !ssh root@{l} apt install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk maven vim pip sysstat gcc-9 libjemalloc-dev nvme-cli curl zip unzip bison flex > /dev/null 2>&1" + " !ssh root@{l} apt install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk maven vim pip sysstat gcc-9 libjemalloc-dev nvme-cli curl zip unzip bison flex linux-tools-common linux-tools-generic linux-tools-`uname -r` > /dev/null 2>&1" ] }, { @@ -1713,7 +1675,26 @@ "heading_collapsed": true }, "source": [ - "# Configure startup" + "# Configure monitor & startups" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "!cd ~\n", + "!git clone https://github.com/trailofbits/tsc_freq_khz.git\n", + "\n", + "for l in clients:\n", + " !scp -r tsc_freq_khz {l}:~/\n", + "\n", + "for l in hclients:\n", + " !ssh {l} 'cd tsc_freq_khz && make && sudo insmod ./tsc_freq_khz.ko' >/dev/null 2>&1\n", + " !ssh root@{l} 'dmesg | grep tsc_freq_khz'" ] }, { @@ -2775,14 +2756,18 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "## Install and deploy Trace-Viewer" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "Clone the master branch of project catapult:\n", "```\n", @@ -2856,21 +2841,27 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "## Deploy perf analysis scripts" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "Create a virtualenv to run the perf analaysis scripts:" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "\n", "```bash\n", @@ -2883,7 +2874,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "\n", "We will put all perf analysis notebooks under `$HOME/PAUS`. Create the directory and start the notebook:\n", @@ -2898,7 +2891,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "Package the virtual environment so that it can be distributed to other nodes:\n", "```bash\n", @@ -2909,7 +2904,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "Distribute to the worker nodes:" ] @@ -2917,7 +2914,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "for l in clients:\n", @@ -2943,7 +2942,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "nbTranslate": { "displayLangs": [ diff --git a/tools/workload/benchmark_velox/tpc_workload.ipynb b/tools/workload/benchmark_velox/tpc_workload.ipynb index 5dcb50a8a066..b9b147619d9a 100644 --- a/tools/workload/benchmark_velox/tpc_workload.ipynb +++ b/tools/workload/benchmark_velox/tpc_workload.ipynb @@ -266,7 +266,7 @@ "outputs": [], "source": [ "if analyze_perf:\n", - " test_tpc.run_perf_analysis(disk_dev, nic_dev)" + " test_tpc.run_perf_analysis(disk_dev, nic_dev, proxy)" ] }, { From d2a3c2abdeb1f8016d3553374766ed7bad57d471 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Fri, 6 Dec 2024 05:35:33 +0000 Subject: [PATCH 07/12] add emon --- .gitignore | 2 + .../analysis/perf_analysis_template.ipynb | 56 +- .../workload/benchmark_velox/analysis/run.py | 4 +- .../analysis/run_perf_analysis.sh | 7 +- .../benchmark_velox/analysis/sparklog.ipynb | 732 ++- tools/workload/benchmark_velox/emon.list | 10 + .../workload/benchmark_velox/initialize.ipynb | 122 + .../native_sql_initialize.ipynb | 37 +- .../benchmark_velox/params.yaml.template | 9 + .../benchmark_velox/sample/tpch_q1.html | 2761 ++++++++-- .../sample/tpch_q1.nbconvert.ipynb | 4776 +++++++++++++---- .../benchmark_velox/tpc_workload.ipynb | 13 +- 12 files changed, 6932 insertions(+), 1597 deletions(-) create mode 100644 tools/workload/benchmark_velox/emon.list diff --git a/.gitignore b/.gitignore index 4ea83cbf7e12..f402cb1e83e0 100644 --- a/.gitignore +++ b/.gitignore @@ -98,3 +98,5 @@ dist/ # For Hive metastore_db/ + +.ipynb_checkpoints diff --git a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb index 973474a08a0d..28f1032b5e21 100644 --- a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb +++ b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb @@ -21,13 +21,13 @@ "disk=''\n", "nic=''\n", "tz=''\n", - "basedir=''\n", + "base_dir=''\n", "name=''\n", "proxy=''\n", "\n", - "compare_appid=''\n", - "compare_basedir=''\n", - "compare_name=''" + "comp_appid=''\n", + "comp_base_dir=''\n", + "comp_name=''" ] }, { @@ -144,6 +144,18 @@ "os.environ[\"http_proxy\"] = proxy" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emonmetric=['emon_cpuutil',\n", + " 'emon_cpufreq',\n", + " 'emon_instr_retired',\n", + " 'emon_ipc']" + ] + }, { "cell_type": "code", "execution_count": null, @@ -174,19 +186,21 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "display(HTML(' 5 App info'))\n", - "display(HTML(' 6 Compare to previous run'))\n", - "display(HTML(' 7 Config compare'))" + "display(HTML(' 5 Self app info'))\n", + "display(HTML(f\" 6 Compare to {comp_name}\"))\n", + "display(HTML(' 7 Config compare'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# App info" + "# Self app info" ] }, { @@ -195,7 +209,7 @@ "metadata": {}, "outputs": [], "source": [ - "app=Application_Run(appid, basedir=basedir)\n", + "app=Application_Run(appid, basedir=base_dir)\n", "appals=app.analysis['app']['als']" ] }, @@ -214,7 +228,7 @@ "metadata": {}, "outputs": [], "source": [ - "summary=app.get_summary(disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + "summary=app.get_summary(show_metric=emonmetric,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", "display(summary.style)" ] }, @@ -226,7 +240,7 @@ }, "outputs": [], "source": [ - "app.generate_trace_view(disk_prefix=disk_prefix,nic_prefix=nic_prefix)" + "app.generate_trace_view(showemon=True,show_metric=emonmetric,disk_prefix=disk_prefix,nic_prefix=nic_prefix)" ] }, { @@ -244,7 +258,8 @@ "metadata": {}, "outputs": [], "source": [ - "shuffle_df, dfx=appals.get_shuffle_stat()" + "if not 'vanilla' in name:\n", + " shuffle_df, dfx=appals.get_shuffle_stat()" ] }, { @@ -273,7 +288,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Compare to previous run" + "# Compare to" ] }, { @@ -282,9 +297,9 @@ "metadata": {}, "outputs": [], "source": [ - "if compare_appid:\n", - " compare_app=Application_Run(comapre_appid,basedir=compare_basedir)\n", - " output=app.compare_app(rapp=compare_app,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + "if comp_appid:\n", + " comp_app=Application_Run(comp_appid,basedir=comp_base_dir)\n", + " output=app.compare_app(rapp=comp_app,show_metric=emonmetric,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", " display(HTML(output))" ] }, @@ -301,15 +316,16 @@ "metadata": {}, "outputs": [], "source": [ - "if compare_appid:\n", - " display(comp_spark_conf(app_als, compare_app_als))" + "if comp_appid:\n", + " comp_appals=comp_app.analysis['app']['als']\n", + " display(comp_spark_conf(appals, comp_appals))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# convert to HTML" + "# Convert to HTML" ] }, { diff --git a/tools/workload/benchmark_velox/analysis/run.py b/tools/workload/benchmark_velox/analysis/run.py index ba8f008d3553..7dedb1210201 100644 --- a/tools/workload/benchmark_velox/analysis/run.py +++ b/tools/workload/benchmark_velox/analysis/run.py @@ -16,11 +16,11 @@ import fire import papermill as pm -def exec(inputnb, outputnb, appid, disk, nic, tz, basedir, name, compare_appid='', compare_basedir='', compare_name=''): +def exec(inputnb, outputnb, appid, disk, nic, tz, base_dir, name, comp_appid='', comp_base_dir='', comp_name='', proxy=''): return pm.execute_notebook( inputnb, outputnb, - parameters=dict(appid=appid,disk=disk,nic=nic,tz=tz,basedir=basedir,name=name,compare_appid=compare_appid,compare_basedir=compare_basedir,compare_name=compare_name)) + parameters=dict(appid=appid,disk=disk,nic=nic,tz=tz,base_dir=base_dir,name=name,comp_appid=comp_appid,comp_base_dir=comp_base_dir,comp_name=comp_name,proxy=proxy)) if __name__ == '__main__': fire.Fire(exec) diff --git a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh index 42b391ffa3f4..23ef530b8cef 100755 --- a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh +++ b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh @@ -15,6 +15,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +set -x + SCRIPT_LOCATION=$(dirname $0) PAUS=$HOME/PAUS @@ -115,7 +117,8 @@ then exit 1 fi hadoop fs -cp -f /$COMP_BASEDIR/$COMP_APPID/app.log /history/$COMP_APPID - EXTRA_ARGS="--compare_appid $COMP_APPID --compare_basedir $COMP_BASEDIR --compare_name $COMP_NAME" + EXTRA_ARGS="--comp_appid $COMP_APPID --comp_base_dir $COMP_BASEDIR --comp_name $COMP_NAME" + sed -i "s/# Compare to/# Compare to $COMP_NAME/g" ${nb_name} fi if [ -n "${PROXY}" ] @@ -125,6 +128,6 @@ fi source ~/paus-env/bin/activate -python3 $SCRIPT_LOCATION/run.py --inputnb $nb_name --outputnb ${nb_name0}.nbconvert.ipynb --appid $APPID --disk $DISK --nic $NIC --tz $SPARK_TZ --basedir $BASEDIR --name $NAME $EXTRA_ARGS +python3 $SCRIPT_LOCATION/run.py --inputnb $nb_name --outputnb ${nb_name0}.nbconvert.ipynb --appid $APPID --disk $DISK --nic $NIC --tz $SPARK_TZ --base_dir $BASEDIR --name $NAME $EXTRA_ARGS jupyter nbconvert --to html --no-input ./${nb_name0}.nbconvert.ipynb --output html/${nb_name0}.html --template classic > /dev/null 2>&1 diff --git a/tools/workload/benchmark_velox/analysis/sparklog.ipynb b/tools/workload/benchmark_velox/analysis/sparklog.ipynb index 876b4f1f2c28..86165394cd0b 100644 --- a/tools/workload/benchmark_velox/analysis/sparklog.ipynb +++ b/tools/workload/benchmark_velox/analysis/sparklog.ipynb @@ -272,6 +272,602 @@ " " ] }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# EMON process" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "def get_alias_name(metric,func):\n", + " return metric+\"_\"+func.__name__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "def splits_fill0(x):\n", + " fi=[]\n", + " for l in x:\n", + " li=re.split(r'\\s+',l.strip())\n", + " li=[l.replace(\",\",\"\") for l in li]\n", + " for j in range(len(li),192*4+5):\n", + " li.append('0')\n", + " fi.append(li)\n", + " return iter(fi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):\n", + " from matplotlib import colors\n", + " rng = M - m\n", + " norm = colors.Normalize(m - (rng * low),\n", + " M + (rng * high))\n", + " normed = norm(s.values)\n", + " c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]\n", + " return ['background-color: {:s}'.format(color) for color in c]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "class Emon_Analysis(Analysis):\n", + " def __init__(self,emon_file):\n", + " Analysis.__init__(self,emon_file)\n", + " \n", + " paths=os.path.split(self.file)\n", + " if fs.exists(paths[0]+\"/emonv.txt\"):\n", + " self.totalcores=0\n", + " self.numberofpackages=0\n", + " self.coresperpackage=0\n", + " self.threadsperpackage=0\n", + " self.tsc=0\n", + " self.unc_cha_cnt=0\n", + " self.unc_mdf_cnt=0\n", + " self.unc_imc_cnt=0\n", + " self.unc_cxlcm_cnt=0\n", + " self.unc_cxldp_cnt=0\n", + " self.unc_mchbm_cnt=0\n", + " self.unc_m2hbm_cnt=0\n", + " self.unc_pmem_fc_cnt=0\n", + " self.unc_pmem_mc_cnt=0\n", + " self.unc_m2m_cnt=0\n", + " self.unc_qpi_cnt=0\n", + " self.unc_r3qpi_cnt=0\n", + " self.unc_iio_cnt=0\n", + " self.unc_irp_cnt=0\n", + " self.unc_pcu_cnt=0\n", + " self.unc_ubox_cnt=0\n", + " self.unc_m2pcie_cnt=0\n", + " self.unc_rdt_cnt=0\n", + " with fs.open(paths[0]+\"/emonv.txt\") as f:\n", + " allcnt = f.read().decode('ascii')\n", + " for l in allcnt.split(\"\\n\"):\n", + " if l.startswith(\"number_of_online_processors\"):\n", + " self.totalcores=int(re.split(\" +\",l)[2])\n", + " elif re.search(\"Number of Packages: +(\\d+)\",l):\n", + " self.numberofpackages=int(re.search(\"Number of Packages: +(\\d+)\",l).group(1))\n", + " elif re.search(\"Cores Per Package: +(\\d+)\",l):\n", + " self.coresperpackage=int(re.search(\"Cores Per Package: +(\\d+)\",l).group(1))\n", + " elif re.search(\"Threads Per Package: +(\\d+)\",l):\n", + " self.threadsperpackage=int(re.search(\"Threads Per Package: +(\\d+)\",l).group(1))\n", + " elif re.search(\"TSC Freq +[.]+ +([0-9.]+)\",l):\n", + " self.tsc=int(float(re.search(\"TSC Freq +[.]+ +([0-9.]+)\",l).group(1))*1000000)\n", + " elif l.startswith(\" cha\"):\n", + " self.unc_cha_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" mdf\"):\n", + " self.unc_mdf_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" imc\"):\n", + " self.unc_imc_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" cxlcm\"):\n", + " self.unc_cxlcm_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" cxldp\"):\n", + " self.unc_cxldp_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" mchbm\"):\n", + " self.unc_mchbm_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" m2hbm\"):\n", + " self.unc_m2hbm_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" pmem_fc\"):\n", + " self.unc_pmem_fc_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" pmem_mc\"):\n", + " self.unc_pmem_mc_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" m2m\"):\n", + " self.unc_m2m_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" qpi\"):\n", + " self.unc_qpi_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" r3qpi\"):\n", + " self.unc_r3qpi_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" iio\"):\n", + " self.unc_iio_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" irp\"):\n", + " self.unc_irp_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" pcu\"):\n", + " self.unc_pcu_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" ubox\"):\n", + " self.unc_ubox_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" m2pcie\"):\n", + " self.unc_m2pcie_cnt=int(re.split(\" +\",l)[-1])*2\n", + " elif l.startswith(\" rdt\"):\n", + " self.unc_rdt_cnt=int(re.split(\" +\",l)[-1])*2\n", + " else:\n", + " raise Exception(\"Wrong, no emonv specified\")\n", + " \n", + " self.begin_clk=0\n", + " self.end_clk=0\n", + " \n", + " self.corecnt=self.totalcores\n", + " \n", + " self.emon_metrics=collections.OrderedDict({\n", + " 'emon_cpuutil':{\n", + " 'sum_func':self.cores_sum, \n", + " 'events':{\n", + " 'a':'CPU_CLK_UNHALTED.REF_TSC'\n", + " },\n", + " 'formula':{\n", + " 'cpu%':'a/({:f}*{:d})'.format(self.tsc,self.corecnt)\n", + " },\n", + " 'fmt':lambda l: F.round(l, 3)\n", + " },\n", + " 'emon_cpufreq':{\n", + " 'sum_func':self.cores_sum, \n", + " 'events':{\n", + " 'a':'CPU_CLK_UNHALTED.THREAD',\n", + " 'b':'CPU_CLK_UNHALTED.REF_TSC'\n", + " },\n", + " 'formula':{\n", + " 'cpu freq':'a/b*{:f}'.format(self.tsc/1000000)\n", + " },\n", + " 'fmt':lambda l: F.round(l, 3)\n", + " },\n", + " 'emon_instr_retired':{\n", + " 'sum_func':self.cores_sum, \n", + " 'events':{\n", + " 'a':'INST_RETIRED.ANY'\n", + " },\n", + " 'formula':{\n", + " 'pathlength':'a/1000000000'\n", + " },\n", + " 'fmt':lambda l: F.round(l, 0)\n", + " },\n", + " 'emon_ipc':{\n", + " 'sum_func':self.cores_sum, \n", + " 'events':{\n", + " 'a':'CPU_CLK_UNHALTED.THREAD',\n", + " 'b':'INST_RETIRED.ANY'\n", + " },\n", + " 'formula':{\n", + " 'ipc':'b/a'\n", + " },\n", + " 'fmt':lambda l: F.round(l, 3)\n", + " }\n", + " })\n", + " self.effective_metric=None\n", + " self.appclients=[] # there is no appid and client column\n", + "\n", + " def count_sum(self,collected_cores):\n", + " return F.expr('+'.join(['_{:d}/_2*{:d}'.format(c+3,self.tsc) for c in collected_cores]))\n", + "\n", + " def cores_sum(self,collected_cores):\n", + " return self.count_sum(collected_cores)\n", + "\n", + " def mem_sum(self,collected_cores):\n", + " return self.count_sum(collected_cores)\n", + "\n", + " def pcie_sum(self,collected_cores):\n", + " return self.count_sum([2,3,7,8])\n", + " \n", + " def list_metric(self):\n", + " if self.effective_metric is None:\n", + " self.get_effective_metric()\n", + " for k in self.effective_metric:\n", + " m=self.emon_metrics[k]\n", + " print(k)\n", + " for fk,fm in m['formula'].items():\n", + " print(\" \",fk)\n", + " \n", + " def load_data(self):\n", + " paths=os.path.split(self.file)\n", + " if fs.exists(paths[0]+\"/emon.parquet/_SUCCESS\"):\n", + " self.df=spark.read.parquet(paths[0]+\"/emon.parquet\")\n", + " self.df.cache()\n", + " return\n", + " \n", + " emondata=sc.textFile(self.file)\n", + " emondf=emondata.mapPartitions(splits_fill0).toDF()\n", + " emondf=emondf.withColumn(\"id\", F.monotonically_increasing_id())\n", + " giddf=emondf.where(emondf._1.rlike(\"======\")).selectExpr(\"id as g_id\")\n", + " \n", + " iddf=emondf.where(emondf._1.rlike(\"\\d\\d/\")).selectExpr(\"_1 as r_1\",\"_2 as r_2\",\"id as r_id\")\n", + " jfid=emondf.where(emondf._2.rlike(\"^[1-9][0-9][0-9]+\")).join(iddf,on=[emondf.id>iddf.r_id]).groupBy('id').agg(F.max('r_id').alias('r_id'))\n", + " iddf=iddf.join(jfid,on='r_id',how='left')\n", + " emondf=emondf.where(emondf._2.rlike(\"^[1-9][0-9][0-9]+\")).join(iddf,on='id',how='left')\n", + " \n", + " jfid=emondf.join(giddf,on=[emondf.id>giddf.g_id]).groupBy('id').agg(F.max('g_id').alias('g_id'))\n", + " giddf=giddf.join(jfid,on='g_id',how='left')\n", + " emondf=emondf.join(giddf,on='id',how='inner')\n", + " \n", + " df=emondf\n", + "\n", + " select_list = []\n", + " for idx, c in enumerate(df.columns):\n", + " if idx >= 2 and c.startswith('_'):\n", + " select_list.append(col(c).cast(LongType()).alias(c))\n", + " else:\n", + " select_list.append(col(c))\n", + " df=df.select(select_list)\n", + "\n", + " df=df.withColumn(\"timestamp\",F.unix_timestamp(F.concat_ws(' ','r_1','r_2'),'MM/dd/yyyy HH:mm:ss')*F.lit(1000)+(F.split(F.col('r_2'),'\\.')[1]).astype(IntegerType()))\n", + " df=df.drop(\"r_1\")\n", + " df=df.drop(\"r_2\")\n", + " \n", + " cores=list(range(0,self.totalcores))\n", + " df=df.withColumn('sum',\n", + " F.when(F.col(\"_1\").startswith(\"UNC_IIO\"),self.pcie_sum(cores))\n", + " .otherwise(self.cores_sum(cores)))\n", + " if self.begin_clk>0 and self.end_clk>0:\n", + " df=df.withColumn('valid',((F.col(\"timestamp\")>F.lit(self.begin_clk)) & (F.col(\"timestamp\")0:\n", + " effective_metric.append(k)\n", + " progress.value=progress.value+1\n", + " self.effective_metric=effective_metric\n", + " emondf.unpersist()\n", + " \n", + " def gen_metric(self,emondf, m):\n", + " join_df=None\n", + " for alias,event in m['events'].items():\n", + " if join_df is None:\n", + " join_df=emondf.where(\"_1='{:s}'\".format(event)).select('timestamp','_1','_2','r_id','g_id',*self.appclients,F.col('sum').alias(alias))\n", + " else:\n", + " tdf=emondf.where(\"_1='{:s}'\".format(event)).select('_1','_2','r_id','g_id',*self.appclients,F.col('sum').alias(alias))\n", + " join_dft=join_df.join(tdf.drop('g_id'),on=['r_id',*self.appclients],how='inner')\n", + " if join_dft.count()==0:\n", + " join_df=join_df.join(tdf.drop('r_id'),on=['g_id',*self.appclients],how='inner')\n", + " else:\n", + " join_df=join_dft\n", + " return join_df\n", + "\n", + " \n", + " \n", + " def generate_trace_view_list(self,id=0, **kwargs):\n", + " trace_events=Analysis.generate_trace_view_list(self,id, **kwargs)\n", + " \n", + " cores=list(range(0,self.totalcores))\n", + " \n", + " emondf=self.df\n", + " if 'collected_cores' in kwargs:\n", + " cores=kwargs.get(\"collected_cores\",None)\n", + " emondf=emondf.withColumn('sum',\n", + " F.when(F.col(\"_1\").startswith(\"UNC_IIO\"),self.pcie_sum(cores))\n", + " .otherwise(self.cores_sum(cores)))\n", + " show_metric= kwargs.get('show_metric', None)\n", + " \n", + " if show_metric is None and self.effective_metric is None:\n", + " self.get_effective_metric()\n", + "\n", + " self.effective_metric=show_metric if show_metric is not None else self.effective_metric\n", + " \n", + " emondf=self.df\n", + " \n", + " tid=0\n", + " for k in self.effective_metric:\n", + " m=self.emon_metrics[k]\n", + " join_df=self.gen_metric(emondf,m)\n", + " rstdf=join_df.select(\n", + " F.lit(tid).alias('tid'),\n", + " F.lit(id).alias('pid'),\n", + " F.lit('C').alias('ph'),\n", + " F.lit(k).alias('name'),\n", + " (F.col('timestamp')-F.lit(self.starttime)).alias(\"ts\"),\n", + " F.struct(*[m['fmt'](F.expr(formula)).alias(col_name) for col_name,formula in m['formula'].items() ]).alias('args')\n", + " ).where(F.col(\"ts\").isNotNull()).orderBy('ts')\n", + " trace_events.extend(rstdf.toJSON().collect())\n", + " trace_events.append(json.dumps({\"name\": \"thread_sort_index\",\"ph\": \"M\",\"pid\":id,\"tid\":tid,\"args\":{\"sort_index \":tid}}))\n", + " tid=tid+1 \n", + "\n", + " return trace_events\n", + " \n", + " def show_emon_metric(self,metric,sub_metric,core,draw=True,metric_define=None, **kwargs):\n", + " if self.df==None:\n", + " self.load_data()\n", + " emondf=self.df\n", + " \n", + " showalltime=kwargs.get(\"showalltime\",True)\n", + " \n", + " if not showalltime:\n", + " emondf=emondf.filter(F.col(\"valid\")==F.lit(True))\n", + " \n", + " if metric is None or metric=='':\n", + " for k in self.effective_metric:\n", + " m=self.emon_metrics[k]\n", + " if sub_metric in m['formula']:\n", + " break\n", + " else:\n", + " print(\"can't find metric\",sub_metric)\n", + " return \n", + " else:\n", + " k=metric\n", + " if metric_define is None:\n", + " m= self.emon_metrics[k]\n", + " else:\n", + " m= metric_define[k]\n", + "\n", + " if type(core)==int:\n", + " core=[core,]\n", + " emondf=emondf.withColumn('sum',\n", + " F.when(F.col(\"_1\").startswith(\"UNC_IIO\"),self.pcie_sum(core))\n", + " .otherwise(self.count_sum(core)))\n", + " \n", + " join_df=self.gen_metric(emondf,m)\n", + " \n", + " rstdf=join_df.select(\n", + " F.col('timestamp').alias('ts'),\n", + " m['fmt'](F.expr(m['formula'][sub_metric])).alias(sub_metric),\n", + " 'r_id'\n", + " ).where(F.col(\"timestamp\").isNotNull()).orderBy('timestamp')\n", + " \n", + " metric_sum=rstdf.select(sub_metric).summary().toPandas()\n", + " display(metric_sum)\n", + " \n", + " if draw:\n", + " pddf=rstdf.toPandas()\n", + " pddf['ts']=(pddf['ts']-pddf.loc[0,'ts'])/1000\n", + " fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True,figsize=(30,8),gridspec_kw = {'width_ratios':[1, 5]})\n", + " plt.subplots_adjust(wspace=0.01)\n", + " sns.violinplot(y=sub_metric, data=pddf, ax=axs[0],palette=['g'])\n", + " axs[0].yaxis.grid(True, which='major')\n", + " ax=axs[1]\n", + " ax.stackplot(pddf['ts'], pddf[sub_metric],colors=['bisque'])\n", + " #ymin, ymax = ax.get_ylim()\n", + " ax2 = ax.twinx()\n", + " ax2.set_ylim(ax.get_ylim())\n", + " ax2.axhline(y=float(metric_sum.loc[4,sub_metric]), linewidth=2, color='r')\n", + " ax2.axhline(y=float(metric_sum.loc[5,sub_metric]), linewidth=2, color='r')\n", + " ax2.axhline(y=float(metric_sum.loc[6,sub_metric]), linewidth=2, color='r')\n", + " ax2.axhline(y=float(metric_sum.loc[7,sub_metric]), linewidth=2, color='r')\n", + " ax.set_xlabel('time (s)')\n", + " ax.yaxis.grid(True, which='major')\n", + " plt.show()\n", + " \n", + " hist_elapsedtime=rstdf.select('`{:s}`'.format(sub_metric)).rdd.flatMap(lambda x: x).histogram(15)\n", + " fig, axs = plt.subplots(figsize=(30, 5))\n", + " ax=axs\n", + " binSides, binCounts = hist_elapsedtime\n", + " binSides=[builtins.round(l,2) for l in binSides]\n", + "\n", + " N = len(binCounts)\n", + " ind = numpy.arange(N)\n", + " width = 0.5\n", + "\n", + " rects1 = ax.bar(ind+0.5, binCounts, width, color='b')\n", + "\n", + " ax.set_ylabel('Frequencies')\n", + " ax.set_title(sub_metric)\n", + " ax.set_xticks(numpy.arange(N+1))\n", + " ax.set_xticklabels(binSides)\n", + " return rstdf\n", + " \n", + "\n", + " def gen_reduce_metric(self,metric,core,sub_metric,agg_func):\n", + " if self.df==None:\n", + " self.load_data()\n", + " emondf=self.df\n", + " \n", + " emondf=emondf.where(F.col(\"valid\")==F.lit(True))\n", + " \n", + " k=metric\n", + " m= self.emon_metrics[k]\n", + "\n", + " if type(core)==int:\n", + " core=[core,]\n", + " \n", + " if len(core)http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{self.appid}.json\"))\n", + " \n", + " def getemonmetric(app,**kwargs):\n", + " emondfs=get_emon_parquets([app.appid],app.basedir)\n", + " emons=Emon_Analysis_All(emondfs)\n", + " metric_msg_map={\n", + " 'emon_instr_retired':F.sum\n", + " }\n", + " \n", + " emonmetric=kwargs.get(\"show_metric\",None)\n", + "\n", + " outdf=None\n", + " for k in emonmetric:\n", + " m=emons.emon_metrics[k]\n", + " for fk,fm in m['formula'].items():\n", + " if k not in metric_msg_map:\n", + " metric_msg_map[k]=F.avg\n", + " df=emons.gen_reduce_metric(k,list(range(0,emons.totalcores)),fk,metric_msg_map[k])\n", + " tmpdf=df.groupBy(\"appid\",'client').agg(*[l(\"`{:s}`\".format(fk)).alias(get_alias_name(fk,l)) for l in [metric_msg_map[k]]]).toPandas()\n", + " tmpdf=tmpdf.set_index(\"client\").drop(columns=['appid']).T\n", + " if outdf is None:\n", + " outdf=tmpdf\n", + " else:\n", + " outdf=outdf.append(tmpdf)\n", + " pandas.options.display.float_format = '{:,.2f}'.format\n", + " return outdf\n", " \n", - " def get_sar_stat(self,**kwargs):\n", + " def get_sar_stat(app,**kwargs):\n", " disk_prefix=kwargs.get(\"disk_prefix\",\"dev259\")\n", " nic_prefix = kwargs.get(\"nic_prefix\",[\"'eth3'\",\"'enp24s0f1'\"])\n", - " cpustat=[self.analysis[\"sar\"][l]['sar_cpu']['als'].get_stat() for l in self.clients]\n", + " cpustat=[app.analysis[\"sar\"][l]['sar_cpu']['als'].get_stat() for l in app.clients]\n", " cpustat=reduce(lambda l,r:l.join(r),cpustat)\n", - " diskstat=[self.analysis[\"sar\"][l]['sar_disk']['als'].get_stat(disk_prefix=disk_prefix) for l in self.clients]\n", + " diskstat=[app.analysis[\"sar\"][l]['sar_disk']['als'].get_stat(disk_prefix=disk_prefix) for l in app.clients]\n", " diskstat=reduce(lambda l,r:l.join(r),diskstat)\n", - " memstat=[self.analysis[\"sar\"][l]['sar_mem']['als'].get_stat() for l in self.clients]\n", + " memstat=[app.analysis[\"sar\"][l]['sar_mem']['als'].get_stat() for l in app.clients]\n", " memstat=reduce(lambda l,r:l.join(r),memstat)\n", - " nicstat=[self.analysis[\"sar\"][l]['sar_nic']['als'].get_stat(nic_prefix=nic_prefix) for l in self.clients]\n", + " nicstat=[app.analysis[\"sar\"][l]['sar_nic']['als'].get_stat(nic_prefix=nic_prefix) for l in app.clients]\n", " nicstat=reduce(lambda l,r:l.join(r),nicstat)\n", - " pagestat=[self.analysis[\"sar\"][l]['sar_page']['als'].get_stat() for l in self.clients]\n", + " pagestat=[app.analysis[\"sar\"][l]['sar_page']['als'].get_stat() for l in app.clients]\n", " pagestat=reduce(lambda l,r:l.join(r),pagestat)\n", " pandas.options.display.float_format = '{:,.2f}'.format\n", " return pandas.concat([cpustat,diskstat,memstat,nicstat,pagestat])\n", - " \n", + " \n", " def get_perf_stat(self, **kwargs):\n", " perfstat=[self.analysis[\"sar\"][l]['perfstat']['als'].get_stat() for l in self.clients]\n", " return reduce(lambda l,r: l.join(r), perfstat)\n", @@ -4640,13 +5278,25 @@ " outcut=out[cmpcolumns]\n", " \n", " pdsout=pandas.DataFrame(outcut.sum(),columns=[lrun])\n", - " pdstime=pdsout\n", + " pdstime=pdsout \n", + "\n", + " if app.show_emon:\n", + " emondf=app.getemonmetric(**kwargs)\n", + " def get_agg(emondf):\n", + " aggs=[]\n", + " for x in emondf.index:\n", + " if x.endswith(\"avg\"):\n", + " aggs.append(emondf.loc[x].mean())\n", + " else:\n", + " aggs.append(emondf.loc[x].sum())\n", "\n", - " node=\"\"\n", - " for l in fs.list_status(app.filedir):\n", - " if l['type']==\"DIRECTORY\" and l['pathSuffix']!=\"summary.parquet\":\n", - " node=l['pathSuffix']\n", - " break\n", + " emondf['agg']=aggs\n", + " return emondf\n", + " emondf=get_agg(emondf)\n", + "\n", + " emonsum=emondf[[\"agg\"]]\n", + "\n", + " emonsum.columns=[lrun]\n", "\n", " print(\"sar metric\")\n", " sardf=app.get_sar_stat(**kwargs)\n", @@ -4669,13 +5319,15 @@ "\n", " sarsum.columns=[lrun]\n", " \n", - " # perf stat\n", - " print(\"perf stat metric\")\n", - " perf_stat = app.get_perf_stat(**kwargs)\n", - " perf_stat = get_sar_agg(perf_stat)[['agg']]\n", - " perf_stat.columns=[lrun]\n", - " \n", - " summary=pandas.concat([pdstime,sarsum,perf_stat])\n", + " summary=pandas.concat([pdstime,sarsum])\n", + " if app.show_emon:\n", + " summary=pandas.concat([summary,emonsum])\n", + " elif app.show_perfstat:\n", + " print(\"perf stat metric\")\n", + " perf_stat = app.get_perf_stat(**kwargs)\n", + " perf_stat = get_sar_agg(perf_stat)[['agg']]\n", + " perf_stat.columns=[lrun]\n", + " summary=pandas.concat([summary,perf_stat])\n", " \n", " df_sum=spark.createDataFrame(summary.T.reset_index())\n", " for c in df_sum.columns:\n", @@ -4713,6 +5365,33 @@ " pdsout2=pandas.DataFrame(out2cut.sum(),columns=[rrun])\n", " pdstime=pdsout.join(pdsout2)\n", "\n", + " showemon=app.show_emon and app2.show_emon\n", + " if showemon:\n", + " print(\"emon metric\")\n", + "\n", + " emondf=app.getemonmetric(**kwargs)\n", + " emondf2=app2.getemonmetric(**kwargs)\n", + " #in case we comare with two clsuter\n", + " emondf.columns=emondf2.columns\n", + " def get_agg(emondf):\n", + " aggs=[]\n", + " for x in emondf.index:\n", + " if x.endswith(\"avg\"):\n", + " aggs.append(emondf.loc[x].mean())\n", + " else:\n", + " aggs.append(emondf.loc[x].sum())\n", + "\n", + " emondf['agg']=aggs\n", + " return emondf\n", + " emondf=get_agg(emondf)\n", + " emondf2=get_agg(emondf2)\n", + "\n", + " emoncolumns=emondf.columns\n", + " emoncmp=emondf.join(emondf2,lsuffix='_'+lrun,rsuffix='_'+rrun)\n", + " emonsum=emoncmp[[\"agg_\"+lrun,\"agg_\"+rrun]]\n", + "\n", + " emonsum.columns=[lrun,rrun]\n", + "\n", " print(\"sar metric\")\n", " sardf=app.get_sar_stat(**kwargs)\n", " sardf2=app2.get_sar_stat(**kwargs)\n", @@ -4741,6 +5420,8 @@ " sarsum.columns=[lrun,rrun]\n", " \n", " summary=pandas.concat([pdstime,sarsum])\n", + " if showemon:\n", + " summary=pandas.concat([summary,emonsum])\n", " \n", " summary[\"diff\"]=numpy.where(summary[rrun] > 0, summary[lrun]/summary[rrun]-1, 0)\n", " \n", @@ -4804,6 +5485,13 @@ " df3[l][j]=[sarcmp[l+\"_\"+lrun][j],sarcmp[l+\"_\"+rrun][j],sarcmp[l+\"_\"+lrun][j]/sarcmp[l+\"_\"+rrun][j]-1]\n", " display_compare(df3,sarcolumns)\n", "\n", + " if showemon:\n", + " df2 = pandas.DataFrame('', index=emoncmp.index, columns=emoncolumns)\n", + " for l in emoncolumns:\n", + " for j in df2.index:\n", + " df2[l][j]=[emoncmp[l+\"_\"+lrun][j],emoncmp[l+\"_\"+rrun][j],emoncmp[l+\"_\"+lrun][j]/emoncmp[l+\"_\"+rrun][j]-1]\n", + " display_compare(df2,emoncolumns)\n", + "\n", " print(\"time breakdown\")\n", " ################################ time breakdown ##################################################################################################\n", " timel=appals.show_time_metric(plot=False)\n", diff --git a/tools/workload/benchmark_velox/emon.list b/tools/workload/benchmark_velox/emon.list new file mode 100644 index 000000000000..552bfefca0f1 --- /dev/null +++ b/tools/workload/benchmark_velox/emon.list @@ -0,0 +1,10 @@ +-q -c -experimental -t0.5 -l100000 -u +-C ( + +INST_RETIRED.ANY +CPU_CLK_UNHALTED.REF_TSC +CPU_CLK_UNHALTED.THREAD +MSR_EVENT:msr=0x611:type=FREERUN:scope=PACKAGE + +) + diff --git a/tools/workload/benchmark_velox/initialize.ipynb b/tools/workload/benchmark_velox/initialize.ipynb index ab17c5f00840..968f2dbe5534 100644 --- a/tools/workload/benchmark_velox/initialize.ipynb +++ b/tools/workload/benchmark_velox/initialize.ipynb @@ -1713,6 +1713,10 @@ "end=$(($(nproc) - 1))\n", "for i in $(seq 0 $end); do echo performance > /sys/devices/system/cpu/cpu$i/cpufreq/scaling_governor; done\n", "for file in $(find /sys/devices/system/cpu/cpu*/power/energy_perf_bias); do echo \"0\" > $file; done\n", + "\n", + "if [ -d /home/{user}/sep_installed ]; then\n", + " /home/{user}/sep_installed/sepdk/src/insmod-sep -g {user}\n", + "fi\n", "'''\n", "\n", "with open('/tmp/tmpstartup', 'w') as f:\n", @@ -1742,6 +1746,124 @@ " !ssh $l \"sudo systemctl status mystartup.service\"" ] }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "## Install Emon" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + " Get the latest offline installer from [link](https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler-download.html?operatingsystem=linux&linux-install-type=offline) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "offline_installer = 'https://registrationcenter-download.intel.com/akdlm/IRC_NAS/e7797b12-ce87-4df0-aa09-df4a272fc5d9/intel-vtune-2025.0.0.1130_offline.sh'\n", + "for l in hclients:\n", + " !ssh {l} \"wget {offline_installer} -q && chmod +x intel-vtune-2025.0.0.1130_offline.sh\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "for l in hclients:\n", + " !ssh {l} \"sudo ./intel-vtune-2025.0.0.1130_offline.sh -a -c -s --eula accept\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "for l in hclients:\n", + " !ssh {l} \"sudo chown -R {user}:{user} /opt/intel/oneapi/vtune/ && rm -f sep_installed && ln -s /opt/intel/oneapi/vtune/latest sep_installed\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "for l in hclients:\n", + " !ssh {l} \"cd sep_installed/sepdk/src/; echo -e \\\"\\\\n\\\\n\\\\n\\\" | ./build-driver\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "for l in hclients:\n", + " !ssh root@{l} \"/home/{user}/sep_installed/sepdk/src/rmmod-sep && /home/{user}/sep_installed/sepdk/src/insmod-sep -g {user}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "for l in hclients:\n", + " !ssh {l} \"source /home/{user}/sep_installed/sep_vars.sh > /dev/null 2>&1; emon -v | head -n 1\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "for l in hclients:\n", + " !ssh {l} 'echo \"source /home/{user}/sep_installed/sep_vars.sh > /dev/null 2>&1\" >> ~/.bashrc'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "for c in hclients:\n", + " !ssh {c} 'tail -n1 ~/.bashrc'" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index 1cdab3a47e02..2e63640c40b7 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -320,6 +320,8 @@ " out=!ssh $l \"ps aux | grep -w perf | grep -v grep | tr -s ' ' | cut -d' ' -f2\"\n", " for x in out:\n", " !ssh root@$l \"kill $x > /dev/null 2>&1\"\n", + " for l in clients:\n", + " !ssh $l \"emon -stop > /dev/null 2>&1\"\n", "\n", "def killnumactl(clients):\n", " for l in clients:\n", @@ -327,7 +329,7 @@ " for x in out:\n", " !ssh $l \"kill $x > /dev/null 2>&1\"\n", "\n", - "def startmonitor(clients,appid,**kwargs):\n", + "def startmonitor(clients, appid, collect_emon, **kwargs):\n", " local_profile_dir=os.path.join(home, 'profile')\n", " prof=os.path.join(local_profile_dir, appid)\n", " !mkdir -p {prof}\n", @@ -337,6 +339,11 @@ " \n", " killsar(clients)\n", " \n", + " if collect_emon:\n", + " !cp -f {emon_list} {home}/emon.list\n", + " for l in clients:\n", + " !scp {home}/emon.list {l}:{home}/emon.list > /dev/null 2>&1\n", + " \n", " perfsyscalls=kwargs.get(\"collect_perf_syscall\",None)\n", " \n", " for l in clients:\n", @@ -345,9 +352,12 @@ " !ssh {l} mkdir -p {prof_client}\n", " !ssh {l} \"sar -o {prof_client}/sar.bin -r -u -d -B -n DEV 1 >/dev/null 2>&1 &\"\n", " !ssh root@{l} \"jps | grep CoarseGrainedExecutorBackend | cut -d' ' -f 1 | xargs -I % bash -c '(cat /proc/%/status >> {prof_client}/%.stat; cat /proc/%/io >> {prof_client}/%.stat)'\"\n", - " !ssh root@{l} \"perf stat -e 'instructions,cycles,cpu_clk_unhalted.thread,cpu_clk_unhalted.ref_tsc' -a -I 500 -o {prof_client}/perfstat.txt >/dev/null 2>&1 & \"\n", - " !ssh {l} \"cat /sys/devices/system/cpu/cpu0/tsc_freq_khz | xargs -I% echo %000 > {prof_client}/tsc_freq 2>/dev/null &\"\n", - " !ssh {l} \"lscpu | grep '^CPU(s):' | cut -d ':' -f 2 | tr -d ' ' > {prof_client}/totalcores 2>/dev/null &\"\n", + " if collect_emon:\n", + " !ssh {l} \"emon -i {home}/emon.list -f {prof_client}/emon.rst >/dev/null 2>&1 & \"\n", + " else:\n", + " !ssh root@{l} \"perf stat -e 'instructions,cycles,cpu_clk_unhalted.thread,cpu_clk_unhalted.ref_tsc' -a -I 500 -o {prof_client}/perfstat.txt >/dev/null 2>&1 & \"\n", + " !ssh {l} \"cat /sys/devices/system/cpu/cpu0/tsc_freq_khz | xargs -I% echo %000 > {prof_client}/tsc_freq 2>/dev/null &\"\n", + " !ssh {l} \"lscpu | grep '^CPU(s):' | cut -d ':' -f 2 | tr -d ' ' > {prof_client}/totalcores 2>/dev/null &\"\n", " if kwargs.get(\"collect_pid\",False):\n", " !ssh {l} \"jps | grep CoarseGrainedExecutorBackend | head -n 1 | cut -d' ' -f 1 | xargs -I % pidstat -h -t -p % 1 > {prof_client}/pidstat.out 2>/dev/null &\"\n", " !ssh root@{l} 'cat /proc/uptime | cut -d\" \" -f 1 | xargs -I ^ date -d \"- ^ seconds\" +%s.%N' > $prof/$l/uptime.txt\n", @@ -369,7 +379,7 @@ " print(\"Missing argument: hbm_nodes. e.g. hbm_nodes = list(range(8,16))\")\n", " return prof\n", "\n", - "def stopmonitor(clients, sc, appid, **kwargs):\n", + "def stopmonitor(clients, sc, appid, collect_emon, **kwargs):\n", " %cd ~\n", " \n", " local_profile_dir=os.path.join(home, 'profile')\n", @@ -386,6 +396,8 @@ " prof_client=os.path.join(prof, l)\n", " !ssh {l} \"sar -f {prof_client}/sar.bin -r > {prof_client}/sar_mem.sar;sar -f {prof_client}/sar.bin -u > {prof_client}/sar_cpu.sar;sar -f {prof_client}/sar.bin -d -p > {prof_client}/sar_disk.sar;sar -f {prof_client}/sar.bin -n DEV > {prof_client}/sar_nic.sar;sar -f {prof_client}/sar.bin -B > {prof_client}/sar_page.sar;\" \n", " !ssh root@{l} \"jps | grep CoarseGrainedExecutorBackend | cut -d' ' -f 1 | xargs -I % bash -c '(cat /proc/%/status >> {prof_client}/%.stat; cat /proc/%/io >> {prof_client}/%.stat)'\"\n", + " if collect_emon:\n", + " !ssh {l} \"source ~/sep_install/sep_vars.sh>/dev/null 2>&1; emon -v \" > {prof}/{l}/emonv.txt\n", " !ssh {l} \"sar -V \" > {prof_client}/sarv.txt\n", " !ssh {l} \"test -f {prof_client}/perfstat.txt && head -n 1 {prof_client}/perfstat.txt > {prof_client}/perfstarttime\"\n", " if l!= socket.gethostname():\n", @@ -796,6 +808,7 @@ " self.table_loaded = False\n", " self.result = {}\n", " self.stopped = False\n", + " self.collect_emon = False\n", " self.perf_html = ''\n", " self.finished_nb = ''\n", " for l in os.listdir(self.tpc_query_path):\n", @@ -805,13 +818,15 @@ " self.query_ids = sorted(self.query_infos.keys(), key=lambda x: str(len(x))+x if x[-1] != 'a' and x[-1] != 'b' else str(len(x)-1) + x)\n", " print(\"http://{}:18080/history/{}/jobs/\".format(local_ip, self.sc.applicationId))\n", " \n", - " def start_monitor(self, clients, **kw):\n", - " startmonitor(clients, self.appid, **kw)\n", + " def start_monitor(self, clients, emon_list='', **kw):\n", + " if emon_list:\n", + " self.collect_emon = True\n", + " startmonitor(clients, self.appid, self.collect_emon, **kw)\n", " \n", " def stop_monitor(self, clients, **kw):\n", " if self.stopped:\n", " return\n", - " stopmonitor(clients, self.sc, self.appid, **kw)\n", + " stopmonitor(clients, self.sc, self.appid, self.collect_emon, **kw)\n", " if self.server:\n", " output_nb = f'{self.nb_name[:-6]}-{self.appid}.ipynb'\n", " if output_nb.startswith(cwd):\n", @@ -819,7 +834,7 @@ " self.finished_nb = f\"http://{localhost}:8888/tree/{output_nb}\"\n", " self.stopped = True\n", "\n", - " def run_perf_analysis(self, disk_dev, nic_dev, proxy):\n", + " def run_perf_analysis(self, disk_dev, nic_dev, proxy, comp_appid, comp_base_dir, comp_name):\n", " if not self.server:\n", " return\n", "\n", @@ -833,6 +848,8 @@ " nic=','.join(nic_dev)\n", "\n", " command =' '.join(['bash', run_script, '--ts', ts, '--base-dir', self.base_dir, '--name', name, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt(), '--proxy', proxy if proxy != '' else \"''\"])\n", + " if comp_appid:\n", + " command += f' --comp-appid {comp_appid} --comp-base-dir {comp_base_dir} --comp-name {comp_name}'\n", " print(command)\n", "\n", " # Block if running on local cluster.\n", @@ -841,7 +858,7 @@ " else:\n", " !ssh {self.server} \"{command} > /dev/null 2>&1 &\"\n", "\n", - " self.perf_html=f'http://{self.server}:8888/view/{self.base_dir}/html/{ts}_{name}_{self.appid}.html'\n", + " self.perf_html=f'http://{self.server}:8889/view/{self.base_dir}/html/{ts}_{name}_{self.appid}.html'\n", " display(HTML(f'{self.perf_html}'))\n", " \n", " def load_table(self, table):\n", diff --git a/tools/workload/benchmark_velox/params.yaml.template b/tools/workload/benchmark_velox/params.yaml.template index 1c70e428bc99..bdb604cfef97 100644 --- a/tools/workload/benchmark_velox/params.yaml.template +++ b/tools/workload/benchmark_velox/params.yaml.template @@ -29,9 +29,18 @@ base_dir: emr # Proxy used to connect to server for perf analysis. proxy: '' +# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable. +# Supported emon events on platform can be verified via `emon -i emon.list` +emon_list: /home/sparkuser/ipython/emon.list + # Whether to upload profile to perf analysis server and run perf analysis scripts. Only takes effect if server is set. analyze_perf: True +# Specify app info to compare for perf analysis +comp_appid: '' +comp_base_dir: '' +comp_name: '' + # Select workload. Can be either 'tpch' or 'tpcds'. workload: tpch diff --git a/tools/workload/benchmark_velox/sample/tpch_q1.html b/tools/workload/benchmark_velox/sample/tpch_q1.html index 57c0f088edbd..c401c35ec87c 100644 --- a/tools/workload/benchmark_velox/sample/tpch_q1.html +++ b/tools/workload/benchmark_velox/sample/tpch_q1.html @@ -3,7 +3,7 @@ -2024_12_02_152940_tpch_gluten_application_1733153225851_0001.nbconvert @@ -13092,27 +13092,27 @@
-
+

