From d3fb6ba67d2a3944c8bf1241cacc6a2bb45aa8e2 Mon Sep 17 00:00:00 2001 From: Arpan Agrawal Date: Mon, 21 May 2018 06:26:37 -0700 Subject: [PATCH] Auto tuning feature enhancements (#379) Auto tuning feature enhancements: Tuning auto switch off when: * parameter converges * the maximum number of tuning iterations is reached * no gain in cost function Remembers and returns the best parameter set when: * tuning is switched off * no new parameter suggestion exists for the job * execution has failed and a retry is attempted Parameters being tune now attain only discrete values defined by the step size Bug fixes --- app-conf/SchedulerConf.xml | 2 +- .../tuning/APIFitnessComputeUtil.java | 198 -------------- .../tuning/AutoTuningAPIHelper.java | 234 +++++++++++----- .../tuning/BaselineComputeUtil.java | 2 +- .../drelephant/tuning/FitnessComputeUtil.java | 257 +++++++++++++++--- .../tuning/JobCompleteDetector.java | 6 +- .../drelephant/tuning/JobTuningInfo.java | 143 ++++++---- .../drelephant/tuning/PSOParamGenerator.java | 6 +- .../drelephant/tuning/ParamGenerator.java | 88 +++--- .../drelephant/util/InfoExtractor.java | 2 +- app/models/FlowExecution.java | 1 - app/models/JobDefinition.java | 2 +- app/models/JobExecution.java | 5 +- app/models/JobSuggestedParamValue.java | 2 +- app/models/TuningJobDefinition.java | 7 +- app/models/TuningJobExecution.java | 6 +- app/models/TuningParameter.java | 1 - conf/evolutions/default/5.sql | 2 +- conf/evolutions/default/6.sql | 11 + scripts/pso/pso_param_generation.py | 105 +++---- .../tuning/PSOParamGeneratorTest.java | 1 + test/resources/test-init.sql | 21 +- test/resources/tunein-test1.sql | 31 ++- 23 files changed, 644 insertions(+), 489 deletions(-) delete mode 100644 app/com/linkedin/drelephant/tuning/APIFitnessComputeUtil.java create mode 100644 conf/evolutions/default/6.sql diff --git a/app-conf/SchedulerConf.xml b/app-conf/SchedulerConf.xml index 0b380cb3a..a8679f5ce 100644 --- a/app-conf/SchedulerConf.xml +++ b/app-conf/SchedulerConf.xml @@ -30,7 +30,7 @@ com.linkedin.drelephant.schedulers.AzkabanScheduler - + diff --git a/app/com/linkedin/drelephant/tuning/APIFitnessComputeUtil.java b/app/com/linkedin/drelephant/tuning/APIFitnessComputeUtil.java deleted file mode 100644 index cc7485d2a..000000000 --- a/app/com/linkedin/drelephant/tuning/APIFitnessComputeUtil.java +++ /dev/null @@ -1,198 +0,0 @@ -/* - * 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. - */ - -package com.linkedin.drelephant.tuning; - -import java.io.IOException; -import java.net.HttpURLConnection; -import java.net.URL; -import java.net.URLEncoder; -import java.util.List; -import java.util.Random; - -import models.JobDefinition; -import models.JobExecution; -import models.TuningJobDefinition; -import models.TuningJobExecution; -import models.TuningJobExecution.ParamSetStatus; - -import org.apache.commons.io.FileUtils; -import org.apache.hadoop.conf.Configuration; -import org.apache.hadoop.security.authentication.client.AuthenticatedURL; -import org.apache.hadoop.security.authentication.client.AuthenticationException; -import org.apache.log4j.Logger; -import org.codehaus.jackson.JsonNode; -import org.codehaus.jackson.map.ObjectMapper; - -import com.linkedin.drelephant.ElephantContext; -import com.linkedin.drelephant.math.Statistics; - -import play.libs.Json; - - -/** - * This is another version of FitnessComputeUtil which uses Dr Elephant API to compute the fitness. - * This can be used during test when Dr Elephant DB is not getting data properly to compute the fitness - */ -public class APIFitnessComputeUtil extends FitnessComputeUtil { - - private static final Logger logger = Logger.getLogger(APIFitnessComputeUtil.class); - - private static final String DR_ELEPHANT_URL = "dr.elephant.api.url"; - private static final String JOB_HISTORY_SERVER_URL = "mapreduce.jobhistory.webapp.address"; - - private String _drElephantURL; - private String _jobHistoryServerURL; - private ObjectMapper _objectMapper = new ObjectMapper(); - - private AuthenticatedURL.Token _token; - private AuthenticatedURL _authenticatedURL; - private long _currentTime = 0; - private long _tokenUpdatedTime = 0; - - private static final long TOKEN_UPDATE_INTERVAL = - Statistics.MINUTE_IN_MS * 30 + new Random().nextLong() % (3 * Statistics.MINUTE_IN_MS); - private static final long FETCH_DELAY = 60000; - - public APIFitnessComputeUtil() { - Configuration configuration = ElephantContext.instance().getAutoTuningConf(); - _drElephantURL = configuration.get(DR_ELEPHANT_URL); - _jobHistoryServerURL = configuration.get(JOB_HISTORY_SERVER_URL); - } - - /** - * Updates the execution metrics - * @param completedExecutions List of completed executions - */ - protected void updateExecutionMetrics(List completedExecutions) { - logger.debug("Updating execution metrics"); - updateAuthToken(); - for (TuningJobExecution tuningJobExecution : completedExecutions) { - logger.debug("Completed executions before updating metric: " + Json.toJson(tuningJobExecution)); - try { - - JobExecution jobExecution = tuningJobExecution.jobExecution; - JobDefinition job = jobExecution.job; - - URL jobExecURL = new URL(new URL(_drElephantURL), - String.format("/rest/jobexec?id=%s", URLEncoder.encode(jobExecution.jobExecId))); - HttpURLConnection conn = (HttpURLConnection) jobExecURL.openConnection(); - JsonNode allApps = _objectMapper.readTree(conn.getInputStream()); - - // job id match and tuning enabled - TuningJobDefinition tuningJobDefinition = TuningJobDefinition.find.select("*") - .fetch(TuningJobDefinition.TABLE.job, "*") - .where() - .eq(TuningJobDefinition.TABLE.job + "." + JobDefinition.TABLE.id, job.id) - .eq(TuningJobDefinition.TABLE.tuningEnabled, 1) - .findUnique(); - - if (allApps != null && allApps.size() > 0) { - Long totalExecutionTime = 0L; - Double totalResourceUsed = 0D; - Double totalInputBytesInBytes = 0D; - - for (JsonNode app : allApps) { - logger.info("Job Execution Update: ApplicationID " + app.get("id").getTextValue()); - Long executionTime = - app.get("finishTime").getLongValue() - app.get("startTime").getLongValue() - app.get("totalDelay") - .getLongValue(); - totalExecutionTime += executionTime; - totalResourceUsed += app.get("resourceUsed").getDoubleValue(); - totalInputBytesInBytes += getTotalInputBytes(app.get("id").getTextValue()); - } - - if (totalExecutionTime != 0) { - jobExecution.executionTime = totalExecutionTime * 1.0 / (1000 * 60); - jobExecution.resourceUsage = totalResourceUsed * 1.0 / (1024 * 3600); - jobExecution.inputSizeInBytes = totalInputBytesInBytes; - logger.info("Job Execution Update: UpdatedValue " + totalExecutionTime + ":" + totalResourceUsed + ":" - + totalInputBytesInBytes); - } - - logger.debug("Job execution " + jobExecution.resourceUsage); - logger.debug("Job details: AvgResourceUsage " + tuningJobDefinition.averageResourceUsage - + ", allowedMaxResourceUsagePercent: " + tuningJobDefinition.allowedMaxResourceUsagePercent); - if (jobExecution.executionState.equals(JobExecution.ExecutionState.FAILED) - || jobExecution.executionState.equals(JobExecution.ExecutionState.CANCELLED)) { - // Todo: Check if the reason of failure is auto tuning and handle cancelled cases - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent - * FileUtils.ONE_GB / (100.0 * tuningJobDefinition.averageInputSizeInBytes); - } else if (jobExecution.resourceUsage > ( - tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent / 100.0)) { - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent - * FileUtils.ONE_GB / (100.0 * totalInputBytesInBytes); - } else { - tuningJobExecution.fitness = jobExecution.resourceUsage * FileUtils.ONE_GB / totalInputBytesInBytes; - } - tuningJobExecution.paramSetState = ParamSetStatus.FITNESS_COMPUTED; - jobExecution.update(); - tuningJobExecution.update(); - - logger.debug("Completed executions after updating metrics: " + Json.toJson(tuningJobExecution)); - } else { - if (jobExecution.executionState.equals(JobExecution.ExecutionState.FAILED) - || jobExecution.executionState.equals(JobExecution.ExecutionState.CANCELLED)) { - // Todo: Check if the reason of failure is auto tuning and handle cancelled cases - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent - * FileUtils.ONE_GB / (100.0 * tuningJobDefinition.averageInputSizeInBytes); - jobExecution.executionTime = 0D; - jobExecution.resourceUsage = 0D; - jobExecution.inputSizeInBytes = 0D; - tuningJobExecution.paramSetState = ParamSetStatus.FITNESS_COMPUTED; - jobExecution.update(); - tuningJobExecution.update(); - } - } - } catch (Exception e) { - logger.error("Error updating fitness of job_exec_id: " + tuningJobExecution.jobExecution.id + "\n Stacktrace: ", - e); - } - } - logger.debug("Execution metrics updated"); - } - - private Long getTotalInputBytes(String applicationID) throws IOException, AuthenticationException { - applicationID = applicationID.replace("application_", "job_"); - URL applicationURL = new URL(new URL(_jobHistoryServerURL), - String.format("/ws/v1/history/mapreduce/jobs/%s/counters", applicationID)); - HttpURLConnection conn = (HttpURLConnection) _authenticatedURL.openConnection(applicationURL, _token); - JsonNode rootNode = _objectMapper.readTree(conn.getInputStream()); - return rootNode.get("jobCounters") - .get("counterGroup") - .get(0) - .get("counter") - .get(5) - .get("totalCounterValue") - .getLongValue(); - } - - /** - * Authenticate and update the token - */ - private void updateAuthToken() { - _currentTime = System.currentTimeMillis() - FETCH_DELAY; - if (_currentTime - _tokenUpdatedTime > TOKEN_UPDATE_INTERVAL) { - logger.info("AnalysisProvider updating its Authenticate Token..."); - _token = new AuthenticatedURL.Token(); - _authenticatedURL = new AuthenticatedURL(); - _tokenUpdatedTime = _currentTime; - } - } -} diff --git a/app/com/linkedin/drelephant/tuning/AutoTuningAPIHelper.java b/app/com/linkedin/drelephant/tuning/AutoTuningAPIHelper.java index 252de349e..14346fa17 100644 --- a/app/com/linkedin/drelephant/tuning/AutoTuningAPIHelper.java +++ b/app/com/linkedin/drelephant/tuning/AutoTuningAPIHelper.java @@ -34,6 +34,7 @@ import models.TuningJobExecution; import models.TuningJobExecution.ParamSetStatus; import models.TuningParameter; +import org.apache.commons.io.FileUtils; import org.apache.hadoop.conf.Configuration; import org.apache.log4j.Logger; @@ -43,52 +44,79 @@ */ public class AutoTuningAPIHelper { - private static final Logger logger = Logger.getLogger(AutoTuningAPIHelper.class); - public static final String ALLOWED_MAX_RESOURCE_USAGE_PERCENT_DEFAULT = "autotuning.default.allowed_max_resource_usage_percent"; - public static final String ALLOWED_MAX_EXECUTION_TIME_PERCENT_DEFAULT = "autotuning.default.allowed_max_execution_time_percent"; + private static final Logger logger = Logger.getLogger(AutoTuningAPIHelper.class); /** - * This method creates a job execution with default parameters. This is required when there is no parameters set - * remains for suggestion. - * @param tuningJobDefinition Job definition - * @return + * For a job, returns the execution with the best parameter set if available else the one with the default parameter set. + * @param jobDefId Sting JobDefId of the job + * @return TuningJobExecution with the best parameter set if available else the one with the default parameter set. */ - private TuningJobExecution createDefaultJobExecution(TuningJobDefinition tuningJobDefinition) { - logger.info("Creating an execution with default parameter values for job: " + tuningJobDefinition.job.jobDefId); - //Get default execution from DB and clone that to create a new default execution - TuningJobExecution tuningJobExecutionDefault = TuningJobExecution.find.select("*") + private TuningJobExecution getBestParamSetTuningJobExecution(String jobDefId) { + TuningJobExecution tuningJobExecutionBestParamSet = TuningJobExecution.find.select("*") .where() - .eq(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.job + "." + JobDefinition.TABLE.id, - tuningJobDefinition.job.id) - .eq(TuningJobExecution.TABLE.isDefaultExecution, true) + .eq(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.job + "." + JobDefinition.TABLE.jobDefId, + jobDefId) + .eq(TuningJobExecution.TABLE.isParamSetBest, true) .setMaxRows(1) .findUnique(); + if (tuningJobExecutionBestParamSet == null) { + tuningJobExecutionBestParamSet = TuningJobExecution.find.select("*") + .where() + .eq(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.job + "." + JobDefinition.TABLE.jobDefId, + jobDefId) + .eq(TuningJobExecution.TABLE.isDefaultExecution, true) + .setMaxRows(1) + .findUnique(); + } + return tuningJobExecutionBestParamSet; + } + + /** + * Returns the param set corresponding to a given job execution id + * @param jobExecutionId Long job execution id of the execution + * @return List: list of parameters + */ + private List getJobExecutionParamSet(Long jobExecutionId) { + return JobSuggestedParamValue.find.where() + .eq(JobSuggestedParamValue.TABLE.jobExecution + "." + JobExecution.TABLE.id, jobExecutionId) + .findList(); + } + + /** + * This method creates a job execution with best parameter set. This is required when there is no parameters set or tuning has been switch off for the job + * remains for suggestion. + * @param tuningJobDefinition Job definition + * @return Tuning Job Execution with best parameters + */ + private TuningJobExecution cloneBestParamSetTuningJobExecution(TuningJobDefinition tuningJobDefinition) { + logger.info("Searching for best param set for job: " + tuningJobDefinition.job.jobName); + + TuningJobExecution bestParamSetTuningJobExecution = + getBestParamSetTuningJobExecution(tuningJobDefinition.job.jobDefId); + List jobSuggestedParamValueList = + getJobExecutionParamSet(bestParamSetTuningJobExecution.jobExecution.id); + TuningJobExecution tuningJobExecution = new TuningJobExecution(); JobExecution jobExecution = new JobExecution(); jobExecution.id = 0L; - jobExecution.job = tuningJobExecutionDefault.jobExecution.job; + jobExecution.job = bestParamSetTuningJobExecution.jobExecution.job; jobExecution.executionState = ExecutionState.NOT_STARTED; jobExecution.save(); tuningJobExecution.jobExecution = jobExecution; - tuningJobExecution.isDefaultExecution = tuningJobExecutionDefault.isDefaultExecution; - tuningJobExecution.tuningAlgorithm = tuningJobExecutionDefault.tuningAlgorithm; + tuningJobExecution.isDefaultExecution = bestParamSetTuningJobExecution.isDefaultExecution; + tuningJobExecution.tuningAlgorithm = bestParamSetTuningJobExecution.tuningAlgorithm; tuningJobExecution.paramSetState = ParamSetStatus.CREATED; tuningJobExecution.save(); logger.debug("Execution with default parameter created with execution id: " + tuningJobExecution.jobExecution.id); - List jobSuggestedParamValueList = JobSuggestedParamValue.find.where() - .eq(JobSuggestedParamValue.TABLE.jobExecution + "." + JobExecution.TABLE.id, - tuningJobExecutionDefault.jobExecution.id) - .findList(); - //Save default parameters corresponding to new default execution for (JobSuggestedParamValue jobSuggestedParamValue : jobSuggestedParamValueList) { JobSuggestedParamValue jobSuggestedParamValue1 = new JobSuggestedParamValue(); @@ -109,25 +137,28 @@ private TuningJobExecution createDefaultJobExecution(TuningJobDefinition tuningJ } /** - * Set default values for allowed max execution time and resource usage in case not provided in API call - * And set tuning algorithm based on value of job type and optimization metric - * @param tuningInput + * Sets the max allowed increase percentage for metrics: execution time and resource usage if not provided in API call + * @param tuningInput TuningInput */ - public void setDefaultValue(TuningInput tuningInput) { + private void setMaxAllowedMetricIncreasePercentage(TuningInput tuningInput) { Configuration configuration = ElephantContext.instance().getAutoTuningConf(); if (tuningInput.getAllowedMaxExecutionTimePercent() == null) { Double allowedMaxExecutionTimePercent = - new Double(Utils.getNonNegativeInt(configuration, "autotuning.default.allowed_max_execution_time_percent", - 150)); + new Double(Utils.getNonNegativeInt(configuration, ALLOWED_MAX_EXECUTION_TIME_PERCENT_DEFAULT, 150)); tuningInput.setAllowedMaxExecutionTimePercent(allowedMaxExecutionTimePercent); } if (tuningInput.getAllowedMaxResourceUsagePercent() == null) { Double allowedMaxResourceUsagePercent = - new Double(Utils.getNonNegativeInt(configuration, "autotuning.default.allowed_max_resource_usage_percent", - 150)); + new Double(Utils.getNonNegativeInt(configuration, ALLOWED_MAX_RESOURCE_USAGE_PERCENT_DEFAULT, 150)); tuningInput.setAllowedMaxResourceUsagePercent(allowedMaxResourceUsagePercent); } + } + /** + * Sets the tuning algorithm based on the job type and optimization metric + * @param tuningInput TuningInput for which tuning algorithm is to be set + */ + private void setTuningAlgorithm(TuningInput tuningInput) { //Todo: Handle algorithm version later TuningAlgorithm tuningAlgorithm = TuningAlgorithm.find.select("*") .where() @@ -137,6 +168,35 @@ public void setDefaultValue(TuningInput tuningInput) { tuningInput.setTuningAlgorithm(tuningAlgorithm); } + /** + * Applies penalty to the given execution + * @param jobExecId String jobExecId of the execution to which penalty has to be applied + */ + private void applyPenalty(String jobExecId) { + Integer penaltyConstant = 3; + logger.info("Execution " + jobExecId + " failed/cancelled. Applying penalty"); + TuningJobExecution tuningJobExecution = TuningJobExecution.find.where() + .eq(TuningJobExecution.TABLE.jobExecution + '.' + JobExecution.TABLE.jobExecId, jobExecId) + .findUnique(); + JobDefinition jobDefinition = tuningJobExecution.jobExecution.job; + TuningJobDefinition tuningJobDefinition = TuningJobDefinition.find.where() + .eq(TuningJobDefinition.TABLE.job + '.' + JobDefinition.TABLE.id, jobDefinition.id) + .findUnique(); + Double averageResourceUsagePerGBInput = + tuningJobDefinition.averageResourceUsage * FileUtils.ONE_GB / tuningJobDefinition.averageInputSizeInBytes; + Double maxDesiredResourceUsagePerGBInput = + averageResourceUsagePerGBInput * tuningJobDefinition.allowedMaxResourceUsagePercent / 100.0; + tuningJobExecution.fitness = penaltyConstant * maxDesiredResourceUsagePerGBInput; + tuningJobExecution.paramSetState = ParamSetStatus.FITNESS_COMPUTED; + tuningJobExecution.update(); + + JobExecution jobExecution = tuningJobExecution.jobExecution; + jobExecution.resourceUsage = 0D; + jobExecution.executionTime = 0D; + jobExecution.inputSizeInBytes = 1D; + jobExecution.save(); + } + /** * Handles the api request and returns param suggestions as response * @param tuningInput Rest api parameters @@ -144,25 +204,75 @@ public void setDefaultValue(TuningInput tuningInput) { */ public Map getCurrentRunParameters(TuningInput tuningInput) { logger.info("Parameter suggestion request for execution: " + tuningInput.getJobExecId()); - setDefaultValue(tuningInput); + List jobSuggestedParamValues; + if (tuningInput.getAllowedMaxExecutionTimePercent() == null + || tuningInput.getAllowedMaxResourceUsagePercent() == null) { + setMaxAllowedMetricIncreasePercentage(tuningInput); + } + setTuningAlgorithm(tuningInput); String jobDefId = tuningInput.getJobDefId(); - TuningJobDefinition tuningJobDefinition = TuningJobDefinition.find.select("*") - .fetch(TuningJobDefinition.TABLE.job, "*") - .where() - .eq(TuningJobDefinition.TABLE.job + "." + JobDefinition.TABLE.jobDefId, jobDefId) - .eq(TuningJobDefinition.TABLE.tuningEnabled, 1) - .findUnique(); + if (tuningInput.getRetry()) { + applyPenalty(tuningInput.getJobExecId()); + TuningJobExecution bestParamSetTuningJobExecution = getBestParamSetTuningJobExecution(jobDefId); + jobSuggestedParamValues = getJobExecutionParamSet(bestParamSetTuningJobExecution.jobExecution.id); + } else { + boolean isJobNewToTuning = false; + boolean isTuningEnabledForJob; + + TuningJobDefinition tuningJobDefinition = TuningJobDefinition.find.select("*") + .fetch(TuningJobDefinition.TABLE.job, "*") + .where() + .eq(TuningJobDefinition.TABLE.job + "." + JobDefinition.TABLE.jobDefId, jobDefId) + .eq(TuningJobDefinition.TABLE.tuningEnabled, 1) + .findUnique(); + + isTuningEnabledForJob = tuningJobDefinition != null; + + if (!isTuningEnabledForJob) { + //Tuning not enabled for the job currently. Either the job is new to tuning or tuning has been turned off for the job + //TuningJobDefinition will have a unique entry for every time a job is turned on for tuning + tuningJobDefinition = TuningJobDefinition.find.select("*") + .fetch(TuningJobDefinition.TABLE.job, "*") + .where() + .eq(TuningJobDefinition.TABLE.job + "." + JobDefinition.TABLE.jobDefId, jobDefId) + .setMaxRows(1) + .orderBy(TuningJobDefinition.TABLE.createdTs + " desc") + .findUnique(); + + isJobNewToTuning = tuningJobDefinition == null; + + if (isJobNewToTuning) { + //The job is new to tuning + logger.debug("Registering job: " + tuningInput.getJobName() + " for auto tuning tuning"); + AutoTuningMetricsController.markNewAutoTuningJob(); + tuningJobDefinition = addNewJobForTuning(tuningInput); + } + } - //If new job for tuning, update db with new job configuration - if (tuningJobDefinition == null) { - logger.debug("New job encountered for tuning"); - AutoTuningMetricsController.markNewAutoTuningJob(); - tuningJobDefinition = addNewJobForTuning(tuningInput); + TuningJobExecution tuningJobExecution; + if (isJobNewToTuning || isTuningEnabledForJob) { + logger.debug("Finding parameter suggestion for job: " + tuningJobDefinition.job.jobName); + tuningJobExecution = getNewTuningJobExecution(tuningJobDefinition); + } else { + //Tuning has been switched off for the job. Returning best param set + tuningJobExecution = cloneBestParamSetTuningJobExecution(tuningJobDefinition); + } + updateJobExecutionParameter(tuningJobExecution, tuningInput); + jobSuggestedParamValues = getJobExecutionParamSet(tuningJobExecution.jobExecution.id); } + logger.debug("Number of output parameters : " + jobSuggestedParamValues.size()); + logger.info("Finishing getCurrentRunParameters"); + return jobSuggestedParamValueListToMap(jobSuggestedParamValues); + } - logger.debug("Finding parameter suggestion for job: " + tuningJobDefinition.job.jobName); + /** + * Returns an execution with unsent parameters corresponding to the given job definition + * @param tuningJobDefinition TuningJobDefinition corresponding to which execution is to be returned + * @return TuningJobExecution corresponding to the given job definition + */ + private TuningJobExecution getNewTuningJobExecution(TuningJobDefinition tuningJobDefinition) { TuningJobExecution tuningJobExecution = TuningJobExecution.find.select("*") .fetch(TuningJobExecution.TABLE.jobExecution, "*") .fetch(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.job, "*") @@ -175,22 +285,23 @@ public Map getCurrentRunParameters(TuningInput tuningInput) { .setMaxRows(1) .findUnique(); - //If no new parameter set for suggestion, create a new suggestion with default parameter + //If no new parameter set for suggestion, create a new suggestion with best parameter set if (tuningJobExecution == null) { logger.info( - "Returning default parameters as no parameter suggestion found for job: " + tuningJobDefinition.job.jobName); + "Returning best parameter set as no parameter suggestion found for job: " + tuningJobDefinition.job.jobName); AutoTuningMetricsController.markParamSetNotFound(); - tuningJobExecution = createDefaultJobExecution(tuningJobDefinition); + tuningJobExecution = cloneBestParamSetTuningJobExecution(tuningJobDefinition); } + return tuningJobExecution; + } - logger.debug("Finding parameters corresponding to execution id: " + tuningJobExecution.jobExecution.id); - List jobSuggestedParamValues = JobSuggestedParamValue.find.where() - .eq(JobSuggestedParamValue.TABLE.jobExecution + "." + JobExecution.TABLE.id, tuningJobExecution.jobExecution.id) - .findList(); - - logger.debug("Number of output parameters : " + jobSuggestedParamValues.size()); + /** + * Returns the list of JobSuggestedParamValue as Map of String to Double + * @param jobSuggestedParamValues List of JobSuggestedParamValue + * @return Map of string to double containing the parameter name and corresponding value + */ + private Map jobSuggestedParamValueListToMap(List jobSuggestedParamValues) { Map paramValues = new HashMap(); - if (jobSuggestedParamValues != null) { for (JobSuggestedParamValue jobSuggestedParamValue : jobSuggestedParamValues) { logger.debug("Param Name is " + jobSuggestedParamValue.tuningParameter.paramName + " And value is " @@ -198,18 +309,14 @@ public Map getCurrentRunParameters(TuningInput tuningInput) { paramValues.put(jobSuggestedParamValue.tuningParameter.paramName, jobSuggestedParamValue.paramValue); } } - - updateJobExecutionParameter(tuningJobExecution, tuningInput); - - logger.info("Finishing getCurrentRunParameters"); return paramValues; } /** *This is to update job execution with IN_PROGRESS and parameter set with IN_PROGRESS. Also update flow_exec_id *, flowExecURL, JobExecID and jobExecURL - * @param tuningJobExecution - * @param tuningInput + * @param tuningJobExecution TuningJobExecution which is to be updated + * @param tuningInput TuningInput corresponding to the TuningJobExecution */ private void updateJobExecutionParameter(TuningJobExecution tuningJobExecution, TuningInput tuningInput) { @@ -291,7 +398,7 @@ private TuningJobDefinition addNewJobForTuning(TuningInput tuningInput) { TuningJobExecution tuningJobExecution = insertDefaultJobExecution(job, flowExecId, jobExecId, flowExecUrl, jobExecUrl, flowDefinition, tuningInput.getTuningAlgorithm()); - insertDefaultParameters(tuningJobExecution.jobExecution, defaultParams); + insertDefaultParameters(tuningJobExecution, defaultParams); logger.info("Added job: " + tuningInput.getJobDefId() + " for tuning"); return tuningJobDefinition; @@ -347,11 +454,14 @@ private TuningJobExecution insertDefaultJobExecution(JobDefinition job, String