add hashjoin benchmark and fix dead link issue

docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md

@@ -16,4 +16,4 @@ Following this [guide](/docs/get-started/xgboost-examples/building-sample-apps/p
 You need to copy the dataset to `/opt/xgboost`. Use the following links to download the data.
 1. [Mortgage dataset](/docs/get-started/xgboost-examples/dataset/mortgage.md)
 2. [Taxi dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
-3. [Agaricus dataset](https://gust.dev/r/xgboost-agaricus)
+3. [Agaricus dataset](https://github.com/dmlc/xgboost/tree/master/demo/data)

docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md

@@ -16,4 +16,4 @@ Following this [guide](/docs/get-started/xgboost-examples/building-sample-apps/s
 You need to copy the dataset to `/opt/xgboost`. Use the following links to download the data.
 1. [Mortgage dataset](/docs/get-started/xgboost-examples/dataset/mortgage.md)
 2. [Taxi dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
-3. [Agaricus dataset](https://gust.dev/r/xgboost-agaricus)
+3. [Agaricus dataset](https://github.com/dmlc/xgboost/tree/master/demo/data)

examples/SQL+DF-Examples/micro-benchmarks/README.md

@@ -3,14 +3,15 @@
 Standard industry benchmarks are a great way to measure performance over 
 a period of time but another barometer to measure performance is to measure
 performance of common operators that are used in the data preprocessing stage or in data analytics.
-The microbenchmark notebook in this repo uses four such queries in the chart shown below:
+The microbenchmark notebook in this repo uses five such queries in the chart shown below:
 
 - **Count Distinct**: a function used to estimate the number of unique page views or 
   unique customers visiting an e-commerce site.
 - **Window**: a critical operator necessary for preprocessing components in analyzing
   timestamped event data in marketing or financial industry.
 - **Intersect**: an operator used to remove duplicates in a dataframe.
 - **Cross-join**: A common use for a cross join is to obtain all combinations of items.
+- **Hash-join**: Joining two tables together by matching rows based on a common column.
 
 These queries were run on a standard eight-nodes CPU cluster with 2 CPU (128 cores),
 512GB memory and 1xA100 GPUs per node. The dataset used was of size 3TB with multiple different data types.

examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-cpu.ipynb

@@ -529,6 +529,34 @@
     "runMicroBenchmark(spark,\"Crossjoin\",query ,1)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "56f915c2-9b9a-4982-8c4e-5b570c17bfeb",
+   "metadata": {},
+   "source": [
+    "### HashJoin\n",
+    "This is a microbenchmark for a HashJoin. The query on GPU will be more than 10x times faster than CPU based on the cluster in the readme."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "040603c9-a96f-4017-bcdb-5f93e12996a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.read.parquet(dataRoot + \"/store_sales\").createOrReplaceTempView(\"store_sales\")\n",
+    "spark.read.parquet(dataRoot + \"/store_returns\").createOrReplaceTempView(\"store_returns\")\n",
+    "\n",
+    "print(\"-\"*50)\n",
+    "query = '''\n",
+    "select  sum(store_sales.ss_ext_wholesale_cost)\n",
+    "from store_sales\n",
+    "join store_returns on (ss_item_sk = sr_item_sk) and (ss_addr_sk=sr_addr_sk)\n",
+    "'''\n",
+    "runMicroBenchmark(spark,\"HashJoin\",query,1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -550,7 +578,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -564,7 +592,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb

@@ -541,18 +541,48 @@
     "runMicroBenchmark(spark,\"Crossjoin\",query,2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "06b351e6-b7bd-4063-a20b-fe4fd71221f9",
+   "metadata": {},
+   "source": [
+    "### HashJoin\n",
+    "This is a microbenchmark for a HashJoin. The query on GPU will be more than 10x times faster than CPU based on the cluster in the readme."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "191d0c9a-2d3a-40f4-89aa-f61dab5caa90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.read.parquet(dataRoot + \"/store_sales\").createOrReplaceTempView(\"store_sales\")\n",
+    "spark.read.parquet(dataRoot + \"/store_returns\").createOrReplaceTempView(\"store_returns\")\n",
+    "\n",
+    "print(\"-\"*50)\n",
+    "query = '''\n",
+    "select  sum(store_sales.ss_ext_wholesale_cost)\n",
+    "from store_sales\n",
+    "join store_returns on (ss_item_sk = sr_item_sk) and (ss_addr_sk=sr_addr_sk)\n",
+    "'''\n",
+    "runMicroBenchmark(spark,\"HashJoin\",query,1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "fc2092e8",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "spark.stop()"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -566,7 +596,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,