Parameters

-
+
-
+
-
+

start analysis cluster and run

-
+
-
+
-
+
@@ -13124,14 +13124,14 @@

start analysis cluster and run
-
24/12/02 15:29:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
+
24/12/06 05:53:36 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

-
24/12/02 15:29:48 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
-24/12/02 15:29:48 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
+
24/12/06 05:53:37 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
+24/12/06 05:53:37 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
@@ -13145,7 +13145,7 @@

start analysis cluster and run +
@@ -13160,13 +13160,13 @@

start analysis cluster and run
+

Sparklog

-
+
@@ -13178,47 +13178,49 @@

Sparklog

-
+
-
+
-
+
-
+
+
+

Content

-
+ -
+
-

App info

+

Self app info

-
+
-
+
-
load data  /sr213/application_1733153225851_0001/app.log
+
load data  /sr213/application_1733153225851_0048/app.log
@@ -13234,12 +13236,7 @@

App info

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http://sr213:18080/history/application_1733153225851_0048
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[Stage 47:(185 + 4) / 200][Stage 48:>(73 + 4) / 200][Stage 49:>  (0 + 8) / 16]
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[Stage 46:(196 + 4) / 200][Stage 47:(105 + 0) / 200][Stage 48:>(86 + 4) / 200]

[Stage 47:(121 + 4) / 200][Stage 48:(105 + 0) / 200][Stage 49:> (0 + 12) / 16]
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@@ -13346,7 +13343,7 @@

App info

appid -application_1733153225851_0001 +application_1733153225851_0048 executor.instances @@ -13386,7 +13383,7 @@

App info

runtime -17.8 +17.65 disk spilled @@ -13410,7 +13407,7 @@

App info

task run time -6.73 +6.79 ser_time @@ -13422,7 +13419,7 @@

App info

gc_time -0.04 +0.03 input read @@ -13430,23 +13427,23 @@

App info

acc_task_time -14.14 +13.99 file read size -5,944.5 +5,951.35 file write size -21.74 +24.52 disk read size -4.95 +5.05 disk write size -12.38 +15.31 disk cancel size @@ -13458,7 +13455,7 @@

App info

-
{'appid': 'application_1733153225851_0001',
+
{'appid': 'application_1733153225851_0048',
  'executor.instances': 4,
  'executor.cores': 4,
  'shuffle.partitions': 32,
@@ -13468,259 +13465,424 @@ 
App info

  'Speculative Tasks': 0,
  'Speculative Killed Tasks': 0,
  'Speculative Stage': 0,
- 'runtime': 17.8,
+ 'runtime': 17.65,
  'disk spilled': 0.0,
  'memspilled': 0.0,
  'local_read': 0.0,
  'remote_read': 0.0,
  'shuffle_write': 0.0,
- 'task run time': 6.73,
+ 'task run time': 6.79,
  'ser_time': 0.0,
  'f_wait_time': 0.0,
- 'gc_time': 0.04,
+ 'gc_time': 0.03,
  'input read': 22.54,
- 'acc_task_time': 14.14,
- 'file read size': 5944.5,
- 'file write size': 21.74,
- 'disk read size': 4.95,
- 'disk write size': 12.38,
+ 'acc_task_time': 13.99,
+ 'file read size': 5951.35,
+ 'file write size': 24.52,
+ 'disk read size': 5.05,
+ 'disk write size': 15.31,
  'disk cancel size': 0.0}
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[Stage 93:(151 + 4) / 200][Stage 94:>(66 + 8) / 200][Stage 95:>  (1 + 4) / 16]
-
-
sar metric
-
+
+
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perf stat metric
+
/sr213/application_1733153225851_0048/sr217/emon.parquet is not found, trying to load data ...
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sar metric
+
- +
- + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + +
 application_1733153225851_0001application_1733153225851_0048
runtime17.800000runtime17.650000
disk spilled0.000000disk spilled0.000000
shuffle_write0.000000shuffle_write0.000000
f_wait_time0.000000f_wait_time0.000000
input read22.540000input read22.540000
acc_task_time14.140000acc_task_time13.990000
output rows1.180000output rows1.180000
%user>90%0.931034%user>90%0.931034
%kernel>10%0.965517%kernel>10%0.965517
%iowait>10%0.620690%iowait>10%0.620690
avg %user41.233793avg %user41.216207
avg %system4.608621avg %system4.514138
avg %iowait0.623793avg %iowait0.743793
avg disk util33.448276avg disk util32.206897
time more than 90%0.000000time more than 90%0.000000
total read (G)5.432129total read (G)5.388613
total write (G)0.994891total write (G)1.121773
avg read bw (MB/s)191.810354avg read bw (MB/s)190.273771
avg write bw (MB/s)35.129954avg write bw (MB/s)39.610183
read bw %75402.394531read bw %75411.578125
read bw %95479.738281read bw %95484.542969
read bw max480.722656read bw max510.351562
time_rd_morethan_950.034483time_rd_morethan_950.034483
write bw %751.074219write bw %751.074219
write bw %9535.035156write bw %95165.687500
write bw max945.855469write bw max812.511719
time_wr_morethan_950.034483time_wr_morethan_950.034483
cached mean97.137931cached mean93.896552
cached 75%152.000000cached 75%145.000000
cached max188.000000cached max188.000000
used mean573.379310used mean834.000000
used 75%593.000000used 75%852.000000
used max597.000000used max859.000000
rx MB/s 75%0.000000rx MB/s 75%0.000000
rx MB/s 95%0.000000rx MB/s 95%0.000000
rx MB/s 99%0.000000rx MB/s 99%0.000000
pgin mean191.965517pgin mean190.206897
pgin 75%402.000000pgin 75%412.000000
pgin max480.000000pgin max509.000000
pgout mean35.103448pgout mean40.965517
pgout 75%1.000000pgout 75%1.000000
pgout max947.000000pgout max840.000000
fault mean117681.655172fault mean117653.310345
fault 75%193074.000000fault 75%205151.000000
fault max287177.000000fault max256538.000000
ipc1.144464cpu%_avg0.448817
instructions962.586404cpu freq_avg3241.915617
cpu_freq3.225843pathlength_sum1933.000000
cpu%7.480452ipc_avg1.137983
@@ -13729,17 +13891,22 @@

App info

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[Stage 288:==============>                                         (3 + 9) / 12]

[Stage 288:==========================================>             (9 + 3) / 12]
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[Stage 341:>                                                        (0 + 1) / 1]
@@ -13754,156 +13921,266 @@

App info

-
http://sr213:1088/tracing_examples/trace_viewer.html#/tracing/test_data/application_1733153225851_0001.json
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[Stage 388:>                                                        (0 + 1) / 1]
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[Stage 396:>                                                        (0 + 1) / 1]
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{'sr217': 200}
+
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[Stage 490:===================>                                     (1 + 2) / 3]
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+
http://sr213:1088/tracing_examples/trace_viewer.html#/tracing/test_data/application_1733153225851_0048.json
-
+
-
gluten tpch_power 663d4f
+
gluten tpch_power 6600a1
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@@ -13911,62 +14188,67 @@

App info
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application_1733153225851_0001
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application_1733153225851_0048
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http://sr213:18080/history/application_1733153225851_0001
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http://sr213:18080/history/application_1733153225851_0048
query time
- +
- - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + @@ -13995,28 +14277,28 @@

App info

- - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + +
 runtimedisk spilledmemspilledlocal_readremote_readshuffle_writedeser_timerun_timeser_timef_wait_timegc_timepeak_memqueryidinput readacc_task_timestagesoutput rowsexecutorscore/exectask.cpusparallelismruntimedisk spilledmemspilledlocal_readremote_readshuffle_writedeser_timerun_timeser_timef_wait_timegc_timepeak_memqueryidinput readacc_task_timestagesoutput rowsexecutorscore/exectask.cpusparallelism
real_queryid
117.8000000.0000000.0000000.0000000.0000000.0000000.3300006.7300000.0000000.0000000.0400001.340000822.54000014.140000[ 8 10 12 15]1.18000044132117.6500000.0000000.0000000.0000000.0000000.0000000.2000006.7900000.0000000.0000000.0300001.340000822.54000013.990000[ 8 10 12 15]1.18000044132
@@ -14027,94 +14309,94 @@

App info

- +
- - + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + +
 0101
AQEShuffleRead02AQEShuffleRead02
AdaptiveSparkPlan01AdaptiveSparkPlan01
ColumnarExchange02ColumnarExchange02
FilterExecTransformer01FilterExecTransformer01
FlushableHashAggregateExecTransformer01FlushableHashAggregateExecTransformer01
InputAdapter02InputAdapter02
InputIteratorTransformer02InputIteratorTransformer02
ProjectExecTransformer02ProjectExecTransformer02
RegularHashAggregateExecTransformer01RegularHashAggregateExecTransformer01
Scan parquet 01Scan parquet 01
ShuffleQueryStage02ShuffleQueryStage02
SortExecTransformer01SortExecTransformer01
VeloxColumnarToRow01VeloxColumnarToRow01
VeloxResizeBatches02VeloxResizeBatches02
@@ -14125,25 +14407,25 @@

App info

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- + - - + + - - + +
 11
ColumnarExchange0.000000ColumnarExchange0.000000
VeloxResizeBatches0.000000VeloxResizeBatches0.000000
@@ -14154,56 +14436,56 @@

App info

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 11
ColumnarExchange0.000000ColumnarExchange0.000000
FlushableHashAggregateExecTransformer0.000000FlushableHashAggregateExecTransformer0.000000
InputIteratorTransformer0.000000InputIteratorTransformer0.000000
ProjectExecTransformer591.600000ProjectExecTransformer591.600000
RegularHashAggregateExecTransformer0.000000RegularHashAggregateExecTransformer0.000000
Scan parquet 591.600000Scan parquet 591.600000
SortExecTransformer0.000000SortExecTransformer0.000000
VeloxColumnarToRow0.000000VeloxColumnarToRow0.000000
VeloxResizeBatches0.000000VeloxResizeBatches0.000000
@@ -14211,18 +14493,18 @@

App info

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-No description has been provided for this image +No description has been provided for this image
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@@ -14249,20 +14531,20 @@

App info

-40%_time of scan and filter -6.95 +44%_time of scan and filter +7.53 -35%_time of project -6.02 +36%_time of project +6.13 -21%_not_counted -3.55 +16%_not_counted +2.69 3%_idle -0.54 +0.55 0%_time of input iterator @@ -14301,19 +14583,19 @@

App info

0.00 -0%_time to compress +0%_time to convert 0.00 -0%_time to spill +0%_time to compress 0.00 -0%_time to decompress +0%_time to spill 0.00 -0%_time to convert +0%_time to decompress 0.00 @@ -14322,41 +14604,1784 @@

App info

-No description has been provided for this image +No description has been provided for this image
-
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Compare to previous run

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Compare to vanilla

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load data  /sr213/application_1733153225851_0029/app.log
+
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Config compare

+
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emon metric
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[Stage 1319:>                                                       (0 + 3) / 3]
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[Stage 1319:==================>                                     (1 + 2) / 3]
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sar metric
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time breakdown
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 application_1733153225851_0029application_1733153225851_0048diff
runtime132.1417.65648.67%
shuffle_write0.000.000.00%
f_wait_time0.000.000.00%
input read22.5422.540.00%
acc_task_time128.0113.99815.01%
output rows1.791.1851.69%
%user>90%0.990.935.91%
%kernel>10%0.990.972.85%
%iowait>10%0.310.62-49.30%
avg %user82.1141.2299.21%
avg %system6.104.5135.11%
avg %iowait0.170.74-76.60%
avg disk util7.1332.21-77.85%
time more than 90%0.000.000.00%
total read (G)5.245.39-2.75%
total write (G)0.021.12-97.81%
avg read bw (MB/s)37.52190.27-80.28%
avg write bw (MB/s)0.1839.61-99.55%
read bw %7559.27411.58-85.60%
read bw %95173.05484.54-64.29%
read bw max236.70510.35-53.62%
time_rd_morethan_950.050.0341.96%
write bw %750.071.07-93.45%
write bw %951.23165.69-99.25%
write bw max1.70812.51-99.79%
time_wr_morethan_950.000.03-100.00%
cached mean88.3393.90-5.93%
cached 75%132.00145.00-8.97%
cached max160.00188.00-14.89%
used mean2,060.73834.00147.09%
used 75%2,343.00852.00175.00%
used max2,346.00859.00173.11%
rx MB/s 75%0.000.000.00%
rx MB/s 95%0.000.000.00%
rx MB/s 99%0.000.000.00%
pgin mean37.37190.21-80.35%
pgin 75%59.00412.00-85.68%
pgin max352.00509.00-30.84%
pgout mean0.1340.97-99.68%
pgout 75%0.001.00-100.00%
pgout max2.00840.00-99.76%
fault mean952,586.87117,653.31709.66%
fault 75%1,426,717.00205,151.00595.45%
fault max2,628,392.00256,538.00924.56%
cpu%_avg0.880.4596.45%
cpu freq_avg3,460.223,241.926.73%
pathlength_sum17,960.001,933.00829.13%
ipc_avg1.271.1411.30%
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 runtimeshuffle_writef_wait_timeinput readacc_task_timeoutput rows
real_queryid      
1 +
132.14
+
17.65
+
648.67%
+		 +
0.00
+
0.00
+
nan%
+		 +
0.00
+
0.00
+
nan%
+		 +
22.54
+
22.54
+
0.00%
+		 +
128.01
+
13.99
+
815.01%
+		 +
1.79
+
1.18
+
51.69%
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 sr217agg
0  
%user>90% +
0.99
+
0.93
+
5.91%
+		 +
0.99
+
0.93
+
5.91%
+
%kernel>10% +
0.99
+
0.97
+
2.85%
+		 +
0.99
+
0.97
+
2.85%
+
%iowait>10% +
0.31
+
0.62
+
-49.30%
+		 +
0.31
+
0.62
+
-49.30%
+
avg %user +
82.11
+
41.22
+
99.21%
+		 +
82.11
+
41.22
+
99.21%
+
avg %system +
6.10
+
4.51
+
35.11%
+		 +
6.10
+
4.51
+
35.11%
+
avg %iowait +
0.17
+
0.74
+
-76.60%
+		 +
0.17
+
0.74
+
-76.60%
+
avg disk util +
7.13
+
32.21
+
-77.85%
+		 +
7.13
+
32.21
+
-77.85%
+
time more than 90% +
0.00
+
0.00
+
nan%
+		 +
0.00
+
0.00
+
nan%
+
total read (G) +
5.24
+
5.39
+
-2.75%
+		 +
5.24
+
5.39
+
-2.75%
+
total write (G) +
0.02
+
1.12
+
-97.81%
+		 +
0.02
+
1.12
+
-97.81%
+
avg read bw (MB/s) +
37.52
+
190.27
+
-80.28%
+		 +
37.52
+
190.27
+
-80.28%
+
avg write bw (MB/s) +
0.18
+
39.61
+
-99.55%
+		 +
0.18
+
39.61
+
-99.55%
+
read bw %75 +
59.27
+
411.58
+
-85.60%
+		 +
59.27
+
411.58
+
-85.60%
+
read bw %95 +
173.05
+
484.54
+
-64.29%
+		 +
173.05
+
484.54
+
-64.29%
+
read bw max +
236.70
+
510.35
+
-53.62%
+		 +
236.70
+
510.35
+
-53.62%
+
time_rd_morethan_95 +
0.05
+
0.03
+
41.96%
+		 +
0.05
+
0.03
+
41.96%
+
write bw %75 +
0.07
+
1.07
+
-93.45%
+		 +
0.07
+
1.07
+
-93.45%
+
write bw %95 +
1.23
+
165.69
+
-99.25%
+		 +
1.23
+
165.69
+
-99.25%
+
write bw max +
1.70
+
812.51
+
-99.79%
+		 +
1.70
+
812.51
+
-99.79%
+
time_wr_morethan_95 +
0.00
+
0.03
+
-100.00%
+		 +
0.00
+
0.03
+
-100.00%
+
cached mean +
88.33
+
93.90
+
-5.93%
+		 +
88.33
+
93.90
+
-5.93%
+
cached 75% +
132.00
+
145.00
+
-8.97%
+		 +
132.00
+
145.00
+
-8.97%
+
cached max +
160.00
+
188.00
+
-14.89%
+		 +
160.00
+
188.00
+
-14.89%
+
used mean +
2,060.73
+
834.00
+
147.09%
+		 +
2,060.73
+
834.00
+
147.09%
+
used 75% +
2,343.00
+
852.00
+
175.00%
+		 +
2,343.00
+
852.00
+
175.00%
+
used max +
2,346.00
+
859.00
+
173.11%
+		 +
2,346.00
+
859.00
+
173.11%
+
rx MB/s 75% +
0.00
+
0.00
+
nan%
+		 +
0.00
+
0.00
+
nan%
+
rx MB/s 95% +
0.00
+
0.00
+
nan%
+		 +
0.00
+
0.00
+
nan%
+
rx MB/s 99% +
0.00
+
0.00
+
nan%
+		 +
0.00
+
0.00
+
nan%
+
pgin mean +
37.37
+
190.21
+
-80.35%
+		 +
37.37
+
190.21
+
-80.35%
+
pgin 75% +
59.00
+
412.00
+
-85.68%
+		 +
59.00
+
412.00
+
-85.68%
+
pgin max +
352.00
+
509.00
+
-30.84%
+		 +
352.00
+
509.00
+
-30.84%
+
pgout mean +
0.13
+
40.97
+
-99.68%
+		 +
0.13
+
40.97
+
-99.68%
+
pgout 75% +
0.00
+
1.00
+
-100.00%
+		 +
0.00
+
1.00
+
-100.00%
+
pgout max +
2.00
+
840.00
+
-99.76%
+		 +
2.00
+
840.00
+
-99.76%
+
fault mean +
952,586.87
+
117,653.31
+
709.66%
+		 +
952,586.87
+
117,653.31
+
709.66%
+
fault 75% +
1,426,717.00
+
205,151.00
+
595.45%
+		 +
1,426,717.00
+
205,151.00
+
595.45%
+
fault max +
2,628,392.00
+
256,538.00
+
924.56%
+		 +
2,628,392.00
+
256,538.00
+
924.56%
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
clientsr217agg
cpu%_avg +
0.88
+
0.45
+
96.45%
+		 +
0.88
+
0.45
+
96.45%
+
cpu freq_avg +
3,460.22
+
3,241.92
+
6.73%
+		 +
3,460.22
+
3,241.92
+
6.73%
+
pathlength_sum +
17,960.00
+
1,933.00
+
829.13%
+		 +
17,960.00
+
1,933.00
+
829.13%
+
ipc_avg +
1.27
+
1.14
+
11.30%
+		 +
1.27
+
1.14
+
11.30%
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 indexStage IDJob IDreal_queryidqueryidtotal_timestdev_timeacc_totaltotal
008818127.981.9199.65%99.65%
11109180.29nan99.87%0.23%
221210180.09nan99.94%0.07%
331511180.07nan100.00%0.06%
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 indexStage IDJob IDreal_queryidqueryidtotal_timestdev_timeacc_totaltotal
00881813.860.3286.65%86.65%
111210180.98nan92.80%6.15%
22109180.74nan97.43%4.63%
331511180.41nan100.00%2.57%
+
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+No description has been provided for this image +
+
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+No description has been provided for this image +
+
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Config compare

