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Peak Unified Memory Heuristic - (Depends on Custom SHS - Requires pea…
…kUnifiedMemory metric) (#281)
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app/com/linkedin/drelephant/spark/heuristics/UnifiedMemoryHeuristic.scala
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| /* | ||
| * Copyright 2016 LinkedIn Corp. | ||
| * | ||
| * Licensed 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. | ||
| */ | ||
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| package com.linkedin.drelephant.spark.heuristics | ||
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| import com.linkedin.drelephant.analysis._ | ||
| import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData | ||
| import com.linkedin.drelephant.spark.data.SparkApplicationData | ||
| import com.linkedin.drelephant.spark.fetchers.statusapiv1.ExecutorSummary | ||
| import com.linkedin.drelephant.util.MemoryFormatUtils | ||
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| import scala.collection.JavaConverters | ||
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| /** | ||
| * A heuristic based on peak unified memory for the spark executors | ||
| * | ||
| * This heuristic reports the fraction of memory used/ memory allocated and if the fraction can be reduced. Also, it checks for the skew in peak unified memory and reports if the skew is too much. | ||
| */ | ||
| class UnifiedMemoryHeuristic(private val heuristicConfigurationData: HeuristicConfigurationData) | ||
| extends Heuristic[SparkApplicationData] { | ||
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| import UnifiedMemoryHeuristic._ | ||
| import JavaConverters._ | ||
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| override def getHeuristicConfData(): HeuristicConfigurationData = heuristicConfigurationData | ||
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| lazy val peakUnifiedMemoryThresholdString: String = heuristicConfigurationData.getParamMap.get(PEAK_UNIFIED_MEMORY_THRESHOLD_KEY) | ||
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| override def apply(data: SparkApplicationData): HeuristicResult = { | ||
| val evaluator = new Evaluator(this, data) | ||
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| var resultDetails = Seq( | ||
| new HeuristicResultDetails("Unified Memory Space Allocated", MemoryFormatUtils.bytesToString(evaluator.maxMemory)), | ||
| new HeuristicResultDetails("Mean peak unified memory", MemoryFormatUtils.bytesToString(evaluator.meanUnifiedMemory)), | ||
| new HeuristicResultDetails("Max peak unified memory", MemoryFormatUtils.bytesToString(evaluator.maxUnifiedMemory)), | ||
| new HeuristicResultDetails("spark.executor.memory", MemoryFormatUtils.bytesToString(evaluator.sparkExecutorMemory)), | ||
| new HeuristicResultDetails("spark.memory.fraction", evaluator.sparkMemoryFraction.toString) | ||
| ) | ||
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| val result = new HeuristicResult( | ||
| heuristicConfigurationData.getClassName, | ||
| heuristicConfigurationData.getHeuristicName, | ||
| evaluator.severity, | ||
| 0, | ||
| resultDetails.asJava | ||
| ) | ||
| result | ||
| } | ||
| } | ||
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| object UnifiedMemoryHeuristic { | ||
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| val EXECUTION_MEMORY = "executionMemory" | ||
| val STORAGE_MEMORY = "storageMemory" | ||
| val SPARK_EXECUTOR_MEMORY_KEY = "spark.executor.memory" | ||
| val SPARK_MEMORY_FRACTION_KEY = "spark.memory.fraction" | ||
| val PEAK_UNIFIED_MEMORY_THRESHOLD_KEY = "peak_unified_memory_threshold" | ||
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| class Evaluator(unifiedMemoryHeuristic: UnifiedMemoryHeuristic, data: SparkApplicationData) { | ||
| lazy val appConfigurationProperties: Map[String, String] = | ||
| data.appConfigurationProperties | ||
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| lazy val DEFAULT_PEAK_UNIFIED_MEMORY_THRESHOLD: SeverityThresholds = SeverityThresholds(low = 0.7 * maxMemory, moderate = 0.6 * maxMemory, severe = 0.4 * maxMemory, critical = 0.2 * maxMemory, ascending = false) | ||
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| lazy val executorSummaries: Seq[ExecutorSummary] = data.executorSummaries | ||
| if (executorSummaries == null) { | ||
