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core/src/main/scala/org/apache/spark/util/MetricUtils.scala
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| package org.apache.spark.util | ||
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| import java.text.NumberFormat | ||
| import java.util.{Arrays, Locale} | ||
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| import scala.concurrent.duration._ | ||
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| import org.apache.spark.SparkException | ||
| import org.apache.spark.util.Utils | ||
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| object MetricUtils { | ||
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| val SUM_METRIC: String = "sum" | ||
| val SIZE_METRIC: String = "size" | ||
| val TIMING_METRIC: String = "timing" | ||
| val NS_TIMING_METRIC: String = "nsTiming" | ||
| val AVERAGE_METRIC: String = "average" | ||
| private val baseForAvgMetric: Int = 1000 | ||
| private val METRICS_NAME_SUFFIX = "(min, med, max (stageId: taskId))" | ||
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| private def toNumberFormat(value: Long): String = { | ||
| val numberFormat = NumberFormat.getNumberInstance(Locale.US) | ||
| numberFormat.format(value.toDouble / baseForAvgMetric) | ||
| } | ||
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| def metricNeedsMax(metricsType: String): Boolean = { | ||
| metricsType != SUM_METRIC | ||
| } | ||
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| /** | ||
| * A function that defines how we aggregate the final accumulator results among all tasks, | ||
| * and represent it in string for a SQL physical operator. | ||
| */ | ||
| def stringValue(metricsType: String, values: Array[Long], maxMetrics: Array[Long]): String = { | ||
| // taskInfo = "(driver)" OR (stage ${stageId}.${attemptId}: task $taskId) | ||
| val taskInfo = if (maxMetrics.isEmpty) { | ||
| "(driver)" | ||
| } else { | ||
| s"(stage ${maxMetrics(1)}.${maxMetrics(2)}: task ${maxMetrics(3)})" | ||
| } | ||
| if (metricsType == SUM_METRIC) { | ||
| val numberFormat = NumberFormat.getIntegerInstance(Locale.US) | ||
| numberFormat.format(values.sum) | ||
| } else if (metricsType == AVERAGE_METRIC) { | ||
| val validValues = values.filter(_ > 0) | ||
| // When there are only 1 metrics value (or None), no need to display max/min/median. This is | ||
| // common for driver-side SQL metrics. | ||
| if (validValues.length <= 1) { | ||
| toNumberFormat(validValues.headOption.getOrElse(0)) | ||
| } else { | ||
| val Seq(min, med, max) = { | ||
| Arrays.sort(validValues) | ||
| Seq( | ||
| toNumberFormat(validValues(0)), | ||
| toNumberFormat(validValues(validValues.length / 2)), | ||
| toNumberFormat(validValues(validValues.length - 1))) | ||
| } | ||
| s"$METRICS_NAME_SUFFIX:\n($min, $med, $max $taskInfo)" | ||
| } | ||
| } else { | ||
| val strFormat: Long => String = if (metricsType == SIZE_METRIC) { | ||
| Utils.bytesToString | ||
| } else if (metricsType == TIMING_METRIC) { | ||
| Utils.msDurationToString | ||
| } else if (metricsType == NS_TIMING_METRIC) { | ||
| duration => Utils.msDurationToString(duration.nanos.toMillis) | ||
| } else { | ||
| throw SparkException.internalError(s"unexpected metrics type: $metricsType") | ||
| } | ||
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| val validValues = values.filter(_ >= 0) | ||
| // When there are only 1 metrics value (or None), no need to display max/min/median. This is | ||
| // common for driver-side SQL metrics. | ||
| if (validValues.length <= 1) { | ||
| strFormat(validValues.headOption.getOrElse(0)) | ||
| } else { | ||
| val Seq(sum, min, med, max) = { | ||
| Arrays.sort(validValues) | ||
| Seq( | ||
| strFormat(validValues.sum), | ||
| strFormat(validValues(0)), | ||
| strFormat(validValues(validValues.length / 2)), | ||
| strFormat(validValues(validValues.length - 1))) | ||
| } | ||
| s"total $METRICS_NAME_SUFFIX\n$sum ($min, $med, $max $taskInfo)" | ||
| } | ||
| } | ||
| } | ||
| } |
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