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
0851_00480851_0029comp
callSite.shortcollect at /tmp/ipykernel_265482/1936321720.py:117collect at /tmp/ipykernel_234307/1936321720.py:117False
spark.app.submitTime17334643016691733457038427False
spark.executor.extraClassPathfile:///data0/home/sparkuser/jars/6600a164407ae0e4f5ea5b33dc4b902f23a27730/gluten-velox-bundle-spark3.3_2.12-centos_7_x86_64-1.3.0-snapshot.jarFalse
spark.executor.extraJavaOptions-xx:+ignoreunrecognizedvmoptions --add-opens=java.base/java.lang=all-unnamed --add-opens=java.base/java.lang.invoke=all-unnamed --add-opens=java.base/java.lang.reflect=all-unnamed --add-opens=java.base/java.io=all-unnamed --add-opens=java.base/java.net=all-unnamed --add-opens=java.base/java.nio=all-unnamed --add-opens=java.base/java.util=all-unnamed --add-opens=java.base/java.util.concurrent=all-unnamed --add-opens=java.base/java.util.concurrent.atomic=all-unnamed --add-opens=java.base/sun.nio.ch=all-unnamed --add-opens=java.base/sun.nio.cs=all-unnamed --add-opens=java.base/sun.security.action=all-unnamed --add-opens=java.base/sun.util.calendar=all-unnamed --add-opens=java.security.jgss/sun.security.krb5=all-unnamed -xx:+useparalleloldgc -xx:parallelgcthreads=2 -xx:newratio=1 -xx:survivorratio=1 -xx:+usecompressedoops -verbose:gc -xx:+printgcdetails -xx:+printgctimestamps -xx:errorfile=/home/sparkuser/logs/java/hs_err_pid%p.log-xx:+ignoreunrecognizedvmoptions --add-opens=java.base/java.lang=all-unnamed --add-opens=java.base/java.lang.invoke=all-unnamed --add-opens=java.base/java.lang.reflect=all-unnamed --add-opens=java.base/java.io=all-unnamed --add-opens=java.base/java.net=all-unnamed --add-opens=java.base/java.nio=all-unnamed --add-opens=java.base/java.util=all-unnamed --add-opens=java.base/java.util.concurrent=all-unnamed --add-opens=java.base/java.util.concurrent.atomic=all-unnamed --add-opens=java.base/sun.nio.ch=all-unnamed --add-opens=java.base/sun.nio.cs=all-unnamed --add-opens=java.base/sun.security.action=all-unnamed --add-opens=java.base/sun.util.calendar=all-unnamed --add-opens=java.security.jgss/sun.security.krb5=all-unnamed -xx:+useparalleloldgc -xx:parallelgcthreads=2 -xx:newratio=1 -xx:survivorratio=1 -xx:+usecompressedoops -verbose:gc -xx:+printgcdetails -xx:+printgctimestamps -xx:errorfile=/data0/home/sparkuser/logs/java/hs_err_pid%p.logFalse
spark.executor.memory10944m29184mFalse
spark.gluten.memory.conservative.task.offHeap.size.in.bytes10041163776NaNFalse
spark.gluten.memory.dynamic.offHeap.sizing.enabledfalseNaNFalse
spark.gluten.memory.offHeap.size.in.bytes80329310208NaNFalse
spark.gluten.memory.overAcquiredMemoryRatio0NaNFalse
spark.gluten.memory.task.offHeap.size.in.bytes20082327552NaNFalse
spark.gluten.memoryOverhead.size.in.bytes1073741824NaNFalse
spark.gluten.numTaskSlotsPerExecutor4NaNFalse
spark.gluten.sql.columnar.backend.libveloxNaNFalse
spark.gluten.sql.columnar.coalesce.batchestrueNaNFalse
spark.gluten.sql.columnar.forceshuffledhashjointrueNaNFalse
spark.gluten.sql.columnar.maxBatchSize4096NaNFalse
spark.gluten.sql.columnar.shuffle.codeclz4NaNFalse
spark.gluten.sql.columnar.shuffle.codecBackendNaNFalse
spark.gluten.sql.session.timeZone.defaultetc/utcNaNFalse
spark.memory.offHeap.size8032931020858368mFalse
spark.pluginsorg.apache.gluten.glutenpluginNaNFalse
spark.repl.class.outputDir/tmp/tmpypqh85b0/tmp/tmpynceqaxdFalse
spark.repl.class.urispark://sr213:40521/classesspark://sr213:34951/classesFalse
spark.shuffle.managerorg.apache.spark.shuffle.sort.columnarshufflemanagerNaNFalse
spark.sql.adaptive.customCostEvaluatorClassorg.apache.spark.sql.execution.adaptive.glutencostevaluatorNaNFalse
spark.sql.extensionsorg.apache.gluten.extension.glutensessionextensionsNaNFalse
spark.sql.files.maxPartitionBytes4gNaNFalse
spark.sql.shuffle.partitions3264False
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diff --git a/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb b/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb index 26430241e69f..122f1c3c7dec 100644 --- a/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb +++ b/tools/workload/benchmark_velox/sample/tpch_q1.nbconvert.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "markdown", - "id": "4e2615af", + "id": "4371325d", "metadata": { "papermill": { - "duration": 0.003759, - "end_time": "2024-12-02T15:29:45.316600", + "duration": 0.003812, + "end_time": "2024-12-06T05:53:34.206544", "exception": false, - "start_time": "2024-12-02T15:29:45.312841", + "start_time": "2024-12-06T05:53:34.202732", "status": "completed" }, "tags": [] @@ -20,19 +20,19 @@ { "cell_type": "code", "execution_count": 1, - "id": "3007d85d", + "id": "c61021c1", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:29:45.324819Z", - "iopub.status.busy": "2024-12-02T15:29:45.324405Z", - "iopub.status.idle": "2024-12-02T15:29:45.330628Z", - "shell.execute_reply": "2024-12-02T15:29:45.330233Z" + "iopub.execute_input": "2024-12-06T05:53:34.214595Z", + "iopub.status.busy": "2024-12-06T05:53:34.214315Z", + "iopub.status.idle": "2024-12-06T05:53:34.220494Z", + "shell.execute_reply": "2024-12-06T05:53:34.220105Z" }, "papermill": { - "duration": 0.011609, - "end_time": "2024-12-02T15:29:45.331772", + "duration": 0.011601, + "end_time": "2024-12-06T05:53:34.221688", "exception": false, - "start_time": "2024-12-02T15:29:45.320163", + "start_time": "2024-12-06T05:53:34.210087", "status": "completed" }, "tags": [ @@ -45,31 +45,31 @@ "disk=''\n", "nic=''\n", "tz=''\n", - "basedir=''\n", + "base_dir=''\n", "name=''\n", "proxy=''\n", "\n", - "compare_appid=''\n", - "compare_basedir=''\n", - "compare_name=''" + "comp_appid=''\n", + "comp_base_dir=''\n", + "comp_name=''" ] }, { "cell_type": "code", "execution_count": 2, - "id": "f8ea15a6", + "id": "f4c7b29a", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:29:45.339630Z", - "iopub.status.busy": "2024-12-02T15:29:45.339237Z", - "iopub.status.idle": "2024-12-02T15:29:45.341862Z", - "shell.execute_reply": "2024-12-02T15:29:45.341478Z" + "iopub.execute_input": "2024-12-06T05:53:34.229763Z", + "iopub.status.busy": "2024-12-06T05:53:34.229436Z", + "iopub.status.idle": "2024-12-06T05:53:34.232162Z", + "shell.execute_reply": "2024-12-06T05:53:34.231769Z" }, "papermill": { - "duration": 0.007763, - "end_time": "2024-12-02T15:29:45.342990", + "duration": 0.008176, + "end_time": "2024-12-06T05:53:34.233387", "exception": false, - "start_time": "2024-12-02T15:29:45.335227", + "start_time": "2024-12-06T05:53:34.225211", "status": "completed" }, "tags": [ @@ -79,26 +79,27 @@ "outputs": [], "source": [ "# Parameters\n", - "appid = \"application_1733153225851_0001\"\n", + "appid = \"application_1733153225851_0048\"\n", "disk = \"nvme0n1\"\n", "nic = \"enp61s0f0\"\n", "tz = \"Etc/GMT+0\"\n", - "basedir = \"sr213\"\n", + "base_dir = \"sr213\"\n", "name = \"tpch_gluten\"\n", - "compare_appid = \"\"\n", - "compare_basedir = \"\"\n", - "compare_name = \"\"\n" + "comp_appid = \"application_1733153225851_0029\"\n", + "comp_base_dir = \"sr213\"\n", + "comp_name = \"vanilla\"\n", + "proxy = \"http://10.239.44.250:8080\"\n" ] }, { "cell_type": "markdown", - "id": "f5b83da6", + "id": "51887dbb", "metadata": { "papermill": { - "duration": 0.003418, - "end_time": "2024-12-02T15:29:45.350391", + "duration": 0.003585, + "end_time": "2024-12-06T05:53:34.240616", "exception": false, - "start_time": "2024-12-02T15:29:45.346973", + "start_time": "2024-12-06T05:53:34.237031", "status": "completed" }, "tags": [] @@ -110,19 +111,19 @@ { "cell_type": "code", "execution_count": 3, - "id": "f1ed544f", + "id": "11b3e5f6", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:29:45.358265Z", - "iopub.status.busy": "2024-12-02T15:29:45.357941Z", - "iopub.status.idle": "2024-12-02T15:29:45.360578Z", - "shell.execute_reply": "2024-12-02T15:29:45.360205Z" + "iopub.execute_input": "2024-12-06T05:53:34.248767Z", + "iopub.status.busy": "2024-12-06T05:53:34.248529Z", + "iopub.status.idle": "2024-12-06T05:53:34.251294Z", + "shell.execute_reply": "2024-12-06T05:53:34.250897Z" }, "papermill": { - "duration": 0.007969, - "end_time": "2024-12-02T15:29:45.361868", + "duration": 0.008331, + "end_time": "2024-12-06T05:53:34.252497", "exception": false, - "start_time": "2024-12-02T15:29:45.353899", + "start_time": "2024-12-06T05:53:34.244166", "status": "completed" }, "tags": [] @@ -136,19 +137,19 @@ { "cell_type": "code", "execution_count": 4, - "id": "ace428dd", + "id": "58fa24f6", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:29:45.369886Z", - "iopub.status.busy": "2024-12-02T15:29:45.369520Z", - "iopub.status.idle": "2024-12-02T15:29:45.372438Z", - "shell.execute_reply": "2024-12-02T15:29:45.372052Z" + "iopub.execute_input": "2024-12-06T05:53:34.260508Z", + "iopub.status.busy": "2024-12-06T05:53:34.260287Z", + "iopub.status.idle": "2024-12-06T05:53:34.263142Z", + "shell.execute_reply": "2024-12-06T05:53:34.262754Z" }, "papermill": { - "duration": 0.008252, - "end_time": "2024-12-02T15:29:45.373616", + "duration": 0.008226, + "end_time": "2024-12-06T05:53:34.264308", "exception": false, - "start_time": "2024-12-02T15:29:45.365364", + "start_time": "2024-12-06T05:53:34.256082", "status": "completed" }, "tags": [] @@ -165,20 +166,20 @@ { "cell_type": "code", "execution_count": 5, - "id": "d25fd5b9", + "id": "6608ae2f", "metadata": { "code_folding": [], "execution": { - "iopub.execute_input": "2024-12-02T15:29:45.381704Z", - "iopub.status.busy": "2024-12-02T15:29:45.381384Z", - "iopub.status.idle": "2024-12-02T15:30:13.999878Z", - "shell.execute_reply": "2024-12-02T15:30:13.999216Z" + "iopub.execute_input": "2024-12-06T05:53:34.272550Z", + "iopub.status.busy": "2024-12-06T05:53:34.272222Z", + "iopub.status.idle": "2024-12-06T05:54:05.226922Z", + "shell.execute_reply": "2024-12-06T05:54:05.226384Z" }, "papermill": { - "duration": 28.624314, - "end_time": "2024-12-02T15:30:14.001501", + "duration": 30.960697, + "end_time": "2024-12-06T05:54:05.228547", "exception": false, - "start_time": "2024-12-02T15:29:45.377187", + "start_time": "2024-12-06T05:53:34.267850", "status": "completed" }, "tags": [] @@ -196,15 +197,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "24/12/02 15:29:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + "24/12/06 05:53:36 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "24/12/02 15:29:48 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.\n", - "24/12/02 15:29:48 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.\n" + "24/12/06 05:53:37 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.\n", + "24/12/06 05:53:37 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.\n" ] }, { @@ -253,19 +254,19 @@ { "cell_type": "code", "execution_count": 6, - "id": "5aed17d6", + "id": "beaceea2", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:14.010905Z", - "iopub.status.busy": "2024-12-02T15:30:14.010558Z", - "iopub.status.idle": "2024-12-02T15:30:14.016526Z", - "shell.execute_reply": "2024-12-02T15:30:14.016117Z" + "iopub.execute_input": "2024-12-06T05:54:05.237940Z", + "iopub.status.busy": "2024-12-06T05:54:05.237576Z", + "iopub.status.idle": "2024-12-06T05:54:05.243620Z", + "shell.execute_reply": "2024-12-06T05:54:05.243229Z" }, "papermill": { - "duration": 0.012011, - "end_time": "2024-12-02T15:30:14.017774", + "duration": 0.01213, + "end_time": "2024-12-06T05:54:05.244853", "exception": false, - "start_time": "2024-12-02T15:30:14.005763", + "start_time": "2024-12-06T05:54:05.232723", "status": "completed" }, "tags": [] @@ -301,13 +302,13 @@ }, { "cell_type": "markdown", - "id": "3247c23f", + "id": "96ff6bfd", "metadata": { "papermill": { - "duration": 0.003742, - "end_time": "2024-12-02T15:30:14.025349", + "duration": 0.004098, + "end_time": "2024-12-06T05:54:05.253289", "exception": false, - "start_time": "2024-12-02T15:30:14.021607", + "start_time": "2024-12-06T05:54:05.249191", "status": "completed" }, "tags": [] @@ -319,19 +320,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "d6126b5d", + "id": "96db8a10", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:14.034277Z", - "iopub.status.busy": "2024-12-02T15:30:14.033993Z", - "iopub.status.idle": "2024-12-02T15:30:16.344751Z", - "shell.execute_reply": "2024-12-02T15:30:16.344056Z" + "iopub.execute_input": "2024-12-06T05:54:05.287175Z", + "iopub.status.busy": "2024-12-06T05:54:05.286902Z", + "iopub.status.idle": "2024-12-06T05:54:07.652568Z", + "shell.execute_reply": "2024-12-06T05:54:07.652028Z" }, "papermill": { - "duration": 2.317118, - "end_time": "2024-12-02T15:30:16.346569", + "duration": 2.397178, + "end_time": "2024-12-06T05:54:07.654334", "exception": false, - "start_time": "2024-12-02T15:30:14.029451", + "start_time": "2024-12-06T05:54:05.257156", "status": "completed" }, "scrolled": false, @@ -370,19 +371,19 @@ { "cell_type": "code", "execution_count": 8, - "id": "71b8be80", + "id": "f2087dbe", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:16.388630Z", - "iopub.status.busy": "2024-12-02T15:30:16.388161Z", - "iopub.status.idle": "2024-12-02T15:30:16.391534Z", - "shell.execute_reply": "2024-12-02T15:30:16.390964Z" + "iopub.execute_input": "2024-12-06T05:54:07.664430Z", + "iopub.status.busy": "2024-12-06T05:54:07.664039Z", + "iopub.status.idle": "2024-12-06T05:54:07.666809Z", + "shell.execute_reply": "2024-12-06T05:54:07.666368Z" }, "papermill": { - "duration": 0.041493, - "end_time": "2024-12-02T15:30:16.392813", + "duration": 0.009062, + "end_time": "2024-12-06T05:54:07.668029", "exception": false, - "start_time": "2024-12-02T15:30:16.351320", + "start_time": "2024-12-06T05:54:07.658967", "status": "completed" }, "tags": [] @@ -396,19 +397,47 @@ { "cell_type": "code", "execution_count": 9, - "id": "7689c4d6", + "id": "df22c6c4", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:16.402338Z", - "iopub.status.busy": "2024-12-02T15:30:16.402063Z", - "iopub.status.idle": "2024-12-02T15:30:16.405000Z", - "shell.execute_reply": "2024-12-02T15:30:16.404449Z" + "iopub.execute_input": "2024-12-06T05:54:07.677489Z", + "iopub.status.busy": "2024-12-06T05:54:07.677198Z", + "iopub.status.idle": "2024-12-06T05:54:07.679618Z", + "shell.execute_reply": "2024-12-06T05:54:07.679199Z" }, "papermill": { - "duration": 0.009284, - "end_time": "2024-12-02T15:30:16.406279", + "duration": 0.008559, + "end_time": "2024-12-06T05:54:07.680791", "exception": false, - "start_time": "2024-12-02T15:30:16.396995", + "start_time": "2024-12-06T05:54:07.672232", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "emonmetric=['emon_cpuutil',\n", + " 'emon_cpufreq',\n", + " 'emon_instr_retired',\n", + " 'emon_ipc']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "44921944", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-06T05:54:07.690327Z", + "iopub.status.busy": "2024-12-06T05:54:07.689959Z", + "iopub.status.idle": "2024-12-06T05:54:07.692308Z", + "shell.execute_reply": "2024-12-06T05:54:07.691898Z" + }, + "papermill": { + "duration": 0.008606, + "end_time": "2024-12-06T05:54:07.693519", + "exception": false, + "start_time": "2024-12-06T05:54:07.684913", "status": "completed" }, "tags": [] @@ -421,20 +450,20 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "de3d224d", + "execution_count": 11, + "id": "e3b53125", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:16.415984Z", - "iopub.status.busy": "2024-12-02T15:30:16.415720Z", - "iopub.status.idle": "2024-12-02T15:30:16.418974Z", - "shell.execute_reply": "2024-12-02T15:30:16.418401Z" + "iopub.execute_input": "2024-12-06T05:54:07.702902Z", + "iopub.status.busy": "2024-12-06T05:54:07.702567Z", + "iopub.status.idle": "2024-12-06T05:54:07.705136Z", + "shell.execute_reply": "2024-12-06T05:54:07.704728Z" }, "papermill": { - "duration": 0.009752, - "end_time": "2024-12-02T15:30:16.420246", + "duration": 0.008666, + "end_time": "2024-12-06T05:54:07.706366", "exception": false, - "start_time": "2024-12-02T15:30:16.410494", + "start_time": "2024-12-06T05:54:07.697700", "status": "completed" }, "tags": [] @@ -447,13 +476,13 @@ }, { "cell_type": "markdown", - "id": "f03ea9cd", + "id": "04d5c054", "metadata": { "papermill": { - "duration": 0.004229, - "end_time": "2024-12-02T15:30:16.428694", + "duration": 0.00437, + "end_time": "2024-12-06T05:54:07.715026", "exception": false, - "start_time": "2024-12-02T15:30:16.424465", + "start_time": "2024-12-06T05:54:07.710656", "status": "completed" }, "tags": [] @@ -464,29 +493,30 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "34fc9194", + "execution_count": 12, + "id": "004663b7", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:16.438322Z", - "iopub.status.busy": "2024-12-02T15:30:16.438042Z", - "iopub.status.idle": "2024-12-02T15:30:16.444163Z", - "shell.execute_reply": "2024-12-02T15:30:16.443602Z" + "iopub.execute_input": "2024-12-06T05:54:07.725014Z", + "iopub.status.busy": "2024-12-06T05:54:07.724613Z", + "iopub.status.idle": "2024-12-06T05:54:07.729656Z", + "shell.execute_reply": "2024-12-06T05:54:07.729270Z" }, "papermill": { - "duration": 0.012553, - "end_time": "2024-12-02T15:30:16.445428", + "duration": 0.011505, + "end_time": "2024-12-06T05:54:07.730819", "exception": false, - "start_time": "2024-12-02T15:30:16.432875", + "start_time": "2024-12-06T05:54:07.719314", "status": "completed" }, + "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ - " 5 App info" + " 5 Self app info" ], "text/plain": [ "" @@ -498,7 +528,7 @@ { "data": { "text/html": [ - " 6 Compare to previous run" + " 6 Compare to vanilla" ], "text/plain": [ "" @@ -510,7 +540,7 @@ { "data": { "text/html": [ - " 7 Config compare" + " 7 Config compare" ], "text/plain": [ "" @@ -521,70 +551,70 @@ } ], "source": [ - "display(HTML(' 5 App info'))\n", - "display(HTML(' 6 Compare to previous run'))\n", - "display(HTML(' 7 Config compare'))" + "display(HTML(' 5 Self app info'))\n", + "display(HTML(f\" 6 Compare to {comp_name}\"))\n", + "display(HTML(' 7 Config compare'))" ] }, { "cell_type": "markdown", - "id": "3fcaa57d", + "id": "64cbb5ba", "metadata": { "papermill": { - "duration": 0.004439, - "end_time": "2024-12-02T15:30:16.454227", + "duration": 0.004589, + "end_time": "2024-12-06T05:54:07.739936", "exception": false, - "start_time": "2024-12-02T15:30:16.449788", + "start_time": "2024-12-06T05:54:07.735347", "status": "completed" }, "tags": [] }, "source": [ - "# App info" + "# Self app info" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "d923e343", + "execution_count": 13, + "id": "0c621763", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:16.463831Z", - "iopub.status.busy": "2024-12-02T15:30:16.463581Z", - "iopub.status.idle": "2024-12-02T15:30:16.505165Z", - "shell.execute_reply": "2024-12-02T15:30:16.504133Z" + "iopub.execute_input": "2024-12-06T05:54:07.749952Z", + "iopub.status.busy": "2024-12-06T05:54:07.749546Z", + "iopub.status.idle": "2024-12-06T05:54:07.832927Z", + "shell.execute_reply": "2024-12-06T05:54:07.832513Z" }, "papermill": { - "duration": 0.048063, - "end_time": "2024-12-02T15:30:16.506705", + "duration": 0.090016, + "end_time": "2024-12-06T05:54:07.834415", "exception": false, - "start_time": "2024-12-02T15:30:16.458642", + "start_time": "2024-12-06T05:54:07.744399", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ - "app=Application_Run(appid, basedir=basedir)\n", + "app=Application_Run(appid, basedir=base_dir)\n", "appals=app.analysis['app']['als']" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "fa84df8a", + "execution_count": 14, + "id": "0195d322", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:30:16.516771Z", - "iopub.status.busy": "2024-12-02T15:30:16.516568Z", - "iopub.status.idle": "2024-12-02T15:30:43.518801Z", - "shell.execute_reply": "2024-12-02T15:30:43.518199Z" + "iopub.execute_input": "2024-12-06T05:54:07.844603Z", + "iopub.status.busy": "2024-12-06T05:54:07.844367Z", + "iopub.status.idle": "2024-12-06T05:54:33.914699Z", + "shell.execute_reply": "2024-12-06T05:54:33.914257Z" }, "papermill": { - "duration": 27.009075, - "end_time": "2024-12-02T15:30:43.520099", + "duration": 26.07688, + "end_time": "2024-12-06T05:54:33.916079", "exception": false, - "start_time": "2024-12-02T15:30:16.511024", + "start_time": "2024-12-06T05:54:07.839199", "status": "completed" }, "tags": [] @@ -594,7 +624,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "load data /sr213/application_1733153225851_0001/app.log\n" + "load data /sr213/application_1733153225851_0048/app.log\n" ] }, { @@ -618,17 +648,7 @@ "output_type": "stream", "text": [ "\r", - "[Stage 1:> (0 + 1) / 1]\r", - "\r", - " \r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "[Stage 5:> (0 + 1) / 1]\r" + "[Stage 1:> (0 + 1) / 1]\r" ] }, { @@ -644,7 +664,7 @@ "output_type": "stream", "text": [ "\r", - "[Stage 8:> (0 + 1) / 1]\r" + "[Stage 4:> (0 + 1) / 1]\r" ] }, { @@ -660,7 +680,7 @@ "output_type": "stream", "text": [ "\r", - "[Stage 15:> (0 + 1) / 1]\r" + "[Stage 5:> (0 + 1) / 1]\r" ] }, { @@ -692,17 +712,9 @@ "output_type": "stream", "text": [ "\r", - "[Stage 39:> (0 + 16) / 200]\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "[Stage 39:=> (6 + 17) / 200]\r", + "[Stage 39:> (0 + 16) / 200]\r", "\r", - "[Stage 39:===> (13 + 16) / 200]\r" + "[Stage 39:=> (6 + 16) / 200]\r" ] }, { @@ -710,9 +722,7 @@ "output_type": "stream", "text": [ "\r", - "[Stage 39:======> (24 + 16) / 200]\r", - "\r", - "[Stage 39:=========> (37 + 16) / 200]\r" + "[Stage 39:===> (14 + 16) / 200]\r" ] }, { @@ -720,9 +730,9 @@ "output_type": "stream", "text": [ "\r", - "[Stage 39:================> (60 + 16) / 200]\r", + "[Stage 39:========> (33 + 16) / 200]\r", "\r", - "[Stage 39:=======================> (88 + 16) / 200]\r" + "[Stage 39:==================> (67 + 16) / 200]\r" ] }, { @@ -730,15 +740,17 @@ "output_type": "stream", "text": [ "\r", - "[Stage 39:=================================> (126 + 16) / 200]\r", + "[Stage 39:==========================> (97 + 16) / 200]\r", "\r", - "[Stage 39:=============================================> (171 + 16) / 200]\r" + "[Stage 39:==================================> (131 + 16) / 200]\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\r", + "[Stage 39:=================================================> (185 + 15) / 200]\r", "\r", " \r" ] @@ -746,7 +758,7 @@ { "data": { "text/html": [ - "http://sr213:18080/history/application_1733153225851_0001" + "http://sr213:18080/history/application_1733153225851_0048" ], "text/plain": [ "" @@ -760,7 +772,7 @@ "output_type": "stream", "text": [ "\r", - "[Stage 42:(173 + 7) / 200][Stage 43:> (0 + 1) / 1][Stage 44:> (0 + 8) / 200]\r", + "[Stage 42:(177 + 5) / 200][Stage 43:> (0 + 1) / 1][Stage 44:>(0 + 11) / 200]\r", "\r", " \r" ] @@ -770,9 +782,9 @@ "output_type": "stream", "text": [ "\r", - "[Stage 44:(129 + 9) / 200][Stage 45:> (0 + 1) / 1][Stage 46:> (0 + 6) / 200]\r", + "[Stage 44:(113 + 12) / 200][Stage 45:> (0 + 1) / 1][Stage 46:> (0 + 3) / 200]\r", "\r", - "[Stage 44:(163 + 4) / 200][Stage 46:>(8 + 12) / 200][Stage 47:> (0 + 0) / 200]\r" + "[Stage 44:(182 + 5) / 200][Stage 46:>(4 + 11) / 200][Stage 47:> (0 + 0) / 200]\r" ] }, { @@ -780,9 +792,9 @@ "output_type": "stream", "text": [ "\r", - "[Stage 44:(185 + 4) / 200][Stage 46:>(46 + 9) / 200][Stage 47:> (0 + 3) / 200]\r", + "[Stage 46:(43 + 16) / 200][Stage 47:> (0 + 0) / 200][Stage 48:> (0 + 0) / 200]\r", "\r", - "[Stage 46:>(91 + 8) / 200][Stage 47:> (7 + 8) / 200][Stage 48:> (0 + 0) / 200]\r" + "[Stage 46:(110 + 8) / 200][Stage 47:> (0 + 8) / 200][Stage 48:> (0 + 0) / 200]\r" ] }, { @@ -790,9 +802,9 @@ "output_type": "stream", "text": [ "\r", - "[Stage 46:(128 + 4) / 200][Stage 47:>(38 + 8) / 200][Stage 48:> (0 + 4) / 200]\r", + "[Stage 46:(155 + 8) / 200][Stage 47:>(47 + 8) / 200][Stage 48:> (0 + 0) / 200]\r", "\r", - "[Stage 46:(160 + 4) / 200][Stage 47:>(99 + 4) / 200][Stage 48:>(25 + 6) / 200]\r" + "[Stage 46:(194 + 4) / 200][Stage 47:>(73 + 4) / 200][Stage 48:> (8 + 8) / 200]\r" ] }, { @@ -800,9 +812,9 @@ "output_type": "stream", "text": [ "\r", - "[Stage 46:(196 + 4) / 200][Stage 47:(105 + 0) / 200][Stage 48:>(86 + 4) / 200]\r", + "[Stage 47:(114 + 8) / 200][Stage 48:>(57 + 4) / 200][Stage 49:> (0 + 4) / 16]\r", "\r", - "[Stage 47:(121 + 4) / 200][Stage 48:(105 + 0) / 200][Stage 49:> (0 + 12) / 16]\r" + "[Stage 47:(185 + 4) / 200][Stage 48:>(73 + 4) / 200][Stage 49:> (0 + 8) / 16]\r" ] }, { @@ -810,7 +822,15 @@ "output_type": "stream", "text": [ "\r", - "[Stage 47:(191 + 4) / 200][Stage 48:(105 + 0) / 200][Stage 49:> (4 + 12) / 16]\r", + "[Stage 48:(126 + 8) / 200][Stage 49:> (0 + 8) / 16][Stage 51:> (0 + 0) / 1]\r", + "\r", + "[Stage 48:(184 + 4) / 200][Stage 49:> (4 + 12) / 16][Stage 51:> (0 + 0) / 1]\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\r", " \r" ] @@ -823,7 +843,7 @@ " \n", " \n", " appid\n", - " application_1733153225851_0001\n", + " application_1733153225851_0048\n", " \n", " \n", " executor.instances\n", @@ -863,7 +883,7 @@ " \n", " \n", " runtime\n", - " 17.8\n", + " 17.65\n", " \n", " \n", " disk spilled\n", @@ -887,7 +907,7 @@ " \n", " \n", " task run time\n", - " 6.73\n", + " 6.79\n", " \n", " \n", " ser_time\n", @@ -899,7 +919,7 @@ " \n", " \n", " gc_time\n", - " 0.04\n", + " 0.03\n", " \n", " \n", " input read\n", @@ -907,23 +927,23 @@ " \n", " \n", " acc_task_time\n", - " 14.14\n", + " 13.99\n", " \n", " \n", " file read size\n", - " 5,944.5\n", + " 5,951.35\n", " \n", " \n", " file write size\n", - " 21.74\n", + " 24.52\n", " \n", " \n", " disk read size\n", - " 4.95\n", + " 5.05\n", " \n", " \n", " disk write size\n", - " 12.38\n", + " 15.31\n", " \n", " \n", " disk cancel size\n", @@ -944,7 +964,7 @@ { "data": { "text/plain": [ - "{'appid': 'application_1733153225851_0001',\n", + "{'appid': 'application_1733153225851_0048',\n", " 'executor.instances': 4,\n", " 'executor.cores': 4,\n", " 'shuffle.partitions': 32,\n", @@ -954,26 +974,26 @@ " 'Speculative Tasks': 0,\n", " 'Speculative Killed Tasks': 0,\n", " 'Speculative Stage': 0,\n", - " 'runtime': 17.8,\n", + " 'runtime': 17.65,\n", " 'disk spilled': 0.0,\n", " 'memspilled': 0.0,\n", " 'local_read': 0.0,\n", " 'remote_read': 0.0,\n", " 'shuffle_write': 0.0,\n", - " 'task run time': 6.73,\n", + " 'task run time': 6.79,\n", " 'ser_time': 0.0,\n", " 'f_wait_time': 0.0,\n", - " 'gc_time': 0.04,\n", + " 'gc_time': 0.03,\n", " 'input read': 22.54,\n", - " 'acc_task_time': 14.14,\n", - " 'file read size': 5944.5,\n", - " 'file write size': 21.74,\n", - " 'disk read size': 4.95,\n", - " 'disk write size': 12.38,\n", + " 'acc_task_time': 13.99,\n", + " 'file read size': 5951.35,\n", + " 'file write size': 24.52,\n", + " 'disk read size': 5.05,\n", + " 'disk write size': 15.31,\n", " 'disk cancel size': 0.0}" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -984,20 +1004,20 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "cc75b864", + "execution_count": 15, + "id": "4be7e21a", "metadata": { "execution": { - 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"name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "sar metric\n" + "\r", + " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "perf stat metric\n" + "/sr213/application_1733153225851_0048/sr217/emon.parquet is not found, trying to load data ...\n" ] }, { @@ -1032,264 +1053,37 @@ "output_type": "stream", "text": [ "\r", - "[Stage 258:> (0 + 1) / 1]\r", + "[Stage 129:> (0 + 2) / 2][Stage 130:> (0 + 2) / 2]\r", "\r", - " \r" + "[Stage 129:> (0 + 2) / 2][Stage 130:> (0 + 2) / 2][Stage 131:> (0 + 4) / 4]\r" ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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 application_1733153225851_0001