| throw new Exception("Executors Summary is null.") | ||
| } | ||
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| val executorList: Seq[ExecutorSummary] = executorSummaries.filterNot(_.id.equals("driver")) | ||
| if (executorList.isEmpty) { | ||
| throw new Exception("No executor information available.") | ||
| } | ||
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| //allocated memory for the unified region | ||
| val maxMemory: Long = executorList.head.maxMemory | ||
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| val PEAK_UNIFIED_MEMORY_THRESHOLDS: SeverityThresholds = if (unifiedMemoryHeuristic.peakUnifiedMemoryThresholdString == null) { | ||
| DEFAULT_PEAK_UNIFIED_MEMORY_THRESHOLD | ||
| } else { | ||
| SeverityThresholds.parse(unifiedMemoryHeuristic.peakUnifiedMemoryThresholdString.split(",").map(_.toDouble * maxMemory).toString, ascending = false).getOrElse(DEFAULT_PEAK_UNIFIED_MEMORY_THRESHOLD) | ||
| } | ||
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| def getPeakUnifiedMemoryExecutorSeverity(executorSummary: ExecutorSummary): Severity = { | ||
| return PEAK_UNIFIED_MEMORY_THRESHOLDS.severityOf(executorSummary.peakUnifiedMemory.getOrElse(EXECUTION_MEMORY, 0).asInstanceOf[Number].longValue | ||
| + executorSummary.peakUnifiedMemory.getOrElse(STORAGE_MEMORY, 0).asInstanceOf[Number].longValue) | ||
| } | ||
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| val sparkExecutorMemory: Long = (appConfigurationProperties.get(SPARK_EXECUTOR_MEMORY_KEY).map(MemoryFormatUtils.stringToBytes)).getOrElse(0L) | ||
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| val sparkMemoryFraction: Double = appConfigurationProperties.getOrElse(SPARK_MEMORY_FRACTION_KEY, 0.6D).asInstanceOf[Number].doubleValue | ||
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| lazy val meanUnifiedMemory: Long = (executorList.map { | ||
| executorSummary => { | ||
| executorSummary.peakUnifiedMemory.getOrElse(EXECUTION_MEMORY, 0).asInstanceOf[Number].longValue | ||
| +executorSummary.peakUnifiedMemory.getOrElse(STORAGE_MEMORY, 0).asInstanceOf[Number].longValue | ||
| } | ||
| }.sum) / executorList.size | ||
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| lazy val maxUnifiedMemory: Long = executorList.map { | ||
| executorSummary => { | ||
| executorSummary.peakUnifiedMemory.getOrElse(EXECUTION_MEMORY, 0).asInstanceOf[Number].longValue | ||
| +executorSummary.peakUnifiedMemory.getOrElse(STORAGE_MEMORY, 0).asInstanceOf[Number].longValue | ||
| } | ||
| }.max | ||
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| lazy val severity: Severity = { | ||
| var severityPeakUnifiedMemoryVariable: Severity = Severity.NONE | ||
| for (executorSummary <- executorList) { | ||
| var peakUnifiedMemoryExecutorSeverity: Severity = getPeakUnifiedMemoryExecutorSeverity(executorSummary) | ||
| if (peakUnifiedMemoryExecutorSeverity.getValue > severityPeakUnifiedMemoryVariable.getValue) { | ||
| severityPeakUnifiedMemoryVariable = peakUnifiedMemoryExecutorSeverity | ||
| } | ||
| } | ||
| severityPeakUnifiedMemoryVariable | ||
| } | ||
| } | ||
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| } |
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24
app/views/help/spark/helpUnifiedMemoryHeuristic.scala.html
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| @* | ||
| * Copyright 2016 LinkedIn Corp. | ||
| * | ||
| * Licensed 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. | ||
| *@ | ||
| <p>Peak Unified Memory Heuristic identifies and flags jobs which have over allocated Unified Memory region.</p> | ||
| <h4>Peak Unified Memory</h4> | ||
| <p>If the job's Peak Unified Memory Consumption is much smaller than the allocated Unified Memory space, then we recommend decreasing the allocated Unified Memory Region for your job.</p> | ||
| <h3>Action Items</h3> | ||
| <p>The Allocated Unified Memory Region can be reduced in the following ways: </p> | ||
| <p>1. If your job's Executor Memory is already low, then reduce <i>spark.memory.fraction</i> which will reduce the amount of space allocated to the Unified Memory Region.</p> | ||
| <p>2. If your job's Executor Memory is high, then we recommend reducing the <i>spark.executor.memory</i> itself which will lower the Allocated Unified Memory space.</p> | ||
| <p>Note:</p> | ||
