spark QueryExecutionMetering 源码
spark QueryExecutionMetering 代码
文件路径:/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/QueryExecutionMetering.scala
/*
* 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.
*/
package org.apache.spark.sql.catalyst.rules
import scala.collection.JavaConverters._
import com.google.common.util.concurrent.AtomicLongMap
import org.apache.spark.sql.catalyst.util.DateTimeConstants.NANOS_PER_SECOND
case class QueryExecutionMetering() {
private val timeMap = AtomicLongMap.create[String]()
private val numRunsMap = AtomicLongMap.create[String]()
private val numEffectiveRunsMap = AtomicLongMap.create[String]()
private val timeEffectiveRunsMap = AtomicLongMap.create[String]()
/** Resets statistics about time spent running specific rules */
def resetMetrics(): Unit = {
timeMap.clear()
numRunsMap.clear()
numEffectiveRunsMap.clear()
timeEffectiveRunsMap.clear()
}
def getMetrics(): QueryExecutionMetrics = {
QueryExecutionMetrics(totalTime, totalNumRuns, totalNumEffectiveRuns, totalEffectiveTime)
}
def totalTime: Long = {
timeMap.sum()
}
def totalNumRuns: Long = {
numRunsMap.sum()
}
def totalNumEffectiveRuns: Long = {
numEffectiveRunsMap.sum()
}
def totalEffectiveTime: Long = {
timeEffectiveRunsMap.sum()
}
def incExecutionTimeBy(ruleName: String, delta: Long): Unit = {
timeMap.addAndGet(ruleName, delta)
}
def incTimeEffectiveExecutionBy(ruleName: String, delta: Long): Unit = {
timeEffectiveRunsMap.addAndGet(ruleName, delta)
}
def incNumEffectiveExecution(ruleName: String): Unit = {
numEffectiveRunsMap.incrementAndGet(ruleName)
}
def incNumExecution(ruleName: String): Unit = {
numRunsMap.incrementAndGet(ruleName)
}
/** Dump statistics about time spent running specific rules. */
def dumpTimeSpent(): String = {
val map = timeMap.asMap().asScala
val maxLengthRuleNames = if (map.isEmpty) {
0
} else {
map.keys.map(_.length).max
}
val colRuleName = "Rule".padTo(maxLengthRuleNames, " ").mkString
val colRunTime = "Effective Time / Total Time".padTo(len = 47, " ").mkString
val colNumRuns = "Effective Runs / Total Runs".padTo(len = 47, " ").mkString
val ruleMetrics = map.toSeq.sortBy(_._2).reverseMap { case (name, time) =>
val timeEffectiveRun = timeEffectiveRunsMap.get(name)
val numRuns = numRunsMap.get(name)
val numEffectiveRun = numEffectiveRunsMap.get(name)
val ruleName = name.padTo(maxLengthRuleNames, " ").mkString
val runtimeValue = s"$timeEffectiveRun / $time".padTo(len = 47, " ").mkString
val numRunValue = s"$numEffectiveRun / $numRuns".padTo(len = 47, " ").mkString
s"$ruleName $runtimeValue $numRunValue"
}.mkString("\n", "\n", "")
s"""
|=== Metrics of Analyzer/Optimizer Rules ===
|Total number of runs: $totalNumRuns
|Total time: ${totalTime / NANOS_PER_SECOND.toDouble} seconds
|
|$colRuleName $colRunTime $colNumRuns
|$ruleMetrics
""".stripMargin
}
}
case class QueryExecutionMetrics(
time: Long,
numRuns: Long,
numEffectiveRuns: Long,
timeEffective: Long) {
def -(metrics: QueryExecutionMetrics): QueryExecutionMetrics = {
QueryExecutionMetrics(
this.time - metrics.time,
this.numRuns - metrics.numRuns,
this.numEffectiveRuns - metrics.numEffectiveRuns,
this.timeEffective - metrics.timeEffective)
}
}
相关信息
相关文章
0
赞
- 所属分类: 前端技术
- 本文标签:
热门推荐
-
2、 - 优质文章
-
3、 gate.io
-
7、 golang
-
9、 openharmony
-
10、 Vue中input框自动聚焦