runtime17.800000
disk spilled0.000000
shuffle_write0.000000
f_wait_time0.000000
input read22.540000
acc_task_time14.140000
output rows1.180000
%user>90%0.931034
%kernel>10%0.965517
%iowait>10%0.620690
avg %user41.233793
avg %system4.608621
avg %iowait0.623793
avg disk util33.448276
time more than 90%0.000000
total read (G)5.432129
total write (G)0.994891
avg read bw (MB/s)191.810354
avg write bw (MB/s)35.129954
read bw %75402.394531
read bw %95479.738281
read bw max480.722656
time_rd_morethan_950.034483
write bw %751.074219
write bw %9535.035156
write bw max945.855469
time_wr_morethan_950.034483
cached mean97.137931
cached 75%152.000000
cached max188.000000
used mean573.379310
used 75%593.000000
used max597.000000
rx MB/s 75%0.000000
rx MB/s 95%0.000000
rx MB/s 99%0.000000
pgin mean191.965517
pgin 75%402.000000
pgin max480.000000
pgout mean35.103448
pgout 75%1.000000
pgout max947.000000
fault mean117681.655172
fault 75%193074.000000
fault max287177.000000
ipc1.144464
instructions962.586404
cpu_freq3.225843
cpu%7.480452
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/iGPPX7p0Cf/++6/SdEFBQeK49C1btkTDhg2Vphs/fjwcHBwAZHfVrWofVGbVqlUIDw+HjY0NfvrpJ63nA6T7TEBAAJydnVWmVdxnVB1zBV1WVhY2bdokfp4yZQosLHKPCG5paYkpU6aInzdu3Ch+j0RU8BhyHiQiIjK2rKwsjBw5EpmZmWjbtq2kHk1EZCratg/Wr18fgwYNEts0VHFwcMCIESPEz4cPH+bvYvpPqVmzJhwdHfN0mYrtkp999lmeLpsov5UuXRq+vr55uswdO3bgw4cPAIDq1avD398/T5dPhRcDo6hQePXqFYKCgjB48GD4+/ujWLFisLS0hKurK6pVq4YRI0bgyJEjWuUVGBgImUwGmUyGU6dOQRAEbNq0Cc2aNYObmxvs7e1RuXJlfP3117h//77G/AYPHizmFxQUBADYu3cv2rVrh5IlS8LW1hblypXD8OHDcfXqVUM2g9527NghllHZS15ufT18+BAzZ85EQEAASpcuDWtrazg5OaFWrVoYM2YMzp8/rzGPyMhILFmyBD169EDlypXh6OgIKysreHh44OOPP8aUKVMQERGhMZ+c66Z4czw6OlrlNhg8eLDafKtUqaJyXkMu+uHh4fjqq69QrVo1uLi4wMbGBl5eXujSpQs2bdqEzMxMjXko2wcjIyMxevRoVK5cGfb29ihWrBgCAgKwadOm/8SP4czMTGzevFn8bKwfJK6uruL72NjYApdfQae4voDh6+zp6SkJoIqOjjYoPysrK1StWtVo+WkrOTlZfO/i4qI2bc7p/4XjWZOcQXGffvqpyrQ+Pj6oX7+++Hn//v1K0ykGxanLz87OTnLDRFV+OcXExOD7778HAEycOFHn64i++0x+7C9paWlYsWIFmjRpAjc3N9jZ2aFKlSqYMmUKXr16BQDw9fUVr2NRUVG58rh8+TLevHkDIPs47dq1q8rlde/eXQyaevnyJcLCwoy+TpqkpaVh/fr16Nq1K3x8fGBrawtnZ2dUr14d48aN0xigtm7dOkk9JjExUW36f//9F7a2tpDJZDA3N8eZM2c0lvHw4cMYNmwYKleuDGdnZ9jZ2aFs2bL47LPPcOLECZ3WV86Q+mBUVJTW9Spt0pqqPih34sQJDB06FBUrVoSjo6O4/fr27Yvdu3drlYeq8srFxMRg2rRp4m8fBwcHlC9fHt27d8eOHTvU5nfz5k2MHTsWderUQbFixWBhYQEHBwdUrFgRXbt2xbJly/Ds2TOdy2lMhp4H88LLly+xcOFCtGzZEl5eXrC1tYW9vT2qVq2KYcOG4ciRI1qfV4ODg9GnTx+ULVsWdnZ2cHR0RMWKFTF06FCcPHlSp3KpOgbu37+Pr7/+GjVq1ICTkxOcnZ1RqVIlDBw4EIcOHVKbZ1JSElasWIE2bdrAy8sLNjY2KF68OGrXro1p06bh+fPnOpWxIO6DQUFBuY7169evY/DgwfDx8YGNjQ08PT3RuXNnvY7jCxcuYPTo0ahevTqKFSsGGxsbeHt7o2fPnti1a5fBZQWA06dPo3fv3qhYsSJsbW1RokQJ1KxZE+PGjZMETeeF169fY+HChWjWrBk8PT1hbW2NEiVKoFGjRvjf//6H+Ph4tfMPGDBAXMcePXpoXN6GDRvE9B4eHnj9+rXa9IZei1UJCQnB2LFj4efnB3d3d1hZWcHV1RVNmjTBrFmz8PDhQ5Xzqvpe9Ul76tSpXNewIUOGiNM3btxoUPtPZmYmNm7ciC5duojnBBcXF1SrVg2jR49GeHi4xjxUlTcwMFD8/9WrVzFkyBBUrVoV9vb2KF68OKpWrYqRI0ca/CCSPlatWoWwsDBYWlpiyZIleb78nGbOnClut5kzZwIAzp49i549e6J06dLieaZv3744deqUVnkKgoCQkBDMmDEDbdq0gbe3N+zs7GBrawtvb2988sknWLRokcZjTJuyCoKAvXv3omPHjihbtixsbGxQqlQp1K1bF9OmTVNa7weM1z5Y2Bj7WmwsCxYsUNumrO2+l9PLly8xb948NGvWTDyf2traokyZMmjRogVmzJihth1dsS300aNHWLBgAXx8fGBnZ4d69erh6NGjAIC3b99i8ODBKFGiBBwcHBAYGIgrV67oVWZTMnb7YK1atcT3SUlJefpQnZwx6oPKzi36plW8J5SamoqJEyfCw8ND3C/k17ZHjx6he/fuKFasGJycnNCpUyel13dTXYtfvHih9pjT9jerYl0iKCgI//zzD/z9/WFra4sKFSpg+fLlALKDgmfPng1fX1/Y2tqiRo0a2LZtm1bLMLQ+WFTcvn0boaGhAABzc3P0798/X8phjGuxsdtmAOXtHX///TfatWuHMmXKwNraGqVLl0b//v01Xt8V2w9z5plzWYovxfqnMm3btlV73GlLcXnPnj1Dz5494ezsDFdXV/Tr10+sX504cQKNGjWCvb09SpYsidGjR0vaelXJysrCX3/9hX79+qF8+fJwcHCAg4MDKlWqhJEjR+pcRy/MGIxIesunnqqItLZ8+XKxG2tNr5YtW2ocLkux2+QjR44IXbp0UZmfra2tsGnTJrX55ew2+csvv1SZn7m5ubBgwQKd1t8YQ+lt375d7XbTtxvDtLQ04euvvxYsLS01fjedO3dWmU+vXr20+n7Nzc2Fr7/+WuXwW4IgaJWPspem7m0rV66scl59hmTJyMgQvvzyS3GoJlWvGjVqCA8ePFCbV859cNu2bZJuJHO+Bg8erHN5TUWxXMYcSm/fvn1ivsWKFRM+fPhglHw7deok5tu/f3+D8xszZoyYX+PGjXWev7ANpRceHi75zu/du2dwnq6urmJ+P//8s8H5yYf0ASB8/fXXKtMproehNm/eLObl4eGhNu2BAwfEtJ6enirTmWIoPWUKwrBHV65cEctgZmYmvHv3Tm16xSHZunTpkmt6bGys5PuNiIhQm5/iEIy1atXSqsw9e/YUAAhlypQRkpOTBUHQbVsqDuHXoEEDtWlXr14tpm3YsKFW5TOW6Oho4aOPPlJ5PfL09BSuX78u6VpZWdfLisNwfvzxxxqXW7t2bTH9kiVLjL9iapw5c0YoV66c2mu7paWlsHDhQrX59OjRQ0w/cOBAlenS0tIkwy5MmzZNbb5Pnz4VAgMDNdaLPv30UyElJUWrdTZGfVCXoe60SWuq+mBycrLQvXt3jfk0a9ZMePPmjRZbL3d5BUEQ9u/fL9jb26vM39nZWWVeU6dOFczMzDSW0czMTHj79q3WZTQ2Q8+DppSVlSXMnTtXsLOz07gdNQ2T8OrVKyEgIEBjPj179hTev3+vVfmUHQPr1q0TLCwsVOav7vr0999/CyVLllRbPnt7e2Hz5s1ala+g7oM5hzbZtGmTYGVlpbJ83bt312qI3Pj4eHF/VvcKDAxUOeyzprJmZGQIw4cPV5v/2LFjDdxC2lu9erXg5OSktjxubm7CkSNHVOaRkJAglC1bVkz/+++/q0z78OFDwdHRUQCyhzhWl68gGO9arCgmJkZo06aNxu/Z3NxcWLRokdI8dBleR1PanMPB6PLS1P5z//59tfU3+fcwevRoITMzU6vtp6xda+bMmWqXoayebkoxMTHifj1+/Hjx//k5lF7O3+0LFixQ24Y0evRotUNspaWlCaVLl9ZqP3F2dhbWr1+vd1mTk5OFDh06qF2Gsnq6MdsHlSmoQ+kZ+1osZ4xhvebPn6+2XPpsx7///lvjdUT+2r17t9I8FLe9suuwlZWVEBoaKvmtJH+5uLgIz54902t7mIqh7YM57d27V7LO2tZBjMVY9UFd2i81pVU8/pXtM87OzkJERIRQpkyZXNN8fX2FpKQkSX6muhY/f/5c7bzaHsuKx3/37t2VXj9WrlwpjB49Wuly9u/frzZ/Y9QH85Iph9KbMGGCmG+7du2MmrcujHEtNnbbjCBI2zs+fPgg9O7dW2V5rKyshMOHD6vMS7FepstLU/uCpnq+tuTp/f39lf4e+fjjj4WQkBCl7We9evVSm/etW7cEPz8/teWUyWTC2LFjta6jm5qp6tG3bt0S8zU3NxdiYmKMljcVfbnHoCAqYJ49eyb2mmNvb4/q1aujfPnycHR0RGpqKh4+fIgLFy6IPUa0atUKZ86cUTusjNzMmTNx/vx5yGQyNG7cGJUrV0ZcXByOHTuGxMREpKSkYMiQIXB1dUX79u015vfbb7+JT8PXrl0bfn5+SE5OxokTJ/D69WtkZmZi8uTJcHFxwciRIw3bMDqoVKkSvvrqK8n/jh8/jsjISL3zTE1NRdu2bSVPBjk5OaFJkybw9PTEhw8fcP/+fVy+fBmZmZm4fv26yrweP34svpc/9erp6Ql7e3skJiYiIiIC169fR2ZmJpYvX474+Hhs3LhRaV451/Pdu3fi8DuOjo4qo4cbNGigdn0HDBiAFy9eKM1XH8OGDZOsg6enJ5o1awZbW1tERESITxncvHkTTZs2RXh4OEqWLKkx3ytXrmDt2rXIyMhAo0aNULVqVSQnJ+P48eNiRHpQUBDatm2rtheUwk4xYrx3796wtrY2OM/jx49LeoNp27atQfndunUL69evN1p++nr9+jW2bduGO3fuICkpCcWKFUPlypXRvHlzrYaz01Z6ejomT54sfq5QoQIqVKhgUJ5v376VPPVWrlw5g/LLzMzEgwcPdM7v3LlzCA0NxfPnz2FpaYlSpUqhcePG8Pf3h5mZ5s4527RpA2tra6SmpuL58+fYvn07+vbtmytdVlaW5MnlL774Qqvy3b17F3fu3EF0dDTS09NRokQJ1K5dG40bN4atra1WeRRkd+7cEd97e3uLQ9upUqNGDfH93bt31eZnYWGBypUra53f/fv3IQiC2qeJDh48iJ07dwIAfvzxR9jZ2anNX5nOnTvjhx9+AACEhobi/PnzaNSoUa50Hz58wOrVq8XPX375pc7L0ldiYiJatmwp9sggk8nQqFEjVKlSBfHx8Th27BhiYmLQo0cPpKSkqM1L8Tv56KOPNC67Ro0a4lPAyr5jU9m3bx969uyJ1NRUAICtrS2aNGkCX19ffPjwAWFhYbh9+zbS09MxadIkZGVlSc6Lin777TeEhobi6dOn2Lx5Mzp27IjevXvnSjd9+nTxSeoGDRqofYr24cOHCAwMxJMnTwBkfyf16tVD1apVYWZmhrt37+L8+fPIysrCH3/8gcTEROzfv1/t/mzM+qAxmaI+mJWVhU6dOkl61KpQoQIaNGgACwsLhIeH4+bNmwCye3Vp3rw5QkNDdT7PhoaGokuXLsjIyICjoyOaNGmC0qVLIzU1FdevX8eNGzdUzrts2TL873//Ez97enqiYcOGcHNzQ0pKCh4/foywsDAkJSUhKysLWVlZOpXNWIxxHjQVQRAwYMAAyVPSNjY2aNKkCby9vZGZmYmoqChcuHABaWlpar+P5ORkBAYGSoZtrVmzJmrXro2MjAxcvHhR7KF4586dSEhIwOHDh3V6IhXIftp2+PDhACD2XOPu7o7k5GSEhYWpPQ/+8ssv+PLLL8V9wcnJSez17d27d7hw4QKioqKQnJyMzz77DObm5krrKHKFZR+8desWduzYgbS0NHh7eyMgIAAWFhYIDQ0VfyPv2rULgwYNwvbt21Xm8/btWzRv3lw89oHsa5C/vz+srKzw6NEjnD17FmlpaTh16hQ++eQTXLhwATY2NjqV95tvvsG6desAZNdPP/74Yzg7O+P169c4d+6c2ANjXpg2bZrkO3Zzc0NAQABKlCiBuLg4nD17Fi9evMCbN2/QsWNHHD16FE2bNs2Vj5OTE7Zu3YqmTZsiIyMDY8eORbNmzXINKZ2VlYWBAwfi3bt3ALKHMW7VqpXK8hnzWiz34MEDNG/eXLx+AtnDRTRs2BDFixdHQkICrl27hjt37iAzMzNPeu8qXbp0rmtdZGQkjh8/DiC7x+sWLVoonVexh9ycYmJiEBAQIOmZpkGDBqhWrRpSUlJw+vRpxMTEQBAErFy5EikpKeK+qYvFixeLdRZPT080atQIxYsXR1xcHC5evCjZ1nnlm2++QWJiItzd3TF9+vQ8X74mR44cwcWLFyEIAqpUqYL69esjMzMTZ86cEdvUVq5cCQcHB8yfP19pHpmZmZLeWcqWLYuPPvoIJUqUgJWVFV6/fo2wsDA8fvwYCQkJGDZsGARBwNChQ3Uu76effiq2n1SvXh01atSAg4MDYmJiEBISgoSEBKXzGbN9sLAw9rXY2OrUqZPrfBMcHIyYmBi98gsPD8enn36KjIwMANl188aNG8PLywtZWVl48eIFwsLC8PLlSwDZbUma7Ny5E4GBgfD09MShQ4cQGxuLtLQ0tG3bFnFxcWjUqBHKly+Pw4cP49WrV4iPj8dvv/2GGTNm6LUOxmaK9kHF39AuLi4oVqyYwXlqqzDUB3fu3ImOHTvCxsYGBw8eRHJyMhISEtCkSRPExcWhdevWKF68OA4cOIDExERERUVh586dGDRokJiHqa7F9vb2avPVx65du1ClShU0aNAAISEh4m+QadOm4d27dyhXrhyaNWuGixcv4vbt2wCARYsWqbwfZqz6YFGQkZFhklErjEHfa7EpTZ06FX/++SecnJzwySefoESJEoiKisKJEyeQmZmJtLQ0DBw4EPfu3YOTk1Ou+QcNGpSrB7xVq1aJ73MeO3IVK1ZUW67OnTvnuk+hmK+url69Cnt7e/Tv3x+vXr0SezG8fPkyOnXqBHNzc/To0QNJSUnYv38/BEHAzp07ERUVpbT3rcuXL6N169ZiL2wWFhZo1KgRKlasiMzMTNy8eRPh4eEQBAHLli3Dhw8fsHbtWr3LX9Ap3vtr2bIlPDw88rE0VOjkY1AWkVbmzp0rjBw5Ujh58qSQnp6uNM3z58+Fli1bilGiEydOVJmf4tMBAITixYsL586dk6SJjY0VWrVqJabx9vYWn2TOSfEJFQCCtbV1rqdZUlJShIEDB4ppHB0dtY5iNUaPUZrKrU+07siRIyVRufPmzVPas8Dz58+FMWPGCOXKlVOZV69evYRZs2YJN27cUJkmPDxc8Pb21vqpBTldotx1YUi+//zzj2Sf+f7773M95XbmzBlJTzjqIsYVv0szMzPB1dU11z797t07oUWLFmK6+vXr61RmU1HcDsbq1ebNmzeSp79zbgttZWVlCS9evBDOnz8vfP7555I869evr/J8pM7r16+FsLAwYcKECYKDg4OYX9myZYWEhASd8zNGj1GqXhYWFsKQIUOE58+f61wuuffv3wuRkZHChg0bhKpVq0qeXvjnn3/0zldu7dq1Yp52dna5nt7S1aFDhyTbQF1vbZq2HwChYsWKwo4dO7Ra9ty5cyXXkYkTJwo3b94UkpKShBcvXghHjx4VmjZtKqbp3Lmz2l4Mcl7rlL1cXFyE2bNnC2lpaTpvK7mC0LuH4rZr0qSJxvRHjhwR01tZWeWavmXLFnF6mTJlNOZ39+5dyXZVd31///69+MRQzrLqui0Ve45wcnIS/ve//wm3b98W3r9/Lzx9+lTYvXu3ULNmTTHNqFGjNOZpTIo9c7m4uAhnz56VTI+Pjxfat2+fa79U1mOUYh3v+++/17jsqVOniulbt25trFVS6+HDh4Kzs7O43P79+wuvXr3Kle6ff/4Rn6q0sLAQrl27pjLPU6dOiU/ZFitWTHjy5Ilk+tmzZ8XpTk5Oas9Zqampkh7xateuLdy6dStXun///VeoVq2amG7VqlVq19tY9UFTPJVo6Dw5LVu2TFLfWr16da40u3btkvTaqe43iSLFY6BevXqCTCYTpk+frrQHoevXryvteSszM1MoUaKEmM/UqVOV9qLw4cMHITg4WGjSpInw+vXrXNPv3r0rfPXVVwa/VDHmedAUcvaKMHbsWCE+Pj5Xuvj4eGHmzJmCtbW1yrzGjRsn5mNraysEBwfnSrNy5UrJ09uajjlBkO7PpUqVEry9vQUrKythxYoVSuunZ86cUdrz3MWLFyW9TH377bdCYmKiJE1WVpawbt068YlWJycnldc5Y+2DpqL41LxiGXM+Tbty5UpJmr1796rMs3PnzmK6cuXKCSEhIbnSPHnyRNJrmDbnBcWy1qlTRzA3NxfKlCmj9Onp9PR0ISgoSJgzZ44WW8Ewf//9t+S3woIFC3LVR9PS0oR58+aJ6by9vdX2hjZ79mwxbYMGDXLtM//73/8k1y519V9TXItTUlIkT2a7ubkJO3fuVJr26tWrQps2bVT25GDMHqOMNU9O3bp1k6xrzn06IyNDmDJliuQY0aZtRrFdq2rVqoKjo6Pg7OwsbN++PVcPR1lZWcI///wj6b138+bNBl+X1PW0o/g78LfffpNMKyg9RgHZv6Nz1j8yMjKESZMmSepjqvbplJQUoU6dOsLatWuFp0+fqlzu1q1bxfYPe3t7pceRurLWq1dPACBUr15dCA0NzZX2/fv3ws8//yz8+uuvuaaZsn1QEApej1HGvhbnZIzzgjKGbEfF3ldbtWqltC0sKytLOHfunDBgwACl9SdBkG777t27i/+/du2apG7VqVMncdrt27fF308565rTp083+Dxz8eJFrbeDsdsHc2rQoIGY52effWZwftoydn3QVD1GKfYMuGfPHsm5dsyYMeK0w4cP63QMmeqYM7Re4OXlJd7bSkhIkFzbPD09xfNOcnKyUL58eQHIbi9TVu8yVn1w//79Bh9zS5cu1XobmqrHKMX9x8nJSeuet03BGNdiU/cYZWZmJrRv3z5XL3ahoaFiD7EAhDVr1mi93qb4XvXNV3GePXv2iP9XbCMFIPz999/iNMW6tbK6RGxsrKTe06pVKyE6OjpXunPnzgmenp4q60dv3741SluPLr0+m6IenZ6eLullc+vWrUbJl/47GBhFRcbr16/FH+7aDi8EQOUPrISEBMkJdt26dUrT5QyMUjVkS3p6ulCjRg0xnTY39gShYAZGXb9+XbLO2nSvHR4ermdJ/49iRbNfv35azVMQA6MaNmyo1XooDpklk8lUDjuWcx88cOCAyjIrNg7oMsSLqSiW21iBUYo3LitWrKjz/Io3jpW92rZtq1MFUHE4JGWvevXqKQ0E0IYpA6Pkr9KlS6u9WZBTzu66c77kDeCGSklJkXRJa2jQR1ZWltC4cWPJ96yOttsPkDaqqLNw4UKNXY2XKVNGWLBggcYucbUJjJK/mjRpIsTFxWm7qSQKwk1sxS6ru3btKv7/+vXrQq1atQRHR0dh+PDhYmPc1atXJeufs3FG8Yaon5+f+P/Hjx8LTZo0ERwcHIRu3bqJQ/bFxcVJ8vv3339VllUesGNmZpbruqjrtszMzBTGjh2r8futVKmSyjqMqSQmJgq2trZiGf744w+l6ZKSknJ1U6/sfFi3bl1xumLD144dOwRPT0/B3d1dcpNoyZIlknNsXlAc9qNPnz5q0+7YsUNMq2lYVsUgr08++US8eZiYmCgZgkjTsBqrVq0S01auXFlpoIdcVFSUGNzj4+OjcogSY9YHC3pgVGZmpmRfnTp1qsq0isNX2tvb57q5pUzO41afoWEjIiLE+TUNy6qOIUNCKL5UMeZ50NhevnwpCWybPn26xnlU7dPx8fGS86Cym79y3333nWT/1HSNV9yf5a9du3ZpLGtO8sZyAMLkyZPVpl2wYIGYVtWQncbaB00lZ2CUumG6Ro0aJaknKbN//34xjZubW67gVUVxcXFCqVKlxPOCunOwsrI6OzsLDx8+1Go9TSXnEFxr165Vm15xG+YMNFGUmZkpCRybOXOmOO3KlStiIIC9vb0QGRmpdpmmuBYr/ra0s7NTGlSsKCsrS+V5oaAHRt25c0ey36kb+kZxCBRtHkzIeW2xtLRUepNOlZxtHvq8VG2TlJQU8SZw7dq1c52DC1JglLohM9u1ayemGzBggMHLVryBp+4apqqsZcuWNelwqfq0DwpCwQuMMva1OKeCGBilGDSj6byujuK237Jli2Ra5cqVxWmbNm2STJM/BOLm5ib5v77DMym+NO0DpmwfVKT4MBgA4cKFCwbnqS1j1wdNFRilGPibmZkpqbefOXNGnJaVlSUOcV63bl2N5S2ogVHDhw+XTFN8wGno0KGSaV9++aU4LWe9x5j1QV3aplW9dPnNaKrAKMWg8pzbOa8Z41ps6sAoX19flQ9NzJo1S0ynGNSqiSm+V33zlae3sbGRtKVduHBBnGZtbS15oOnSpUvitAkTJuTKc+LEieL0gIAAtQ84Ky4n57CsytoR9Hnpcp0yRT1asdMJJycntQ/hECmjeXwXokLCzc1NHM4mJiZGMuyZKlWrVkXXrl2VTnNycsKoUaPEz+q60JdzcXFRObSRhYUFxo8fr1N+BdWyZcvE9y1atMCQIUM0zlO7dm2Dl9u8eXPxfXh4uMH55Qf50Bty6rpob9euHT7++GMAgCAIkiE9VKlRowbatWundJqvry9q1qwpflYc1qMoUexKc+DAgUbL18fHBwcPHsTBgwdRvHhxg/NzdXXFli1bcPHiRaVdpJraxx9/jCVLliAsLAzx8fFIT0/Hq1evcPDgQfTo0UNM9+zZM3Tu3DlXN7W6kslkGDFiBB49eoQ+ffoYWnzMmDEDDx8+BAA4Oztj1qxZBuW3du1anDt3DkD2+fqnn35Sm97BwQEjRozAnj178PjxY3z48AHJycn4999/sXjxYnh6eoppV6xYgUWLFmksw3fffYd79+6he/fuSqfb2NigX79+6Nu3r8Yh+szNzdGlSxds3LgRt2/fRlJSElJTUxEdHY0tW7agTp06YtqQkBAMGDAAgiBoLGNB9P79e/G94tA0Y8aMwfXr1/Hu3TusW7dO7No659CaycnJWuX3/fffIyQkBElJSQgODhb3EU35yd2+fVvcD4YNG2bwddHMzAxLly7F9evXVXZJXqxYMQwcOBBdunQxaFm6+ueff8Th8cqVK4devXopTWdvb48xY8ZozE/Zd5KSkoJBgwYhJiYGr169wldffYVHjx4BkH4nqr4PY3r69Cl27doFALCyspLUk5Tp3bs3SpcuDSB7GAr5MBLKzJo1C/Xr1wcAnDhxAj///DMA4OuvvxbXt3///hgwYIDaZSqWaeHChWqHnPbx8UG3bt0AANHR0bh06ZLGPPOyPpgfzpw5g6dPnwLI3r++++47lWk///xzsRvv5ORkBAcH67Qsf39/fPPNNzqXUd6lOgCUKlVK5/nzgrHPg8b2yy+/4MOHDwCAypUrazWUkqp1CA4OFs+DpUuXVjsM0ZQpU2BlZQUg+5gLCQnRqdxdunQRj1ltXbhwQTy2S5YsqXYYTiB7SAJLS0sAwF9//aU0TWHYBxVNmTJF5bRJkyaJ78+dO6d0WC/Fc+D333+PMmXKqMzPxcVFPEcmJyfjwIEDOpV15syZKFu2rE7zGNvOnTvFIbj8/PwwcuRItenHjRsnvle1zwDZ9ZktW7bAxcUFADB37lyEhobiw4cP6N+/vziE0tKlS9UOb2yqa7FiPlOmTNE4pK9MJitw5zZtKbY3NGjQQO2QhYrDT4WEhCA6OlqnZX355ZeoV6+e7oU0gblz54rDqS9fvlyrodDzg5mZmeTclJNi3SQ4OFgcTlJfhra//fzzz0ZpN1GlKLQPmuJaXBgo1heMNfRNzmukj4+P+L5cuXKSaV5eXgCyh8NV9zssLxmzfTApKUlyP6Nv375qhws3tsJSH1TcZ8zMzMT9ApDuMzKZTKzjaXOvqaDS5xgBcq+zqeqDhdXr16+xb98+8XNBGkYPMP21WB9jxoyBra2t0mmdO3cW30dERORVkUzCy8sL5ubm4mfFY87LywsWFhaSz3I5j7mUlBT8+uuv4ufly5eLdQFlGjRogIYNGwLI/h2rODx2UaE49GzPnj1V7k9EqlhoTkJUcLx8+RIXLlzA7du3ER8fj/fv30tu5Cqe6F+8eKGxAq5qnGS5du3aiTfbL1++DEEQIJPJVKZv0aJFrhukOfOTe/DgAd6+fQtXV1e1ZSiI5GPiAsCIESOMmvfdu3dx6dIlPHz4EAkJCSobcwrrj5HQ0FDxfYUKFdQ27gLZFcLLly/nmlcVVWOXKy7z+vXrALIr70XN1atXce3aNQDZP171CYz69NNPxUaDxMRERERE4OrVq4iOjkafPn3w3XffYdKkSZLKrTrt2rUTz0VJSUm4d+8eLl26hLdv32L48OG4cuUK5s6dm6eVuLFjxyptdCtRogTatm2Ltm3bYvPmzRg0aBAEQcDjx48xZ84cLF26VGPeZcuWFcfzTk9Px/Pnz3Hu3DnExsbi119/RWRkJFauXCkGsurjyJEjksClX3/9Fe7u7nrnd+PGDXz77bfi53nz5mm86fH48WMUK1Ys1/+rVq2KqlWrYtCgQejYsSMuXrwIIDsIcuDAgWqvS0ePHsWUKVMQHh4OW1tbBAQEwMfHB+/fv0doaCju37+PH3/8EcuWLcPy5cvVnn937typtHze3t7o378/+vTpg9GjR4vjje/fvx///POPymDhgkx+ExuAeGMZADIzM5Wmz3mdlt+4NlV+cl988QXS09Ph7OyMefPmKU2jqz/++AMzZszAnTt34OTkhGbNmsHDwwMJCQkICQnBs2fP8MMPP+Cnn37Cpk2bJI0MpiTf7wGgTZs2autO7du3V3uTB1D+nQiCgKysLEk6eZ1Q8TtR9X0Y09GjR8X9IzAwUOP5SH7T9NmzZ3j//j1u3rwJf39/pWktLCywbds2+Pn54d27d5g6dSrev3+PoKAgANnn3NWrV6td3qNHj3D37l0A2cFoHTp00LhOdevWFYP4L168KDasKDJlfbCgUayDBQQEqA0ss7CwQPv27fH777+L8+rSQDpkyBC1x4wqijeWIiIicOfOHY31TGUCAwNNFihrivOgMR05ckR8P3ToUK3resoo7jPt2rVTm5eLiwsCAgJw/PhxcV5VAa/KDBs2TOfyHTp0SHzfpUsXtb9hgeyA8CpVquDmzZu4e/cuYmNjczWyG2sfzAtubm5qgzJ8fX1RpUoVREZGQhAEXLp0SdJg/eHDB5w6dUr83Lt3b43LrFu3rvj+4sWL6Nu3r1ZltbCwwKBBg7RKa0qK+4w261upUiU4OTkhMTFR4+9Yb29v/Prrr+jduzcyMjIwYMAANG/eHLdv3wYA9OjRA8OHD1ebhymuxQ8ePBAfxJDJZPj888/V5lnYKX5PHTt2VJu2WrVqKF++vBhQdOnSJckNH010PW8FBQWJdR9jioyMFAN2+/bti8aNGxt9Gcbi7++vNoikSZMmcHBwQFJSEpKTk3Hr1i3JwzA5ZWVlISwsDFeuXEFMTAwSExMlQSLyG9+A7u1v7u7u6NSpk07zKFPU2wdNcS0uDDw8PPD48WMAwN9//63VwxWayINr5RTr6jnbReTTBEFAQkKC2C4fFRVlcDk0yYv2wVGjRonXLi8vL6xYscIoZddWYakP6rPPKAZ9FTb6rC+Qe52NWR+cOXOmxoDQgm7Lli1iEH/ZsmXRpEmTfC7R/zHWtdjY1N27qlChgvi+sN+3MtYxd/HiRSQkJADIPp78/Pw0Lrtu3bpixwwXL14UH6Ly9fUttA9Fy7169Qr79+8XPxe0YEQqHBgYRYXCnTt3MHHiRBw4cEDljcmctOkhQNON72rVqonvExMT8eTJE3h7e+udX8mSJVG8eHHExsYCyP6BoEujd0Hw4sULyROzxqrwHTx4EN9//z2uXLmiVfq86AHCFOSNuwAkvTepohg8ok0PTzmf8sjJ3t5efJ+UlKQxv8JGsbeopk2b6vWk1cSJE3P97/bt2/j8889x7tw5TJs2DRcuXMCuXbvURujLKWv0ffLkCcaMGYN//vkHP//8M86ePYtjx47ByclJ5/LqQ1nATE4DBw7EtWvXxN5JfvvtNyxYsEDSg44yH330EVauXCn5X1paGtasWYNJkybhzJkzqF+/Pvbt24dPPvlE57LfuXMHffr0EQMixowZo9WPcVVevXqFrl27isETnTt3VroP5KRpG7q6umLXrl2oWLEikpOT8eHDB/z6668qe59YunQpxo8fD0EQ0LZtWwQFBaFkyZKSNFu2bMGIESOQkpKCkSNHwsnJSWXvW5rKZ25ujpUrVyI8PFwMvly+fHmhDIxS3CfT0tLE90uXLsXgwYMRHR2NXr16iT3q5GxQz9noqCq/2bNn4/79+7h16xZatGghBtNpyg/Ivplz+vRpANlP2JcoUUKndVRm3LhxYrDioEGDsHz5csk5JDMzE0uXLsV3332HhIQE9OzZE8ePH0dAQIDBy9ZE8ckuxbqUMpUrV4aFhYXap3WVfSd2dnb47bffMGnSJGRlZWH69OniNVDxO8mLoFPFHpViYmIwevRojfPcuXNHfP/s2TOVgVFA9rV91apV+Oyzz5CWliaeR+RBU5quHYrls7Ky0qo3IsU6h+KNMTlT1QcLKlPX3xTpuy3Lli0LPz8/XLt2DWlpaWjSpAlGjRqFTp06oXbt2pInEvODKc6DxhYWFia+N3Sf1mefkQdG6brP6HMjX/G8cOvWLa3OW2/evBHfx8TE5LoZW9D3QUXVqlXTGIBYrVo1REZGAsi+rin2qHr9+nXxemRhYaFVoJ+81zlA+XlVXTm0qbubmuI+c/r0aa3WQd52k5CQgKSkJDg4OKhM26tXLwwdOhTr16/H/fv3cf/+fQBAmTJl8Ntvv+lUPmNdi+V1ZCD7Rk3OunlRo895Sx4Ypct5y9nZGdWrV9e9gCbwxRdfIC0tDXZ2dvjxxx/zuzhqaWpvtLCwQKVKlcQ2tYiICKWBUZmZmVixYgUWL14sOS+po2v7W4MGDQwKLv6vtA+a4lpcGHTv3l38HTty5EicP38ePXv2RJMmTSRtlrrIGVSm+IBTzmmK7XiG9qymK1O3Dy5atAhbt24FkL0N/vzzzzx/ILuw1Af12Wfyen8xJmMdI6auDxY2ivchPvvsM70ecDIVQ6/FpqLu3lVRum9limMuNTVVq7qCYhCiLr87C4MtW7aI7ce+vr6F7t46FQwFoyZCpMbRo0fRuXNnSW8B2tCmO1xNDVuOjo6wsbERlx0bG6s2MEqbhjJ3d3cxMEr+tzB59eqV+N7S0lIyXJS+5s6dix9++EGneQpKd8e6iouLE99rs78oplGcVxVNPy4UK+g5e9oo7NLS0iTd/xszYrxq1ao4duwYmjZtisuXL2Pfvn1YsGCBzvutnJeXF3bt2oVu3bphz549uHz5MsaNGyf2LlFQTJgwQQyMev/+PS5cuCDpsl5bVlZWGDt2LMqUKYOePXsiJSUFffr0QWRkpE4Nea9evUL79u3FY6Ft27Zi+fTx/v17dOrUSRyOyt/fH1u3bjXaD1kPDw/0799f7PL2+PHjSgOjLly4gG+//RaCIKBSpUrYtWuX0mCOAQMGIDk5WewW/euvv0a3bt00Plmqirm5OcaNG4d+/foBAM6fP48PHz5oDH4raOzs7MT3inWFunXr4tatW7nS5/yRmbPxVVV+5cqVk/SEpG1+sbGxYrBdlSpVtPoRq8kff/whNiY3bdoU69evzzXsh7m5Ob799lu8efMGCxYsQHp6OkaPHi32GmhKivUbTT02WFpawsXFRdLIn5Oq72TQoEFKe9FQ/E70bVzXxcuXL8X3t27dUrrfqaPNk6cDBw7EwYMHJUMxT58+XathERTLFxcXh1WrVhlcPlPUBwsyU9ffFBkyXNbvv/+OFi1aID4+Hm/evMHcuXPFp85r1qyJ2rVro0WLFujYsaPe1w59mOI8aGyJiYmS84uhw5jk1T7j7Oys101RxfPC+fPncf78eZ3mV3XeKqj7YE7a/m6Xy/m7XXH7ZWRkGOW8qkp+D6Enp7jOhw8fxuHDh3WaPz4+XuNv1eXLl+Ps2bO4d+8egP8bZk+bwDBTXIsVr3X5MfR5Xsur85avr2+BuHG4adMmsee3KVOmqB0OsyAw9LwFZLeZdOvWTefhPHVtfzPkvPVfah801bW4oJs5cyaOHz+OmzdvIj09HevWrcO6detgZmaGqlWrws/PDwEBAejZs6fWQT05b/4rBt+omybv6SU/Gat98K+//pL0xLx+/fo8HUJPUWGoD+qzzxSE/UVfxjpG8qI+WFiEh4fj5s2b4ueC1nNNQfkNkZO671+xfljYezYyxTEXHR1t0t+dhYFiMOLAgQMLxG8KKnwK5sDpRP/f27dv8emnn4qN1B4eHli0aBGuX7+Od+/eISsrC4IgiK9mzZqJ82pz8dTm5q9iGk1PIRk7v4Lo3bt34ntj3Gw8ceKEpNGjbt262LhxI+7fvy8Olaj4KuwUv3Nt9hfF4AhtIuX/y5WBPXv24O3btwCyt1uvXr2Mmr+NjY3YzT6QPU63rgGbiszMzLBs2TLx88aNGwtcFL+HhwfKly8vfpYPxaSvHj16IDAwEEB2l7jaPP0tl5ycjI4dO4pdgtepUwd//fWX3k+bZWZmok+fPuKTF76+vti/f7/Rf6Ar9s6javstXLhQ0gOWuh5uhg0bJvay8fr1a50btdWVLzU1FdHR0Qbllx8UuydWF1wjp9gds5WVVa7tbUh+OecHsm+0yPNZunSpVj3NaTJ//nzx/cSJE3MFRSmaOHGi+KP7xo0buHr1qsHL10TXa52mNIZ8Jzm/D1NQrBvpQ9ubOTmfrNO2ocsU5TN2fbCgM3X9TZEhvUfWrl0b165dw6BBgyQBhSkpKQgNDcWaNWvQs2dPVKhQASdOnNB7OboyxXnQ2HIeJ4bu13m1z+i7v5jqvFVQ98GcDP3dnlfnfcCwc4Ix5cU629raSoadtra2Vjt0mCJe6wxX0M9bxpSQkIAJEyYAyP4dKH9fkBmjvXHu3LmS3489evTAvn37EBMTg9TUVEnb28mTJ8V0urbF6fsd/9faB/PyWlKQODs748KFC5g+fbrknJ+VlYWIiAhs3boVo0aNgre3t6TNzBQKyn5kaPvgmTNnMHDgQHF95s+fj/79+xu9nNoqLPVB0iznMfJfPW8poxig0bhxY42jeOS1glDfUua/fO9KGzzmVAsLC5M8/FLQghGp8GCPUVSg/frrr+KTZxUrVsT58+fh5uamMr2uNx60CWpQTKNYmc+L/AoiR0dH8b0xArsWLlwovu/Zsyd27NihspvPwt6FJqC61wtV5MN7Af+NxlhDrF+/XnzfrVs3yb5qLAEBAbC1tUVKSgri4+MRFhZm0FArvr6+qFy5Mu7cuYPMzEycPHlSHO6roChZsqQ4RIIxerlr06aN+GTu0aNHJU+0qZKRkYFevXqJw1lUqFABBw4cMCiI6YsvvsDevXsBAG5ubjh8+LDWN150ofhkr7Ltl5WVJQ6dA0DjE30WFhb4+OOPxQbtsLAwcaxwQ8unqowFnWLwnjaNh4ppFOdV9r8XL14gKytLbeCRYn729vaSxl3g/7owtrKywpIlS7BkyRKl+ciHjAGyA5jatm0rfj506JD4/tWrV5JenzTtM8WLF0flypXFIU7CwsLUDttmDIo3ynStGylTvnx5HDt2DIBxvmNjU7zeLF68GOPHjzf6Mk6fPi0JiAOAr776Co0aNdLYAKdYvk6dOmHPnj0Gl8fY9UFd5EePl3lZf1N3vtGGj48PgoKCsGbNGly8eBHh4eE4ffo0Tp06Jdalnz59ivbt2+PixYvw8/OTzH/v3j2j3IRSHFbX2OdBU8hZb0xOToazs7Pe+eXVPqPv/qK4vn///Te6d++uVz7KGLoP5gVDf7crbr8aNWrgxo0bxitcDoaeE4zF0dFRbJsJDw9H7dq1jb6MefPm4ezZs+LnlJQU9O3bFxcuXJAMP6GqfHLGuhb/F6918psvBe28tWXLFqU9t+qiQYMG4m/tR48eiYH0MplM7XDiik/qL168GDt27AAAtGrVShxaOy8Yet5KS0sTe5wFgGXLluHrr79WmZch7W/6nrf+a+2DprwWF3T29vaYNWsWZsyYgWvXruHSpUu4cOECjh49iufPnwPI7uH7m2++gbW1tdhrtinNmDFDfNhSXwMHDkT9+vX1mlff9sGIiAh06dJF7DV5zJgxmDx5sl5lMKb8qA8WtZERCiJj1gcPHDhg8MOeFStWxNixYw3KQx+pqamSUSuU9WSe3/LiNwSPOdNTrCuMGTMGy5cv1zuv2NhYpSNZ6Gr27Nn5MpSv4r2/Ro0aoUKFCnleBioaGBhFBZrizeKZM2eqDYoCdB8zVbGBQ5l3795JGhY0deGuKT9A2h27Nl3CFzSKXXOnp6cjJiZG7+FTMjMzxQAJmUyGpUuXqh37+OnTp3otpyBR/M5fvHihMb3iPlUY95e8EhMTgyNHjoifTRUxbmZmhmLFiomNwMbYJxW7Bi+I+/j79+/F98YIztN1fQVBwLBhw3Dw4EEAQKlSpXD48GGNw3OpM336dLG3KgcHBxw4cACVKlXSOz91NG2/N2/eSBp1tendRvFcoE3vOdqWDyicAZiVK1cW30dHRyMpKUlt0Jzi0yWK8yr7X0ZGBu7cuYOqVatqlV/FihVVPv2UlpamdTfjcXFxKtNGRUVJPuf1PqMNxeXl7FErp/T0dI3Dryh+JxERERqXr+k7NjbF85FiPc9YYmNjMWDAALHR6ZNPPsGJEyeQmJiIfv36ISQkRG3veaYonzHrg4C00U7Tk9uGPjGnj8JYf7O1tUXz5s3RvHlzTJgwAampqVi3bh0mT56MpKQkpKamYvbs2di1a5dkvmfPnuncPbsyioFRcsY6D5qCk5OTZAj1qKgog/bpgr7PmPq8Bei/D+YFQ3+358X2K2jc3d3F67Up1vn8+fOYNWsWgOz66EcffYRLly7hypUrmDJlChYvXqyxfHKmuNblrH/pozBc6+TLLWjnrWPHjmHjxo0G5ZGUlKQ0yODRo0fi0OqaKA7TmPNhCFMz9Lx16dIl8futVKmS2qAoIO/bJv6L7YP/xWtJTmZmZqhduzZq164tBj/t378f48aNE4dVnT59Oj7//HO1+4MxbNy40eAetOvWrat3YBSge3vZ48eP0bZtW3GopD59+pi8ly1dGVofLOjXzv8aY9YHL126ZPDvzmbNmuVLYNTu3bvF7WBjY4PevXvneRlMhcdcwWLMukJiYqJR2nomTJiQ54FRHz58wPbt28XP7C2KDFEwHj0jUkEx0ElTBPqjR4+0arxRpHjjTBl57wpAduOct7e3Qfm9fPlS0htHtWrVNJZR8UZXQYjCLlWqFMqUKSN+VnyiU1dv3rxBWloagOyLfOnSpdWmv3Dhgs7LKGjdcyp+54rjUKui+PSxNvvLf9WmTZuQmZkJIHv4t5YtW5pkOZmZmZJj2Nra2uA8FSu1xsjPmDIyMiS9Nxhyg1BO1/X97rvvsGnTJgDZASCHDx82qHviVatWYc6cOQCye64IDg7Gxx9/rHd+mty+fVt8r2z75fyRqc3Y34pBJOqG3dOGYvkAmKTXLFOrXr262EV0VlaWODyiKufPnxffN27cONf0YsWKoUqVKuJnTdceTfkZW37vM9pQvF5pCmSKjIwUz9+qNGrUSHx//fp1SQ8FOSUnJ0uunXnxnSieQ+Q94xjT8OHDxYbx/v374/Dhw2KDe2hoqMYnvhTLd/36dfFpYkMYsz4ISHs10NQDgLwXQ10YWh8sCvU3a2trfPXVV9i8ebP4vzNnzuRjiQqeunXriu8N3acL+j5j6vOWMgVpH/z33381NvQrtgXk/E5q1aol9mD08uXLQjkUsa5Muc8kJCSgf//+Yn1g6dKl2Llzpxj8vWTJEo2BkqYon2Ke9+/f17m9KSde68gQmtobMzIyJEO35/xOFNtXtek9Vp/2N0PkRfsgYPw2VkPyy4trcUFrU9ZGhw4dcOrUKfF36+vXr3O1WxRVurSXvX37Fm3atBF/J7Zp0wabNm0qcO3gOelaHzT1tdPYCvr2N1R+/IYoiBSH0evcubNBPQ0XNIXtmCvqeMxl2717t9j+bW1tjU8//TR/C0SFGgOjqEBTjFDW1MOBYoVEW5q665T3UAJkX4Q0dUF5/PhxtTebFPPz9fXVqscTxYpVXvTyoI1WrVqJ7+W9ruhDcXvGx8drvDGqz3dsY2Mjvld3IzWvKD45dP/+fdy5c0dtesXhburVq2eychV2QUFB4vsBAwaY7Emyc+fOSXqR8/X1NSi/6OhoSeCRofkZ26FDh8QfQTKZDAEBAQbnqdgToKb1Xbx4MX766ScA2cEc+/btQ82aNfVe9l9//SU+GWtmZoatW7eaLIhObufOneL7pk2b5pru5uYGS0tL8bNikI0ymZmZCAsLEz9rCtjVpXxVq1ZFiRIlDMovP1haWkq+xz///FNl2sePH0uG4Wjfvr3SdIr/V5dfSkoK9u3bpza/a9euQRAEja8ZM2aI8zRr1kwyTVHO4DVN+0x8fLzkWmPoPqMNxeH9jhw5ovbms2LdSJWPP/5Y7DU0NTUV//zzj8q0wcHBSE9PB5A9VGSdOnW0LbbeWrZsKdZpzpw5Y9TGoTVr1iA4OBhA9nAEq1atgoWFBbZs2SL28LZw4UKcOHFCZR7ly5cXhxTM+ZSVIYxVHwSyA1/l58LY2Fi1w1go9hCpLUPrg4r1t7NnzyIhIUFl2oyMDMl+XdDqb61btxbfK3vCMzAwUKtzlqaXImOfB01FcZ/esGGDxt8m6ijuMwcPHlSbV1xcHEJCQsTPebHPtGnTRny/a9cuJCYmmnyZcpr2wbzw5s0btYHUUVFRiIyMFD/n/E5sbW0l9WJ9fqcWNor7jOJDKcYwcuRIsUemrl27Yvjw4fDy8sLq1asBZAeFDxo0SG2POaa4FpcvX158IEMQBKxbt86g/BTr2fKeUFTJ72udYv1WmYiICDx8+FD8bOrzVlBQkMHXJcU2Az8/P63n8/HxEefbsGGD0vzywtWrV8UhxpQJCQkRf7vb2tqiRo0akum6tK8mJCSI9c+8khftg4Dx21gNyS8vrsUFsU1ZG56enqhevbr4OS/qC1FRUQafZwYPHqz38nVpH0xOTkaHDh3EukrDhg3x999/S9qWCjpt64PaXjtTU1MLxEMfBe0+hLEZsz44c+ZMg485eU+Deenp06c4evSo+LkgDqNnCFO3zZhCUT7uGjZsKA6nFxUVZdA+7+vra5S2nvy4f5UzGFGb0ROIVGFgFBVo8ps4ACQVjpwiIyM1dq2uar7du3crnZaYmIi1a9eKn/v27asxv4SEBKxZs0bptIyMDCxZskSn/ACgQoUK4hM+d+/eLRDdKyt2UXr8+HGtGiSU9Rjh6uoq/kjX9ANm06ZNej25Xbx4cXH7vX79WqueNUzJ19dXcsN49uzZKtMePHhQEgDRr18/k5atsDp37pzkpr+uXWlqapSWS09Px9SpU8XPpUqVgp+fn975Adm9IclZWVnhk08+0XpefWl7DLx79w4TJ04UP7do0QIlS5bMlS4qKgoZGRla5XngwAGcPHlS/NyuXTuVaTdt2iQu39LSEjt37jSo55djx45JhqJau3YtevbsqXM+mZmZGp+YkVu3bh3Cw8PFz/3798+VxtzcHE2aNBE/r1ixItfwdoo2bdokuQ4oC+zS9ju+ceOGeMNJVfkKC8UGyM2bN6vsuWHu3LniDfZ69eqpfLp90KBB4pN+R44cUflUzpIlS8RGPC8vL5MH2gHZgU1ly5YVPy9atEjt079LliwRG6vMzc0RGBho6iKia9euYsPEgwcPVHaLn5ycjBUrVmjMz8zMTHJunz9/vtIGuIyMDCxYsED8rPg9mpKPjw+6du0KIPtJ7OHDh4vBWepkZmbi2rVrKqdHRETg22+/BZC9DTZt2iTWmypUqCDWK7OysjBw4EC1DVaKdbdJkyZpPfy04jlMXZ6G1AeB7KfZFYc0VbXPPH78WPJ0sbYMrQ82bdpU7LkgNTUVP/74o8q0v/32m3jj0tbWFt26ddO5vPpISEjQ6nqsuM9p6o3hv2bkyJHiuevOnTtq6+lyqvZpxfPgs2fPsH79epV5LFiwQOwlo0yZMkoDqY2tSZMmYo/MiYmJ+Oqrr7QKQEtNTVW5zoVtH5w/f77KaYrHeMOGDZU2/iqeA3/88UdJ7znq3Lp1S/y+C5PevXuLwdkPHz7U2FuhXGJiotrfR+vXr8cff/wBIPv3lWKgbd++fcXfwC9fvsSgQYNU7qemuhYrDjc2f/58rYb0VZXmo48+Et/fvHlT0ruPor///lvjA1TKKD54pypvdRTbqC5evKi2DU7x/KjqGCHjEgQBCxcuVDld8bzVtWvXXL3EKravhoeHS3rCzmncuHF5HriaF+2DACQ9AysGJevLkPxMcS1WV77Q0FCtzoumlJGRofYBA7n3799Lrh2Ftc5qivbB9PR09OjRQ2yjqF69Ovbv3y8+NJPfjF0fVLx2njhxQuXvuJUrV2q1b5maodfigs5U9cHCZOPGjWIbXMmSJSXBYkWBqdtmTKEoH3d2dnb4/PPPxc9ffvml1nW0K1eumKpYeerJkyc4duyY+JnD6JHBBKIC7LfffhMACAAEa2trYc+ePbnSnDx5UvD09BQACFZWVmL6kydPKs2zWbNmYhoAQvHixYVz585J0sTFxQmtWrUS05QuXVp49+6d0vwGDRokyc/a2lrYvXu3JE1KSoowcOBAMY29vb3w5MkTrbfDJ598Is47ePBgISsrS+t5VVEs94YNG3Se//PPPxfnNzc3F+bNmyekpKTkShcbGytMmzZNqFChgtJ8+vfvL+ZToUIFISIiQjI9MzNTWL16tWBpaSn5fnU5ffn5+YnzzJw5U7cVVeHRo0dinj4+PjrNGxwcLFmP77//XsjIyJCkOXPmjODq6iqm6dKli8r8dPkuDf3ejU1xO6g6ZjUZNmyYmIe/v7/O85uZmQm9e/cWDhw4IKSnpytNExkZKTkOAQjLly9XmtbX11do27at8Ndffwnv379XmubJkyfCp59+Kslv/PjxOpd9xowZ4vzNmjXTap5vvvlGaNWqlRAcHCx8+PBBaZrw8HDJcWNhYSFcunRJZRl8fHyEhQsXCtHR0UrTpKamCmvWrBHs7e3FPMuUKSMkJycrTb93717BwsJCACCYmZkJ27Zt02rdVLl06ZLg4OAgLnvBggV65xUXFye4uroK06dPF+7du6c0TXJysjB37lzB3Nxcq2N4586dkn2hVatWwvPnz3Ol+/PPPyXbsGXLlkrz+/jjj4Vhw4YJ586dU3q9yMrKEv766y/JOaZMmTIqr3Pq6LMPmkrt2rXFsvj5+QkxMTGS6StXrhRkMpmYZv/+/Wrz69atm+Q8f/v2bcn0v//+W7C2thbTrFq1yqDy67Itf/rpJ8k+M2DAACE+Pl6SJjMzU1ixYoVkPxw+fLhBZdTF119/rbauFR8fL7Rv316yHgCER48eKc3v+fPngq2trZjus88+k5zDlNW1Xr16ZcpVlLh7967g6OgoLr9p06bCrVu3lKaNiYkRFi9eLJQvX17luSElJUWoUaOGmN93332nNF3nzp3FNJ07d1ZZvpSUFMl53cvLSzhw4IDStElJScKWLVuEpk2bCi4uLmrX21j1QUEQhG+//VbMq0SJEsKVK1ck0yMjI4WPPvpIUh/UpQ5maH1wyZIl4vxmZmbCmjVrcqXZtWuXYGNjI6YbO3asVnnrU7/NKTg4WPDy8hLmzZsnPHjwQGmaa9euCVWqVBGXNWbMGL2XZwoF4Zoyb948yfcxduzYXOdXQci+1v/000+Cvb29yrzGjh0r5mNraysEBwfnSrNq1SrJtWnJkiUay2jI7xBFISEhYn0LgNC9e3chKipKadoHDx4Is2bNEjw8PFTu1wV9H9ywYUOua860adOEzMxMSbqVK1dK0ij73gQhuz7Vrl07MZ2Li4uwZcuWXPkJQnZdODg4WOjYsaMgk8mEuLg4rcs6aNAgPdfY+LZv3y7ZNqNGjRJev36tNO2NGzeECRMmCC4uLir36zt37kjqtsquS3FxcYKXl5eYZvHixSrLZ+xrsSBkXz9r1qwp5unm5ibs3LlT5fr07NlT7XdWp04dMa8GDRrkqqvs379fcHR0lFzrtN0H3r59K5iZmYnznTp1Sqv5FHXp0kWyriEhIZLpGRkZwtSpU7U6RhSdPHky38/v+vLx8RHLntdtKIrXRQCCTCbLVf/IzMwUJk2aJKmjhIWF5corMzNTcix16tRJePv2rSRNYmKiMGLECAGQtq9q850plnXGjBl6rW9etA+eOXNG8nvh5s2bepVVLioqSjzuzMzMhGPHjuk0v7GvxcqUL19ezH/69Ok6lU8VxbZ1Xdry4uLiBAcHB2HMmDHChQsXlLZXPH/+XPJbvEaNGkrzUmzfzPkbUt9pxmbs9sGsrCyhb9++Yrpy5crlavfIb8auD2ZkZAju7u5i2q5du+Zqu9qwYUOu84Ky85DifmusaTkZ41qsjD51Q8V5cl6/9J0mCMavD+YVxbqIIb+7K1SoIOYxbtw4I5bQcMa4FguC8dtmdNnu+nxHXbt2FecZMmSI1vOZoizq6k