| <p><i>spark.memory.fraction</i>: This is the fraction of JVM Used Memory (Executor memory - Reserved memory) dedicated to the unified memory region (execution + storage). It basically partitions user memory from execution and storage memory.</p> |
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83 changes: 83 additions & 0 deletions
83
test/com/linkedin/drelephant/spark/heuristics/UnifiedMemoryHeuristicTest.scala
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| package com.linkedin.drelephant.spark.heuristics | ||
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| import com.linkedin.drelephant.analysis.{ApplicationType, Severity} | ||
| import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData | ||
| import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkRestDerivedData} | ||
| import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl} | ||
| import org.scalatest.{FunSpec, Matchers} | ||
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| import scala.collection.JavaConverters | ||
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| class UnifiedMemoryHeuristicTest extends FunSpec with Matchers { | ||
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| import UnifiedMemoryHeuristicTest._ | ||
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| val heuristicConfigurationData = newFakeHeuristicConfigurationData() | ||
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| val memoryFractionHeuristic = new UnifiedMemoryHeuristic(heuristicConfigurationData) | ||
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| val executorData = Seq( | ||
| newDummyExecutorData("1", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94567)), | ||
| newDummyExecutorData("2", 400000, Map("executionMemory" -> 200000, "storageMemory" -> 34568)), | ||
| newDummyExecutorData("3", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 34569)), | ||
| newDummyExecutorData("4", 400000, Map("executionMemory" -> 20000, "storageMemory" -> 3456)), | ||
| newDummyExecutorData("5", 400000, Map("executionMemory" -> 200000, "storageMemory" -> 34564)), | ||
| newDummyExecutorData("6", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94561)) | ||
| ) | ||
| describe(".apply") { | ||
| val data = newFakeSparkApplicationData(executorData) | ||
| val heuristicResult = memoryFractionHeuristic.apply(data) | ||
| val heuristicResultDetails = heuristicResult.getHeuristicResultDetails | ||
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| it("has severity") { | ||
| heuristicResult.getSeverity should be(Severity.CRITICAL) | ||
| } | ||
| } | ||
| } | ||
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| object UnifiedMemoryHeuristicTest { | ||
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| import JavaConverters._ | ||
|
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| def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData = | ||
| new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava) | ||
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| def newDummyExecutorData( | ||
| id: String, | ||
| maxMemory: Long, | ||
| peakUnifiedMemory: Map[String, Long] | ||
| ): ExecutorSummaryImpl = new ExecutorSummaryImpl( | ||
| id, | ||
| hostPort = "", | ||
| rddBlocks = 0, | ||
| memoryUsed = 0, | ||
| diskUsed = 0, | ||
| activeTasks = 0, | ||
| failedTasks = 0, | ||
| completedTasks = 0, | ||
| totalTasks = 0, | ||
| totalDuration = 0, | ||
| totalInputBytes = 0, | ||
| totalShuffleRead = 0, | ||
| totalShuffleWrite = 0, | ||
| maxMemory, | ||
| totalGCTime = 0, | ||
| totalMemoryBytesSpilled = 0, | ||
| executorLogs = Map.empty, | ||
| peakJvmUsedMemory = Map.empty, | ||
| peakUnifiedMemory | ||
| ) | ||
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| def newFakeSparkApplicationData(executorSummaries: Seq[ExecutorSummaryImpl]): SparkApplicationData = { | ||
| val appId = "application_1" | ||
| val restDerivedData = SparkRestDerivedData( | ||
| new ApplicationInfoImpl(appId, name = "app", Seq.empty), | ||
| jobDatas = Seq.empty, | ||
| stageDatas = Seq.empty, | ||
| executorSummaries = executorSummaries, | ||
| stagesWithFailedTasks = Seq.empty | ||
| ) | ||
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| SparkApplicationData(appId, restDerivedData, logDerivedData = None) | ||
| } | ||
| } |
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