f /** * Inserts default execution parameters in database - * @param jobExecution Job Execution + * @param tuningJobExecution Tuning Job Execution * @param defaultParams Default parameters map as string */ @SuppressWarnings("unchecked") - private void insertDefaultParameters(JobExecution jobExecution, String defaultParams) { + private void insertDefaultParameters(TuningJobExecution tuningJobExecution, String defaultParams) { + JobExecution jobExecution = tuningJobExecution.jobExecution; + TuningAlgorithm.JobType jobType = tuningJobExecution.tuningAlgorithm.jobType; + ObjectMapper mapper = new ObjectMapper(); Map paramValueMap = null; try { diff --git a/app/com/linkedin/drelephant/tuning/BaselineComputeUtil.java b/app/com/linkedin/drelephant/tuning/BaselineComputeUtil.java index f03aac577..03a75571d 100644 --- a/app/com/linkedin/drelephant/tuning/BaselineComputeUtil.java +++ b/app/com/linkedin/drelephant/tuning/BaselineComputeUtil.java @@ -146,7 +146,7 @@ private Long getAvgInputSizeInBytes(String jobDefId) { + "FROM yarn_app_result yar INNER JOIN yarn_app_heuristic_result yahr " + "ON yar.id=yahr.yarn_app_result_id " + "INNER JOIN yarn_app_heuristic_result_details yahrd " + "ON yahr.id=yahrd.yarn_app_heuristic_result_id " + "WHERE job_def_id=:jobDefId AND yahr.heuristic_name='" + CommonConstantsHeuristic.MAPPER_SPEED + "' " - + "AND yahrd.name='Total input size in MB' " + + "AND yahrd.name='" + CommonConstantsHeuristic.TOTAL_INPUT_SIZE_IN_MB + "' " + "GROUP BY job_exec_id ORDER BY start_time DESC LIMIT :num ) temp"; logger.debug("Running query for average input size computation " + sql); diff --git a/app/com/linkedin/drelephant/tuning/FitnessComputeUtil.java b/app/com/linkedin/drelephant/tuning/FitnessComputeUtil.java index f3a8e6e0a..658ecbbee 100644 --- a/app/com/linkedin/drelephant/tuning/FitnessComputeUtil.java +++ b/app/com/linkedin/drelephant/tuning/FitnessComputeUtil.java @@ -16,21 +16,30 @@ package com.linkedin.drelephant.tuning; +import com.avaje.ebean.Expr; import com.linkedin.drelephant.AutoTuner; import com.linkedin.drelephant.ElephantContext; import com.linkedin.drelephant.mapreduce.heuristics.CommonConstantsHeuristic; import com.linkedin.drelephant.util.Utils; import controllers.AutoTuningMetricsController; import java.util.ArrayList; +import java.util.Arrays; +import java.util.HashMap; +import java.util.HashSet; import java.util.List; +import java.util.Map; +import java.util.Set; import models.AppHeuristicResult; import models.AppHeuristicResultDetails; import models.AppResult; import models.JobDefinition; import models.JobExecution; +import models.JobSuggestedParamValue; +import models.TuningAlgorithm; import models.TuningJobDefinition; import models.TuningJobExecution; import models.TuningJobExecution.ParamSetStatus; +import models.TuningParameter; import org.apache.commons.io.FileUtils; import org.apache.hadoop.conf.Configuration; import org.apache.log4j.Logger; @@ -45,7 +54,9 @@ */ public class FitnessComputeUtil { private static final Logger logger = Logger.getLogger(FitnessComputeUtil.class); - public static final String FITNESS_COMPUTE_WAIT_INTERVAL = "fitness.compute.wait_interval.ms"; + private static final String FITNESS_COMPUTE_WAIT_INTERVAL = "fitness.compute.wait_interval.ms"; + private static final int MAX_TUNING_EXECUTIONS = 39; + private static final int MIN_TUNING_EXECUTIONS = 18; private Long waitInterval; public FitnessComputeUtil() { @@ -56,24 +67,187 @@ public FitnessComputeUtil() { /** * Updates the metrics (execution time, resource usage, cost function) of the completed executions whose metrics are * not computed. - * @return List of job execution */ - public List updateFitness() { + public void updateFitness() { logger.info("Computing and updating fitness for completed executions"); List completedExecutions = getCompletedExecutions(); updateExecutionMetrics(completedExecutions); updateMetrics(completedExecutions); - return completedExecutions; + + Set jobDefinitionSet = new HashSet(); + for (TuningJobExecution tuningJobExecution : completedExecutions) { + jobDefinitionSet.add(tuningJobExecution.jobExecution.job); + } + checkToDisableTuning(jobDefinitionSet); + } + + /** + * Checks if the tuning parameters converge + * @param jobExecutions List of previous executions on which parameter convergence is to be checked + * @return true if the parameters converge, else false + */ + private boolean doesParameterSetConverge(List jobExecutions) { + boolean result = false; + int num_param_set_for_convergence = 3; + + TuningJobExecution tuningJobExecution = TuningJobExecution.find.where() + .eq(TuningJobExecution.TABLE.jobExecution + '.' + JobExecution.TABLE.id, jobExecutions.get(0).id) + .findUnique(); + TuningAlgorithm.JobType jobType = tuningJobExecution.tuningAlgorithm.jobType; + + if (jobType == TuningAlgorithm.JobType.PIG) { + Map> paramValueSet = new HashMap>(); + for (JobExecution jobExecution : jobExecutions) { + List jobSuggestedParamValueList = new ArrayList(); + try { + jobSuggestedParamValueList = JobSuggestedParamValue.find.where() + .eq(JobSuggestedParamValue.TABLE.jobExecution + '.' + JobExecution.TABLE.id, jobExecution.id) + .or(Expr.eq(JobSuggestedParamValue.TABLE.tuningParameter + '.' + TuningParameter.TABLE.id, 2), + Expr.eq(JobSuggestedParamValue.TABLE.tuningParameter + '.' + TuningParameter.TABLE.id, 5)) + .findList(); + } catch (NullPointerException e) { + logger.info("Checking param convergence: Map memory and reduce memory parameter not found"); + } + if (jobSuggestedParamValueList.size() > 0) { + num_param_set_for_convergence -= 1; + for (JobSuggestedParamValue jobSuggestedParamValue : jobSuggestedParamValueList) { + Set tmp; + if (paramValueSet.containsKey(jobSuggestedParamValue.id)) { + tmp = paramValueSet.get(jobSuggestedParamValue.id); + } else { + tmp = new HashSet(); + } + tmp.add(jobSuggestedParamValue.paramValue); + paramValueSet.put(jobSuggestedParamValue.id, tmp); + } + } + + if (num_param_set_for_convergence == 0) { + break; + } + } + + result = true; + for (Integer paramId : paramValueSet.keySet()) { + if (paramValueSet.get(paramId).size() > 1) { + result = false; + } + } + } + + if (result) { + logger.info( + "Switching off tuning for job: " + jobExecutions.get(0).job.jobName + " Reason: parameter set converged"); + } + return result; + } + + /** + * Checks if the median gain (from tuning) during the last 6 executions is negative + * Last 6 executions constitutes 2 iterations of PSO (given the swarm size is three). Negative average gains in + * latest 2 algorithm iterations (after a fixed number of minimum iterations) imply that either the algorithm hasn't + * converged or there isn't enough scope for tuning. In both the cases, switching tuning off is desired + * @param jobExecutions List of previous executions + * @return true if the median gain is negative, else false + */ + private boolean isMedianGainNegative(List jobExecutions) { + int num_fitness_for_median = 6; + Double[] fitnessArray = new Double[num_fitness_for_median]; + int entries = 0; + for (JobExecution jobExecution : jobExecutions) { + TuningJobExecution tuningJobExecution = TuningJobExecution.find.where() + .eq(TuningJobExecution.TABLE.jobExecution + '.' + JobExecution.TABLE.id, jobExecution.id) + .findUnique(); + if (jobExecution.executionState == JobExecution.ExecutionState.SUCCEEDED + && tuningJobExecution.paramSetState == ParamSetStatus.FITNESS_COMPUTED) { + fitnessArray[entries] = tuningJobExecution.fitness; + entries += 1; + if (entries == num_fitness_for_median) { + break; + } + } + } + Arrays.sort(fitnessArray); + double medianFitness; + if (fitnessArray.length % 2 == 0) { + medianFitness = (fitnessArray[fitnessArray.length / 2] + fitnessArray[fitnessArray.length / 2 - 1]) / 2; + } else { + medianFitness = fitnessArray[fitnessArray.length / 2]; + } + + JobDefinition jobDefinition = jobExecutions.get(0).job; + TuningJobDefinition tuningJobDefinition = TuningJobDefinition.find.where(). + eq(TuningJobDefinition.TABLE.job + '.' + JobDefinition.TABLE.id, jobDefinition.id).findUnique(); + double baselineFitness = + tuningJobDefinition.averageResourceUsage * FileUtils.ONE_GB / tuningJobDefinition.averageInputSizeInBytes; + + if (medianFitness > baselineFitness) { + logger.info( + "Switching off tuning for job: " + jobExecutions.get(0).job.jobName + " Reason: unable to tune enough"); + return true; + } else { + return false; + } + } + + /** + * Switches off tuning for the given job + * @param jobDefinition Job for which tuning is to be switched off + */ + private void disableTuning(JobDefinition jobDefinition, String reason) { + TuningJobDefinition tuningJobDefinition = TuningJobDefinition.find.where() + .eq(TuningJobDefinition.TABLE.job + '.' + JobDefinition.TABLE.id, jobDefinition.id) + .findUnique(); + if (tuningJobDefinition.tuningEnabled == 1) { + logger.info("Disabling tuning for job: " + tuningJobDefinition.job.jobDefId); + tuningJobDefinition.tuningEnabled = 0; + tuningJobDefinition.tuningDisabledReason = reason; + tuningJobDefinition.save(); + } + } + + /** + * Checks and disables tuning for the given job definitions. + * Tuning can be disabled if: + * - Number of tuning executions >= MAX_TUNING_EXECUTIONS + * - or number of tuning executions >= MIN_TUNING_EXECUTIONS and parameters converge + * - or number of tuning executions >= MIN_TUNING_EXECUTIONS and median gain (in cost function) in last 6 executions is negative + * @param jobDefinitionSet Set of jobs to check if tuning can be switched off for them + */ + private void checkToDisableTuning(Set jobDefinitionSet) { + for (JobDefinition jobDefinition : jobDefinitionSet) { + try { + List jobExecutions = JobExecution.find.where() + .eq(JobExecution.TABLE.job + '.' + JobDefinition.TABLE.id, jobDefinition.id) + .isNotNull(JobExecution.TABLE.jobExecId) + .orderBy("id desc") + .findList(); + if (jobExecutions.size() >= MIN_TUNING_EXECUTIONS) { + if (doesParameterSetConverge(jobExecutions)) { + logger.info("Parameters converged. Disabling tuning for job: " + jobDefinition.jobName); + disableTuning(jobDefinition, "Parameters converged"); + } else if (isMedianGainNegative(jobExecutions)) { + logger.info("Unable to get gain while tuning. Disabling tuning for job: " + jobDefinition.jobName); + disableTuning(jobDefinition, "Unable to get gain"); + } else if (jobExecutions.size() >= MAX_TUNING_EXECUTIONS) { + logger.info("Maximum tuning executions limit reached. Disabling tuning for job: " + jobDefinition.jobName); + disableTuning(jobDefinition, "Maximum executions reached"); + } + } + } catch (NullPointerException e) { + logger.info("No execution found for job: " + jobDefinition.jobName); + } + } } /** * This method update metrics for auto tuning monitoring for fitness compute daemon - * @param completedExecutions + * @param completedExecutions List of completed tuning job executions */ private void updateMetrics(List completedExecutions) { int fitnessNotUpdated = 0; for (TuningJobExecution tuningJobExecution : completedExecutions) { - if (tuningJobExecution.paramSetState.equals(ParamSetStatus.FITNESS_COMPUTED) == false) { + if (!tuningJobExecution.paramSetState.equals(ParamSetStatus.FITNESS_COMPUTED)) { fitnessNotUpdated++; } else { AutoTuningMetricsController.markFitnessComputedJobs(); @@ -120,11 +294,13 @@ private List getCompletedExecutions() { * Updates the execution metrics * @param completedExecutions List of completed executions */ - protected void updateExecutionMetrics(List completedExecutions) { - for (TuningJobExecution tuningJobExecution : completedExecutions) { + private void updateExecutionMetrics(List completedExecutions) { - logger.info("Updating execution metrics and fitness for execution: " + tuningJobExecution.jobExecution.jobExecId); + //To artificially increase the cost function value 3 times (as a penalty) in case of metric value violation + Integer penaltyConstant = 3; + for (TuningJobExecution tuningJobExecution : completedExecutions) { + logger.info("Updating execution metrics and fitness for execution: " + tuningJobExecution.jobExecution.jobExecId); try { JobExecution jobExecution = tuningJobExecution.jobExecution; JobDefinition job = jobExecution.job; @@ -151,6 +327,8 @@ protected void updateExecutionMetrics(List completedExecutio Double totalResourceUsed = 0D; Double totalInputBytesInBytes = 0D; + Map counterValuesMap = new HashMap(); + for (AppResult appResult : results) { totalResourceUsed += appResult.resourceUsed; totalInputBytesInBytes += getTotalInputBytes(appResult); @@ -178,40 +356,45 @@ protected void updateExecutionMetrics(List completedExecutio } //Compute fitness - if (jobExecution.executionState.equals(JobExecution.ExecutionState.FAILED) - || jobExecution.executionState.equals(JobExecution.ExecutionState.CANCELLED)) { - logger.info("Execution " + jobExecution.jobExecId + " failed/cancelled. Applying penalty"); - // Todo: Check if the reason of failure is auto tuning and handle cancelled cases - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent - * FileUtils.ONE_GB / (100.0 * tuningJobDefinition.averageInputSizeInBytes); - } else if (jobExecution.resourceUsage > ( - // Todo: Check execution time constraint as well - tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent / 100.0)) { - logger.info("Execution " + jobExecution.jobExecId + " violates constraint on resource usage"); - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent - * FileUtils.ONE_GB / (100.0 * totalInputBytesInBytes); + Double averageResourceUsagePerGBInput = + tuningJobDefinition.averageResourceUsage * FileUtils.ONE_GB / tuningJobDefinition.averageInputSizeInBytes; + Double maxDesiredResourceUsagePerGBInput = + averageResourceUsagePerGBInput * tuningJobDefinition.allowedMaxResourceUsagePercent / 100.0; + Double averageExecutionTimePerGBInput = + tuningJobDefinition.averageExecutionTime * FileUtils.ONE_GB / tuningJobDefinition.averageInputSizeInBytes; + Double maxDesiredExecutionTimePerGBInput = + averageExecutionTimePerGBInput * tuningJobDefinition.allowedMaxExecutionTimePercent / 100.0; + Double resourceUsagePerGBInput = + jobExecution.resourceUsage * FileUtils.ONE_GB / jobExecution.inputSizeInBytes; + Double executionTimePerGBInput = + jobExecution.executionTime * FileUtils.ONE_GB / jobExecution.inputSizeInBytes; + + if (resourceUsagePerGBInput > maxDesiredResourceUsagePerGBInput + || executionTimePerGBInput > maxDesiredExecutionTimePerGBInput) { + logger.info("Execution " + jobExecution.jobExecId + " violates constraint on resource usage per GB input"); + tuningJobExecution.fitness = penaltyConstant * maxDesiredResourceUsagePerGBInput; } else { - tuningJobExecution.fitness = jobExecution.resourceUsage * FileUtils.ONE_GB / totalInputBytesInBytes; + tuningJobExecution.fitness = resourceUsagePerGBInput; } tuningJobExecution.paramSetState = ParamSetStatus.FITNESS_COMPUTED; jobExecution.update(); tuningJobExecution.update(); - } else { - if (jobExecution.executionState.equals(JobExecution.ExecutionState.FAILED) - || jobExecution.executionState.equals(JobExecution.ExecutionState.CANCELLED)) { - // Todo: Check if the reason of failure is auto tuning and handle cancelled cases - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent - * FileUtils.ONE_GB / (100.0 * tuningJobDefinition.averageInputSizeInBytes); - jobExecution.executionTime = 0D; - jobExecution.resourceUsage = 0D; - jobExecution.inputSizeInBytes = 0D; - tuningJobExecution.paramSetState = ParamSetStatus.FITNESS_COMPUTED; - jobExecution.update(); - tuningJobExecution.update(); + } + + TuningJobExecution currentBestTuningJobExecution; + try { + currentBestTuningJobExecution = + TuningJobExecution.find.where().eq("jobExecution.job.id", tuningJobExecution.jobExecution.job.id). + eq(TuningJobExecution.TABLE.isParamSetBest, 1).findUnique(); + if (currentBestTuningJobExecution.fitness > tuningJobExecution.fitness) { + currentBestTuningJobExecution.isParamSetBest = false; + tuningJobExecution.isParamSetBest = true; + currentBestTuningJobExecution.save(); + tuningJobExecution.save(); } + } catch (NullPointerException e) { + tuningJobExecution.isParamSetBest = true; + tuningJobExecution.save(); } } catch (Exception e) { logger.error("Error updating fitness of execution: " + tuningJobExecution.jobExecution.id + "\n Stacktrace: ", diff --git a/app/com/linkedin/drelephant/tuning/JobCompleteDetector.java b/app/com/linkedin/drelephant/tuning/JobCompleteDetector.java index 51083dbe1..694cfb329 100644 --- a/app/com/linkedin/drelephant/tuning/JobCompleteDetector.java +++ b/app/com/linkedin/drelephant/tuning/JobCompleteDetector.java @@ -42,8 +42,8 @@ public abstract class JobCompleteDetector { /** * Updates the status of completed executions * @return List of completed executions - * @throws MalformedURLException - * @throws URISyntaxException + * @throws MalformedURLException MalformedURLException + * @throws URISyntaxException URISyntaxException */ public List updateCompletedExecutions() throws MalformedURLException, URISyntaxException { logger.info("Checking execution status"); @@ -57,7 +57,7 @@ public List updateCompletedExecutions() throws MalformedURLE /** * This method is for updating metrics for auto tuning monitoring for job completion daemon - * @param completedExecutions + * @param completedExecutions List completed job executions */ private void updateMetrics(List completedExecutions) { for (TuningJobExecution tuningJobExecution : completedExecutions) { diff --git a/app/com/linkedin/drelephant/tuning/JobTuningInfo.java b/app/com/linkedin/drelephant/tuning/JobTuningInfo.java index 55ed47ecb..3da56b6ab 100644 --- a/app/com/linkedin/drelephant/tuning/JobTuningInfo.java +++ b/app/com/linkedin/drelephant/tuning/JobTuningInfo.java @@ -18,71 +18,96 @@ import models.TuningParameter; import models.JobDefinition; + import java.util.List; +import models.TuningAlgorithm.JobType; /** * This class holds the tuning information for the job. */ class JobTuningInfo { - private JobDefinition _tuningJob; - - /** - * Comprises of: - * - archive: best configuration encountered - * - prev_population: pen-ultimate configuration - * - current_population: current configuration - * - rnd_state: state of random number generator - */ - private String _tunerState; - private List _parametersToTune; - - /** - * Sets the job being tuned - * @param tuningJob Job - */ - public void setTuningJob(JobDefinition tuningJob) { - this._tuningJob = tuningJob; - } - - /** - * Returns the job being tuned - * @return Job - */ - public JobDefinition getTuningJob() { - return _tuningJob; - } - - /** - * Sets the string tuner state - * @param stringTunerState String tuner state - */ - public void setTunerState(String stringTunerState) { - this._tunerState = stringTunerState; - } - - /** - * Returns string tuner state - * @return String tuner state - */ - public String getTunerState() { - return _tunerState; - } - - /** - * Sets parameters to tune - * @param parameters Parameters to tune - */ - public void setParametersToTune(List parameters) { - this._parametersToTune = parameters; - } - - /** - * Returns parameters to tune - * @return Parameters to tune - */ - public List getParametersToTune() { - return _parametersToTune; - } + private JobDefinition _tuningJob; + + private JobType _jobType; + + /** + * Comprises of: + * - archive: best configuration encountered + * - prev_population: pen-ultimate configuration + * - current_population: current configuration + * - rnd_state: state of random number generator + */ + private String _tunerState; + private List _parametersToTune; + + + /** + * Sets the jobtype of job being tuned + */ + public void setJobType(JobType jobType) { + this._jobType = jobType; + } + + /** + * Returns the job type + */ + public JobType getJobType() { + return _jobType; + } + + /** + * Sets the job being tuned + * + * @param tuningJob Job + */ + public void setTuningJob(JobDefinition tuningJob) { + this._tuningJob = tuningJob; + } + + /** + * Returns the job being tuned + * + * @return Job + */ + public JobDefinition getTuningJob() { + return _tuningJob; + } + + /** + * Sets the string tuner state + * + * @param stringTunerState String tuner state + */ + public void setTunerState(String stringTunerState) { + this._tunerState = stringTunerState; + } + + /** + * Returns string tuner state + * + * @return String tuner state + */ + public String getTunerState() { + return _tunerState; + } + + /** + * Sets parameters to tune + * + * @param parameters Parameters to tune + */ + public void setParametersToTune(List parameters) { + this._parametersToTune = parameters; + } + + /** + * Returns parameters to tune + * + * @return Parameters to tune + */ + public List getParametersToTune() { + return _parametersToTune; + } } diff --git a/app/com/linkedin/drelephant/tuning/PSOParamGenerator.java b/app/com/linkedin/drelephant/tuning/PSOParamGenerator.java index bd1b32e7e..b553f8772 100644 --- a/app/com/linkedin/drelephant/tuning/PSOParamGenerator.java +++ b/app/com/linkedin/drelephant/tuning/PSOParamGenerator.java @@ -75,6 +75,7 @@ public JobTuningInfo generateParamSet(JobTuningInfo jobTuningInfo) { JobTuningInfo newJobTuningInfo = new JobTuningInfo(); newJobTuningInfo.setTuningJob(jobTuningInfo.getTuningJob()); newJobTuningInfo.setParametersToTune(jobTuningInfo.getParametersToTune()); + newJobTuningInfo.setJobType(jobTuningInfo.getJobType()); JsonNode jsonJobTuningInfo = Json.toJson(jobTuningInfo); logger.info("Job Tuning Info for " + jobTuningInfo.getTuningJob().jobName + ": " + jsonJobTuningInfo); @@ -82,14 +83,15 @@ public JobTuningInfo generateParamSet(JobTuningInfo jobTuningInfo) { logger.info("Parameters to tune for job: " + parametersToTune); String stringTunerState = jobTuningInfo.getTunerState(); stringTunerState = stringTunerState.replaceAll("\\s+", ""); + String jobType = jobTuningInfo.getJobType().toString(); List error = new ArrayList(); try { logger.info( - "Calling PSO with StringTunerState= " + stringTunerState + "\nand Parameters to tune: " + parametersToTune); + "Calling PSO with Job type = " + jobType + " StringTunerState= " + stringTunerState + "\nand Parameters to tune: " + parametersToTune); Process p = Runtime.getRuntime() - .exec(PYTHON_PATH + " " + TUNING_SCRIPT_PATH + " " + stringTunerState + " " + parametersToTune); + .exec(PYTHON_PATH + " " + TUNING_SCRIPT_PATH + " " + stringTunerState + " " + parametersToTune + " " + jobType); BufferedReader inputStream = new BufferedReader(new InputStreamReader(p.getInputStream())); BufferedReader errorStream = new BufferedReader(new InputStreamReader(p.getErrorStream())); String updatedStringTunerState = inputStream.readLine(); diff --git a/app/com/linkedin/drelephant/tuning/ParamGenerator.java b/app/com/linkedin/drelephant/tuning/ParamGenerator.java index e8ab3ff47..952a9e22b 100644 --- a/app/com/linkedin/drelephant/tuning/ParamGenerator.java +++ b/app/com/linkedin/drelephant/tuning/ParamGenerator.java @@ -37,6 +37,8 @@ import java.util.ArrayList; import java.util.List; +import static java.lang.Math.*; + /** * This is an abstract class for generating parameter suggestions for jobs @@ -88,7 +90,6 @@ private List fetchJobsForParamSuggestion() { List jobsForParamSuggestion = new ArrayList(); List pendingParamExecutionList = new ArrayList(); - //Todo: Check if the find works correctly? try { pendingParamExecutionList = TuningJobExecution.find.select("*") .fetch(TuningJobExecution.TABLE.jobExecution, "*") @@ -128,7 +129,7 @@ private List fetchJobsForParamSuggestion() { } if (jobsForParamSuggestion.size() > 0) { for (TuningJobDefinition tuningJobDefinition : jobsForParamSuggestion) { - logger.info("New parameter suggestion needed for job:" + tuningJobDefinition.job.jobName); + logger.info("New parameter suggestion needed for job: " + tuningJobDefinition.job.jobName); } } else { logger.info("None of the jobs need new parameter suggestion"); @@ -207,8 +208,10 @@ private List getJobsTuningInfo(List tuningJo } catch (NullPointerException e) { logger.error("Error extracting default value of params for job " + tuningJobDefinition.job.jobDefId, e); } + JobTuningInfo jobTuningInfo = new JobTuningInfo(); jobTuningInfo.setTuningJob(job); + jobTuningInfo.setJobType(tuningJobDefinition.tuningAlgorithm.jobType); jobTuningInfo.setParametersToTune(tuningParameterList); JobSavedState jobSavedState = JobSavedState.find.byId(job.id); @@ -310,7 +313,6 @@ private List getParamValueList(Particle particle, List jobTuningInfoList) { - logger.info("Updating new parameter suggestion in database"); if (jobTuningInfoList == null) { logger.info("No new parameter suggestion to update"); @@ -338,11 +340,18 @@ private void updateDatabase(List jobTuningInfoList) { .eq(TuningJobDefinition.TABLE.tuningEnabled, 1) .findUnique(); - List derivedParameterList = TuningParameter.find.where() - .eq(TuningParameter.TABLE.tuningAlgorithm + "." + TuningAlgorithm.TABLE.id, - tuningJobDefinition.tuningAlgorithm.id) - .eq(TuningParameter.TABLE.isDerived, 1) - .findList(); + List derivedParameterList = new ArrayList(); + try { + derivedParameterList = TuningParameter.find.where() + .eq(TuningParameter.TABLE.tuningAlgorithm + "." + TuningAlgorithm.TABLE.id, + tuningJobDefinition.tuningAlgorithm.id) + .eq(TuningParameter.TABLE.isDerived, 1) + .findList(); + } catch (NullPointerException e) { + logger.info("No derived parameters for job: " + job.jobName); + } + logger.info("No. of derived tuning params for job " + tuningJobDefinition.job.jobName + ": " + + derivedParameterList.size()); JsonNode jsonTunerState = Json.parse(stringTunerState); JsonNode jsonSuggestedPopulation = jsonTunerState.get(JSON_CURRENT_POPULATION_KEY); @@ -399,11 +408,15 @@ private void updateDatabase(List jobTuningInfoList) { tuningJobExecution.jobExecution = jobExecution; tuningJobExecution.tuningAlgorithm = tuningJobDefinition.tuningAlgorithm; tuningJobExecution.isDefaultExecution = false; - if (isParamConstraintViolated(jobSuggestedParamValueList)) { + if (isParamConstraintViolated(jobSuggestedParamValueList, tuningJobExecution.tuningAlgorithm.jobType, job.id)) { logger.info("Parameter constraint violated. Applying penalty."); + int penaltyConstant = 3; + Double averageResourceUsagePerGBInput = + tuningJobDefinition.averageResourceUsage * FileUtils.ONE_GB / tuningJobDefinition.averageInputSizeInBytes; + Double maxDesiredResourceUsagePerGBInput = + averageResourceUsagePerGBInput * tuningJobDefinition.allowedMaxResourceUsagePercent / 100.0; + tuningJobExecution.fitness = penaltyConstant * maxDesiredResourceUsagePerGBInput; tuningJobExecution.paramSetState = TuningJobExecution.ParamSetStatus.FITNESS_COMPUTED; - tuningJobExecution.fitness = - 3 * tuningJobDefinition.averageResourceUsage * tuningJobDefinition.allowedMaxResourceUsagePercent / 100.0; } else { tuningJobExecution.paramSetState = TuningJobExecution.ParamSetStatus.CREATED; } @@ -435,40 +448,43 @@ private void updateDatabase(List jobTuningInfoList) { * @param jobSuggestedParamValueList * @return true if the constraint is violated, false otherwise */ - private boolean isParamConstraintViolated(List jobSuggestedParamValueList) { - logger.info("Checking whether parameter values are within constraints"); + private boolean isParamConstraintViolated(List jobSuggestedParamValueList, + TuningAlgorithm.JobType jobType, Integer jobDefinitionId) { + logger.info("Checking whether parameter values are within constraints"); Integer violations = 0; - Double mrSortMemory = null; - Double mrMapMemory = null; - Double pigMaxCombinedSplitSize = null; - for (JobSuggestedParamValue jobSuggestedParamValue : jobSuggestedParamValueList) { - if (jobSuggestedParamValue.tuningParameter.paramName.equals("mapreduce.task.io.sort.mb")) { - mrSortMemory = jobSuggestedParamValue.paramValue; - } else if (jobSuggestedParamValue.tuningParameter.paramName.equals("mapreduce.map.memory.mb")) { - mrMapMemory = jobSuggestedParamValue.paramValue; - } else if (jobSuggestedParamValue.tuningParameter.paramName.equals("pig.maxCombinedSplitSize")) { - pigMaxCombinedSplitSize = jobSuggestedParamValue.paramValue / FileUtils.ONE_MB; + if (jobType.equals(TuningAlgorithm.JobType.PIG)) { + Double mrSortMemory = null; + Double mrMapMemory = null; + Double pigMaxCombinedSplitSize = null; + + for (JobSuggestedParamValue jobSuggestedParamValue : jobSuggestedParamValueList) { + if (jobSuggestedParamValue.tuningParameter.paramName.equals("mapreduce.task.io.sort.mb")) { + mrSortMemory = jobSuggestedParamValue.paramValue; + } else if (jobSuggestedParamValue.tuningParameter.paramName.equals("mapreduce.map.memory.mb")) { + mrMapMemory = jobSuggestedParamValue.paramValue; + } else if (jobSuggestedParamValue.tuningParameter.paramName.equals("pig.maxCombinedSplitSize")) { + pigMaxCombinedSplitSize = jobSuggestedParamValue.paramValue / FileUtils.ONE_MB; + } } - } - if (mrSortMemory != null && mrMapMemory != null) { - if (mrSortMemory > 0.6 * mrMapMemory) { - logger.info("Constraint violated: Sort memory > 60% of map memory"); - violations++; + if (mrSortMemory != null && mrMapMemory != null) { + if (mrSortMemory > 0.6 * mrMapMemory) { + logger.info("Constraint violated: Sort memory > 60% of map memory"); + violations++; + } + if (mrMapMemory - mrSortMemory < 768) { + logger.info("Constraint violated: Map memory - sort memory < 768 mb"); + violations++; + } } - if (mrMapMemory - mrSortMemory < 768) { - logger.info("Constraint violated: Map memory - sort memory < 768 mb"); + + if (pigMaxCombinedSplitSize != null && mrMapMemory != null && (pigMaxCombinedSplitSize > 1.8 * mrMapMemory)) { + logger.info("Constraint violated: Pig max combined split size > 1.8 * map memory"); violations++; } } - - if (pigMaxCombinedSplitSize != null && mrMapMemory != null && (pigMaxCombinedSplitSize > 1.8 * mrMapMemory)) { - logger.info("Constraint violated: Pig max combined split size > 1.8 * map memory"); - violations++; - } - if (violations == 0) { return false; } else { diff --git a/app/com/linkedin/drelephant/util/InfoExtractor.java b/app/com/linkedin/drelephant/util/InfoExtractor.java index 48433a277..6ff0f4d1b 100644 --- a/app/com/linkedin/drelephant/util/InfoExtractor.java +++ b/app/com/linkedin/drelephant/util/InfoExtractor.java @@ -268,7 +268,7 @@ public static WorkflowClient getWorkflowClientInstance(String scheduler, String for (SchedulerConfigurationData data : _configuredSchedulers) { if (data.getSchedulerName().equals(scheduler)) { try { - String workflowClass = data.getParamMap().get("exception_class"); + String workflowClass = data.getParamMap().get("workflow_client"); Class schedulerClass = Class.forName(workflowClass); Object instance = schedulerClass.getConstructor(String.class).newInstance(url); if (!(instance instanceof WorkflowClient)) { diff --git a/app/models/FlowExecution.java b/app/models/FlowExecution.java index d64166ad1..3fb66ef72 100644 --- a/app/models/FlowExecution.java +++ b/app/models/FlowExecution.java @@ -41,7 +41,6 @@ public static class TABLE { public static final String id = "id"; public static final String flowExecId = "flowExecId"; public static final String flowExecUrl = "flowExecUrl"; - public static final String flowDefinitionId = "flowDefinitionId"; public static final String flowDefinition = "flowDefinition"; } diff --git a/app/models/JobDefinition.java b/app/models/JobDefinition.java index 93c264948..2b4d5732e 100644 --- a/app/models/JobDefinition.java +++ b/app/models/JobDefinition.java @@ -50,7 +50,7 @@ public static class TABLE { public static final String username = "username"; public static final String jobName = "jobName"; public static final String jobDefUrl = "jobDefUrl"; - public static final String flowDefinitionId = "flowDefinitionId"; + public static final String flowDefinition = "flowDefinition"; public static final String createdTs = "createdTs"; public static final String updatedTs = "updatedTs"; } diff --git a/app/models/JobExecution.java b/app/models/JobExecution.java index 274236ae4..8c0c6a2d6 100644 --- a/app/models/JobExecution.java +++ b/app/models/JobExecution.java @@ -53,10 +53,9 @@ public static class TABLE { public static final String executionState = "executionState"; public static final String resourceUsage = "resourceUsage"; public static final String executionTime = "executionTime"; - public static final String inputSizeInMb = "inputSizeInMb"; + public static final String inputSizeInBytes = "inputSizeInBytes"; public static final String jobExecUrl = "jobExecUrl"; - public static final String flowExecutionId = "flowExecutionId"; - public static final String jobDefinitionId = "jobDefinitionId"; + public static final String jobDefinition = "jobDefinition"; public static final String createdTs = "createdTs"; public static final String updatedTs = "updatedTs"; public static final String flowExecution = "flowExecution"; diff --git a/app/models/JobSuggestedParamValue.java b/app/models/JobSuggestedParamValue.java index ddace5e8f..84dcef981 100644 --- a/app/models/JobSuggestedParamValue.java +++ b/app/models/JobSuggestedParamValue.java @@ -42,7 +42,7 @@ public static class TABLE { public static final String TABLE_NAME = "job_suggested_param_value"; public static final String id = "id"; public static final String jobExecution = "jobExecution"; - public static final String tuningParameterId = "tuningParameterId"; + public static final String tuningParameter = "tuningParameter"; public static final String paramValue = "paramValue"; public static final String createdTs = "createdTs"; public static final String updatedTs = "updatedTs"; diff --git a/app/models/TuningJobDefinition.java b/app/models/TuningJobDefinition.java index a5a22cddd..1c0891f75 100644 --- a/app/models/TuningJobDefinition.java +++ b/app/models/TuningJobDefinition.java @@ -42,7 +42,7 @@ public class TuningJobDefinition extends Model { public static class TABLE { public static final String TABLE_NAME = "tuning_job_definition"; public static final String client = "client"; - public static final String tuningAlgorithmId = "tuningAlgorithmId"; + public static final String tuningAlgorithm = "tuningAlgorithm"; public static final String tuningEnabled = "tuningEnabled"; public static final String averageResourceUsage = "averageResourceUsage"; public static final String averageExecutionTime = "averageExecutionTime"; @@ -52,6 +52,7 @@ public static class TABLE { public static final String job = "job"; public static final String createdTs = "createdTs"; public static final String updatedTs = "updatedTs"; + public static final String tuningDisabledReason = "tuningDisabledReason"; } @ManyToOne(cascade = CascadeType.ALL) @@ -100,4 +101,8 @@ public Double getAverageInputSizeInGB() { @Column(nullable = false) @UpdatedTimestamp public Timestamp updatedTs; + + + @Column(nullable = true) + public String tuningDisabledReason; } diff --git a/app/models/TuningJobExecution.java b/app/models/TuningJobExecution.java index 661074b61..6f57acaa7 100644 --- a/app/models/TuningJobExecution.java +++ b/app/models/TuningJobExecution.java @@ -42,12 +42,12 @@ public enum ParamSetStatus { public static class TABLE { public static final String TABLE_NAME = "tuning_job_execution"; - public static final String jobExecutionId = "jobExecutionId"; - public static final String tuningAlgorithmId = "tuningAlgorithmId"; public static final String paramSetState = "paramSetState"; public static final String isDefaultExecution = "isDefaultExecution"; public static final String fitness = "fitness"; + public static final String isParamSetBest = "isParamSetBest"; public static final String jobExecution = "jobExecution"; + public static final String tuningAlgorithm = "tuningAlgorithm"; } @OneToOne(cascade = CascadeType.ALL) @@ -66,6 +66,8 @@ public static class TABLE { public Double fitness; + public Boolean isParamSetBest; + public static Model.Finder find = new Model.Finder(Long.class, TuningJobExecution.class); } diff --git a/app/models/TuningParameter.java b/app/models/TuningParameter.java index 4075c49da..d19e70b3b 100644 --- a/app/models/TuningParameter.java +++ b/app/models/TuningParameter.java @@ -48,7 +48,6 @@ public static class TABLE { public static final String TABLE_NAME = "tuning_parameter"; public static final String id = "id"; public static final String paramName = "paramName"; - public static final String tuningAlgorithmId = "tuningAlgorithmId"; public static final String defaultValue = "defaultValue"; public static final String minValue = "minValue"; public static final String maxValue = "maxValue"; diff --git a/conf/evolutions/default/5.sql b/conf/evolutions/default/5.sql index 6bb0535ff..174d42ee0 100644 --- a/conf/evolutions/default/5.sql +++ b/conf/evolutions/default/5.sql @@ -77,7 +77,7 @@ CREATE TABLE IF NOT EXISTS flow_definition ( /** * This table represent the job to be optimized. This table contains general info other than auto tuning related - * information. Broken job defintion info in two table, as this can be used for Dr Elephant basic info. + * information. Broken job definition info in two table, as this can be used for Dr Elephant basic info. * As not all jobs will be enabled for auto tuning. */ CREATE TABLE IF NOT EXISTS job_definition ( diff --git a/conf/evolutions/default/6.sql b/conf/evolutions/default/6.sql new file mode 100644 index 000000000..f16030727 --- /dev/null +++ b/conf/evolutions/default/6.sql @@ -0,0 +1,11 @@ +# --- Support for auto tuning spark +# --- !Ups + +ALTER TABLE tuning_algorithm ADD UNIQUE KEY tuning_algorithm_uk1(optimization_algo, optimization_algo_version); +ALTER TABLE tuning_job_execution ADD COLUMN is_param_set_best tinyint(4) default 0 NOT NULL; +ALTER TABLE tuning_job_definition ADD COLUMN tuning_disabled_reason text; + +# --- !Downs +ALTER TABLE tuning_job_definition DROP COLUMN tuning_disabled_reason; +ALTER TABLE tuning_job_execution DROP COLUMN is_param_set_best; +ALTER TABLE tuning_algorithm DROP INDEX tuning_algorithm_uk1; \ No newline at end of file diff --git a/scripts/pso/pso_param_generation.py b/scripts/pso/pso_param_generation.py index 715b891e5..3d1581886 100644 --- a/scripts/pso/pso_param_generation.py +++ b/scripts/pso/pso_param_generation.py @@ -25,6 +25,7 @@ param_default_value = [] param_name = [] iteration = 0 +job_type = "" LARGE_DUMMY_FITNESS = 10000 @@ -40,6 +41,7 @@ ARG_TUNING_STATE_KEY = 'json_tuning_state' ARG_PARAMETERS_TO_TUNE_KEY = 'parameters_to_tune' +ARG_JOB_TYPE = "job_type" TUNING_STATE_ARCHIVE_KEY = 'archive' TUNING_STATE_PREV_POPULATION_KEY = 'prev_population' @@ -60,38 +62,10 @@ INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE = (0.5, 0.8) INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE = (1.05, 1.1) INITIAL_DERIVED_SORT_MEMORY_PARAM_RANGE = (0.0, 0.25) +# POPULATION_SIZE = 3 performs the best as was found in the experimentation POPULATION_SIZE = 3 -# The below method is intentionally commented -# Todo: Update the below method to implement step size -# def fix_data_type(params): -# """ -# :param params: -# :return: -# """ -# params_as_json = {} -# -# for i in xrange(0, len(params)): -# params[i] = int(round(params[i])) -# params[i] *= param_step_size[i] -# params_as_json[param_name[i]] = float(params[i]) -# -# if param_name[i] == PARAM_PIG_MAX_COMBINED_SPLIT_SIZE: -# params_as_json[PARAM_PIG_MAX_COMBINED_SPLIT_SIZE] = int(params[i]) * 1024 * 1024 -# if PARAM_MAPREDUCE_INPUT_FILEINPUTFORMAT_SPLIT_MAXSIZE not in param_name: -# params_as_json[PARAM_MAPREDUCE_INPUT_FILEINPUTFORMAT_SPLIT_MAXSIZE] = int(params[i]) * 1024 * 1024 -# -# elif param_name[i] == PARAM_MAPREDUCE_MAP_MEMORY_MB: -# if PARAM_MAPREDUCE_MAP_JAVA_OPTS not in param_name: -# params_as_json[PARAM_MAPREDUCE_MAP_JAVA_OPTS] = '-Xmx%dm' % (0.75 * int(params[i])) -# -# elif param_name[i] == PARAM_MAPREDUCE_REDUCE_MEMORY_MB: -# if PARAM_MAPREDUCE_REDUCE_JAVA_OPTS not in param_name: -# params_as_json[PARAM_MAPREDUCE_REDUCE_JAVA_OPTS] = '-Xmx%dm' % (0.75 * int(params[i])) -# return params_as_json - - def initialize_params(parameters_to_tune): """Initializes data structures for generating new parameter suggestion :param parameters_to_tune: The list of parameters to be tuned in json format @@ -118,52 +92,56 @@ def initial_population_generator(random, args): for i in range(0, len(param_name)): if param_name[i] == PARAM_MAPREDUCE_TASK_IO_SORT_FACTOR: - sort_factor_index = i + mr_sort_factor_index = i elif param_name[i] == PARAM_MAPREDUCE_TASK_IO_SORT_MB: - sort_memory_index = i + mr_sort_memory_index = i elif param_name[i] == PARAM_MAPREDUCE_MAP_SORT_SPILL_PERCENT: - spill_percent_index = i + mr_spill_percent_index = i elif param_name[i] == PARAM_MAPREDUCE_MAP_MEMORY_MB: - map_memory_index = i + mr_map_memory_index = i elif param_name[i] == PARAM_MAPREDUCE_REDUCE_MEMORY_MB: - reduce_memory_index = i + mr_reduce_memory_index = i elif param_name[i] == PARAM_PIG_MAX_COMBINED_SPLIT_SIZE: - max_combined_split_size_index = i + pig_max_combined_split_size_index = i global iteration if iteration == 0: iteration += 1 initial_population = param_default_value - else: initial_population = [random.uniform(x, y) for x, y in param_value_range] - if iteration % 2 == 1: - initial_population[map_memory_index] = random.uniform(INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[0], - INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[1]) * \ - param_default_value[map_memory_index] - initial_population[reduce_memory_index] = random.uniform(INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[0], - INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[1]) * \ - param_default_value[ - reduce_memory_index] - - if iteration % 2 == 0: - initial_population[map_memory_index] = random.uniform(INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[0], - INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[1]) * \ - param_default_value[map_memory_index] - initial_population[reduce_memory_index] = random.uniform(INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[0], - INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[1]) * \ - param_default_value[ - reduce_memory_index] - - initial_population[sort_memory_index] = random.uniform(INITIAL_DERIVED_SORT_MEMORY_PARAM_RANGE[0], - INITIAL_DERIVED_SORT_MEMORY_PARAM_RANGE[1]) * initial_population[ - map_memory_index] - initial_population[max_combined_split_size_index] = param_default_value[max_combined_split_size_index] - iteration += 1 + + if job_type == 'PIG': + if iteration % 2 == 1: + initial_population[mr_map_memory_index] = random.uniform(INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[0], + INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[1]) * \ + param_default_value[mr_map_memory_index] + initial_population[mr_reduce_memory_index] = random.uniform(INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[0], + INITIAL_DERIVED_LOWER_MEMORY_PARAM_RANGE[ + 1]) * param_default_value[ + mr_reduce_memory_index] + + if iteration % 2 == 0: + initial_population[mr_map_memory_index] = random.uniform(INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[0], + INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[1]) * \ + param_default_value[mr_map_memory_index] + initial_population[mr_reduce_memory_index] = random.uniform(INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[0], + INITIAL_DERIVED_UPPER_MEMORY_PARAM_RANGE[ + 1]) * param_default_value[ + mr_reduce_memory_index] + + initial_population[mr_sort_memory_index] = random.uniform(INITIAL_DERIVED_SORT_MEMORY_PARAM_RANGE[0], + INITIAL_DERIVED_SORT_MEMORY_PARAM_RANGE[1]) * \ + initial_population[mr_map_memory_index] + initial_population[pig_max_combined_split_size_index] = param_default_value[ + pig_max_combined_split_size_index] + iteration += 1 for i in range(0, len(param_name)): (min_val, max_val) = param_value_range[i] + step = param_step_size[i] + initial_population[i] = int(round(initial_population[i] * 1.0 / step)) * step initial_population[i] = max(min_val, min(max_val, initial_population[i])) return initial_population @@ -192,8 +170,9 @@ def bounder(candidate, args): param_value_lower_bound = get_params_lower_bound() param_value_upper_bound = get_params_upper_bound() bounded_candidate = candidate - for i, (c, lo, hi) in enumerate(zip(candidate, param_value_lower_bound, - param_value_upper_bound)): + for i, (c, lo, hi, step) in enumerate( + zip(candidate, param_value_lower_bound, param_value_upper_bound, param_step_size)): + c = int(round(c * 1.0 / step)) * step bounded_candidate[i] = max(min(c, hi), lo) return bounded_candidate @@ -234,7 +213,7 @@ def generate_tuning_state(pso, pseudo_random_number_generator, population): data[TUNING_STATE_ARCHIVE_KEY] = archive data[TUNING_STATE_PREV_POPULATION_KEY] = prev_population - data[TUNING_STATE_CURRENT_POPULATION_KEY] = current_population # todo: send 2 or 3? + data[TUNING_STATE_CURRENT_POPULATION_KEY] = current_population data[TUNING_STATE_RANDOM_STATE_KEY] = rnd_state data_dump = json.dumps(data) return data_dump @@ -313,9 +292,11 @@ def main(json_tuning_state, display=False): parser = argparse.ArgumentParser() parser.add_argument(ARG_TUNING_STATE_KEY, help='Saved tuning state object') parser.add_argument(ARG_PARAMETERS_TO_TUNE_KEY) + parser.add_argument(ARG_JOB_TYPE) args = parser.parse_args() json_tuning_state = args.json_tuning_state parameters_to_tune = args.parameters_to_tune + job_type = args.job_type parameters_to_tune = json.loads(parameters_to_tune) initialize_params(parameters_to_tune) main(json_tuning_state) diff --git a/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java b/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java index 8696e3194..99aa1f0dd 100644 --- a/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java +++ b/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java @@ -105,6 +105,7 @@ public void run() { jobTuningInfo.setTuningJob(jobDefinition); jobTuningInfo.setParametersToTune(tuningParameterList); jobTuningInfo.setTunerState("{}"); + jobTuningInfo.setJobType(TuningAlgorithm.JobType.PIG); PSOParamGenerator psoParamGenerator = new PSOParamGenerator(); diff --git a/test/resources/test-init.sql b/test/resources/test-init.sql index 12f1c4091..024cd9155 100644 --- a/test/resources/test-init.sql +++ b/test/resources/test-init.sql @@ -7,24 +7,31 @@ insert into yarn_app_heuristic_result(id,yarn_app_result_id,heuristic_class,heur insert into yarn_app_heuristic_result_details (yarn_app_heuristic_result_id,name,value,details) values (137594512,'Group A','1 tasks @ 4 MB avg','NULL'), (137594512,'Group B','1 tasks @ 79 MB avg','NULL'), (137594512,'Number of tasks','2','NULL'), (137594513,'Avg task CPU time (ms)','11510','NULL'), (137594513,'Avg task GC time (ms)','76','NULL'), (137594513,'Avg task runtime (ms)','11851','NULL'), (137594513,'Number of tasks','2','NULL'), (137594513,'Task GC/CPU ratio','0.006602953953084275 ','NULL'), (137594516,'Average task input size','42 MB','NULL'), (137594516,'Average task runtime','11 sec','NULL'), (137594516,'Max task runtime','12 sec','NULL'), (137594516,'Min task runtime','11 sec','NULL'), (137594516,'Number of tasks','2','NULL'), (137594520,'Median task input size','42 MB','NULL'), (137594520,'Median task runtime','11 sec','NULL'), (137594520,'Median task speed','3 MB/s','NULL'), (137594520,'Number of tasks','2','NULL'), (137594523,'Avg output records per task','56687','NULL'), (137594523,'Avg spilled records per task','79913','NULL'), (137594523,'Number of tasks','2','NULL'), (137594523,'Ratio of spilled records to output records','1.4097111356119074','NULL'), (137594525,'Avg Physical Memory (MB)','522','NULL'), (137594525,'Avg task runtime','11 sec','NULL'), (137594525,'Avg Virtual Memory (MB)','3307','NULL'), (137594525,'Max Physical Memory (MB)','595','NULL'), (137594525,'Min Physical Memory (MB)','449','NULL'), (137594525,'Number of tasks','2','NULL'), (137594525,'Requested Container Memory','2 GB','NULL'), (137594530,'Group A','11 tasks @ 868 KB avg','NULL'), (137594530,'Group B','9 tasks @ 883 KB avg ','NULL'), (137594530,'Number of tasks','20','NULL'), (137594531,'Avg task CPU time (ms)','8912','NULL'), (137594531,'Avg task GC time (ms)','73','NULL'), (137594531,'Avg task runtime (ms)','11045','NULL'), (137594531,'Number of tasks','20','NULL'), (137594531,'Task GC/CPU ratio','0.008191202872531419 ','NULL'), (137594534,'Average task runtime','11 sec','NULL'), (137594534,'Max task runtime','14 sec','NULL'), (137594534,'Min task runtime','8 sec','NULL'), (137594534,'Number of tasks','20','NULL'), (137594537,'Avg Physical Memory (MB)','416','NULL'), (137594537,'Avg task runtime','11 sec','NULL'), (137594537,'Avg Virtual Memory (MB)','3326','NULL'), (137594537,'Max Physical Memory (MB)','497','NULL'), (137594537,'Min Physical Memory (MB)','354','NULL'), (137594537,'Number of tasks','20','NULL'), (137594537,'Requested Container Memory','2 GB','NULL'), (137594540,'Average code runtime','1 sec','NULL'), (137594540,'Average shuffle time','9 sec (5.49x)','NULL'), (137594540,'Average sort time','(0.04x)','NULL'), (137594540,'Number of tasks','20','NULL'), (137594612,'Group A','1 tasks @ 4 MB avg','NULL'), (137594612,'Group B','1 tasks @ 79 MB avg','NULL'), (137594612,'Number of tasks','2','NULL'), (137594613,'Avg task CPU time (ms)','11510','NULL'), (137594613,'Avg task GC time (ms)','76','NULL'), (137594613,'Avg task runtime (ms)','11851','NULL'), (137594613,'Number of tasks','2','NULL'), (137594613,'Task GC/CPU ratio','0.006602953953084275 ','NULL'), (137594616,'Average task input size','42 MB','NULL'), (137594616,'Average task runtime','11 sec','NULL'), (137594616,'Max task runtime','12 sec','NULL'), (137594616,'Min task runtime','11 sec','NULL'), (137594616,'Number of tasks','2','NULL'), (137594620,'Median task input size','42 MB','NULL'), (137594620,'Median task runtime','11 sec','NULL'), (137594620,'Median task speed','3 MB/s','NULL'), (137594620,'Number of tasks','2','NULL'), (137594623,'Avg output records per task','56687','NULL'), (137594623,'Avg spilled records per task','79913','NULL'), (137594623,'Number of tasks','2','NULL'), (137594623,'Ratio of spilled records to output records','1.4097111356119074','NULL'), (137594625,'Avg Physical Memory (MB)','522','NULL'), (137594625,'Avg task runtime','11 sec','NULL'), (137594625,'Avg Virtual Memory (MB)','3307','NULL'), (137594625,'Max Physical Memory (MB)','595','NULL'), (137594625,'Min Physical Memory (MB)','449','NULL'), (137594625,'Number of tasks','2','NULL'), (137594625,'Requested Container Memory','2 GB','NULL'), (137594630,'Group A','11 tasks @ 868 KB avg','NULL'), (137594630,'Group B','9 tasks @ 883 KB avg ','NULL'), (137594630,'Number of tasks','20','NULL'), (137594631,'Avg task CPU time (ms)','8912','NULL'), (137594631,'Avg task GC time (ms)','73','NULL'), (137594631,'Avg task runtime (ms)','11045','NULL'), (137594631,'Number of tasks','20','NULL'), (137594631,'Task GC/CPU ratio','0.008191202872531419 ','NULL'), (137594634,'Average task runtime','11 sec','NULL'), (137594634,'Max task runtime','14 sec','NULL'), (137594634,'Min task runtime','8 sec','NULL'), (137594634,'Number of tasks','20','NULL'), (137594637,'Avg Physical Memory (MB)','416','NULL'), (137594637,'Avg task runtime','11 sec','NULL'), (137594637,'Avg Virtual Memory (MB)','3326','NULL'), (137594637,'Max Physical Memory (MB)','497','NULL'), (137594637,'Min Physical Memory (MB)','354','NULL'), (137594637,'Number of tasks','20','NULL'), (137594637,'Requested Container Memory','2 GB','NULL'), (137594640,'Average code runtime','1 sec','NULL'), (137594640,'Average shuffle time','9 sec (5.49x)','NULL'), (137594640,'Average sort time','(0.04x)','NULL'), (137594640,'Number of tasks','20','NULL'); -INSERT INTO flow_definition VALUES (10003,'https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow','https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow'); +INSERT INTO flow_definition(id, flow_def_id, flow_def_url) VALUES (10003,'https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow','https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow'); -INSERT