76Tnv9+rVQpkwZMU316tWF8+fPK11+bGyssHbtWsHPz0+oVauW1uU2FWPUo+fMmSPm4e7urvL+HZG2/m+gaaIC6LPPPsPPP/+M27dvIzU1FZ07d4a/vz8++ugjyGQyXLt2DTdv3gQA9OzZE69fv8bp06e1zr9Bgwa4ePEimjRpgiZNmqBy5cqIi4vD0aNHxS6MzczMsHbtWjg4OGidX9euXVGnTh3UqlUL79+/x/Hjx/H69Wsx3Y8//ogyZcpoXc5JkyaJQ6QEBQUhLCwMjRo1knSRO2XKFKVPWGzZskUybJCcYu8XW7ZskfRMJDd27FhUrFhRaZmWL1+OO3fu4MyZM8jMzMS0adOwcOFCBAQEwNPTEx8+fMCjR4/EJ5IUux9X9P333yM4OBjv37/H/fv3UatWLTRp0gTlypVDcnIyzp49i5iYGADZPamMGTNGzZZSrn///uKTKDNnzkRISAgCAgLg5OQkpqlWrZqkG1+52NhYpU8/KEZmx8bGYvTo0bnSVKxYUfIksVzXrl0xYMAAbNmyBUB2DyYbNmxAs2bNYGtri4iICISGhopPipUqVQqrVq3SbaULmJiYGJVDVskNHz4813G2bt061K1bV+U879+/lwxzpU/3tVlZWfjzzz/x559/ws7ODv7+/vDx8YGTkxPevXuHiIgIXL9+XfLkXo8ePfDll18qzU8QBBw6dAiHDh2CtbU1atSogQoVKsDZ2RkpKSm4c+cOwsLCJL2dNG7cWGOvBNOnT5cMrQgAL168EN+HhYXl6sGqbt26uYZ8EAQBR48exdGjR2Fraws/Pz+ULVsWjo6OSEpKwrVr1yRPH8pkMqxatUoypnVO0dHRmDRpEiZNmoSyZcuiZs2acHNzg0wmQ0xMDC5evCh5ItXe3h47duyQjFsu9+bNG/Tu3Vt8wszX1xfnzp3DuXPn1G4fIPscPGDAgFz/b9++vdjLU4kSJfDkyROlx2xOqo7ht2/fYvbs2Zg9ezZ8fHxQq1YtuLm5wczMDE+fPsX58+cl3eBXq1ZN7VALPXr0QL9+/bBt2zYA2T0kli1bFs2aNYOPjw/ev3+PS5cuSZ56cXd3xy+//KI0v7S0NPz+++/4/fff4erqCn9/f3h6esLW1hZv3rzBxYsXJb3FODo64p9//tF4nTPWPmgqmzdvRv369cX9uHz58mjTpg2KFSuGy5cv49atW2LakSNHajwnrV69GqGhoYiJiUF0dDRq1KiBVq1awcPDQzxPy7Vv3x6jRo0y2brlNGbMGOzdu1es82zZsgV79uxBs2bN4OHhgcTERJw7dw5PnjwR56lUqZLap8yNbc6cOThw4ADu37+P2NjYXHWtY8eOISEhAZUqVUJCQoLa4XGA7GvhL7/8Ij4ZtGnTJhw9ehTNmzcHAJw8eVIyvMi6devydGjIihUrYsuWLejdu7f4lHuNGjVQs2ZNVK9eHQ4ODoiNjcXNmzdx584d8ZqiOESEom+//Vas4/r7+2POnDlK061btw41atTAy5cvsWfPHqxcuVLp+c3GxgY7d+5E8+bN8eTJEzx58gTt27eHt7c36tWrB1dXV7x79w7379/H1atXxafJFYffUMZY9UEAGDVqFFasWIG0tDS8fv0adevWRf369eHh4YFnz57h8uXLyMrKwpw5c/DDDz+oLZcyhtQHgezjbvfu3Th9+jSysrLwxRdfYPHixWjYsCHMzc1x5coVSY8xVapUwdy5c3UupyGePHmCadOmYdq0afD19YWfnx9KlCiBd+/e4cGDB7h8+bKYtkSJEpg2bVqelq8wmDJlCm7cuCH2YLNs2TL8+uuvaNKkCby9vZGZmYnHjx/jwoULGrvpnzt3Lg4fPozIyEikpKSgW7duqFWrFvz9/ZGZmYkLFy5Ihkxp1qyZpHcaU2vcuDFWrFiBr776CllZWdi1axf++ecf1K1bF5UqVYKNjQ3evHmDa9eu4dGjR1rlWVj2wdq1a+PWrVuYN28etmzZgoCAAFhYWODixYuSIfR69+4t9kKUk0wmw+bNmxEYGIhbt24hPj4eAwYMwPjx49GoUSO4u7vj/fv3iI6ORlhYWJEY1qFPnz64ceOG2NvW2rVrsX79ejRo0ADlypWDhYUFXr58ifDwcPE3vCppaWno06cPkpOTAWQ//aysN1kXFxds2rQJLVq0QFZWFqZMmYLAwECxlxVFxr4WA/93/QwMDERMTAzevHmDnj17okyZMmjYsCGKFSuGxMRE3Lp1S6xrqvtNOnbsWLEuc/HiRfj6+qJx48awt7fH7du3cefOHdja2uLbb79V26uZMsWLF0e7du2wf/9+ANnD3fTo0QM1atSQDDHSunVrlb2mrlq1ChcvXsTLly/x5s0bBAQEoH79+vjoo4+QkpKCU6dOSb7bAQMGqDxGyLjk7Y1ffPEFli9fjvr16yMjIwNnzpzB48ePxXQTJkxQOpS0mZkZ5s2bJ+5/e/fuRfny5dGwYUOULl0aL168wKlTp5CUlARra2vMmzcPEyZMyLP1A/KmfTAgIACNGjXC+fPnkZycjHr16qFDhw6S3rFr1aol6Z1BHR8fH/Tp0wfbtm1DVlYW2rRpg3bt2sHb21vsubZMmTKYPHmy0vlNcS3OadKkSRgxYgSA7N7ejhw5Aj8/P5ibm4tpFi1alKuXMSC7HqSslxfF/y1btgw7d+7MlWb27NkoXrx4rv8nJSVhxYoVWLFiBVxdXVGnTh2ULl0aWVlZePLkCUJCQsSeFc3NzfUaIaKgMHb74OrVqyXDoletWhXz5s3Tqizq2viNzZj1QXNzc4wePVpsm9+9eze8vLzQqFEjWFhY4Pr164iOjkbJkiXRq1cvrFy50uTrp46h1+LQ0FClvfAo1k9DQ0OV/uZv3769xnYuYzBmfdBU9uzZk+t+Ts7e/5WNAqGuF1EAOHv2rOT3W1EbRk/O1G0zxta/f39xVKANGzbg33//RevWrVGsWDHJtVjVyBHa3CNQlsbV1RWzZs3Sv+BacnNzw86dO9G2bVvEx8fj1q1baNSoESpVqoTatWvD2dkZ8fHxiIyMxK1bt8RrSq1atUxeNkXKjinFc8D06dOxdOlSyfTZs2ejc+fOavNVvKfSr18/sTd4Ir3lY1AWkVYePHggVKpUSRKhm/PVrVs3ITExUasnVhTTHD58WGjbtq3KfG1sbISgoCC15cvZA8/QoUNV5mdubi7873//02s7TJ8+Xe02uHr1qsby6frS9NRPamqq8NVXX0meblL16tSpk8p8Dhw4IHmyM+fLzMxMmD17tiAI+kVqp6amCgEBAWrLp+ppC8UnsnV9qXuqLj09XRgxYoTkCXFlr6pVqwp3795Vu36Foccofbejpn1w48aNYloLCwu9egfJ+aShupeFhYUwdepUIS0tTWV+ms5Xii+ZTCZ8/vnnWvXUo8+xrGwfVOy9QNOrZMmSSnvqU/S///1PpzJVr15dCA8PV5mfIcecquPYmMdwXFycTnl8+umnQkJCgtptKAiCkJaWJowbN07jOQHI7g0pMjJSZV61atXSunz+/v65ekJSxVj7oCmdPn1a7EVS1evzzz9XewwrunnzpuRJRmWvLl26aPUda6JrTymJiYmSp6rVvT755BPh2bNnBpdRV1FRUUK1atVUlsvT01O4fv265AkiTU/trly5UtJTV86XjY2NsHbt2rxZQSXOnz+vcZ+Rv+zs7IQ5c+bkyuOff/4R09ja2uZ6Wj6n/fv3S9b/xo0bKtM+f/5caNOmjVblMzMzEzp06KBxnY1VHxQEQVizZo3KeWUymTBjxgy9e8sxpD4ol5SUJOlNQ9WrUaNGwsuXL7Uum+K8+jp8+LDk6WR1r6pVq6rsRSU/FYQeowQh+2n8WbNmSXqpU/VS1YuB3MuXL4VGjRppzKdz585CUlKSVuUzVo9Rcnv27JE8garu5eLiIvz+++9K8yno+2DOJ+3XrVun9rzVrVs3lb2rKkpMTBT69eunVR0OgFCvXj2VvUaoKmtBs27dOqF48eJarW+pUqWU9gQ1btw4MU2VKlU0bpOJEyeK6StVqqT2eDHGtTinp0+fCi1bttSYn5mZmfDjjz+qzCcrK0vo16+fyvkdHR2FvXv36r0PPHjwQPDw8FBbRk3tAHfv3hWqVq2qNg+ZTCaMGDFC6ye22WOUfnL2/DBz5ky138uXX36psYf52bNnqz1fFStWTNi7d6/O35mxeqkwdfugIGRfRxW/15wvdb3IKfP27VvB399fZX7a9NhgrGuxMllZWcLgwYPV5qmqJ8Ocoy7o8lL2uy4xMVGr+pV8X1TVQ58gFI4eo4zdPpizFzldXvr20q8rU9QHU1JShMDAQJX5lCpVSrh48aLG81Be9BglCIZdi5X1cKrtK+c6m6rHKDlj1AdNRd/tqMmQIUPEtDVr1syDNdGNsa7FgmDcthldtrEuaRXl7Hkv50tdXUbfY07ZOqtbnr7T5CIjI4V69eppVTZLS0th1KhRmjecEemzDTXVrU+fPi1Jn7P3MiJ9MLSOCrxy5crhypUrWLNmDXbu3Cn2HlWqVCn4+/tj0KBBej+hZmVlhf379+P3339HUFAQbt++jQ8fPqB06dJo27Ytxo4diwoVKuiU5++//462bdvi119/xfXr15GYmAgPDw80b94co0ePVvpkozZmzZqFRo0aYfny5QgNDUVsbKxWY8+bkpWVFVauXIlx48Zh48aNOHHiBB48eIDY2FhYWVnB19cXTZo0Qb9+/RAQEKAyn3bt2iEiIgJLlizBwYMHER0dDXNzc3h6eiIwMBAjRoxQ+sSbLuU8fvw4NmzYgJ07d+LGjRuIi4sTn0DKDxYWFvjll18wfPhwrF+/HidPnkRMTAzS0tLg5uYGf39/dO/eHQMHDmQUtBobNmwQ37dt21av3kFiY2Nx4sQJnD9/HlevXsXDhw/x8uVLvH//HpaWlihevDg++ugjNGvWDIMHD4anp6fa/G7cuIGzZ88iJCQE4eHhePDgAZ4/f46kpCSYm5ujWLFiqFy5Mpo0aYJBgwbl2RNbcjNmzECdOnVw5swZ3Lx5E0+fPkVsbCxSU1Nha2sLNzc31KlTB+3atcOAAQMkTzQpM2XKFHTt2hUnTpzApUuXcPv2bTx+/BgJCQlIT0+Hk5MTSpcujY8//hg9evRAu3btYGZmlkdra3wuLi4IDQ3FwYMHcfnyZTx48AAvXrxAcnIyBEGAi4sLfH190bRpUwwaNAg1a9bUKl9LS0v8/PPPGDVqFH7//XecOXMG9+7dQ0JCAmxsbFCyZEnUq1cPPXv2RNeuXdVuw+DgYOzZswcXLlxAZGQkYmJikJiYiPT0dDg6OsLDwwONGjVC9+7d0b59e/HpmaKgadOmuHnzJn777Tf8/fffePjwIZKTk+Hh4YGGDRvi888/R2BgoNb5Va9eHeHh4diwYQN27NiBe/fuIT4+HiVLloS/vz+GDBmCLl26mG6F1HB0dMSWLVswfvx4BAUF4dy5c3j06BHevXsHOzs7eHp6omHDhujXrx9atmyZL2X08fHB1atXsXbtWvzxxx9iryleXl7o0qULvv32W8nT2dr46quv0LJlS6xduxaHDx/G06dPAWQ//dW2bVuMGjUKlSpVMsXqaKVhw4a4desWdu3ahb179+LixYt49eoVkpOT4eTkhHLlyqF27dpo2bIl2rZtC0dHR8n8MTExGDp0qPh54cKFKnt1kGvfvj2++OILrFmzBh8+fECfPn0QFham9KnvUqVK4dChQzh//jz++OMPnD59Gs+ePUN8fDxsbW3h5eWFmjVrIjAwEB06dNCqh1Nj1QeB7CcTq1WrhsWLF4u973l4eKBBgwYYM2YMGjdujKioKI1lUlVOQ+uD9vb22L17N44dO4YtW7bg7NmzePnyJTIzM+Hu7o769evj008/Rffu3fP83Nq6dWs8f/4chw4dwpkzZ8SeBRITE2FlZSWet7p3747evXtLep4lKZlMhunTp2PEiBHYsGEDjh07hsjISLx9+xZmZmbw8vJCw4YN0bt3b6U93Chyd3dHSEgIdu3ahT/++AOhoaF49eoVzM3NUbJkSTRp0gQDBw7Mt/M0AHTq1AmtW7fGtm3bcODAAYSHh+P169dITU2Fi4sLKlSogLp166J169Zo1aoVrK2tleZT2PbBYcOGoWbNmliyZAnOnj2LV69eoXjx4qhbty6GDh2Kbt26aZWPo6Mjtm7dismTJ2Pr1q04ceIEoqOjERcXBysrK3h4eKBGjRpo2rQpOnTokOd1f1MYNmwYevXqhU2bNuHw4cO4ceMG3rx5g4yMDBQvXhyVK1dG/fr10bp1awQGBkp6JQGAQ4cOiU8LW1paYsuWLUqvWYrmzp2Lo0eP4tq1a7h79y5Gjx4t+S2oyNBrsTKlS5fG0aNHcfbsWfz55584c+YMnj17hoSEBDg6OqJKlSpo0aIFPvvsM7XfsUwmw5YtW9CiRQusX78eN2/eRHp6Ory9vdGuXTuMGTMG5cqVU9vbrDrlypXDjRs3sGrVKhw6dAh37txBYmKipDcSTSpWrIgbN25g8+bN2LVrF65evYo3b97AysoKnp6eaN68OYYOHaq2R2EyjRkzZog9DF2+fBlv376Fu7s7GjVqhFGjRok9qarzww8/oHXr1li+fDnOnDmDV69ewcHBAd7e3ujcuTNGjBiB0qVL49SpU6ZfISVM3T4IZPdKffXqVfz444/YvXs3oqKi8OHDB73zK168OM6dO4dly5bhjz/+wN27d/H+/Xud8jDWtVgZmUyGDRs2oE2bNvjll19w9epVJCQk6LqaRuHo6Ii3b9/i+PHjOHnyJC5fvox79+4hNjYWMplMbH9r164dBg0aBFdX13wpp7EU9PZBUzBFfdDGxgZHjhzBihUrsHXrVty5cwcymQy+vr7o1q0bvvrqK5QsWRIHDx7MgzXUzBjX4sLA0PpgYZOcnIy//vpL/CzvgbGoMmXbjCls374d3bp1w9atW3H16lXxGlqUVK5cGaGhoTh06BCCg4MREhKC58+fIzExEY6OjvD29oafnx9atGiB9u3bw83NLb+LbDDF33vVq1eHv79/PpaGigqZkN+RFUR5LDAwUBx65uTJkzrdIFVm8ODB2LhxI4DsE/XgwYMNLCERERHRf4evry+io6MBAI8ePYKvr2/+FoiIiIqcoKAgDBkyBED2sBf6Bp4QEeWVmTNnikO0zJgxAzNnzszfAhERERERERVihbfLBiIiIiIiIiIiIiIiIiIiIiIiIhUYGEVEREREREREREREREREREREREUOA6OIiIiIiIiIiIiIiIiIiIiIiKjIYWAUEREREREREREREREREREREREVOQyMIiIiIiIiIiIiIiIiIiIiIiKiIoeBUUREREREREREREREREREREREVOTIBEEQ8rsQqmRlZSEmJgaOjo6QyWT5XRwioiLrq6++wvbt242W36RJkzBp0iSj5UdEhZ+fnx8eP35stPz27NmDJk2aGC0/IiIiyl/8TWKYx48fw8/Pz6h5xsbGGjU/IiIiIiIiIiJjEgQB7969g6enJ8zMVPcLVaADo54+fQovL6/8LgYRERERERERERERERERERERERUwT548QZkyZVROt8jDsujM0dERQPZKODk55XNpiIiIiPLPr9+czu8iEBERERERERFRATBiabP8LgIRERFRvktMTISXl5cYW6RKgQ6Mkg+f5+TkxMAoIiIi+k/rcP67/C4CEREREREREREVAE5Ot/O7CEREREQFhjy2SBXVg+wREREREREREREREREREREREREVUgyMIiIiIiIiIiIiIiIiIiIiIiKiIoeBUUREREREREREREREREREREREVOQwMIqIiIiIiIiIiIiIiIiIiIiIiIocBkYREREREREREREREREREREREVGRw8AoIiIiIiIiIiIiIiIiIiIiIiIqchgYRURERERERERERERERERERERERQ4Do4iIiIiIiIiIiIiIiIiIiIiIqMhhYBQRERERERERERERERERERERERU5DIwiIiIiIiIiIiIiIiIiIiIiIqIih4FRRERERERERERERERERERERERU5DAwioiIiIiIiIiIiIiIiIiIiIiIihwGRhERERERERERERERERERERERUZHDwCgiIiIiIiIiIiIiIiIiIiIiIipyGBhFRERERERERERERERERERERERFDgOjiIiIiIiIiIiIiIiIiIiIiIioyGFgFBERERERERERERERERERERERFTkMjCIiIiIiIiIiIiIiIiIiIiIioiKHgVFERERERERERERERERERERERFTkMDCKiIiIiIiIiIiIiIiIiIiIiIiKHAZGERERERERERERERERERERERFRkcPAKCIiIiIiIiIiIiIiIiIiIiIiKnIYGEVEREREREREREREREREREREREUOA6OIiIiIiIiIiIiIiIiIiIiIiKjIYWAUEREREREREREREREREREREREVOQyMIiIiIiIiIiIiIiIiIiIiIiKiIscivwtARERERJr1nsJqGxERERERERERATfzuwBEREREhQh7jCIiIiIiIiIiIiIiIiIiIiIioiJH58Co9PR0zJkzB61bt4aTkxNkMhlOnTqVK50gCFi7di1q1qwJW1tbuLu7o3PnzkhKSjJGuYmIiIiIiIiIiIiIiIiIiIiIiFTSOTAqOTkZ06dPx/3791GjRg2V6aZOnYovvvgCVapUwfLlyzFlyhRYWloiJSXFoAITERERERERERERERERERERERFpYqHrDI6Ojnj8+DG8vLywc+dOnD9/PleayMhILFq0CFOnTsW8efPE/48bN86w0hIREREREREREREREREREREREWlB5x6jzM3N4eXlpTbN9u3bYWlpiSlTpgAAh88jIiIiIiIiIiIiIiIiIiIiIqI8pXNglDZCQ0NRo0YN7NmzB+7u7nB0dISXlxe2b99uisURERERERERERERERERERERERFJmCQw6tmzZ3j9+jVGjRqFiRMn4o8//kC5cuXQv39/XLlyReV8qampSExMlLyIiIiIiIiIiIiIiIiIiIiIiIh0ZZLAqPfv3yMqKgoLFizAxIkT0bt3b+zfvx8ODg5YtGiRyvnmz58PZ2dn8aVpyD4iIiIiIiIiIiIiIiIiIiIiIiJlTBIYZWVlBQDo3r27+D8HBwc0atQIN27cUDnflClTkJCQIL6ePHliiuIREREREREREREREREREREREVERZ5LAqBIlSkj+yhUvXhyvXr1SOZ+1tTWcnJwkLyIiIiIiIiIiIiIiIiIiIiIiIl2ZJDCqWrVqAIAXL15I/v/69Wt4enqaYpFEREREREREREREREREREREREQikwRGtW3bFgCwbds28X9v377FuXPnULduXVMskoiIiIiIiIiIiIiIiIiIiIiISGShz0wrV65EfHw8IiIiAACbN29GSEgIXFxcMHr0aHTu3Bl16tTB1KlT8erVK3h7e+O3335DZmYmJk+ebNQVICIiIiIiIiIiIiIiIiIiIiIiykkmCIKg60y+vr6Ijo7O9X8fHx9ERUUByB42b8KECdi3bx+Sk5Ph7++P+fPnIzAwUOvlJCYmwtnZGQkJCXByctK1mERERERFRo2NNfK7CEREREREREREVADcHHQzv4tARERElO+0jSnSq8coefCTOiVKlMDGjRv1yZ6IiIiIiIiIiIiIiIiIiIiIiMggZvldACIiIiIiIiIiIiIiIiIiIiIiImNjYBQRERERERERERERERERERERERU5DIwiIiIiIiIiIiIiIiIiIiIiIqIih4FRRERERERERERERERERERERERU5DAwioiIiIiIiIiIiIiIiIiIiIiIihwGRhERERERERERERERERERERERUZHDwCgiIiIiIiIiIiIiIiIiIiIiIipyGBhFRERERERERERERERERERERERFDgOjiIiIiIiIiIiIiIiIiIiIiIioyGFgFBERERERERERERERERERERERFTkMjCIiIiIiIiIiIiIiIiIiIiIioiKHgVFERERERERERERERERERERERFTkMDCKiIiIiIiIiIiIiIiIiIiIiIiKHAZGERERERERERERERERERERERFRkcPAKCIiIiIiIiIiIiIiIiIiIiIiKnIYGEVEREREREREREREREREREREREUOA6OIiIiIiIiIiIiIiIiIiIiIiKjIYWAUEREREREREREREREREREREREVOQyMIiIiIiIiIiIiIiIiIiIiIiKiIoeBUUREREREREREREREREREREREVOQwMIqIiIiIiIiIiIiIiIiIiIiIiIocBkYREREREREREREREREREREREVGRw8AoIiIiIiIiIiIiIiIiIiIiIiIqchgYRURERERERERERERERERERERERQ4Do4iIiIiIiIiIiIiIiIiIiIiIqMhhYBQRERERERERERERERERERERERU5DIwiIiIiIiIiIiIiIiIiIiIiIqIih4FRRERERERERERERERERERERERU5DAwioiIiIiIiIiIiIiIiIiIiIiIihwGRhERERERERERERERERERERERUZHDwCgiIiIiIiIiIiIiIiIiIiIiIipyGBhFRERERERERERERERERERERERFDgOjiIiIiIiIiIiIiIiIiIiIiIioyGFgFBERERERERERERERERERERERFTkMjCIiIiIiIiIiIiIiIiIiIiIioiKHgVFERERERERERERERERERERERFTkMDCKiIiIiIiIiIiIiIiIiIiIiIiKHAZGERERERERERERERERERERERFRkcPAKCIiIiIiIiIiIiIiIiIiIiIiKnIYGEVEREREREREREREREREREREREUOA6OIiIiIiIiIiIiIiIiIiIiIiKjIYWAUEREREREREREREREREREREREVOQyMIiIiIiIiIiIiIiIiIiIiIiKiIoeBUUREREREREREREREREREREREVOQwMIqIiIiIiIiIiIiIiIiIiIiIiIocBkYREREREREREREREREREREREVGRw8AoIiIiIiIiIiIiIiIiIiIiIiIqchgYRURERERERERERERERERERERERQ4Do4iIiIiIiIiIiIiIiIiIiIiIqMhhYBQRERERERERERERERERERERERU5DIwiIiIiIiIiIiIiIiIiIiIiIqIixyK/C0BEREREREREREREREREVFRkZmYiPT09v4tBRERUqFhYWMDc3Bwymcy4+Ro1NyIiIiIiIiIiIiIiIiKi/yBBEPDixQvEx8fnd1GIiIgKJXNzc7i7u8PZ2dloAVIMjCIiIiIiIiIiIiIiIiIiMpA8KMrd3R12dnZG7/GCiIioqBIEARkZGUhMTMTz58+RkpICDw8Po+TNwCgiIiIiIiIiIiIiIiIiIgNkZmaKQVGurq75XRwiIqJCydHREdbW1njz5g3c3d1hbm5ucJ5mRigXEREREREREREREREREdF/Vnp6OgDAzs4un0tCRERUuNnb20MQBPHaaigGRhERERERERERERERERERGQGHzyMiIjKMsa+lDIwiIiIiIiIiIiIiIiIiIiIiIqIiR+fAqPT0dMyZMwetW7eGk5MTZDIZTp06pXaeCRMmQCaTYfTo0fqWk4iIiIiIiIiIiIiIiIiIiIiISGs6B0YlJydj+vTpuH//PmrUqKEx/cOHD/Hrr7/qVTgiIiIiIiIiIiIiIiIiIiIiIiJ96BwY5ejoiMePH+Phw4cYN26cxvTfffcdhgwZolfhiIiIiIiIiIiIiIiIiIiICiqZTIbAwMD8LobJCYKAhQsXomLFirCysoJMJsPgwYPzu1ikxuDBgyGTyRAVFWW0PPft24c6derAwcEBMpkMvr6+KtOeOnUKMpkMM2fOVDr92rVrkMlkkldQUJDRykokZ6HrDObm5vDy8tIqbUhICA4fPowHDx5g+fLlOheOiIiIiIiIiIiIiIiIiKiw8528P7+LoFLUgg4myXfgwIHYsmULypcvj/v370um3b17F5s3b8bBgwcRFRWFpKQklC1bFj179sTkyZNhb28vpv3w4QMmTZqEHTt2QBAEjBs3DpMmTYKZmc59gGgkD/IwZiBJUbFt2zZMnjwZderUwYQJE2BlZQU/P7/8LhbloYcPH6JHjx5wdXXFqFGj4ODgABcXF73zK1WqFGbMmAEgO0jqn3/+0XpeHqukC50Do7QlCALGjx+PcePGwd3d3VSLISIiIiIiIiIiIiIiIiKiAuTAgQP466+/VE6fPXs2tm7dinr16mHAgAEwMzPDkSNHMHfuXOzduxchISFwcHAAAHzzzTfYtGkTBg0ahKSkJPzwww+wsbHRanSjvHD79m3Y2dnldzFM7sCBAwCAvXv3wsPDI59LQ/nh+PHjSEtLw+LFi9G3b1+N6evVq4fbt2/Dzc1N6fRSpUqJvUkFBQXpFBhFpAuTBUZt3boVDx8+xIQJE7SeJzU1FampqeLnxMREUxSNiIiIiIiIiIiIiIiIiIhM4N27dxg1ahS+++47zJkzR2maNm3aYOrUqahWrZr4v4yMDLRv3x5Hjx7FihUrMGXKFKSnp2PDhg0ICgoSAzGqVKmC1atXF5jAqCpVquR3EfLE8+fPAYBBUf9huu4DdnZ2/5njgwo24/cvCCAlJQVTp07FlClT4OTkpPV88+fPh7Ozs/jSdsg+IiIiIiIiIiIiIiIiIiLKf9999x2srKwwdepUlWkGDhwoCYoCAAsLC3z++ecAgJCQEADZQVZpaWmStB999BFevXpltPIGBgZCJpNBJpMhOjoa0dHR4meZTCYO2aVqHplMhsDAQKV5z5w5EzKZDIsWLULJkiXh4eGB3bt3Y/ny5ShWrBjKly+PI0eO5JovIiICffr0QcmSJWFtbY3KlStj4cKFyMzMNHh94+LiMHbsWPj4+MDKygoeHh4YMmQInj59mivt4MGDxXU8efIkAEjWe/DgwQaXZ8eOHWjcuDHc3Nxgb2+PypUrY/jw4bmGX1Qs/5QpU1CtWjXY2trC3d0drVq1wu7duyXp7t69i4kTJ8Lf3x/FihWDra0tqlWrhtmzZyMlJSVXvr6+vvD19cWzZ8/Qq1cvuLi4wNHREZ9++ini4uIMWseEhAQsWLAATZs2RalSpWBtbQ1vb2+MGDECz549y5Vevt3v3LmDiRMnwtPTEzY2NmjcuDFu3LihdBnLly9H5cqVYWNjg2rVqmHbtm0GlVkuKChI/L7lw941b95c7fGhuI/IZDKxVyhD6XOsZmRkYOnSpfDz84OtrS1cXFzQuXNn3Lp1S+Vy5Md0fHw8Ro0aBQ8PD9jY2KBixYrYunWrUdaF8pZJeoxavHgxBEHAV199pdN8U6ZMwfjx48XPiYmJDI4iIiIiIiIiIiIiIiIiIioEzpw5g19++QX79++HjY2NzvNbWGTfvra1tQUAFC9eHBUqVMD06dPx22+/ITk5GfPnz0eDBg0k80VFRSEhIQG1atXSeZmDBw8WA5uWLl0KIHv4PjkXFxe188yaNUvjMtavX4/evXvjl19+weDBg+Hm5obBgwdjw4YNGDp0qCQo6eTJk+jYsSMyMjLQvXt3lC5dGqGhoZg8eTIiIyOxYcMGnddRLiUlBc2aNcPNmzfRvHlz9OvXD7dv30ZQUBCOHj2KK1euwN3dXUzftWtXMdgkKCgI0dHRYnAMAPj5+eldFgBYtWoVRo8ejbJly6Jfv36wsbHB/fv38ccff6Bly5aoUKGCJH1MTAyaNGmCR48eoXHjxujUqRMyMjJw6tQpfPPNN+jatauYdteuXVi+fDmaN2+Oxo0bw9bWFpcvX8aMGTNw7tw5HDp0CDKZTJJ/amoqWrRoAR8fHwwfPhwnTpzAn3/+iaysLLVDQ2py+/ZtTJkyBQ0aNEDHjh3h5uaGBw8eICgoCAcPHsTly5dRqlSpXPONHDkSsbGx6NOnD+7du4d9+/ahQ4cOuHfvnuT4mj59OubMmYPy5cvj66+/xsuXLzFkyBCjxFr4+fmJ3/mpU6dw+vRpDBo0SNwvlB0f8vRRUVHYuHGjwWWQ0/VYzczMRNeuXbF//3589NFHGDlyJJKSkrBz5040atQI586dQ40aNZQuKysrC61bt0ZsbCx69+4NMzMzXLp0CWFhYejfv7/R1onyhtEDoxISErBw4UJMmjQJb968kUxLTk7G06dPUbJkSVhaWuaa19raGtbW1sYuEhERERERERERERERERERmVBKSgqGDx+O7t27o127dnrl8ffffwMAOnToIP5v7dq16Nq1K0qWLAkAKFmypBgcdPr0aSxbtgzHjx/HvHnz9A6MkgsKCgIAjT3cKM6jTWDUkiVL0LZtW7x+/Rp//PEHNm/ejE6dOsHW1hbz58/H06dPUaZMGXz48AH9+/dHVlYWLly4gNq1a4t5fPnll1izZg2GDRuGJk2a6LKKopUrV+LmzZsYOnQofv/9d/H/c+bMwfTp0zF79mysXLlS/H/Xrl3FYKNTp04hOjraaL3/ANkBYzY2Nrhy5YokqOXdu3dITk7OlX7UqFF49OgRfvzxR0ycOFEyLWfPW126dMHw4cPh5uYm+b98O548eRKffPKJZNqLFy/w2WefYeHChQCA9PR0VKtWDcHBwUhMTNRptCxF5cqVQ2RkJCpXriz5/59//olPP/0Uq1evxuzZs3PNl5WVhbCwMFhZWQEABg0ahE2bNuHEiRNo3749AODZs2eYP38+vLy8cOXKFbGMrVq1wsCBA/UqryI/Pz8xAG7mzJk4ffq0JEBJGfk+curUKaMHRslpc6yuXLkS+/fvR9++fbFp0yYx8HLq1KmoUaMGxo0bh2PHjimd9/z582jTpg3OnTsniW158eKFwetBec/oQ+nFxcUhKSkJP/zwA7y8vMQXkL1zenl54fr168ZeLBERERERERERERERERER5ZMZM2bg+fPnYk8uujp69Ci2bdsGPz8/SUBHixYtcOvWLaxevRq//fYbwsLCcOnSJfj7+2Po0KFo3LgxoqOjMXr0aCOtifH5+PgAALy9vQEA5cuXBwDxPro82OKff/7B8+fPMXLkSElQFABx5KWdO3fqXY5du3YBACZPniz5/9dffw1bW1txel7JzMyEubl5rk5VHB0dc/WgFBMTg71796JSpUqSUajkWrduLflctWrVXEFRwP8F3d28eVNpmSZNmiS+t7S0ROvWrZGZmYl79+5pt1JKuLu75wqK0qYs48aNE4OiAKBjx44Asnugktu7dy8yMjIwdOhQSeBW37594enpqXeZi4K1a9fC3NwcK1asEIOigOxAtW7duuHEiRMqh0kUBAFLlizJtW8q69mLCj6j9xhVsmRJ7N27N9f/O3XqhA4dOmDUqFGoWLGisRdLRERERERERERERERERET5ICwsDD///DN+/PFHlClTRuf579+/j379+qF48eL4+++/JUEMQHZgUbdu3bBmzRrUrVsXNWrUwKxZs9CxY0eYmRm9LxCjkw97Jv8rHypQ/jklJQUAcPnyZQDA48ePc/WEk5GRASB7W+nr9u3bsLe3z3W/3tnZGWXLlsW///6LhIQEODs7670MXfTo0QPTp09HgwYN0LdvX9SvXx8ff/yx0p6Zrly5AgAICAiAubm5xryzsrKwbt06bNy4Ebdu3cK7d+8gCII4/d27d7nmcXNzQ/HixXP9DwCSkpJ0Wrecjh8/jiVLluDy5ct4+/YtMjMz1ZYFACpVqqSxLPIgqZxDwpmbm6N69eqIiYkxqNyF1bt37xAZGYlixYphxYoVuaZHRUVBEAQ8ePAAdevWzTW9TJkyubY/FV56BUatXLkS8fHxiIiIAABs3rwZISEhcHFxwejRo8VIxZx8fX1VTiMiIiIiIiIiIiIiIiIiosJn2LBhqFatGr7++mud533+/DnatGmDDx8+4NixYyhXrpxk+q1bt/Djjz9i//79+PTTT3Hy5EmULl0a3377LYYNGwYbGxsMGzYM33//fa6AqoJCJpMBgBjEJf8s/ysPkomPjwcABAcHIzg4WGleyoaY01ZSUpLKXoRcXV0BZAeU5FVg1LRp0+Dq6or169fj+++/hyAIsLS0RK9evbBmzRpJgJR822jbY8/XX3+NVatWoWTJkujZsyc8PDxgYWGBqKgobNy4UQw0U2Rvb5/rf/LvSDGoSld//vkn+vTpA2tra3Tq1AnlypUTg+JmzZqltCzKyqOsLPL9IWdAF/B/3+l/UUJCAoDsEc/UDXep6njSJ8CTCi69rgw//fQToqOjxc/r168HkB2pW5C7KCQiIiIiIiIiIiIiIiIiIuO6ceMGAOQadgoAHjx4AJlMhlq1auHatWuSabGxsWjTpg1iYmKwf/9+1K9fP9f8d+7cgb+/P1asWCEG7PTq1QtHjx7FgAEDkJKSggULFsDMzAzTp083/srlIRcXFwDZw6OZosMRBwcHxMbGKp329u1bANnD2OUVMzMzfPnll/jyyy+RkJCA06dP46effsK2bdvg4uKCVatWiWnl2+b58+ca83316hVWr16NatWqITQ0FA4ODuK0v/76Cxs3bjT6uqgza9YsWFtbIzw8HNWqVRP///r1a7VBO9qQr5uy71X+nf4Xyc8VderUQVhYmM7zKzuXUeGlV2BUVFSUzvMYEkFJREREREREREREREREREQF09ixY5X+f9myZXB2dsbgwYNRunRpybSkpCS0a9cOd+7cQXBwMD755BOlefTo0UPy+f3799i1axf27duHdu3aAcgOfpg+fbrBgVHm5uZITU01KA9DyIf0unz5skkCo6pWrYqLFy/i3r17kuH0EhMT8ejRI5QqVSrPeovKydnZGZ07d0bbtm3h5uaG06dPS6bXqVMHMpkMZ86cQWZmptrh9B4+fAhBENCuXTtJUBQAXLp0ySTlV+fBgweoXr26JCjKWGX56KOPAAA3b96UHCuZmZm4deuWwfnnFfn39P79e63SazpWHR0dUblyZURGRuLdu3d5GvBHBU/BH3CViIiIiIiIiIiIiIiIiIgKrKVLlyp9AYCbmxuWLl2KiRMniulTUlLQsWNHXLlyBTt27ED79u11XqZ8WDE5Y/TwUqJECbx8+RJxcXEG56WPLl26oGTJkvj5559x/fr1XNPv3buHu3fv6p1/9+7dAQBz586VdGyyaNEipKSk5ApCM7WTJ0/m6mDl2bNnSE5ORrFixST/9/DwQMeOHXH//n0sWrQoV16nTp0S33t7ewPIDjxSzP/GjRtYs2aNEddAO97e3rh//76kB6fY2FhMmzbN4Lw7deoES0tLbNiwAW/evBH/v337dsTExBicf16pUKECAODixYtapdfmWB0xYgSSk5PxzTffID09XTItPT0dR44c0b/AVKgUzEFWiYiIiIiIiIiIiIiIiIioSBo1ahROnz6NevXq4fr167mCgFxcXPDNN98ondfOzg5du3ZF3759MWDAALx//x5bt25VmV4X7dq1Q2hoKNq1a4eePXvCwcEBjo6O6N+/v5jm1KlTkiAcIHvEpZkzZ4qfBw8eDF9fX52Xb2triy1btqBz586oW7cuOnTogMqVK+P9+/cIDQ3F5cuXsX37dlSqVEmv9Rs9ejQ2b96MTZs24f79+2jQoAEiIiJw+PBhlC5dOs+HIuzWrRucnZ3RsGFD+Pj4IC4uDrt27YIgCEq/zzVr1uDWrVuYMmUK9u7di4CAAGRkZCAkJAQvXrwQR77y9PREt27dEBwcjEaNGiEgIABPnjzB7t278cknn+DAgQN5up6jR4/G2LFjUadOHXTr1g1JSUk4cOAA/Pz8lAbA6aJUqVKYNm0aZs6cibp166Jz585ITEzE9u3bUb58eTx48MBIa6GdoKAg8XuQ/z116pTk+FB8L+fn54cGDRpg8+bNyMjIEPfxb775RhxGUZE2x+rYsWNx7NgxrF+/HiEhIQgMDISrqyvu37+P48ePo0SJEoiMjDTWqlMBxsAoIiIiIiIiIiIiIiIiIiITilrQIb+LUKBER0cDyO7RR9lwYj4+PmoDnX7//XeMHz8e27dvh7W1NcaPH49Zs2YZXK7JkycjPj4eO3fuxOTJk5GZmQkfH59cgVE5lxUdHS35X2BgoF6BUQDQsmVLhIeHY/78+Thx4gQOHDgAV1dXVK5cGYsXL0bLli31yhfIDrySlz84OBiXL19G8eLFMXjwYMyZMwfu7u56562P+fPnY9++fTh37hx2794NV1dX1K9fHxMnTkRgYGCu9KVLl0ZYWBh+/PFH7N69G0uXLoWDgwP8/PzEHsrkNm7cCB8fHwQHB2P58uUoX748li5disqVK+d5YNSYMWNgYWGBVatWYe3atXBzc0Pfvn0xZ84c2NnZGZz/jBkzUKxYMaxcuRK//PILypYtiw0bNuDIkSP5EhiVcxjE06dPS/6nLDAKAIKDgzF69GgcPnwY27dvB5AdZKgsMEqbY9Xc3Bx79uzBL7/8gk2bNmHr1q0QBAFeXl7o1q0b+vbta/gKU6EgE3L2TVeAJCYmwtnZGQkJCXBycsrv4hARERHlmxoba+R3EYiIiIiIiIiIqAC4OehmfheBlPjw4QMePXqEsmXLwsbGJr+LQ0REVGhpe03VNqbIzBSFJCIiIiIiIiIiIiIiIiIiIiIiyk8MjCIiIiIiIiIiIiIiIiIiIiIioiKHgVFERERERERERERERERERERERFTkWOR3AYiIiIiIiIiIiIiIiIiIiAzVp08fXLx4Uau0FhYWuH//volLRERE+Y2BUUREREREREREREREREREVOjt2LEjv4tAREQFDIfSIyIiIiIiIiIiIiIiIiIiIiKiIoeBUUREREREREREREREREREREREVOQwMIqIiIiIiIiIiIiIiIiIiIiIiIocBkYREREREREREREREREREREREVGRw8AoIiIiIiIiIiIiIiIiIiIiIiIqchgYRURERERERERERERERERERERERQ4Do4iIiIiIiIiIiIiIiIiIiIiIqMhhYBQRERERERERERERERERERERERU5DIwiIiIiIiIiIiIiIiIiIiIiIqIixyK/C0BEREREREREREREREREVKTNdM7vEqg2MyG/S1CoyWQyNGvWDKdOncrvopiUIAj48ccfsW7dOkRHRyM9PR2DBg1CUFBQfhfNJP4r3yvRfwF7jCIiIiIiIiIiIiIiIiIiIr3du3cPQ4YMgZ+fH1xdXWFnZ4eqVatizJgxiImJyZX+1KlTkMlkSl9lypSRpP3w4QPGjh2LkiVLwt3dHfPnz0dWVpZJ1sPX1xe+vr4mybuw27ZtGyZPngxnZ2dMmDABM2bMQNeuXfO7WEVaUFAQZDJZkQ0+I8or7DGKiIiIiIiIiP4fe/cd1nX1/3/88XbEVBwojhTIAWrmiPo4E0eluXBrWqJWfr6miVofycHIDMcnV6aWA8TMSlypNBxAWYqj3JqSQllm5WCjMn5/+Hu/P74FFN4spfvturjwfV7nnNfzNYjrisd1DgAAAAAAFjtx4oQ2bNig9u3bq23btqpQoYJOnz6tpUuXau3atfr+++/l7u6ebVyHDh3k6elp1laxYkWzzz4+PgoNDdXw4cOVlJSk6dOny9raWhMmTCjKS8qzU6dOydbWtqTLKHLh4eGSpK1bt6pmzZolXE3R+6c8V+CfgGAUAADAA+DY+V9KugQAAAAAAAAAyNHTTz+ty5cvq3z58mbt69at0/PPPy9/f399+umn2cZ5enoqICAg13lv3ryp4OBghYSEaMiQIZIkd3d3LVmy5L4JRuUU+CqNLl68KEn/iFCU9M95rsA/AVvpAQAAAAAAAAAAAAAsZmdnly0UJUndu3eXdGtFKUskJibqxo0baty4samtSZMm+vPPPy0rNAeenp6mbfzi4uIUFxdntrVfTlvr3T7GYDBkW/XKKCAgQAaDQXPnzpWTk5Nq1qypzZs3a9GiRapcubLq1aunr7/+Otu4EydOaPDgwXJycpKVlZXc3Nw0e/ZsZWRkFPh6r169qvHjx8vZ2VkPPfSQatasqREjRujChQvZ+np7e5uuMSIiQpLMrtvb29viOozbKQYEBOi9997TI488Imtraz322GP6+OOPcx1nvPdZWVmaM2eOGjZsKGtra9WqVUuvv/66Wd9Tp05pwIABcnR0lJWVlRo2bCh/f3+lpaVlmzc9PT3bto65PVejbdu2qVOnTnJwcJCtra1atWqlLVu25Nr/6tWrevPNN9W4cWPZ2NioevXqevrpp7V58+Zs98VgMGjEiBGSpBEjRpjVxdZ6QP6wYhQAAAAAAAAAAAAAoNB9/vnnkqQGDRrkeDwmJkaLFi1Samqq6tevr2effVb29vam41WqVFH9+vXl5+en5cuXKzk5WUFBQWrVqpXZPLGxsYqPj1ezZs3yXaO3t7cpALNgwQJJt7bvM6pUqdJdxwQGBt7zHKtWrdLAgQP1wQcfyNvbW46OjvL29lZwcLBGjhxpFkqKiIhQjx49lJ6err59+6p27dq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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "appals.show_critical_path_time_breakdown().T" + ] + }, + { + "cell_type": "markdown", + "id": "94f75901", + "metadata": { + "papermill": { + "duration": 0.014921, + "end_time": "2024-12-06T05:56:21.656661", + "exception": false, + "start_time": "2024-12-06T05:56:21.641740", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Compare to vanilla" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f9051ad7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-06T05:56:21.687372Z", + "iopub.status.busy": "2024-12-06T05:56:21.687092Z", + "iopub.status.idle": "2024-12-06T05:56:57.099205Z", + "shell.execute_reply": "2024-12-06T05:56:57.098700Z" + }, + "papermill": { + "duration": 35.429191, + "end_time": "2024-12-06T05:56:57.100672", + "exception": false, + "start_time": "2024-12-06T05:56:21.671481", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load data /sr213/application_1733153225851_0029/app.log\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "emon metric\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 1319:> (0 + 3) / 3]\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 1319:==================> (1 + 2) / 3]\r", + "\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sar metric\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time breakdown\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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runtime132.1417.65648.67%
shuffle_write0.000.000.00%
f_wait_time0.000.000.00%
input read22.5422.540.00%
acc_task_time128.0113.99815.01%
output rows1.791.1851.69%
%user>90%0.990.935.91%
%kernel>10%0.990.972.85%
%iowait>10%0.310.62-49.30%
avg %user82.1141.2299.21%
avg %system6.104.5135.11%
avg %iowait0.170.74-76.60%
avg disk util7.1332.21-77.85%
time more than 90%0.000.000.00%
total read (G)5.245.39-2.75%
total write (G)0.021.12-97.81%
avg read bw (MB/s)37.52190.27-80.28%
avg write bw (MB/s)0.1839.61-99.55%
read bw %7559.27411.58-85.60%
read bw %95173.05484.54-64.29%
read bw max236.70510.35-53.62%
time_rd_morethan_950.050.0341.96%
write bw %750.071.07-93.45%
write bw %951.23165.69-99.25%
write bw max1.70812.51-99.79%
time_wr_morethan_950.000.03-100.00%
cached mean88.3393.90-5.93%
cached 75%132.00145.00-8.97%
cached max160.00188.00-14.89%
used mean2,060.73834.00147.09%
used 75%2,343.00852.00175.00%
used max2,346.00859.00173.11%
rx MB/s 75%0.000.000.00%
rx MB/s 95%0.000.000.00%
rx MB/s 99%0.000.000.00%
pgin mean37.37190.21-80.35%
pgin 75%59.00412.00-85.68%
pgin max352.00509.00-30.84%
pgout mean0.1340.97-99.68%
pgout 75%0.001.00-100.00%
pgout max2.00840.00-99.76%
fault mean952,586.87117,653.31709.66%
fault 75%1,426,717.00205,151.00595.45%
fault max2,628,392.00256,538.00924.56%
cpu%_avg0.880.4596.45%
cpu freq_avg3,460.223,241.926.73%
pathlength_sum17,960.001,933.00829.13%
ipc_avg1.271.1411.30%
\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 runtimeshuffle_writef_wait_timeinput readacc_task_timeoutput rows
real_queryid      
1\n", + "
132.14
\n", + "
17.65
\n", + "
648.67%
\n", + " 		\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + " 		\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + " 		\n", + "
22.54
\n", + "
22.54
\n", + "
0.00%
\n", + " 		\n", + "
128.01
\n", + "
13.99
\n", + "
815.01%
\n", + " 		\n", + "
1.79
\n", + "
1.18
\n", + "
51.69%
\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 sr217agg
0  
%user>90%\n", + "
0.99
\n", + "
0.93
\n", + "
5.91%
\n", + " 		\n", + "
0.99
\n", + "
0.93
\n", + "
5.91%
\n", + "
%kernel>10%\n", + "
0.99
\n", + "
0.97
\n", + "
2.85%
\n", + " 		\n", + "
0.99
\n", + "
0.97
\n", + "
2.85%
\n", + "
%iowait>10%\n", + "
0.31
\n", + "
0.62
\n", + "
-49.30%
\n", + " 		\n", + "
0.31
\n", + "
0.62
\n", + "
-49.30%
\n", + "
avg %user\n", + "
82.11
\n", + "
41.22
\n", + "
99.21%
\n", + " 		\n", + "
82.11
\n", + "
41.22
\n", + "
99.21%
\n", + "
avg %system\n", + "
6.10
\n", + "
4.51
\n", + "
35.11%
\n", + " 		\n", + "
6.10
\n", + "
4.51
\n", + "
35.11%
\n", + "
avg %iowait\n", + "
0.17
\n", + "
0.74
\n", + "
-76.60%
\n", + " 		\n", + "
0.17
\n", + "
0.74
\n", + "
-76.60%
\n", + "
avg disk util\n", + "
7.13
\n", + "
32.21
\n", + "
-77.85%
\n", + " 		\n", + "
7.13
\n", + "
32.21
\n", + "
-77.85%
\n", + "
time more than 90%\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + " 		\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + "
total read (G)\n", + "
5.24
\n", + "
5.39
\n", + "
-2.75%
\n", + " 		\n", + "
5.24
\n", + "
5.39
\n", + "
-2.75%
\n", + "
total write (G)\n", + "
0.02
\n", + "
1.12
\n", + "
-97.81%
\n", + " 		\n", + "
0.02
\n", + "
1.12
\n", + "
-97.81%
\n", + "
avg read bw (MB/s)\n", + "
37.52
\n", + "
190.27
\n", + "
-80.28%
\n", + " 		\n", + "
37.52
\n", + "
190.27
\n", + "
-80.28%
\n", + "
avg write bw (MB/s)\n", + "
0.18
\n", + "
39.61
\n", + "
-99.55%
\n", + " 		\n", + "
0.18
\n", + "
39.61
\n", + "
-99.55%
\n", + "
read bw %75\n", + "
59.27
\n", + "
411.58
\n", + "
-85.60%
\n", + " 		\n", + "
59.27
\n", + "
411.58
\n", + "
-85.60%
\n", + "
read bw %95\n", + "
173.05
\n", + "
484.54
\n", + "
-64.29%
\n", + " 		\n", + "
173.05
\n", + "
484.54
\n", + "
-64.29%
\n", + "
read bw max\n", + "
236.70
\n", + "
510.35
\n", + "
-53.62%
\n", + " 		\n", + "
236.70
\n", + "
510.35
\n", + "
-53.62%
\n", + "
time_rd_morethan_95\n", + "
0.05
\n", + "
0.03
\n", + "
41.96%
\n", + " 		\n", + "
0.05
\n", + "
0.03
\n", + "
41.96%
\n", + "
write bw %75\n", + "
0.07
\n", + "
1.07
\n", + "
-93.45%
\n", + " 		\n", + "
0.07
\n", + "
1.07
\n", + "
-93.45%
\n", + "
write bw %95\n", + "
1.23
\n", + "
165.69
\n", + "
-99.25%
\n", + " 		\n", + "
1.23
\n", + "
165.69
\n", + "
-99.25%
\n", + "
write bw max\n", + "
1.70
\n", + "
812.51
\n", + "
-99.79%
\n", + " 		\n", + "
1.70
\n", + "
812.51
\n", + "
-99.79%
\n", + "
time_wr_morethan_95\n", + "
0.00
\n", + "
0.03
\n", + "
-100.00%
\n", + " 		\n", + "
0.00
\n", + "
0.03
\n", + "
-100.00%
\n", + "
cached mean\n", + "
88.33
\n", + "
93.90
\n", + "
-5.93%
\n", + " 		\n", + "
88.33
\n", + "
93.90
\n", + "
-5.93%
\n", + "
cached 75%\n", + "
132.00
\n", + "
145.00
\n", + "
-8.97%
\n", + " 		\n", + "
132.00
\n", + "
145.00
\n", + "
-8.97%
\n", + "
cached max\n", + "
160.00
\n", + "
188.00
\n", + "
-14.89%
\n", + " 		\n", + "
160.00
\n", + "
188.00
\n", + "
-14.89%
\n", + "
used mean\n", + "
2,060.73
\n", + "
834.00
\n", + "
147.09%
\n", + " 		\n", + "
2,060.73
\n", + "
834.00
\n", + "
147.09%
\n", + "
used 75%\n", + "
2,343.00
\n", + "
852.00
\n", + "
175.00%
\n", + " 		\n", + "
2,343.00
\n", + "
852.00
\n", + "
175.00%
\n", + "
used max\n", + "
2,346.00
\n", + "
859.00
\n", + "
173.11%
\n", + " 		\n", + "
2,346.00
\n", + "
859.00
\n", + "
173.11%
\n", + "
rx MB/s 75%\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + " 		\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + "
rx MB/s 95%\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + " 		\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + "
rx MB/s 99%\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + " 		\n", + "
0.00
\n", + "
0.00
\n", + "
nan%
\n", + "
pgin mean\n", + "
37.37
\n", + "
190.21
\n", + "
-80.35%
\n", + " 		\n", + "
37.37
\n", + "
190.21
\n", + "
-80.35%
\n", + "
pgin 75%\n", + "
59.00
\n", + "
412.00
\n", + "
-85.68%
\n", + " 		\n", + "
59.00
\n", + "
412.00
\n", + "
-85.68%
\n", + "
pgin max\n", + "
352.00
\n", + "
509.00
\n", + "
-30.84%
\n", + " 		\n", + "
352.00
\n", + "
509.00
\n", + "
-30.84%
\n", + "
pgout mean\n", + "
0.13
\n", + "
40.97
\n", + "
-99.68%
\n", + " 		\n", + "
0.13
\n", + "
40.97
\n", + "
-99.68%
\n", + "
pgout 75%\n", + "
0.00
\n", + "
1.00
\n", + "
-100.00%
\n", + " 		\n", + "
0.00
\n", + "
1.00
\n", + "
-100.00%
\n", + "
pgout max\n", + "
2.00
\n", + "
840.00
\n", + "
-99.76%
\n", + " 		\n", + "
2.00
\n", + "
840.00
\n", + "
-99.76%
\n", + "
fault mean\n", + "
952,586.87
\n", + "
117,653.31
\n", + "
709.66%
\n", + " 		\n", + "
952,586.87
\n", + "
117,653.31
\n", + "
709.66%
\n", + "
fault 75%\n", + "
1,426,717.00
\n", + "
205,151.00
\n", + "
595.45%
\n", + " 		\n", + "
1,426,717.00
\n", + "
205,151.00
\n", + "
595.45%
\n", + "
fault max\n", + "
2,628,392.00
\n", + "
256,538.00
\n", + "
924.56%
\n", + " 		\n", + "
2,628,392.00
\n", + "
256,538.00
\n", + "
924.56%
\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
clientsr217agg
cpu%_avg\n", + "
0.88
\n", + "
0.45
\n", + "
96.45%
\n", + " 		\n", + "
0.88
\n", + "
0.45
\n", + "
96.45%
\n", + "
cpu freq_avg\n", + "
3,460.22
\n", + "
3,241.92
\n", + "
6.73%
\n", + " 		\n", + "
3,460.22
\n", + "
3,241.92
\n", + "
6.73%
\n", + "
pathlength_sum\n", + "
17,960.00
\n", + "
1,933.00
\n", + "
829.13%
\n", + " 		\n", + "
17,960.00
\n", + "
1,933.00
\n", + "
829.13%
\n", + "
ipc_avg\n", + "
1.27
\n", + "
1.14
\n", + "
11.30%
\n", + " 		\n", + "
1.27
\n", + "
1.14
\n", + "
11.30%
\n", + "
\n", + "\n", + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 indexStage IDJob IDreal_queryidqueryidtotal_timestdev_timeacc_totaltotal
008818127.981.9199.65%99.65%
11109180.29nan99.87%0.23%
221210180.09nan99.94%0.07%
331511180.07nan100.00%0.06%
\n", + "		\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 indexStage IDJob IDreal_queryidqueryidtotal_timestdev_timeacc_totaltotal
00881813.860.3286.65%86.65%
111210180.98nan92.80%6.15%
22109180.74nan97.43%4.63%
331511180.41nan100.00%2.57%
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "appals.get_app_info(disk_prefix=disk_prefix,nic_prefix=nic_prefix)" + "if comp_appid:\n", + " comp_app=Application_Run(comp_appid,basedir=comp_base_dir)\n", + " output=app.compare_app(rapp=comp_app,show_metric=emonmetric,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + " display(HTML(output))" + ] + }, + { + "cell_type": "markdown", + "id": "572607be", + "metadata": { + "papermill": { + "duration": 0.019224, + "end_time": "2024-12-06T05:56:57.140390", + "exception": false, + "start_time": "2024-12-06T05:56:57.121166", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Config compare" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "8d703114", + "execution_count": 22, + "id": "0b4f3632", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:31:52.348015Z", - "iopub.status.busy": "2024-12-02T15:31:52.347758Z", - "iopub.status.idle": "2024-12-02T15:31:54.312505Z", - "shell.execute_reply": "2024-12-02T15:31:54.312032Z" + "iopub.execute_input": "2024-12-06T05:56:57.179356Z", + "iopub.status.busy": "2024-12-06T05:56:57.179070Z", + "iopub.status.idle": "2024-12-06T05:56:58.328465Z", + "shell.execute_reply": "2024-12-06T05:56:58.327997Z" }, "papermill": { - "duration": 1.978758, - "end_time": "2024-12-02T15:31:54.314515", + "duration": 1.170805, + "end_time": "2024-12-06T05:56:58.330214", "exception": false, - "start_time": "2024-12-02T15:31:52.335757", + "start_time": "2024-12-06T05:56:57.159409", "status": "completed" }, - "scrolled": true, "tags": [] }, "outputs": [ @@ -2134,112 +4491,274 @@ " \n", " \n", " \n", - " 0\n", + " 0851_0048\n", + " 0851_0029\n", + " comp\n", " \n", " \n", " \n", " \n", - " 40%_time of scan and filter\n", - " 6.95\n", + " callSite.short\n", + " collect at /tmp/ipykernel_265482/1936321720.py:117\n", + " collect at /tmp/ipykernel_234307/1936321720.py:117\n", + " False\n", " \n", " \n", - " 35%_time of project\n", - " 6.02\n", + " spark.app.submitTime\n", + " 1733464301669\n", + " 1733457038427\n", + " False\n", " \n", " \n", - " 21%_not_counted\n", - " 3.55\n", + " spark.executor.extraClassPath\n", + " file:///data0/home/sparkuser/jars/6600a164407ae0e4f5ea5b33dc4b902f23a27730/gluten-velox-bundle-spark3.3_2.12-centos_7_x86_64-1.3.0-snapshot.jar\n", + " \n", + " False\n", " \n", " \n", - " 3%_idle\n", - " 0.54\n", + " spark.executor.extraJavaOptions\n", + " -xx:+ignoreunrecognizedvmoptions --add-opens=java.base/java.lang=all-unnamed --add-opens=java.base/java.lang.invoke=all-unnamed --add-opens=java.base/java.lang.reflect=all-unnamed --add-opens=java.base/java.io=all-unnamed --add-opens=java.base/java.net=all-unnamed --add-opens=java.base/java.nio=all-unnamed --add-opens=java.base/java.util=all-unnamed --add-opens=java.base/java.util.concurrent=all-unnamed --add-opens=java.base/java.util.concurrent.atomic=all-unnamed --add-opens=java.base/sun.nio.ch=all-unnamed --add-opens=java.base/sun.nio.cs=all-unnamed --add-opens=java.base/sun.security.action=all-unnamed --add-opens=java.base/sun.util.calendar=all-unnamed --add-opens=java.security.jgss/sun.security.krb5=all-unnamed -xx:+useparalleloldgc -xx:parallelgcthreads=2 -xx:newratio=1 -xx:survivorratio=1 -xx:+usecompressedoops -verbose:gc -xx:+printgcdetails -xx:+printgctimestamps -xx:errorfile=/home/sparkuser/logs/java/hs_err_pid%p.log\n", + " -xx:+ignoreunrecognizedvmoptions --add-opens=java.base/java.lang=all-unnamed --add-opens=java.base/java.lang.invoke=all-unnamed --add-opens=java.base/java.lang.reflect=all-unnamed --add-opens=java.base/java.io=all-unnamed --add-opens=java.base/java.net=all-unnamed --add-opens=java.base/java.nio=all-unnamed --add-opens=java.base/java.util=all-unnamed --add-opens=java.base/java.util.concurrent=all-unnamed --add-opens=java.base/java.util.concurrent.atomic=all-unnamed --add-opens=java.base/sun.nio.ch=all-unnamed --add-opens=java.base/sun.nio.cs=all-unnamed --add-opens=java.base/sun.security.action=all-unnamed --add-opens=java.base/sun.util.calendar=all-unnamed --add-opens=java.security.jgss/sun.security.krb5=all-unnamed -xx:+useparalleloldgc -xx:parallelgcthreads=2 -xx:newratio=1 -xx:survivorratio=1 -xx:+usecompressedoops -verbose:gc -xx:+printgcdetails -xx:+printgctimestamps -xx:errorfile=/data0/home/sparkuser/logs/java/hs_err_pid%p.log\n", + " False\n", " \n", " \n", - " 0%_time of input iterator\n", - " 0.06\n", + " spark.executor.memory\n", + " 10944m\n", + " 29184m\n", + " False\n", " \n", " \n", - " 0%_time of aggregation\n", - " 0.03\n", + " spark.gluten.memory.conservative.task.offHeap.size.in.bytes\n", + " 10041163776\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to append / split batches\n", - " 0.00\n", + " spark.gluten.memory.dynamic.offHeap.sizing.enabled\n", + " false\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time of rowConstruction\n", - " 0.00\n", + " spark.gluten.memory.offHeap.size.in.bytes\n", + " 80329310208\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to split\n", - " 0.00\n", + " spark.gluten.memory.overAcquiredMemoryRatio\n", + " 0\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to deserialize\n", - " 0.00\n", + " spark.gluten.memory.task.offHeap.size.in.bytes\n", + " 20082327552\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time of sort\n", - " 0.00\n", + " spark.gluten.memoryOverhead.size.in.bytes\n", + " 1073741824\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time of extraction\n", - " 0.00\n", + " spark.gluten.numTaskSlotsPerExecutor\n", + " 4\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_shuffle write time\n", - " 0.00\n", + " spark.gluten.sql.columnar.backend.lib\n", + " velox\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to compress\n", - " 0.00\n", + " spark.gluten.sql.columnar.coalesce.batches\n", + " true\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to spill\n", - " 0.00\n", + " spark.gluten.sql.columnar.forceshuffledhashjoin\n", + " true\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to decompress\n", - " 0.00\n", + " spark.gluten.sql.columnar.maxBatchSize\n", + " 4096\n", + " NaN\n", + " False\n", " \n", " \n", - " 0%_time to convert\n", - " 0.00\n", + " spark.gluten.sql.columnar.shuffle.codec\n", + " lz4\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.gluten.sql.columnar.shuffle.codecBackend\n", + " \n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.gluten.sql.session.timeZone.default\n", + " etc/utc\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.memory.offHeap.size\n", + " 80329310208\n", + " 58368m\n", + " False\n", + " \n", + " \n", + " spark.plugins\n", + " org.apache.gluten.glutenplugin\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.repl.class.outputDir\n", + " /tmp/tmpypqh85b0\n", + " /tmp/tmpynceqaxd\n", + " False\n", + " \n", + " \n", + " spark.repl.class.uri\n", + " spark://sr213:40521/classes\n", + " spark://sr213:34951/classes\n", + " False\n", + " \n", + " \n", + " spark.shuffle.manager\n", + " org.apache.spark.shuffle.sort.columnarshufflemanager\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.sql.adaptive.customCostEvaluatorClass\n", + " org.apache.spark.sql.execution.adaptive.glutencostevaluator\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.sql.extensions\n", + " org.apache.gluten.extension.glutensessionextensions\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.sql.files.maxPartitionBytes\n", + " 4g\n", + " NaN\n", + " False\n", + " \n", + " \n", + " spark.sql.shuffle.partitions\n", + " 32\n", + " 64\n", + " False\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " 0\n", - "40%_time of scan and filter 6.95\n", - "35%_time of project 6.02\n", - "21%_not_counted 3.55\n", - " 3%_idle 0.54\n", - " 0%_time of input iterator 0.06\n", - " 0%_time of aggregation 0.03\n", - " 0%_time to append / split batches 0.00\n", - " 0%_time of rowConstruction 0.00\n", - " 0%_time to split 0.00\n", - " 0%_time to deserialize 0.00\n", - " 0%_time of sort 0.00\n", - " 0%_time of extraction 0.00\n", - " 0%_shuffle write time 0.00\n", - " 0%_time to compress 0.00\n", - " 0%_time to spill 0.00\n", - " 0%_time to decompress 0.00\n", - " 0%_time to convert 0.00" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" + " 0851_0048 \\\n", + "callSite.short collect at /tmp/ipykernel_265482/1936321720.py:117 \n", + "spark.app.submitTime 1733464301669 \n", + "spark.executor.extraClassPath file:///data0/home/sparkuser/jars/6600a164407ae0e4f5ea5b33dc4b902f23a27730/gluten-velox-bundle-spark3.3_2.12-centos_7_x86_64-1.3.0-snapshot.jar \n", + "spark.executor.extraJavaOptions -xx:+ignoreunrecognizedvmoptions --add-opens=java.base/java.lang=all-unnamed --add-opens=java.base/java.lang.invoke=all-unnamed --add-opens=java.base/java.lang.reflect=all-unnamed --add-opens=java.base/java.io=all-unnamed --add-opens=java.base/java.net=all-unnamed --add-opens=java.base/java.nio=all-unnamed --add-opens=java.base/java.util=all-unnamed --add-opens=java.base/java.util.concurrent=all-unnamed --add-opens=java.base/java.util.concurrent.atomic=all-unnamed --add-opens=java.base/sun.nio.ch=all-unnamed --add-opens=java.base/sun.nio.cs=all-unnamed --add-opens=java.base/sun.security.action=all-unnamed --add-opens=java.base/sun.util.calendar=all-unnamed --add-opens=java.security.jgss/sun.security.krb5=all-unnamed -xx:+useparalleloldgc -xx:parallelgcthreads=2 -xx:newratio=1 -xx:survivorratio=1 -xx:+usecompressedoops -verbose:gc -xx:+printgcdetails -xx:+printgctimestamps -xx:errorfile=/home/sparkuser/logs/java/hs_err_pid%p.log \n", + "spark.executor.memory 10944m \n", + "spark.gluten.memory.conservative.task.offHeap.size.in.bytes 10041163776 \n", + "spark.gluten.memory.dynamic.offHeap.sizing.enabled false \n", + "spark.gluten.memory.offHeap.size.in.bytes 80329310208 \n", + "spark.gluten.memory.overAcquiredMemoryRatio 0 \n", + "spark.gluten.memory.task.offHeap.size.in.bytes 20082327552 \n", + "spark.gluten.memoryOverhead.size.in.bytes 1073741824 \n", + "spark.gluten.numTaskSlotsPerExecutor 4 \n", + "spark.gluten.sql.columnar.backend.lib velox \n", + "spark.gluten.sql.columnar.coalesce.batches true \n", + "spark.gluten.sql.columnar.forceshuffledhashjoin true \n", + "spark.gluten.sql.columnar.maxBatchSize 4096 \n", + "spark.gluten.sql.columnar.shuffle.codec lz4 \n", + "spark.gluten.sql.columnar.shuffle.codecBackend \n", + "spark.gluten.sql.session.timeZone.default etc/utc \n", + "spark.memory.offHeap.size 80329310208 \n", + "spark.plugins org.apache.gluten.glutenplugin \n", + "spark.repl.class.outputDir /tmp/tmpypqh85b0 \n", + "spark.repl.class.uri spark://sr213:40521/classes \n", + "spark.shuffle.manager org.apache.spark.shuffle.sort.columnarshufflemanager \n", + "spark.sql.adaptive.customCostEvaluatorClass org.apache.spark.sql.execution.adaptive.glutencostevaluator \n", + "spark.sql.extensions org.apache.gluten.extension.glutensessionextensions \n", + "spark.sql.files.maxPartitionBytes 4g \n", + "spark.sql.shuffle.partitions 32 \n", + "\n", + " 0851_0029 \\\n", + "callSite.short collect at /tmp/ipykernel_234307/1936321720.py:117 \n", + "spark.app.submitTime 1733457038427 \n", + "spark.executor.extraClassPath \n", + "spark.executor.extraJavaOptions -xx:+ignoreunrecognizedvmoptions --add-opens=java.base/java.lang=all-unnamed --add-opens=java.base/java.lang.invoke=all-unnamed --add-opens=java.base/java.lang.reflect=all-unnamed --add-opens=java.base/java.io=all-unnamed --add-opens=java.base/java.net=all-unnamed --add-opens=java.base/java.nio=all-unnamed --add-opens=java.base/java.util=all-unnamed --add-opens=java.base/java.util.concurrent=all-unnamed --add-opens=java.base/java.util.concurrent.atomic=all-unnamed --add-opens=java.base/sun.nio.ch=all-unnamed --add-opens=java.base/sun.nio.cs=all-unnamed --add-opens=java.base/sun.security.action=all-unnamed --add-opens=java.base/sun.util.calendar=all-unnamed --add-opens=java.security.jgss/sun.security.krb5=all-unnamed -xx:+useparalleloldgc -xx:parallelgcthreads=2 -xx:newratio=1 -xx:survivorratio=1 -xx:+usecompressedoops -verbose:gc -xx:+printgcdetails -xx:+printgctimestamps -xx:errorfile=/data0/home/sparkuser/logs/java/hs_err_pid%p.log \n", + "spark.executor.memory 29184m \n", + "spark.gluten.memory.conservative.task.offHeap.size.in.bytes NaN \n", + "spark.gluten.memory.dynamic.offHeap.sizing.enabled NaN \n", + "spark.gluten.memory.offHeap.size.in.bytes NaN \n", + "spark.gluten.memory.overAcquiredMemoryRatio NaN \n", + "spark.gluten.memory.task.offHeap.size.in.bytes NaN \n", + "spark.gluten.memoryOverhead.size.in.bytes NaN \n", + "spark.gluten.numTaskSlotsPerExecutor NaN \n", + "spark.gluten.sql.columnar.backend.lib NaN \n", + "spark.gluten.sql.columnar.coalesce.batches NaN \n", + "spark.gluten.sql.columnar.forceshuffledhashjoin NaN \n", + "spark.gluten.sql.columnar.maxBatchSize NaN \n", + "spark.gluten.sql.columnar.shuffle.codec NaN \n", + "spark.gluten.sql.columnar.shuffle.codecBackend NaN \n", + "spark.gluten.sql.session.timeZone.default NaN \n", + "spark.memory.offHeap.size 58368m \n", + "spark.plugins NaN \n", + "spark.repl.class.outputDir /tmp/tmpynceqaxd \n", + "spark.repl.class.uri spark://sr213:34951/classes \n", + "spark.shuffle.manager NaN \n", + "spark.sql.adaptive.customCostEvaluatorClass NaN \n", + "spark.sql.extensions NaN \n", + "spark.sql.files.maxPartitionBytes NaN \n", + "spark.sql.shuffle.partitions 64 \n", + "\n", + " comp \n", + "callSite.short False \n", + "spark.app.submitTime False \n", + "spark.executor.extraClassPath False \n", + "spark.executor.extraJavaOptions False \n", + "spark.executor.memory False \n", + "spark.gluten.memory.conservative.task.offHeap.size.in.bytes False \n", + "spark.gluten.memory.dynamic.offHeap.sizing.enabled False \n", + "spark.gluten.memory.offHeap.size.in.bytes False \n", + "spark.gluten.memory.overAcquiredMemoryRatio False \n", + "spark.gluten.memory.task.offHeap.size.in.bytes False \n", + "spark.gluten.memoryOverhead.size.in.bytes False \n", + "spark.gluten.numTaskSlotsPerExecutor False \n", + "spark.gluten.sql.columnar.backend.lib False \n", + "spark.gluten.sql.columnar.coalesce.batches False \n", + "spark.gluten.sql.columnar.forceshuffledhashjoin False \n", + "spark.gluten.sql.columnar.maxBatchSize False \n", + "spark.gluten.sql.columnar.shuffle.codec False \n", + "spark.gluten.sql.columnar.shuffle.codecBackend False \n", + "spark.gluten.sql.session.timeZone.default False \n", + "spark.memory.offHeap.size False \n", + "spark.plugins False \n", + "spark.repl.class.outputDir False \n", + "spark.repl.class.uri False \n", + "spark.shuffle.manager False \n", + "spark.sql.adaptive.customCostEvaluatorClass False \n", + "spark.sql.extensions False \n", + "spark.sql.files.maxPartitionBytes False \n", + "spark.sql.shuffle.partitions False " ] }, "metadata": {}, @@ -2247,130 +4766,44 @@ } ], "source": [ - "appals.show_critical_path_time_breakdown().T" - ] - }, - { - "cell_type": "markdown", - "id": "bde3cd7c", - "metadata": { - "papermill": { - "duration": 0.012768, - "end_time": "2024-12-02T15:31:54.340986", - "exception": false, - "start_time": "2024-12-02T15:31:54.328218", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Compare to previous run" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "888c7084", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-02T15:31:54.367369Z", - "iopub.status.busy": "2024-12-02T15:31:54.367111Z", - "iopub.status.idle": "2024-12-02T15:31:54.370128Z", - "shell.execute_reply": "2024-12-02T15:31:54.369683Z" - }, - "papermill": { - "duration": 0.017843, - "end_time": "2024-12-02T15:31:54.371339", - "exception": false, - "start_time": "2024-12-02T15:31:54.353496", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "if compare_appid:\n", - " compare_app=Application_Run(comapre_appid,basedir=compare_basedir)\n", - " output=app.compare_app(rapp=compare_app,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", - " display(HTML(output))" - ] - }, - { - "cell_type": "markdown", - "id": "bb199e20", - "metadata": { - "papermill": { - "duration": 0.012114, - "end_time": "2024-12-02T15:31:54.396409", - "exception": false, - "start_time": "2024-12-02T15:31:54.384295", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Config compare" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ae5a681e", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-02T15:31:54.422666Z", - "iopub.status.busy": "2024-12-02T15:31:54.422386Z", - "iopub.status.idle": "2024-12-02T15:31:54.424818Z", - "shell.execute_reply": "2024-12-02T15:31:54.424396Z" - }, - "papermill": { - "duration": 0.01699, - "end_time": "2024-12-02T15:31:54.426008", - "exception": false, - "start_time": "2024-12-02T15:31:54.409018", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "if compare_appid:\n", - " display(comp_spark_conf(app_als, compare_app_als))" + "if comp_appid:\n", + " comp_appals=comp_app.analysis['app']['als']\n", + " display(comp_spark_conf(appals, comp_appals))" ] }, { "cell_type": "markdown", - "id": "6118d3af", + "id": "20b5f6f2", "metadata": { "papermill": { - "duration": 0.012438, - "end_time": "2024-12-02T15:31:54.451132", + "duration": 0.020157, + "end_time": "2024-12-06T05:56:58.371233", "exception": false, - "start_time": "2024-12-02T15:31:54.438694", + "start_time": "2024-12-06T05:56:58.351076", "status": "completed" }, "tags": [] }, "source": [ - "# convert to HTML" + "# Convert to HTML" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "902ebd2c", + "execution_count": 23, + "id": "bd866a20", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:31:54.477825Z", - "iopub.status.busy": "2024-12-02T15:31:54.477584Z", - "iopub.status.idle": "2024-12-02T15:31:54.480969Z", - "shell.execute_reply": "2024-12-02T15:31:54.480577Z" + "iopub.execute_input": "2024-12-06T05:56:58.412619Z", + "iopub.status.busy": "2024-12-06T05:56:58.412337Z", + "iopub.status.idle": "2024-12-06T05:56:58.416007Z", + "shell.execute_reply": "2024-12-06T05:56:58.415586Z" }, "papermill": { - "duration": 0.018124, - "end_time": "2024-12-02T15:31:54.482237", + "duration": 0.025916, + "end_time": "2024-12-06T05:56:58.417156", "exception": false, - "start_time": "2024-12-02T15:31:54.464113", + "start_time": "2024-12-06T05:56:58.391240", "status": "completed" }, "tags": [] @@ -2396,20 +4829,20 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "1f55d7ac", + "execution_count": 24, + "id": "83323888", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:31:54.509184Z", - "iopub.status.busy": "2024-12-02T15:31:54.508952Z", - "iopub.status.idle": "2024-12-02T15:31:54.511234Z", - "shell.execute_reply": "2024-12-02T15:31:54.510830Z" + "iopub.execute_input": "2024-12-06T05:56:58.459405Z", + "iopub.status.busy": "2024-12-06T05:56:58.459137Z", + "iopub.status.idle": "2024-12-06T05:56:58.461591Z", + "shell.execute_reply": "2024-12-06T05:56:58.461165Z" }, "papermill": { - "duration": 0.016976, - "end_time": "2024-12-02T15:31:54.512431", + "duration": 0.024889, + "end_time": "2024-12-06T05:56:58.462703", "exception": false, - "start_time": "2024-12-02T15:31:54.495455", + "start_time": "2024-12-06T05:56:58.437814", "status": "completed" }, "tags": [] @@ -2421,20 +4854,20 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "45249902", + "execution_count": 25, + "id": "98b1ba3b", "metadata": { "execution": { - "iopub.execute_input": "2024-12-02T15:31:54.539435Z", - "iopub.status.busy": "2024-12-02T15:31:54.539198Z", - "iopub.status.idle": "2024-12-02T15:31:54.541484Z", - "shell.execute_reply": "2024-12-02T15:31:54.541077Z" + "iopub.execute_input": "2024-12-06T05:56:58.505858Z", + "iopub.status.busy": "2024-12-06T05:56:58.505587Z", + "iopub.status.idle": "2024-12-06T05:56:58.508041Z", + "shell.execute_reply": "2024-12-06T05:56:58.507614Z" }, "papermill": { - "duration": 0.01708, - "end_time": "2024-12-02T15:31:54.542686", + "duration": 0.024884, + "end_time": "2024-12-06T05:56:58.509167", "exception": false, - "start_time": "2024-12-02T15:31:54.525606", + "start_time": "2024-12-06T05:56:58.484283", "status": "completed" }, "tags": [] @@ -2477,24 +4910,25 @@ }, "papermill": { "default_parameters": {}, - "duration": 132.778369, - "end_time": "2024-12-02T15:31:57.173789", + "duration": 207.873445, + "end_time": "2024-12-06T05:57:01.150405", "environment_variables": {}, "exception": null, - "input_path": "2024_12_02_152940_tpch_gluten_application_1733153225851_0001.ipynb", - "output_path": "2024_12_02_152940_tpch_gluten_application_1733153225851_0001.nbconvert.ipynb", + "input_path": "2024_12_06_055328_tpch_gluten_application_1733153225851_0048.ipynb", + "output_path": "2024_12_06_055328_tpch_gluten_application_1733153225851_0048.nbconvert.ipynb", "parameters": { - "appid": "application_1733153225851_0001", - "basedir": "sr213", - "compare_appid": "", - "compare_basedir": "", - "compare_name": "", + "appid": "application_1733153225851_0048", + "base_dir": "sr213", + "comp_appid": "application_1733153225851_0029", + "comp_base_dir": "sr213", + "comp_name": "vanilla", "disk": "nvme0n1", "name": "tpch_gluten", "nic": "enp61s0f0", + "proxy": "http://10.239.44.250:8080", "tz": "Etc/GMT+0" }, - "start_time": "2024-12-02T15:29:44.395420", + "start_time": "2024-12-06T05:53:33.276960", "version": "2.6.0" }, "toc": { diff --git a/tools/workload/benchmark_velox/tpc_workload.ipynb b/tools/workload/benchmark_velox/tpc_workload.ipynb index b9b147619d9a..15aba310b002 100644 --- a/tools/workload/benchmark_velox/tpc_workload.ipynb +++ b/tools/workload/benchmark_velox/tpc_workload.ipynb @@ -44,9 +44,18 @@ "# Proxy used to connect to server for perf analysis.\n", "proxy=''\n", "\n", + "# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable.\n", + "# Supported emon events on platform can be verified via `emon -i emon.list`\n", + "emon_list: '/home/sparkuser/ipython/emon_srf.list'\n", + "\n", "# Whether to upload profile to perf analysis server and run perf analysis scripts. Only takes effect if server is set.\n", "analyze_perf=True\n", "\n", + "# Specify app info to compare for perf analysis\n", + "comp_appid: ''\n", + "comp_base_dir: ''\n", + "comp_name: ''\n", + "\n", "# Select workload. Can be either 'tpch' or 'tpcds'.\n", "workload='tpch'\n", "\n", @@ -238,7 +247,7 @@ "metadata": {}, "outputs": [], "source": [ - "test_tpc.start_monitor(clients)" + "test_tpc.start_monitor(clients, emon_list=emon_list)" ] }, { @@ -266,7 +275,7 @@ "outputs": [], "source": [ "if analyze_perf:\n", - " test_tpc.run_perf_analysis(disk_dev, nic_dev, proxy)" + " test_tpc.run_perf_analysis(disk_dev, nic_dev, proxy, comp_appid, comp_base_dir, comp_name)" ] }, { From c4493e86e0991ac2c678b3374fab5b16b93da7cd Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Fri, 6 Dec 2024 09:54:10 +0000 Subject: [PATCH 08/12] fix --- .../native_sql_initialize.ipynb | 10 +- .../sample/trace_result_tpch_q1.json | 1148 ++++++++++------- .../benchmark_velox/tpc_workload.ipynb | 2 +- 3 files changed, 689 insertions(+), 471 deletions(-) diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index 2e63640c40b7..d3cf1f9ca984 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -387,10 +387,7 @@ " !mkdir -p {prof}\n", "\n", " killsar(clients)\n", - " killnumactl(clients) \n", - " \n", - " with open(f\"{prof}/starttime\",\"w\") as f:\n", - " f.write(\"{:d}\".format(int(time.time()*1000)))\n", + " killnumactl(clients)\n", " \n", " for l in clients:\n", " prof_client=os.path.join(prof, l)\n", @@ -405,7 +402,10 @@ " \n", " if sc is not None:\n", " sc.stop()\n", - " \n", + "\n", + " with open(f\"{prof}/starttime\",\"w\") as f:\n", + " f.write(\"{:d}\".format(int(time.time()*1000)))\n", + "\n", " if hdfs_event_dir != '':\n", " !hadoop fs -copyToLocal {hdfs_event_dir}/{appid} {prof}/app.log\n", " elif local_event_dir != '':\n", diff --git a/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json b/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json index 4099df0c4ada..8f13dcbf8cc9 100644 --- a/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json +++ b/tools/workload/benchmark_velox/sample/trace_result_tpch_q1.json @@ -3,515 +3,733 @@ "traceEvents": [ {"name": "process_name", "ph": "M", "pid": 100300, "tid": 0, "args": {"name": "sr217.3"}}, -{"tid": 100300, "ts": -29221, "dur": 1658, "pid": 100300, "ph": "X", "name": "stg0", "args": {"job id": 0, "stage id": 0, "tskid": 0, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -31061, "dur": 1687, "pid": 100300, "ph": "X", "name": "stg0", "args": {"job id": 0, "stage id": 0, "tskid": 0, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"name": "process_name", "ph": "M", "pid": 100100, "tid": 0, "args": {"name": "sr217.1"}}, +{"tid": 100100, "ts": -27468, "dur": 1597, "pid": 100100, "ph": "X", "name": "stg1", "args": {"job id": 1, "stage id": 1, "tskid": 1, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, {"name": "process_name", "ph": "M", "pid": 100200, "tid": 0, "args": {"name": "sr217.2"}}, -{"tid": 100200, "ts": -25482, "dur": 1673, "pid": 100200, "ph": "X", "name": "stg1", "args": {"job id": 1, "stage id": 1, "tskid": 1, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100300, "ts": -23725, "dur": 44, "pid": 100300, "ph": "X", "name": "stg2", "args": {"job id": 2, "stage id": 2, "tskid": 2, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100200, "ts": -25774, "dur": 1571, "pid": 100200, "ph": "X", "name": "stg2", "args": {"job id": 2, "stage id": 2, "tskid": 2, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -24113, "dur": 57, "pid": 100300, "ph": "X", "name": "stg3", "args": {"job id": 3, "stage id": 3, "tskid": 3, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -23974, "dur": 31, "pid": 100300, "ph": "X", "name": "stg4", "args": {"job id": 4, "stage id": 4, "tskid": 4, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100200, "ts": -23861, "dur": 65, "pid": 100200, "ph": "X", "name": "stg5", "args": {"job id": 5, "stage id": 5, "tskid": 5, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -23721, "dur": 32, "pid": 100300, "ph": "X", "name": "stg6", "args": {"job id": 6, "stage id": 6, "tskid": 6, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, +{"tid": 100300, "ts": -23618, "dur": 29, "pid": 100300, "ph": "X", "name": "stg7", "args": {"job id": 7, "stage id": 7, "tskid": 7, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, {"name": "process_name", "ph": "M", "pid": 100400, "tid": 0, "args": {"name": "sr217.4"}}, -{"tid": 100400, "ts": -23602, "dur": 1568, "pid": 100400, "ph": "X", "name": "stg3", "args": {"job id": 3, "stage id": 3, "tskid": 3, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100400, "ts": -21959, "dur": 45, "pid": 100400, "ph": "X", "name": "stg4", "args": {"job id": 4, "stage id": 4, "tskid": 4, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100300, "ts": -21844, "dur": 33, "pid": 100300, "ph": "X", "name": "stg5", "args": {"job id": 5, "stage id": 5, "tskid": 5, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"name": "process_name", "ph": "M", "pid": 100100, "tid": 0, "args": {"name": "sr217.1"}}, -{"tid": 100100, "ts": -21745, "dur": 1580, "pid": 100100, "ph": "X", "name": "stg6", "args": {"job id": 6, "stage id": 6, "tskid": 6, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100400, "ts": -20094, "dur": 34, "pid": 100400, "ph": "X", "name": "stg7", "args": {"job id": 7, "stage id": 7, "tskid": 7, "input": 0.0, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100303, "ts": -18980, "dur": 13476, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 20, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100302, "ts": -18981, "dur": 13530, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 16, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100300, "ts": -18985, "dur": 13603, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 8, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100402, "ts": -18981, "dur": 13669, "pid": 100400, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 19, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100400, "ts": -18982, "dur": 13677, "pid": 100400, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 11, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100301, "ts": -18982, "dur": 13895, "pid": 100300, "ph": "X", "name": "stg8", "args": {"job id": 8, "stage id": 8, "tskid": 12, "input": 315.73, "spill": 0.0, "Shuffle Read Metrics": "", "|---Local Read": 0.0, "|---Remote Read": 0.0, "Shuffle Write Metrics": "", "|---Write": 0.0}}, -{"tid": 100401, "ts": 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+{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-6453,"args":{"ipc":1.134}}, +{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-5953,"args":{"ipc":1.094}}, +{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-5453,"args":{"ipc":0.973}}, +{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-4952,"args":{"ipc":1.042}}, +{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-4452,"args":{"ipc":1.055}}, +{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-3952,"args":{"ipc":1.195}}, +{"tid":3,"pid":200,"ph":"C","name":"emon_ipc","ts":-3451,"args":{"ipc":1.375}}, +{"name": "thread_sort_index", "ph": "M", "pid": 200, "tid": 3, "args": {"sort_index ": 3}}, {"name": "process_sort_index", "ph": "M", "pid": 0, "tid": 0, "args": {"sort_index ": 0}}, {"name": "process_sort_index", "ph": "M", "pid": 100, "tid": 0, "args": {"sort_index ": 100}}, {"name": "process_sort_index", "ph": "M", "pid": 200, "tid": 0, "args": {"sort_index ": 200}}, {"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, +{"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}}, +{"name": "process_sort_index", "ph": "M", "pid": 100400, "tid": 0, "args": {"sort_index ": 100400}}, +{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, +{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, {"name": "process_sort_index", "ph": "M", "pid": 100400, "tid": 0, "args": {"sort_index ": 100400}}, {"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, -{"name": "process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, -{"name": "process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, {"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, -{"name": "process_sort_index", "ph": 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"process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, -{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, {"name": "process_sort_index", "ph": "M", "pid": 100400, "tid": 0, "args": {"sort_index ": 100400}}, +{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, {"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}}, {"name": "process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, -{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, -{"name": "process_sort_index", "ph": "M", "pid": 100400, "tid": 0, "args": {"sort_index ": 100400}}, {"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}}, {"name": "process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, +{"name": "process_sort_index", "ph": "M", "pid": 100400, "tid": 0, "args": {"sort_index ": 100400}}, {"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}}, -{"name": "process_sort_index", "ph": "M", "pid": 100600, "tid": 0, "args": {"sort_index ": 100600}}, -{"name": "process_sort_index", "ph": "M", "pid": 100500, "tid": 0, "args": {"sort_index ": 100500}} +{"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}}, +{"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}}, +{"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}}, +{"name": "process_sort_index", "ph": "M", "pid": 100300, "tid": 0, "args": {"sort_index ": 100300}} ], "displayTimeUnit": "ns" } \ No newline at end of file diff --git a/tools/workload/benchmark_velox/tpc_workload.ipynb b/tools/workload/benchmark_velox/tpc_workload.ipynb index 15aba310b002..bd08ff2ed534 100644 --- a/tools/workload/benchmark_velox/tpc_workload.ipynb +++ b/tools/workload/benchmark_velox/tpc_workload.ipynb @@ -46,7 +46,7 @@ "\n", "# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable.\n", "# Supported emon events on platform can be verified via `emon -i emon.list`\n", - "emon_list: '/home/sparkuser/ipython/emon_srf.list'\n", + "emon_list: '/home/sparkuser/ipython/emon.list'\n", "\n", "# Whether to upload profile to perf analysis server and run perf analysis scripts. 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z_=$hSh53kk`b1X(_wIMh*+0XW{!fs%|0qW0GxPSJr0e!aXSh7r298D!&Gw;xM&&m! z+kMzeE76vV`ccZWrMVFlgdjiiMNgXQ9ocj~HH|J30qd9hs-o4`M>WWTHu<y%S{Y~OTUS1@Kn-RN$G+>wQ`c}R^NU{Oy=;-*?tN3Y=hL!yD4_V zc1{H!k+%PBT$fKs+dpyT{&gzCzZFo^_2}kpguqP;OsT+5d1WnPtOepMy)sicfS_Bn z9)?y{kzqZNXglvsI1YcoRU*JM(5i*YXsBTCM>Pg7nprhbN(P(2*LMO{6m8rtbVRN) zgkQ+`W+Q5em*Ct>HzE334YNRN$AxH7p4^=7?2m2M*Hw<-vfkW@(rL0!WIM7=T)eIM)JOzFR{Zg` zw$s@6a^!#souTb_>MF7g_x7Q17mBFEG5J<}EoqL9re`)+T~WxpxQ8@bP*5 zY9Mu*A~4QZAZ=JjE7z`j$tIo9*2Z8ySX|e1$myBw`2lPW$pO*15nZ%bIlH}>Ho}`# zPWBRNN@zWc_7R!A^2Uru zH%F&CY@ZDCO!7rdPaN>Aw9~W5w4TF6|5A}U{m^jY9k zLB<-YN`Ad`)t`e&9lo!=&1!+nqbmi*5B)LQb&a;ocOI@^M_FmLeJOMN?_4g!Z@mKK zU*zwx>&zbdgY7o)<%O_^5{NQs7AtozSM9J0VOV7<2=5t-_w3&iF|QCYU&1d$b_PH4 z&{KqA2M_R6EPh}HJf#p(uGk+@Zj Date: Tue, 17 Dec 2024 03:36:05 +0000 Subject: [PATCH 10/12] update to support sending email --- .../analysis/perf_analysis_template.ipynb | 44 +++- .../analysis/run_perf_analysis.sh | 84 ++++++-- .../benchmark_velox/analysis/sparklog.ipynb | 188 +++++++++++++++++- .../workload/benchmark_velox/initialize.ipynb | 61 ++---- .../native_sql_initialize.ipynb | 57 +++--- .../benchmark_velox/params.yaml.template | 52 +++-- .../benchmark_velox/tpc_workload.ipynb | 64 +++--- 7 files changed, 399 insertions(+), 151 deletions(-) diff --git a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb index 28f1032b5e21..cf541517b9c9 100644 --- a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb +++ b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb @@ -23,11 +23,18 @@ "tz=''\n", "base_dir=''\n", "name=''\n", + "notebook=''\n", + "notebook_html=''\n", "proxy=''\n", + "emails=''\n", + "pr=''\n", "\n", "comp_appid=''\n", "comp_base_dir=''\n", - "comp_name=''" + "comp_name=''\n", + "\n", + "baseline_appid=''\n", + "baseline_base_dir=''" ] }, { @@ -219,7 +226,7 @@ "metadata": {}, "outputs": [], "source": [ - "appals.get_basic_state()" + "stats=appals.get_basic_state()" ] }, { @@ -240,7 +247,7 @@ }, "outputs": [], "source": [ - "app.generate_trace_view(showemon=True,show_metric=emonmetric,disk_prefix=disk_prefix,nic_prefix=nic_prefix)" + "traceview=app.generate_trace_view(showemon=True,show_metric=emonmetric,disk_prefix=disk_prefix,nic_prefix=nic_prefix)" ] }, { @@ -284,6 +291,18 @@ "appals.show_critical_path_time_breakdown().T" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if emails:\n", + " mail_list=' '.join(emails.split(','))\n", + " body,title=generate_email_body_title(appid, base_dir, name, comp_appid, comp_base_dir, comp_name, baseline_appid, baseline_base_dir, notebook, notebook_html, traceview, stats, summary, pr)\n", + " !mail -a \"Content-type: text/html; charset=utf-8\" -s \"$title\" $mail_list < $body" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -321,6 +340,25 @@ " display(comp_spark_conf(appals, comp_appals))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare to baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if baseline_appid:\n", + " baseline_app=Application_Run(baseline_appid,basedir=baseline_base_dir)\n", + " output=app.compare_app(rapp=baseline_app,show_metric=emonmetric,show_queryplan_diff=False,disk_prefix=disk_prefix,nic_prefix=nic_prefix)\n", + " display(HTML(output))" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh index 23ef530b8cef..5c6cfd9d10c8 100755 --- a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh +++ b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh @@ -22,11 +22,6 @@ PAUS=$HOME/PAUS while [[ $# -gt 0 ]]; do case $1 in - --ts) - TS="$2" - shift # past argument - shift # past value - ;; --base-dir) BASEDIR="$2" shift # past argument @@ -42,6 +37,11 @@ while [[ $# -gt 0 ]]; do shift # past argument shift # past value ;; + --pr) + PR="$2" + shift # past argument + shift # past value + ;; --disk) DISK="$2" shift # past argument @@ -62,6 +62,11 @@ while [[ $# -gt 0 ]]; do shift # past argument shift # past value ;; + --emails) + EMAILS="$2" + shift # past argument + shift # past value + ;; --comp-appid) COMP_APPID="$2" shift # past argument @@ -77,6 +82,16 @@ while [[ $# -gt 0 ]]; do shift # past argument shift # past value ;; + --baseline-appid) + BASELINE_APPID="$2" + shift # past argument + shift # past value + ;; + --baseline-base-dir) + BASELINE_BASEDIR="$2" + shift # past argument + shift # past value + ;; *) echo "Error: Unknown argument: $1" exit 1 @@ -85,7 +100,7 @@ while [[ $# -gt 0 ]]; do done # Validation: Check if any of the required variables are empty -if [[ -z "${TS+x}" || -z "${BASEDIR+x}" || -z "${NAME+x}" || -z "${APPID+x}" || -z "${DISK+x}" || -z "${NIC+x}" || -z "${SPARK_TZ+x}" ]]; then +if [[ -z "${BASEDIR+x}" || -z "${NAME+x}" || -z "${APPID+x}" || -z "${DISK+x}" || -z "${NIC+x}" || -z "${SPARK_TZ+x}" ]]; then echo "Error: One or more required arguments are missing or empty." exit 1 fi @@ -98,16 +113,16 @@ if [ ! -f "$PAUS/sparklog.ipynb" ]; then cp $SCRIPT_LOCATION/sparklog.ipynb $PAUS/ fi -mkdir -p $PAUS/$BASEDIR -cd $PAUS/$BASEDIR -mkdir -p html +workdir=$PAUS/$BASEDIR +mkdir -p $workdir +mkdir -p $workdir/html -nb_name0=${TS}_${NAME}_${APPID} +nb_name0=${NAME}_${APPID} nb_name=${nb_name0}.ipynb -cp -f $PAUS/perf_analysis_template.ipynb $nb_name -hadoop fs -mkdir -p /history -hadoop fs -cp -f /$BASEDIR/$APPID/app.log /history/$APPID +cp -f $PAUS/perf_analysis_template.ipynb $workdir/$nb_name +hdfs dfs -mkdir -p /history +hdfs dfs -ls /history/$APPID >/dev/null 2>&1 || { hdfs dfs -cp /$BASEDIR/$APPID/app.log /history/$APPID || exit 1; } EXTRA_ARGS="" if [ -v COMP_APPID ] @@ -116,18 +131,49 @@ then echo "Missing --comp-base-dir or --comp-name" exit 1 fi - hadoop fs -cp -f /$COMP_BASEDIR/$COMP_APPID/app.log /history/$COMP_APPID - EXTRA_ARGS="--comp_appid $COMP_APPID --comp_base_dir $COMP_BASEDIR --comp_name $COMP_NAME" - sed -i "s/# Compare to/# Compare to $COMP_NAME/g" ${nb_name} + hdfs dfs -ls /history/$COMP_APPID >/dev/null 2>&1 || { hdfs dfs -cp /$COMP_BASEDIR/$COMP_APPID/app.log /history/$COMP_APPID || exit 1; } + EXTRA_ARGS=$EXTRA_ARGS" -r comp_appid $COMP_APPID -r comp_base_dir $COMP_BASEDIR -r comp_name $COMP_NAME" + sed -i "s/# Compare to/# Compare to $COMP_NAME/g" $workdir/$nb_name +fi +if [ -v BASELINE_APPID ] +then + if [[ -z "${BASELINE_BASEDIR+x}" ]]; then + echo "Missing --baseline-base-dir" + exit 1 + fi + hdfs dfs -ls /history/$BASELINE_APPID >/dev/null 2>&1 || { hdfs dfs -cp /$BASELINE_BASEDIR/$BASELINE_APPID/app.log /history/$BASELINE_APPID || exit 1; } + EXTRA_ARGS=$EXTRA_ARGS" -r baseline_appid $BASELINE_APPID -r baseline_base_dir $BASELINE_BASEDIR" +fi + + +if [ -n "${PR}" ] +then + EXTRA_ARGS=$EXTRA_ARGS" -r pr $PR" fi if [ -n "${PROXY}" ] then - EXTRA_ARGS=$EXTRA_ARGS" --proxy $PROXY" + EXTRA_ARGS=$EXTRA_ARGS" -r proxy $PROXY" +fi + +if [ -n "${EMAILS}" ] +then + EXTRA_ARGS=$EXTRA_ARGS" -r emails $EMAILS" fi source ~/paus-env/bin/activate -python3 $SCRIPT_LOCATION/run.py --inputnb $nb_name --outputnb ${nb_name0}.nbconvert.ipynb --appid $APPID --disk $DISK --nic $NIC --tz $SPARK_TZ --base_dir $BASEDIR --name $NAME $EXTRA_ARGS +notebook_html=html/${nb_name0}.html + +papermill --cwd $workdir \ + -r appid $APPID \ + -r disk $DISK \ + -r nic $NIC \ + -r tz $SPARK_TZ \ + -r base_dir $BASEDIR \ + -r name $NAME \ + -r notebook $nb_name \ + -r notebook_html $notebook_html \ + $EXTRA_ARGS $workdir/$nb_name $workdir/$nb_name -jupyter nbconvert --to html --no-input ./${nb_name0}.nbconvert.ipynb --output html/${nb_name0}.html --template classic > /dev/null 2>&1 +jupyter nbconvert --to html --no-input $workdir/$nb_name --output $workdir/$notebook_html --template classic > /dev/null 2>&1 diff --git a/tools/workload/benchmark_velox/analysis/sparklog.ipynb b/tools/workload/benchmark_velox/analysis/sparklog.ipynb index 86165394cd0b..79713c8d1be3 100644 --- a/tools/workload/benchmark_velox/analysis/sparklog.ipynb +++ b/tools/workload/benchmark_velox/analysis/sparklog.ipynb @@ -268,8 +268,9 @@ " with open(outputfolder, 'w') as outfile: \n", " outfile.write(output)\n", " \n", - " display(HTML(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appidx}.json\"))\n", - " " + " traceview_link=f'http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{appidx}.json'\n", + " display(HTML(f\"{traceview_link}\"))\n", + " return traceview_link" ] }, { @@ -5217,9 +5218,11 @@ "\n", " with open('/home/sparkuser/trace_result/'+self.appid+'.json', 'w') as outfile: \n", " outfile.write(output)\n", - "\n", - " display(HTML(f\"http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{self.appid}.json\"))\n", " \n", + " traceview_link=f'http://{localhost}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{self.appid}.json'\n", + " display(HTML(f\"{traceview_link}\"))\n", + " return traceview_link\n", + "\n", " def getemonmetric(app,**kwargs):\n", " emondfs=get_emon_parquets([app.appid],app.basedir)\n", " emons=Emon_Analysis_All(emondfs)\n", @@ -5749,6 +5752,183 @@ " return pdrst.drop(columns=['rst'])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cvt_number(n):\n", + " try:\n", + " if str(n).isdigit():\n", + " return f'{n:,}'\n", + " else:\n", + " return f'{round(float(n),2):,}'\n", + " except ValueError:\n", + " return n\n", + "\n", + "def parse_changelog(changelog):\n", + " out=[]\n", + " if fs.exists(changelog):\n", + " with fs.open(changelog) as f:\n", + " for l in f.readlines():\n", + " l = l.decode('ascii')\n", + " if l.startswith(\"commit\"):\n", + " out.append(re.sub(r\"commit +(.+)\",r\"commit \\1\",l))\n", + " elif l.startswith(\"Author\"):\n", + " out.append(re.sub(r\"Author: +([^<]+) <(.+)>\",r\"Author: \\1 <\\2> \",l))\n", + " elif l.startswith(\"Date\"):\n", + " out.append(re.sub(r\"Date: +(\\d\\d\\d\\d-\\d\\d-\\d\\d)\",r\"Author: \\1\",l))\n", + " else:\n", + " out.append(l)\n", + " else:\n", + " out.append(f'{os.path.basename(changelog)} not found!')\n", + " return out\n", + "\n", + "def generate_query_diff(name, comp_name, query_time_file, comp_query_time_file):\n", + " result = []\n", + " if fs.exists(query_time_file) and fs.exists(comp_query_time_file):\n", + " result.append(['query', name, comp_name, 'difference', 'percentage'])\n", + " \n", + " qtimes = {}\n", + " comp_qtimes = {}\n", + " with fs.open(query_time_file) as f:\n", + " qtimes = json.loads(f.read().decode('ascii'))\n", + " with fs.open(comp_query_time_file) as f:\n", + " comp_qtimes = json.loads(f.read().decode('ascii'))\n", + " \n", + " query_ids = sorted(qtimes.keys(), key=lambda x: str(len(x))+x if x[-1] != 'a' and x[-1] != 'b' else str(len(x)-1) + x)\n", + " \n", + " if len(comp_qtimes) != len(qtimes):\n", + " raise Exception('Number of queries mismatch!')\n", + " \n", + " query_ids.append('total')\n", + " qtimes['total'] = sum([float(i) for i in qtimes.values()])\n", + " comp_qtimes['total'] = sum([float(i) for i in comp_qtimes.values()])\n", + " \n", + " for q in query_ids:\n", + " t1 = qtimes.get(q)\n", + " t2 = comp_qtimes.get(q)\n", + " delta = str(\"{:.2f}\".format(float(t2) - float(t1)))\n", + " perc = str(\"{:.2f}\".format((float(t2) / float(t1)) * 100)) + '%'\n", + " result.append([q, str(t1), str(t2), delta, perc])\n", + " return result\n", + "\n", + "def append_summary(appid, base_dir, name, comp_appid, comp_base_dir, comp_name, baseline_appid, baseline_base_dir, statsall, output):\n", + " with open(output,\"a\") as linkfile:\n", + "\n", + " difftable=''' \n", + " '''\n", + " for k,v in statsall.items():\n", + " difftable+=f'''\n", + " \n", + " \n", + " \n", + " '''\n", + " difftable+='''\n", + " \n", + "
{k}{cvt_number(v)}
\\n'''\n", + " linkfile.write(difftable)\n", + " linkfile.write(\"\\n
\\n\")\n", + " \n", + " linkfile.write(\"\\n gluten gitlog in last 2 days
\\n\")\n", + " out=parse_changelog(os.path.join('/', base_dir, appid, 'changelog_gluten'))\n", + " linkfile.write(\"
\".join(out))\n", + " linkfile.write(\"\\n
\\n\")\n", + " \n", + " linkfile.write(\"\\n velox gitlog in last 2 days
\\n\")\n", + " out=parse_changelog(os.path.join('/', base_dir, appid, 'changelog_velox'))\n", + " linkfile.write(\"
\".join(out))\n", + " linkfile.write(\"\\n
\\n\")\n", + " \n", + " linkfile.write('''
\\n''')\n", + " \n", + " def append_query_diff(their_appid, their_base_dir, their_name):\n", + " query_diff=generate_query_diff(name, their_name, os.path.join('/', base_dir, appid, 'query_time.json'), os.path.join('/', their_base_dir, their_appid, 'query_time.json'))\n", + " if query_diff:\n", + " difftable='''\n", + " \n", + " '''\n", + " for l in query_diff:\n", + " difftable+='''\n", + " '''\n", + " base=0\n", + " pr=0\n", + " if re.match(r\"[0-9.]+\",l[1]):\n", + " base=float(l[1])\n", + " l[1]=\"{:.2f}\".format(base)\n", + " if re.match(r\"[0-9.]+\",l[2]):\n", + " pr=float(l[2])\n", + " l[2]=\"{:.2f}\".format(pr)\n", + "\n", + " for d in l:\n", + " color='#000000'\n", + " if base > pr:\n", + " color='#6F9915'\n", + " elif base < pr:\n", + " color='#F92663'\n", + " difftable += f'''\n", + " '''\n", + "\n", + " difftable+='''\n", + " '''\n", + "\n", + " difftable+='''\n", + " \n", + "
{d}
'''\n", + " linkfile.write(difftable)\n", + " linkfile.write(\"\\n
\\n\")\n", + " # return percentage\n", + " return query_diff[-1][-1]\n", + " return ''\n", + "\n", + " baseline_perc = ''\n", + " if comp_appid:\n", + " append_query_diff(comp_appid, comp_base_dir, comp_name)\n", + " if baseline_appid:\n", + " baseline_perc = append_query_diff(baseline_appid, baseline_base_dir, 'Vanilla Spark')\n", + "\n", + " linkfile.write(\"
\")\n", + " \n", + " return baseline_perc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_email_body_title(appid, base_dir, name, comp_appid, comp_base_dir, comp_name, baseline_appid, baseline_base_dir, notebook, notebook_html, traceview, stats, summary, pr=''):\n", + " statsall=collections.OrderedDict()\n", + " for k,v in stats.items():\n", + " statsall[k]=v\n", + " for k,v in summary.to_dict()[appals.appid].items():\n", + " statsall[k]=v\n", + " \n", + " pr_link=''\n", + " if pr:\n", + " pr_link=f'https://github.com/apche/incubator-gluten/pull/{pr}'\n", + " title=!wget --quiet -O - $pr_link | sed -n -e 's!.*\\(.*\\).*!\\1!p'\n", + " pr_link=f'pr link: {title[0]}
'\n", + " \n", + " output=f'/tmp/{appid}.html'\n", + " with open(output, 'w+') as f:\n", + " f.writelines(f'''\n", + "\n", + "history event: http://{local_ip}:18080/tmp/sparkEventLog/{appid}/jobs/
\n", + "notebook: http://{local_ip}:8889/notebooks/{base_dir}/{notebook}
\n", + "notebook html: http://{local_ip}:8889/view/{base_dir}/{notebook_html}
\n", + "traceview: {traceview}
\n", + "{pr_link}\n", + "
''')\n", + " baseline_perc = append_summary(appid, base_dir, name, comp_appid, comp_base_dir, comp_name, baseline_appid, baseline_base_dir, statsall, output)\n", + " \n", + " title_prefix = f\"[ {datetime.now().strftime('%m_%d_%Y')} ]\" if not pr else f\"[ PR {pr} ]\"\n", + " title = f'{title_prefix} {name} {appid} {baseline_perc}'\n", + " return output,title" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/tools/workload/benchmark_velox/initialize.ipynb b/tools/workload/benchmark_velox/initialize.ipynb index 968f2dbe5534..f7962c90017f 100644 --- a/tools/workload/benchmark_velox/initialize.ipynb +++ b/tools/workload/benchmark_velox/initialize.ipynb @@ -23,7 +23,7 @@ "```bash\n", "apt update\n", "\n", - "apt install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk maven vim pip sysstat gcc-9 libjemalloc-dev nvme-cli curl zip unzip bison flex linux-tools-common linux-tools-generic linux-tools-`uname -r`\n", + "apt install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk maven vim pip sysstat gcc-9 libjemalloc-dev nvme-cli curl zip unzip bison flex linux-tools-common linux-tools-generic linux-tools-`uname -r` mailutils\n", "\n", "python3 -m pip install notebook==6.5.2\n", "python3 -m pip install jupyter_server==1.23.4\n", @@ -2182,35 +2182,8 @@ }, "outputs": [], "source": [ - "!sudo docker pull apache/gluten:vcpkg-centos-7" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "import os\n", - "http_proxy=os.getenv('http_proxy')\n", - "https_proxy=os.getenv('https_proxy')\n", - "\n", - "container=!sudo docker run -e http_proxy={http_proxy} -e https_proxy={https_proxy} -itd apache/gluten:vcpkg-centos-7\n", - "containerid = container[0]\n", - "containerid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "!sudo docker exec {containerid} bash -c \"cd /opt && git clone https://github.com/apache/incubator-gluten.git gluten\"" + "%cd ~\n", + "!git clone https://github.com/apache/incubator-gluten.git gluten" ] }, { @@ -2221,7 +2194,7 @@ }, "outputs": [], "source": [ - "!sudo docker exec {containerid} bash -c \"cd /opt && source /opt/rh/devtoolset-9/enable && cd gluten && ./dev/builddeps-veloxbe.sh --enable_vcpkg=ON --enable_hdfs=ON > build.log\"" + "!sudo docker pull apache/gluten:vcpkg-centos-7" ] }, { @@ -2232,10 +2205,7 @@ }, "outputs": [], "source": [ - "import os\n", - "if os.path.exists(f'/home/{user}/.m2/settings.xml'):\n", - " !sudo docker exec {containerid} bash -c \"mkdir -p ~/.m2\"\n", - " !sudo docker cp /home/{user}/.m2/settings.xml {containerid}:/root/.m2/settings.xml" + "%cd ~/gluten" ] }, { @@ -2246,7 +2216,9 @@ }, "outputs": [], "source": [ - "!sudo docker exec {containerid} bash -c \"cd /opt/gluten && mvn clean package -DskipTests -Pspark-3.3 -Pbackends-velox\"" + "!sed -i 's/3.2//' ./dev/buildbundle-veloxbe.sh\n", + "!sed -i 's/3.4//' ./dev/buildbundle-veloxbe.sh\n", + "!sed -i 's/3.5//' ./dev/buildbundle-veloxbe.sh" ] }, { @@ -2257,7 +2229,16 @@ }, "outputs": [], "source": [ - "!sudo docker cp {containerid}:/opt/gluten/package/target/gluten-velox-bundle-spark3.3_2.12-centos_7_x86_64-1.3.0-SNAPSHOT.jar ~/" + "!sudo docker run --rm \\\n", + " -v /home/{user}/gluten:/root/gluten \\\n", + " -v /home/{user}/.cache/vcpkg:/root/.cache/vcpkg \\\n", + " -v /home/{user}/.m2:/root/.m2 \\\n", + " -v /home/{user}/.ccache:/root/.ccache \\\n", + " -e http_proxy \\\n", + " -e https_proxy \\\n", + " --workdir /root/gluten \\\n", + " apache/gluten:vcpkg-centos-7 \\\n", + " ./dev/package-vcpkg.sh" ] }, { @@ -2268,8 +2249,8 @@ }, "outputs": [], "source": [ - "for l in clients:\n", - " !scp ~/gluten-velox-bundle-spark3.3_2.12-centos_7_x86_64-1.3.0-SNAPSHOT.jar {l}:~/" + "for l in hclients:\n", + " !scp ~/gluten/package/target/gluten-velox-bundle-spark3.3*.jar {l}:~/" ] }, { @@ -2879,6 +2860,7 @@ { "cell_type": "markdown", "metadata": { + "heading_collapsed": true, "hidden": true }, "source": [ @@ -2964,6 +2946,7 @@ { "cell_type": "markdown", "metadata": { + "heading_collapsed": true, "hidden": true }, "source": [ diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index d3cf1f9ca984..b3e369a91979 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -287,6 +287,7 @@ "import socket\n", "import os\n", "import sys\n", + "import json\n", "\n", "def upload_profile(server, base_dir, appid):\n", " local_profile_dir = os.path.join(home, 'profile')\n", @@ -377,9 +378,8 @@ " !ssh $hbm_l \"while :; do echo \\$(numactl -H | grep '$hbm_numa_nodes' | grep 'size' | awk '{ print \\$4 }' | awk '{ s += \\$1 } END { print s }'), \\$(numactl -H | grep '$hbm_numa_nodes' | grep 'free' | awk '{ print \\$4 }' | awk '{ s += \\$1 } END { print s }') | ts '%Y-%m-%d %H:%M:%S,' >> $hbm_prof/$hbm_l/numactl.csv; sleep 1; done >/dev/null 2>&1 &\"\n", " else:\n", " print(\"Missing argument: hbm_nodes. e.g. hbm_nodes = list(range(8,16))\")\n", - " return prof\n", "\n", - "def stopmonitor(clients, sc, appid, collect_emon, **kwargs):\n", + "def stopmonitor(clients, sc, appid, result, collect_emon, **kwargs):\n", " %cd ~\n", " \n", " local_profile_dir=os.path.join(home, 'profile')\n", @@ -402,9 +402,15 @@ " \n", " if sc is not None:\n", " sc.stop()\n", - "\n", + " \n", + " !git --git-dir=\"{gluten_home}/.git\" log --format=\"commit %H%nAuthor: %an <%ae>%nDate: %cs%n %n %s %n\" --since=`date --date='2 days ago' +'%m/%d/%Y'` > {prof}/changelog_gluten\n", + " !git --git-dir=\"{gluten_home}/ep/build-velox/build/velox_ep/.git\" log --format=\"commit %H%nAuthor: %an <%ae>%nDate: %cs%n %n %s %n\" --since=`date --date='2 days ago' +'%m/%d/%Y'` > {prof}/changelog_velox\n", + " \n", " with open(f\"{prof}/starttime\",\"w\") as f:\n", " f.write(\"{:d}\".format(int(time.time()*1000)))\n", + " \n", + " with open(f'{prof}/query_time.json', 'w') as f:\n", + " json.dump(result, f)\n", "\n", " if hdfs_event_dir != '':\n", " !hadoop fs -copyToLocal {hdfs_event_dir}/{appid} {prof}/app.log\n", @@ -798,6 +804,7 @@ " self.spark = spark\n", " self.sc = spark.sparkSession.sparkContext\n", " self.appid = self.sc.applicationId\n", + " self.app_name = self.sc.appName\n", " self.run_gluten = run_gluten\n", " self.workload = workload\n", " self.table_dir = table_dir\n", @@ -826,30 +833,28 @@ " def stop_monitor(self, clients, **kw):\n", " if self.stopped:\n", " return\n", - " stopmonitor(clients, self.sc, self.appid, self.collect_emon, **kw)\n", + " stopmonitor(clients, self.sc, self.appid, self.result, self.collect_emon, **kw)\n", + "\n", " if self.server:\n", " output_nb = f'{self.nb_name[:-6]}-{self.appid}.ipynb'\n", " if output_nb.startswith(cwd):\n", " output_nb = os.path.relpath(output_nb, home)\n", " self.finished_nb = f\"http://{localhost}:8888/tree/{output_nb}\"\n", + " upload_profile(self.server, self.base_dir, self.appid)\n", + "\n", " self.stopped = True\n", "\n", - " def run_perf_analysis(self, disk_dev, nic_dev, proxy, comp_appid, comp_base_dir, comp_name):\n", + " def run_perf_analysis(self, disk_dev, nic_dev, proxy, emails):\n", " if not self.server:\n", " return\n", "\n", - " upload_profile(self.server, self.base_dir, self.appid)\n", - "\n", - " ts=time.strftime(\"%Y_%m_%d_%H%M%S\")\n", - " name=f'{self.workload}_gluten' if self.run_gluten else f'{self.workload}_vanilla'\n", " run_script=f'{gluten_home}/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh'\n", " \n", " disk=','.join(disk_dev)\n", " nic=','.join(nic_dev)\n", "\n", - " command =' '.join(['bash', run_script, '--ts', ts, '--base-dir', self.base_dir, '--name', name, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt(), '--proxy', proxy if proxy != '' else \"''\"])\n", - " if comp_appid:\n", - " command += f' --comp-appid {comp_appid} --comp-base-dir {comp_base_dir} --comp-name {comp_name}'\n", + " appname='_'.join(self.app_name.split(' '))\n", + " command =' '.join(['bash', run_script, '--base-dir', self.base_dir, '--name', appname, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt(), '--proxy', proxy if proxy != '' else \"''\", '--emails', ','.join(emails) if emails else \"''\"])\n", " print(command)\n", "\n", " # Block if running on local cluster.\n", @@ -858,7 +863,7 @@ " else:\n", " !ssh {self.server} \"{command} > /dev/null 2>&1 &\"\n", "\n", - " self.perf_html=f'http://{self.server}:8889/view/{self.base_dir}/html/{ts}_{name}_{self.appid}.html'\n", + " self.perf_html=f'http://{self.server}:8889/view/{self.base_dir}/html/{appname}_{self.appid}.html'\n", " display(HTML(f'{self.perf_html}'))\n", " \n", " def load_table(self, table):\n", @@ -1026,7 +1031,7 @@ " if run_gluten:\n", " offheap_ratio = gluten_offheap_ratio\n", " else:\n", - " offheap_ratio = vanilla_offheap_ratio\n", + " offheap_ratio = spark_offheap_ratio\n", " driver_memory = convert_to_bytes('20g')\n", " executor_memory_overhead = convert_to_bytes('1g')\n", " \n", @@ -1154,7 +1159,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Vanilla Spark" + "## Spark" ] }, { @@ -1163,10 +1168,10 @@ "metadata": {}, "outputs": [], "source": [ - "def vanilla_tpch_conf_overwrite(conf):\n", + "def spark_tpch_conf_overwrite(conf):\n", " return conf\n", "\n", - "def vanilla_tpcds_conf_overwrite(conf):\n", + "def spark_tpcds_conf_overwrite(conf):\n", " conf.set('spark.sql.optimizer.runtime.bloomFilter.applicationSideScanSizeThreshold', '0')\\\n", " .set('spark.sql.optimizer.runtime.bloomFilter.enabled', 'true')\n", " return conf" @@ -1178,7 +1183,7 @@ "metadata": {}, "outputs": [], "source": [ - "def create_cntx_vanilla(executors_per_node, cores_per_executor, task_per_core, memory_per_node, extra_jars, app_name='', master='yarn', conf_overwrite=None):\n", + "def create_cntx_spark(executors_per_node, cores_per_executor, task_per_core, memory_per_node, extra_jars, app_name='', master='yarn', conf_overwrite=None):\n", " conf = default_conf(executors_per_node, cores_per_executor, task_per_core, memory_per_node, extra_jars, app_name, master, run_gluten=False)\n", " conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\",20480)\\\n", " .set(\"spark.sql.parquet.columnarReaderBatchSize\",20480)\\\n", @@ -1257,12 +1262,12 @@ "\n", " if workload.lower() == 'tpch':\n", " if not app_name:\n", - " app_name = 'tpch_power'\n", + " app_name = f\"tpch_spark{''.join(spark_version.split('.'))}\"\n", " tabledir = tpch_tabledir\n", " is_tpch_workload=True\n", " elif workload.lower() == 'tpcds':\n", " if not app_name:\n", - " app_name = 'tpcds_power'\n", + " app_name = f\"tpcds_spark{''.join(spark_version.split('.'))}\"\n", " tabledir = tpcds_tabledir\n", " is_tpcds_workload=True\n", " else:\n", @@ -1304,14 +1309,14 @@ " task_per_core = gluten_tpcds_task_per_core\n", " workload_conf_overwrite = gluten_tpcds_conf_overwrite\n", " else:\n", - " app_name = ' '.join(['vanilla', app_name, lastgit[:6]])\n", - " create_cntx_func=create_cntx_vanilla\n", + " app_name = ' '.join(['spark', app_name, lastgit[:6]])\n", + " create_cntx_func=create_cntx_spark\n", " if is_tpch_workload:\n", - " task_per_core = vanilla_tpch_task_per_core\n", - " workload_conf_overwrite = vanilla_tpch_conf_overwrite\n", + " task_per_core = spark_tpch_task_per_core\n", + " workload_conf_overwrite = spark_tpch_conf_overwrite\n", " elif is_tpcds_workload:\n", - " task_per_core = vanilla_tpcds_task_per_core\n", - " workload_conf_overwrite = vanilla_tpcds_conf_overwrite\n", + " task_per_core = spark_tpcds_task_per_core\n", + " workload_conf_overwrite = spark_tpcds_conf_overwrite\n", " \n", " conf_overwrite = lambda conf: app_conf_overwrite(workload_conf_overwrite(conf))\n", " \n", diff --git a/tools/workload/benchmark_velox/params.yaml.template b/tools/workload/benchmark_velox/params.yaml.template index bdb604cfef97..73e02b728f7b 100644 --- a/tools/workload/benchmark_velox/params.yaml.template +++ b/tools/workload/benchmark_velox/params.yaml.template @@ -20,31 +20,10 @@ disk_dev: nic_dev: - ens787f0 -# Hostname or IP to server for perf analysis. Able to connect via ssh. -server: '' - -# Specify the directory on perf analysis server. Usually a codename for this run. -base_dir: emr - -# Proxy used to connect to server for perf analysis. -proxy: '' - -# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable. -# Supported emon events on platform can be verified via `emon -i emon.list` -emon_list: /home/sparkuser/ipython/emon.list - -# Whether to upload profile to perf analysis server and run perf analysis scripts. Only takes effect if server is set. -analyze_perf: True - -# Specify app info to compare for perf analysis -comp_appid: '' -comp_base_dir: '' -comp_name: '' - # Select workload. Can be either 'tpch' or 'tpcds'. workload: tpch -# Run with gluten. If False, run vanilla Spark. +# Run with gluten. If False, run Spark. run_gluten: True # TPC tables @@ -57,20 +36,20 @@ cores_per_executor: 8 gluten_tpch_task_per_core: 2 gluten_tpcds_task_per_core: 2 -vanilla_tpch_task_per_core: 4 -vanilla_tpcds_task_per_core: 4 +spark_tpch_task_per_core: 4 +spark_tpcds_task_per_core: 4 # Physical memory on each worker node. memory_per_node: 1000g -# Offheap ratio. 0 to disable offheap for vanilla Spark. +# Offheap ratio. 0 to disable offheap for Spark. # onheap:offheap = 1:2 -vanilla_offheap_ratio: 2.0 +spark_offheap_ratio: 2.0 # onheap:offheap = 1:7 gluten_offheap_ratio: 7.0 # spark.io.compression.codec -vanilla_codec: lz4 +spark_codec: lz4 # spark.gluten.sql.columnar.shuffle.codec gluten_codec: lz4 # spark.gluten.sql.columnar.shuffle.codecBackend @@ -78,3 +57,22 @@ gluten_codec_backend: '' # spark.gluten.sql.columnar.maxBatchSize max_batch_size: 4096 +# Hostname or IP to server for perf analysis. Able to connect via ssh. +server: '' + +# Specify the directory on perf analysis server. Usually a codename for this run. +base_dir: test + +# Proxy used to connect to server for perf analysis. +proxy: '' + +# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable. +# Supported emon events on platform can be verified via `emon -i emon.list` +emon_list: /home/sparkuser/ipython/emon.list + +# Whether to run perf analysis scripts. Only takes effect if server is set. +analyze_perf: True + +# List of email to receive perf analysis results. +emails: + - diff --git a/tools/workload/benchmark_velox/tpc_workload.ipynb b/tools/workload/benchmark_velox/tpc_workload.ipynb index bd08ff2ed534..c0232d1d52f6 100644 --- a/tools/workload/benchmark_velox/tpc_workload.ipynb +++ b/tools/workload/benchmark_velox/tpc_workload.ipynb @@ -35,31 +35,10 @@ "# List of network devices. e.g. ['ens787f0']\n", "nic_dev=[]\n", "\n", - "# Hostname or IP to server for perf analysis. Able to connect via ssh.\n", - "server=''\n", - "\n", - "# Specify the directory on perf analysis server. Usually a codename for this run.\n", - "base_dir=''\n", - "\n", - "# Proxy used to connect to server for perf analysis.\n", - "proxy=''\n", - "\n", - "# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable.\n", - "# Supported emon events on platform can be verified via `emon -i emon.list`\n", - "emon_list: '/home/sparkuser/ipython/emon.list'\n", - "\n", - "# Whether to upload profile to perf analysis server and run perf analysis scripts. Only takes effect if server is set.\n", - "analyze_perf=True\n", - "\n", - "# Specify app info to compare for perf analysis\n", - "comp_appid: ''\n", - "comp_base_dir: ''\n", - "comp_name: ''\n", - "\n", "# Select workload. Can be either 'tpch' or 'tpcds'.\n", "workload='tpch'\n", "\n", - "# Run with gluten. If False, run vanilla Spark.\n", + "# Run with gluten. If False, run Spark.\n", "run_gluten=True\n", "\n", "# TPC tables\n", @@ -68,30 +47,49 @@ "\n", "# Parallelism\n", "executors_per_node=32\n", - "cores_per_executor=8\n", + "cores_per_executor=7\n", "\n", "gluten_tpch_task_per_core=2\n", "gluten_tpcds_task_per_core=4\n", - "vanilla_tpch_task_per_core=8\n", - "vanilla_tpcds_task_per_core=8\n", + "spark_tpch_task_per_core=8\n", + "spark_tpcds_task_per_core=8\n", "\n", "# Physical memory on each worker node.\n", "memory_per_node='1000g'\n", "\n", - "# Offheap ratio. 0 to disable offheap for vanilla Spark.\n", + "# Offheap ratio. 0 to disable offheap for Spark.\n", "# onheap:offheap = 1:2\n", - "vanilla_offheap_ratio=2.0\n", + "spark_offheap_ratio=2.0\n", "# onheap:offheap = 1:7\n", "gluten_offheap_ratio=7.0\n", "\n", "# spark.io.compression.codec\n", - "vanilla_codec='lz4'\n", + "spark_codec='lz4'\n", "# spark.gluten.sql.columnar.shuffle.codec\n", "gluten_codec='lz4'\n", "# spark.gluten.sql.columnar.shuffle.codecBackend\n", "gluten_codec_backend=''\n", "# spark.gluten.sql.columnar.maxBatchSize\n", - "max_batch_size=4096" + "max_batch_size=4096\n", + "\n", + "# Hostname or IP to server for perf analysis. Able to connect via ssh.\n", + "server=''\n", + "\n", + "# Specify the directory on perf analysis server. Usually a codename for this run.\n", + "base_dir=''\n", + "\n", + "# Proxy used to connect to server for perf analysis.\n", + "proxy=''\n", + "\n", + "# Emon event file for `emon -i`. Set to emptry string '' if emon is unavailable.\n", + "# Supported emon events on platform can be verified via `emon -i emon.list`\n", + "emon_list=''\n", + "\n", + "# Whether to run perf analysis scripts. Only takes effect if server is set.\n", + "analyze_perf=False\n", + "\n", + "# List of email to receive perf analysis results.\n", + "emails = []" ] }, { @@ -185,8 +183,8 @@ " pass\n", " return conf\n", "\n", - "def vanilla_conf_overwrite(conf):\n", - " conf.set('spark.io.compression.codec', vanilla_codec)\\\n", + "def spark_conf_overwrite(conf):\n", + " conf.set('spark.io.compression.codec', spark_codec)\\\n", " .set('spark.executorEnv.LD_LIBRARY_PATH',f\"{os.getenv('HADOOP_HOME')}/lib/native/\") \\\n", " .set('spark.yarn.appMasterEnv.LD_LIBRARY_PATH',f\"{os.getenv('HADOOP_HOME')}/lib/native/\") \\\n", "\n", @@ -199,7 +197,7 @@ "def app_conf_overwrite(conf):\n", " if run_gluten:\n", " return gluten_conf_overwrite(conf)\n", - " return vanilla_conf_overwrite(conf)" + " return spark_conf_overwrite(conf)" ] }, { @@ -275,7 +273,7 @@ "outputs": [], "source": [ "if analyze_perf:\n", - " test_tpc.run_perf_analysis(disk_dev, nic_dev, proxy, comp_appid, comp_base_dir, comp_name)" + " test_tpc.run_perf_analysis(disk_dev, nic_dev, proxy, emails)" ] }, { From 8a52634155f4cb90b0752b2dd70098de94bf0730 Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Tue, 17 Dec 2024 14:39:27 +0000 Subject: [PATCH 11/12] remove unused --- .../workload/benchmark_velox/analysis/run.py | 26 ------------------- 1 file changed, 26 deletions(-) delete mode 100644 tools/workload/benchmark_velox/analysis/run.py diff --git a/tools/workload/benchmark_velox/analysis/run.py b/tools/workload/benchmark_velox/analysis/run.py deleted file mode 100644 index 7dedb1210201..000000000000 --- a/tools/workload/benchmark_velox/analysis/run.py +++ /dev/null @@ -1,26 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import fire -import papermill as pm - -def exec(inputnb, outputnb, appid, disk, nic, tz, base_dir, name, comp_appid='', comp_base_dir='', comp_name='', proxy=''): - return pm.execute_notebook( - inputnb, - outputnb, - parameters=dict(appid=appid,disk=disk,nic=nic,tz=tz,base_dir=base_dir,name=name,comp_appid=comp_appid,comp_base_dir=comp_base_dir,comp_name=comp_name,proxy=proxy)) - -if __name__ == '__main__': - fire.Fire(exec) From 4a38a7a5fb3bc61eaab2a2962ffd19cf6e08883c Mon Sep 17 00:00:00 2001 From: Rong Ma Date: Wed, 18 Dec 2024 13:03:39 +0000 Subject: [PATCH 12/12] add records file for comparison --- .../analysis/perf_analysis_template.ipynb | 96 ++++++------------- .../analysis/run_perf_analysis.sh | 3 +- .../native_sql_initialize.ipynb | 87 ++++++++++++++--- 3 files changed, 102 insertions(+), 84 deletions(-) diff --git a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb index cf541517b9c9..c750e2a44fc0 100644 --- a/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb +++ b/tools/workload/benchmark_velox/analysis/perf_analysis_template.ipynb @@ -1,12 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, { "cell_type": "code", "execution_count": null, @@ -37,21 +30,19 @@ "baseline_base_dir=''" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# start analysis cluster and run" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import findspark\n", - "findspark.init()" + "%%html\n", + "" ] }, { @@ -60,25 +51,30 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "def get_py4jzip():\n", - " spark_home=os.environ['SPARK_HOME']\n", - " py4jzip = !ls {spark_home}/python/lib/py4j*.zip\n", - " return py4jzip[0]" + "import warnings\n", + "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "code_folding": [] - }, + "metadata": {}, "outputs": [], "source": [ - "from pyspark import SparkConf, SparkContext\n", - "from pyspark.sql import SQLContext\n", + "import findspark\n", + "findspark.init()\n", + "\n", + "import os\n", "import time\n", "import sys\n", + "from pyspark import SparkConf, SparkContext\n", + "from pyspark.sql import SQLContext\n", + "\n", + "def get_py4jzip():\n", + " spark_home=os.environ['SPARK_HOME']\n", + " py4jzip = !ls {spark_home}/python/lib/py4j*.zip\n", + " return py4jzip[0]\n", + "\n", "conf = (SparkConf()\n", " .set('spark.app.name', f'perf_analysis_{appid}')\n", " .set('spark.serializer','org.apache.spark.serializer.KryoSerializer')\n", @@ -108,28 +104,6 @@ "time.sleep(10)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%html\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sparklog" - ] - }, { "cell_type": "code", "execution_count": null, @@ -163,16 +137,6 @@ " 'emon_ipc']" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, { "cell_type": "code", "execution_count": null, @@ -183,13 +147,6 @@ "nic_prefix=[f\"'{dev}'\" for dev in nic.split(',')]" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Content" - ] - }, { "cell_type": "code", "execution_count": null, @@ -198,16 +155,17 @@ }, "outputs": [], "source": [ - "display(HTML(' 5 Self app info'))\n", - "display(HTML(f\" 6 Compare to {comp_name}\"))\n", - "display(HTML(' 7 Config compare'))" + "display(HTML(' 1 App info'))\n", + "display(HTML(f\" 2 Compare to {comp_name}\"))\n", + "display(HTML(' 3 Config compare'))\n", + "display(HTML(' 4 Compare to baseline'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Self app info" + "# App info" ] }, { diff --git a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh index 5c6cfd9d10c8..af30250d4812 100755 --- a/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh +++ b/tools/workload/benchmark_velox/analysis/run_perf_analysis.sh @@ -120,6 +120,7 @@ mkdir -p $workdir/html nb_name0=${NAME}_${APPID} nb_name=${nb_name0}.ipynb +# Upload eventlog cp -f $PAUS/perf_analysis_template.ipynb $workdir/$nb_name hdfs dfs -mkdir -p /history hdfs dfs -ls /history/$APPID >/dev/null 2>&1 || { hdfs dfs -cp /$BASEDIR/$APPID/app.log /history/$APPID || exit 1; } @@ -133,7 +134,7 @@ then fi hdfs dfs -ls /history/$COMP_APPID >/dev/null 2>&1 || { hdfs dfs -cp /$COMP_BASEDIR/$COMP_APPID/app.log /history/$COMP_APPID || exit 1; } EXTRA_ARGS=$EXTRA_ARGS" -r comp_appid $COMP_APPID -r comp_base_dir $COMP_BASEDIR -r comp_name $COMP_NAME" - sed -i "s/# Compare to/# Compare to $COMP_NAME/g" $workdir/$nb_name + sed -i "s/# Compare to\"/# Compare to $COMP_NAME\"/g" $workdir/$nb_name fi if [ -v BASELINE_APPID ] then diff --git a/tools/workload/benchmark_velox/native_sql_initialize.ipynb b/tools/workload/benchmark_velox/native_sql_initialize.ipynb index b3e369a91979..0772232d70c9 100644 --- a/tools/workload/benchmark_velox/native_sql_initialize.ipynb +++ b/tools/workload/benchmark_velox/native_sql_initialize.ipynb @@ -251,7 +251,7 @@ "import spylon_kernel\n", "from collections import namedtuple\n", "from concurrent.futures import ThreadPoolExecutor\n", - "from datetime import date\n", + "from datetime import date, datetime\n", "from functools import reduce\n", "from IPython.display import display, HTML\n", "from matplotlib import rcParams\n", @@ -766,6 +766,32 @@ " return etc_gmt" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_last_run(records_file, appid=''):\n", + " if os.path.exists(records_file):\n", + " if appid:\n", + " lines=!tail -n2 $records_file\n", + " if len(lines) == 2:\n", + " # Check appid match\n", + " last_appid = lines[1].split('\\t')[1]\n", + " if last_appid != appid:\n", + " print(f'appid not match. Required {appid}. Got {last_appid}')\n", + " else:\n", + " l=lines[0].split('\\t')\n", + " return l[1],l[2],l[3]\n", + " else:\n", + " lines=!tail -n1 $records_file\n", + " if len(lines) == 1:\n", + " l=lines[0].split('\\t')\n", + " return l[1],l[2],l[3]\n", + " return None, None, None" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -800,11 +826,16 @@ " tpctables=[]\n", " tpc_query_path = ''\n", " \n", + " RECORDS_SPARK_TPCH = f\"records_spark_tpch.csv\"\n", + " RECORDS_SPARK_TPCDS = f\"records_spark_tpcds.csv\"\n", + " RECORDS_GLUTEN_TPCH = f\"records_gluten_tpch.csv\"\n", + " RECORDS_GLUTEN_TPCDS = f\"records_gluten_tpcds.csv\"\n", + " \n", " def __init__(self, spark, table_dir, run_gluten, workload, server, base_dir, nb_name, data_source = 'parquet'):\n", " self.spark = spark\n", " self.sc = spark.sparkSession.sparkContext\n", " self.appid = self.sc.applicationId\n", - " self.app_name = self.sc.appName\n", + " self.app_name = '_'.join(self.sc.appName.split(' '))\n", " self.run_gluten = run_gluten\n", " self.workload = workload\n", " self.table_dir = table_dir\n", @@ -814,6 +845,7 @@ " self.data_source = data_source\n", " self.table_loaded = False\n", " self.result = {}\n", + " self.duration = 0\n", " self.stopped = False\n", " self.collect_emon = False\n", " self.perf_html = ''\n", @@ -835,13 +867,29 @@ " return\n", " stopmonitor(clients, self.sc, self.appid, self.result, self.collect_emon, **kw)\n", "\n", + " output_nb = f'{self.nb_name[:-6]}-{self.appid}.ipynb'\n", + " \n", + " record_file = ''\n", + " if self.workload == 'tpch':\n", + " if self.run_gluten:\n", + " record_file = self.RECORDS_GLUTEN_TPCH\n", + " else:\n", + " record_file = self.RECORDS_SPARK_TPCH\n", + " else:\n", + " if self.run_gluten:\n", + " record_file = self.RECORDS_GLUTEN_TPCDS\n", + " else:\n", + " record_file = self.RECORDS_SPARK_TPCDS\n", + " record_file = os.path.join(cwd, record_file)\n", + " with open(record_file, 'a+') as f:\n", + " f.write(f'{datetime.now()}\\t{self.appid}\\t{self.base_dir}\\t{self.app_name}\\t{output_nb}\\t{self.duration}')\n", + "\n", " if self.server:\n", - " output_nb = f'{self.nb_name[:-6]}-{self.appid}.ipynb'\n", " if output_nb.startswith(cwd):\n", - " output_nb = os.path.relpath(output_nb, home)\n", + " output_nb = os.path.relpath(output_nb, cwd)\n", " self.finished_nb = f\"http://{localhost}:8888/tree/{output_nb}\"\n", " upload_profile(self.server, self.base_dir, self.appid)\n", - "\n", + " \n", " self.stopped = True\n", "\n", " def run_perf_analysis(self, disk_dev, nic_dev, proxy, emails):\n", @@ -853,8 +901,21 @@ " disk=','.join(disk_dev)\n", " nic=','.join(nic_dev)\n", "\n", - " appname='_'.join(self.app_name.split(' '))\n", - " command =' '.join(['bash', run_script, '--base-dir', self.base_dir, '--name', appname, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt(), '--proxy', proxy if proxy != '' else \"''\", '--emails', ','.join(emails) if emails else \"''\"])\n", + " command =' '.join(['bash', run_script, '--base-dir', self.base_dir, '--name', self.app_name, '--appid', self.appid, '--disk', disk, '--nic', nic, '--tz', convert_to_etc_gmt(), '--proxy', proxy if proxy != '' else \"''\", '--emails', ','.join(emails) if emails else \"''\"])\n", + " \n", + " if self.run_gluten:\n", + " if self.workload == 'tpch':\n", + " comp_file = os.path.join(cwd, self.RECORDS_GLUTEN_TPCH)\n", + " baseline_file = os.path.join(cwd, self.RECORDS_SPARK_TPCH)\n", + " else:\n", + " comp_file = os.path.join(cwd, self.RECORDS_GLUTEN_TPCDS)\n", + " baseline_file = os.path.join(cwd, self.RECORDS_SPARK_TPCDS)\n", + " comp_appid, comp_base_dir, comp_name = get_last_run(comp_file, self.appid)\n", + " if comp_appid:\n", + " command += ' '.join(['', '--comp-appid', comp_appid, '--comp-base-dir', comp_base_dir, '--comp-name', comp_name])\n", + " baseline_appid, baseline_base_dir, _ = get_last_run(baseline_file, '')\n", + " if baseline_appid:\n", + " command += ' '.join(['', '--baseline-appid', baseline_appid, '--baseline-base-dir', baseline_base_dir])\n", " print(command)\n", "\n", " # Block if running on local cluster.\n", @@ -863,7 +924,7 @@ " else:\n", " !ssh {self.server} \"{command} > /dev/null 2>&1 &\"\n", "\n", - " self.perf_html=f'http://{self.server}:8889/view/{self.base_dir}/html/{appname}_{self.appid}.html'\n", + " self.perf_html=f'http://{self.server}:8889/view/{self.base_dir}/html/{self.app_name}_{self.appid}.html'\n", " display(HTML(f'{self.perf_html}'))\n", " \n", " def load_table(self, table):\n", @@ -902,6 +963,7 @@ " display(HTML(('Completed Query. Time(sec): {:f}'.format(duration))))\n", " \n", " self.result[query] = duration\n", + " self.duration += float(duration)\n", " if print_result:\n", " print(collect)\n", "\n", @@ -914,18 +976,15 @@ " def print_result(self):\n", " print(self.result)\n", " print()\n", - " durations = [float(i) for i in self.result.values()]\n", - " print(\"total duration:\")\n", - " print(sum(durations))\n", - " print()\n", + " print(f\"total duration:\\n{self.duration}\\n\")\n", " if self.server:\n", " print(self.finished_nb)\n", " print(f\"http://{self.server}:1088/tracing_examples/trace_viewer.html#/tracing/test_data/{self.appid}.json\")\n", " print(f\"http://{self.server}:18080/history/{self.appid}\")\n", " print(self.perf_html)\n", " print(self.appid)\n", - " for i in durations:\n", - " print(i)\n", + " for t in self.result.values():\n", + " print(t)\n", " \n", "class TestTPCH(TestTPC):\n", " tpctables = ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier']\n",