INTO job_definition VALUES (100003,'https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow&job=countByCountryFlow_countByCountry',10003,'countByCountryFlow_countByCountry','https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow&job=countByCountryFlow_countByCountry','azkaban','mkumar1','2018-02-12 08:40:42','2018-02-12 08:40:43'); +INSERT INTO job_definition(id, job_def_id, flow_definition_id, job_name, job_def_url, scheduler, username, created_ts, updated_ts) VALUES (100003,'https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow&job=countByCountryFlow_countByCountry',10003,'countByCountryFlow_countByCountry','https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow&job=countByCountryFlow_countByCountry','azkaban','mkumar1','2018-02-12 08:40:42','2018-02-12 08:40:43'); --INSERT INTO tuning_algorithm VALUES (1,'PIG','PSO',1,'RESOURCE','2018-02-06 17:03:23','2018-02-06 17:03:23'); -- INSERT INTO tuning_parameter VALUES (1,'mapreduce.task.io.sort.mb',1,100,50,1920,50,0,'2018-02-06 17:03:23','2018-02-06 17:03:23'),(2,'mapreduce.map.memory.mb',1,2048,1536,8192,128,0,'2018-02-06 17:03:23','2018-02-06 17:03:23'),(3,'mapreduce.task.io.sort.factor',1,10,10,150,10,0,'2018-02-06 17:03:23','2018-02-06 17:03:23'),(4,'mapreduce.map.sort.spill.percent',1,0.8,0.6,0.9,0.1,0,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(5,'mapreduce.reduce.memory.mb',1,2048,1536,8192,128,0,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(6,'pig.maxCombinedSplitSize',1,536870912,536870912,536870912,128,0,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(7,'mapreduce.reduce.java.opts',1,1536,1152,6144,128,1,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(8,'mapreduce.map.java.opts',1,1536,1152,6144,128,1,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(9,'mapreduce.input.fileinputformat.split.maxsize',1,536870912,536870912,536870912,128,1,'2018-02-06 17:03:24','2018-02-06 17:03:24'); -INSERT INTO tuning_job_definition VALUES (100003,'azkaban',1,1,40.29456456163195,5.178423333333334,324168876088,150,150,'2018-02-12 08:40:42','2018-02-12 08:40:43'); +INSERT INTO tuning_job_definition(job_definition_id, client, tuning_algorithm_id, tuning_enabled, average_resource_usage, average_execution_time, average_input_size_in_bytes, allowed_max_resource_usage_percent, allowed_max_execution_time_percent, created_ts, updated_ts, tuning_disabled_reason) +VALUES (100003,'azkaban',1,1,40.29456456163195,5.178423333333334,324168876088,150,150,'2018-02-12 08:40:42','2018-02-12 08:40:43', NULL); -INSERT INTO flow_execution VALUES (1496,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293',10003),(1497,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389',10003),(1498,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495',10003),(1499,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589',10003),(1500,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680',10003),(1501,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818',10003),(1502,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057',10003); +INSERT INTO flow_execution(id, flow_exec_id, flow_exec_url, flow_definition_id) VALUES (1496,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293',10003),(1497,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389',10003),(1498,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495',10003),(1499,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589',10003),(1500,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680',10003),(1501,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818',10003),(1502,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057',10003); -INSERT INTO job_execution VALUES (1541,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0',100003,1496,'SUCCEEDED',21.132545572916666,3.2694833333333335,324713861757,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(1542,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0',100003,1497,'SUCCEEDED',23.334004991319443,3.6118166666666665,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1543,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0',100003,1498,'SUCCEEDED',21.28552951388889,3.2940833333333335,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1544,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0',100003,1499,'SUCCEEDED',21.630970052083335,3.9560833333333334,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1545,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0',100003,1500,'SUCCEEDED',22.328486328125,3.7285166666666667,324713861757,'2018-02-14 07:29:47','2018-02-14 07:29:48'),(1546,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0',100003,1501,'SUCCEEDED',32.16945149739583,5.203783333333333,324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(1547,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0',100003,1502,'SUCCEEDED', 27.2955078125, 4.047583333333334, 324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48'); +INSERT INTO job_execution(id, job_exec_id, job_exec_url, job_definition_id, flow_execution_id, execution_state, resource_usage, execution_time, input_size_in_bytes, created_ts, updated_ts) VALUES (1541,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0',100003,1496,'SUCCEEDED',21.132545572916666,3.2694833333333335,324713861757,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(1542,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0',100003,1497,'SUCCEEDED',23.334004991319443,3.6118166666666665,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1543,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0',100003,1498,'SUCCEEDED',21.28552951388889,3.2940833333333335,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1544,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0',100003,1499,'SUCCEEDED',21.630970052083335,3.9560833333333334,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1545,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0',100003,1500,'SUCCEEDED',22.328486328125,3.7285166666666667,324713861757,'2018-02-14 07:29:47','2018-02-14 07:29:48'),(1546,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0',100003,1501,'SUCCEEDED',32.16945149739583,5.203783333333333,324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(1547,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0',100003,1502,'SUCCEEDED', 27.2955078125, 4.047583333333334, 324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48'); -INSERT INTO tuning_job_execution VALUES (1541,1,'FITNESS_COMPUTED',0,0.06987967161749142),(1542,1,'FITNESS_COMPUTED',0,0.07715930864495756),(1543,1,'FITNESS_COMPUTED',0,0.07038554856075895),(1544,1,'FITNESS_COMPUTED',0,0.07152782795578526),(1545,1,'FITNESS_COMPUTED',0,0.07383432757503201),(1546,1,'FITNESS_COMPUTED',0,0.10637576523832741),(1547,1,'FITNESS_COMPUTED',0,0.09025893809095505); +INSERT INTO tuning_job_execution(job_execution_id, tuning_algorithm_id, param_set_state, is_default_execution, fitness, is_param_set_best) VALUES (1541,1,'FITNESS_COMPUTED',0,0.06987967161749142,0), +(1542,1,'FITNESS_COMPUTED',0,0.07715930864495756,0), +(1543,1,'FITNESS_COMPUTED',0,0.07038554856075895,0), +(1544,1,'FITNESS_COMPUTED',0,0.07152782795578526,0), +(1545,1,'FITNESS_COMPUTED',0,0.07383432757503201,0), +(1546,1,'FITNESS_COMPUTED',0,0.10637576523832741,0), +(1547,1,'FITNESS_COMPUTED',0,0.09025893809095505,0); -- INSERT INTO job_saved_state (job_definition_id, saved_state) VALUES (100003,'{\"current_population\":[{\"birthdate\":1.518593387909695E9,\"maximize\":false,\"candidate\":[159.88928672056016,1536.0,10.0,0.770164839202443,2863.372720073011,5.36870912E8],\"fitness\":10000.0,\"paramSetId\":1545,\"_candidate\":[159.88928672056016,1536.0,10.0,0.770164839202443,2863.372720073011,5.36870912E8]},{\"birthdate\":1.518593387909696E9,\"maximize\":false,\"candidate\":[201.64225529876035,1536.0,10.29839988592941,0.7635183100860585,2789.189282499988,5.36870912E8],\"fitness\":10000.0,\"paramSetId\":1546,\"_candidate\":[201.64225529876035,1536.0,10.29839988592941,0.7635183100860585,2789.189282499988,5.36870912E8]},{\"birthdate\":1.518593387909698E9,\"maximize\":false,\"candidate\":[149.52419594024295,1536.0,10.0,0.7630834894363029,2844.1716734703073,5.36870912E8],\"fitness\":10000.0,\"paramSetId\":1547,\"_candidate\":[149.52419594024295,1536.0,10.0,0.7630834894363029,2844.1716734703073,5.36870912E8]}],\"prev_population\":[{\"_candidate\":[162.7907384046847,1536.0,10.0,0.7632081784681852,2809.5806453243313,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909403E9,\"fitness\":0.07715930864495756},{\"_candidate\":[124.05878355054111,1536.0,12.521341191290857,0.7622909149004323,2041.562366831904,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909406E9,\"fitness\":0.07038554856075895},{\"_candidate\":[149.51252503919468,1536.0,10.0,0.7619961998308155,2844.326081249364,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909407E9,\"fitness\":0.07152782795578526}],\"archive\":[{\"birthday\":1.518589785163133E9,\"_candidate\":[214.91248065459718,1536.0,10.0,0.7684337983774014,2810.072296417105,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387904912E9,\"fitness\":0.06798720858096566},{\"_candidate\":[124.05878355054111,1536.0,12.521341191290857,0.7622909149004323,2041.562366831904,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909406E9,\"fitness\":0.07038554856075895},{\"birthday\":1.518589785163142E9,\"_candidate\":[149.5180195836781,1536.0,10.0,0.7624156183109899,2844.240241069899,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387904921E9,\"fitness\":0.06753575007445276}],\"rnd_state\":\"[3, 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07:29:48'),(3271,1547,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'); +INSERT INTO job_suggested_param_value(id, job_execution_id, tuning_parameter_id, param_value, created_ts, updated_ts) VALUES (3209,1541,1,149.5239493606563,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3210,1541,2,1536,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3211,1541,3,10,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3212,1541,4,0.761466551875019,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3213,1541,5,2844.365182469904,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3214,1541,6,536870912,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3215,1541,7,2133.273886852428,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3216,1541,8,1152,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3217,1541,9,536870912,'2018-02-14 05:30:42','2018-02-14 05:30:42'), (3218,1542,1,162.7907384046847,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3219,1542,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3220,1542,3,10,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3221,1542,4,0.7632081784681852,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3222,1542,5,2809.5806453243313,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3223,1542,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3224,1542,7,2107.1854839932485,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3225,1542,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3226,1542,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3227,1543,1,124.05878355054111,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3228,1543,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3229,1543,3,12.521341191290857,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3230,1543,4,0.7622909149004323,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3231,1543,5,2041.562366831904,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3232,1543,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3233,1543,7,1531.171775123928,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3234,1543,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3235,1543,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3236,1544,1,149.51252503919468,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3237,1544,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3238,1544,3,10,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3239,1544,4,0.7619961998308155,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3240,1544,5,2844.326081249364,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3241,1544,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3242,1544,7,2133.244560937023,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3243,1544,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3244,1544,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3245,1545,1,159.88928672056016,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3246,1545,2,1536,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3247,1545,3,10,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3248,1545,4,0.770164839202443,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3249,1545,5,2863.372720073011,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3250,1545,6,536870912,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3251,1545,7,2147.5295400547584,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3252,1545,8,1152,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3253,1545,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3254,1546,1,201.64225529876035,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3255,1546,2,1536,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3256,1546,3,10.29839988592941,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3257,1546,4,0.7635183100860585,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3258,1546,5,2789.189282499988,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3259,1546,6,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3260,1546,7,2091.891961874991,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3261,1546,8,1152,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3262,1546,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3263,1547,1,149.52419594024295,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3264,1547,2,1536,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3265,1547,3,10,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3266,1547,4,0.7630834894363029,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3267,1547,5,2844.1716734703073,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3268,1547,6,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3269,1547,7,2133.1287551027303,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3270,1547,8,1152,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3271,1547,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'); diff --git a/test/resources/tunein-test1.sql b/test/resources/tunein-test1.sql index ec851f0be..ebb33dd33 100644 --- a/test/resources/tunein-test1.sql +++ b/test/resources/tunein-test1.sql @@ -1,12 +1,25 @@ -INSERT INTO `flow_definition` VALUES 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runtime','2 min 41 sec',NULL),(1511868,'Min task runtime','41 sec',NULL),(1511868,'Number of tasks','204',NULL),(1511869,'Median task input size','377 MB',NULL),(1511869,'Median task runtime','1 min 19 sec',NULL),(1511869,'Median task speed','4 MB/s',NULL),(1511869,'Number of tasks','204',NULL),(1511869,'Total input size in MB','78404.13675880432',NULL),(1511870,'Avg output records per task','1411730',NULL),(1511870,'Avg spilled records per task','0',NULL),(1511870,'Number of tasks','204',NULL),(1511870,'Ratio of spilled records to output records','0.0',NULL),(1511871,'Avg Physical Memory (MB)','722',NULL),(1511871,'Avg task runtime','1 min 21 sec',NULL),(1511871,'Avg Virtual Memory (MB)','2358',NULL),(1511871,'Max Physical Memory (MB)','768',NULL),(1511871,'Min Physical Memory (MB)','669',NULL),(1511871,'Number of tasks','204',NULL),(1511871,'Requested Container Memory','2 GB',NULL),(1511872,'Group A','0 tasks @ 0 bytes avg',NULL),(1511872,'Group B','0 tasks @ 0 bytes 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(MB)','826',NULL),(1519433,'Min Physical Memory (MB)','707',NULL),(1519433,'Number of tasks','2',NULL),(1519433,'Requested Container Memory','1 GB',NULL),(1519434,'Group A','0 tasks @ 0 bytes avg',NULL),(1519434,'Group B','0 tasks @ 0 bytes avg',NULL),(1519434,'Number of tasks','0',NULL),(1519435,'Avg task CPU time (ms)','0',NULL),(1519435,'Avg task GC time (ms)','0',NULL),(1519435,'Avg task runtime (ms)','0',NULL),(1519435,'Number of tasks','0',NULL),(1519435,'Task GC/CPU ratio','0.0',NULL),(1519436,'Average task runtime','0 sec',NULL),(1519436,'Max task runtime','0 sec',NULL),(1519436,'Min task runtime','0 sec',NULL),(1519436,'Number of tasks','0',NULL),(1519437,'Avg Physical Memory (MB)','0',NULL),(1519437,'Avg task runtime','0 sec',NULL),(1519437,'Avg Virtual Memory (MB)','0',NULL),(1519437,'Max Physical Memory (MB)','0',NULL),(1519437,'Min Physical Memory (MB)','0',NULL),(1519437,'Number of tasks','0',NULL),(1519437,'Requested Container Memory','1 GB',NULL),(1519438,'Average code runtime','0 sec',NULL),(1519438,'Average shuffle time','0 sec ',NULL),(1519438,'Average sort time','0 sec ',NULL),(1519438,'Number of tasks','0',NULL),(1519440,'Group A','1 tasks @ 304 MB avg',NULL),(1519440,'Group B','1 tasks @ 512 MB avg',NULL),(1519440,'Number of tasks','2',NULL),(1519441,'Avg task CPU time (ms)','117545',NULL),(1519441,'Avg task GC time (ms)','2388',NULL),(1519441,'Avg task runtime (ms)','79402',NULL),(1519441,'Number of tasks','2',NULL),(1519441,'Task GC/CPU ratio','0.020315623803649666',NULL),(1519442,'Average task input size','408 MB',NULL),(1519442,'Average task runtime','1 min 19 sec',NULL),(1519442,'Max task runtime','1 min 37 sec',NULL),(1519442,'Min task runtime','1 min 1 sec',NULL),(1519442,'Number of tasks','2',NULL),(1519443,'Median task input size','408 MB',NULL),(1519443,'Median task runtime','1 min 19 sec',NULL),(1519443,'Median task speed','5 MB/s',NULL),(1519443,'Number of tasks','2',NULL),(1519443,'Total input size in MB','817.0285835266113',NULL),(1519444,'Avg output records per task','1193587',NULL),(1519444,'Avg spilled records per task','0',NULL),(1519444,'Number of tasks','2',NULL),(1519444,'Ratio of spilled records to output records','0.0',NULL),(1519445,'Avg Physical Memory (MB)','742',NULL),(1519445,'Avg task runtime','1 min 19 sec',NULL),(1519445,'Avg Virtual Memory (MB)','2921',NULL),(1519445,'Max Physical Memory (MB)','807',NULL),(1519445,'Min Physical Memory (MB)','676',NULL),(1519445,'Number of tasks','2',NULL),(1519445,'Requested Container Memory','1 GB',NULL),(1519446,'Group A','0 tasks @ 0 bytes avg',NULL),(1519446,'Group B','0 tasks @ 0 bytes avg',NULL),(1519446,'Number of tasks','0',NULL),(1519447,'Avg task CPU time (ms)','0',NULL),(1519447,'Avg task GC time (ms)','0',NULL),(1519447,'Avg task runtime (ms)','0',NULL),(1519447,'Number of tasks','0',NULL),(1519447,'Task GC/CPU ratio','0.0',NULL),(1519448,'Average task runtime','0 sec',NULL),(1519448,'Max task runtime','0 sec',NULL),(1519448,'Min task runtime','0 sec',NULL),(1519448,'Number of tasks','0',NULL),(1519449,'Avg Physical Memory (MB)','0',NULL),(1519449,'Avg task runtime','0 sec',NULL),(1519449,'Avg Virtual Memory (MB)','0',NULL),(1519449,'Max Physical Memory (MB)','0',NULL),(1519449,'Min Physical Memory (MB)','0',NULL),(1519449,'Number of tasks','0',NULL),(1519449,'Requested Container Memory','1 GB',NULL),(1519450,'Average code runtime','0 sec',NULL),(1519450,'Average shuffle time','0 sec ',NULL),(1519450,'Average sort time','0 sec ',NULL),(1519450,'Number of tasks','0',NULL),(1519452,'Group A','1 tasks @ 271 MB avg',NULL),(1519452,'Group B','1 tasks @ 510 MB avg',NULL),(1519452,'Number of tasks','2',NULL),(1519453,'Avg task CPU time (ms)','56870',NULL),(1519453,'Avg task GC time (ms)','442',NULL),(1519453,'Avg task runtime (ms)','52394',NULL),(1519453,'Number of tasks','2',NULL),(1519453,'Task GC/CPU ratio','0.007772111834007386',NULL),(1519454,'Average task input size','391 MB',NULL),(1519454,'Average task runtime','52 sec',NULL),(1519454,'Max task runtime','1 min 5 sec',NULL),(1519454,'Min task runtime','39 sec',NULL),(1519454,'Number of tasks','2',NULL),(1519455,'Median task input size','391 MB',NULL),(1519455,'Median task runtime','52 sec',NULL),(1519455,'Median task speed','7 MB/s',NULL),(1519455,'Number of tasks','2',NULL),(1519455,'Total input size in MB','782.293210029602',NULL),(1519456,'Avg output records per task','1244941',NULL),(1519456,'Avg spilled records per task','0',NULL),(1519456,'Number of tasks','2',NULL),(1519456,'Ratio of spilled records to output records','0.0',NULL),(1519457,'Avg Physical Memory (MB)','623',NULL),(1519457,'Avg task runtime','52 sec',NULL),(1519457,'Avg Virtual Memory (MB)','2923',NULL),(1519457,'Max Physical Memory (MB)','624',NULL),(1519457,'Min Physical Memory (MB)','622',NULL),(1519457,'Number of tasks','2',NULL),(1519457,'Requested Container Memory','1 GB',NULL),(1519458,'Group A','0 tasks @ 0 bytes 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avg',NULL),(1519464,'Group B','100 tasks @ 488 MB avg',NULL),(1519464,'Number of tasks','149',NULL),(1519465,'Avg task CPU time (ms)','65602',NULL),(1519465,'Avg task GC time (ms)','717',NULL),(1519465,'Avg task runtime (ms)','56899',NULL),(1519465,'Number of tasks','149',NULL),(1519465,'Task GC/CPU ratio','0.010929544830950277',NULL),(1519466,'Average task input size','427 MB',NULL),(1519466,'Average task runtime','56 sec',NULL),(1519466,'Max task runtime','1 min 43 sec',NULL),(1519466,'Min task runtime','28 sec',NULL),(1519466,'Number of tasks','149',NULL),(1519467,'Median task input size','474 MB',NULL),(1519467,'Median task runtime','56 sec',NULL),(1519467,'Median task speed','7 MB/s',NULL),(1519467,'Number of tasks','149',NULL),(1519467,'Total input size in MB','63770.37501811981',NULL),(1519468,'Avg output records per task','1560839',NULL),(1519468,'Avg spilled records per task','0',NULL),(1519468,'Number of tasks','149',NULL),(1519468,'Ratio of spilled records to output 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sec',NULL),(1520066,'Min task runtime','18 sec',NULL),(1520066,'Number of tasks','512',NULL),(1520067,'Median task input size','948 MB',NULL),(1520067,'Median task runtime','1 min 22 sec',NULL),(1520067,'Median task speed','12 MB/s',NULL),(1520067,'Number of tasks','512',NULL),(1520067,'Total input size in MB','539270.6440467834',NULL),(1520068,'Avg output records per task','736811',NULL),(1520068,'Avg spilled records per task','235320',NULL),(1520068,'Number of tasks','512',NULL),(1520068,'Ratio of spilled records to output records','0.3193772050486382',NULL),(1520069,'Avg Physical Memory (MB)','892',NULL),(1520069,'Avg task runtime','1 min 27 sec',NULL),(1520069,'Avg Virtual Memory (MB)','2924',NULL),(1520069,'Max Physical Memory (MB)','1001',NULL),(1520069,'Min Physical Memory (MB)','864',NULL),(1520069,'Number of tasks','512',NULL),(1520069,'Requested Container Memory','1 GB',NULL),(1520070,'Group A','291 tasks @ 7 MB avg',NULL),(1520070,'Group B','275 tasks @ 7 MB 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avg',NULL),(1520076,'Group B','840 tasks @ 1 GB avg',NULL),(1520076,'Number of tasks','1774',NULL),(1520077,'Avg task CPU time (ms)','64423',NULL),(1520077,'Avg task GC time (ms)','1714',NULL),(1520077,'Avg task runtime (ms)','46509',NULL),(1520077,'Number of tasks','1774',NULL),(1520077,'Task GC/CPU ratio','0.026605404901976003',NULL),(1520078,'Average task input size','944 MB',NULL),(1520078,'Average task runtime','46 sec',NULL),(1520078,'Max task runtime','2 min 23 sec',NULL),(1520078,'Min task runtime','23 sec',NULL),(1520078,'Number of tasks','1774',NULL),(1520079,'Median task input size','926 MB',NULL),(1520079,'Median task runtime','43 sec',NULL),(1520079,'Median task speed','21 MB/s',NULL),(1520079,'Number of tasks','1774',NULL),(1520079,'Total input size in MB','1676399.0118570328',NULL),(1520080,'Avg output records per task','3497298',NULL),(1520080,'Avg spilled records per task','6994597',NULL),(1520080,'Number of tasks','1774',NULL),(1520080,'Ratio of spilled records to 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sec',NULL),(1520085,'Avg Virtual Memory (MB)','3297',NULL),(1520085,'Max Physical Memory (MB)','1792',NULL),(1520085,'Min Physical Memory (MB)','1389',NULL),(1520085,'Number of tasks','999',NULL),(1520085,'Requested Container Memory','1 GB',NULL),(1520086,'Average code runtime','29 sec',NULL),(1520086,'Average shuffle time','15 sec (0.54x)',NULL),(1520086,'Average sort time','6 sec (0.22x)',NULL),(1520086,'Number of tasks','999',NULL),(1536115,'Group A','0 tasks @ 0 bytes avg',NULL),(1536115,'Group B','1 tasks @ 515 MB avg',NULL),(1536115,'Number of tasks','1',NULL),(1536116,'Avg task CPU time (ms)','109330',NULL),(1536116,'Avg task GC time (ms)','6065',NULL),(1536116,'Avg task runtime (ms)','102946',NULL),(1536116,'Number of tasks','1',NULL),(1536116,'Task GC/CPU ratio','0.05547425226378853',NULL),(1536117,'Average task input size','515 MB',NULL),(1536117,'Average task runtime','1 min 42 sec',NULL),(1536117,'Max task runtime','1 min 42 sec',NULL),(1536117,'Min task runtime','1 min 42 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CPU time (ms)','2700',NULL),(1536122,'Avg task GC time (ms)','109',NULL),(1536122,'Avg task runtime (ms)','8496',NULL),(1536122,'Number of tasks','1',NULL),(1536122,'Task GC/CPU ratio','0.04037037037037037',NULL),(1536123,'Average task runtime','8 sec',NULL),(1536123,'Max task runtime','8 sec',NULL),(1536123,'Min task runtime','8 sec',NULL),(1536123,'Number of tasks','1',NULL),(1536124,'Avg Physical Memory (MB)','398',NULL),(1536124,'Avg task runtime','8 sec',NULL),(1536124,'Avg Virtual Memory (MB)','4100',NULL),(1536124,'Max Physical Memory (MB)','398',NULL),(1536124,'Min Physical Memory (MB)','398',NULL),(1536124,'Number of tasks','1',NULL),(1536124,'Requested Container Memory','3 GB',NULL),(1536125,'Average code runtime','',NULL),(1536125,'Average shuffle time','7 sec (9.49x)',NULL),(1536125,'Average sort time',' (0.05x)',NULL),(1536125,'Number of tasks','1',NULL),(1536163,'Group A','0 tasks @ 0 bytes avg',NULL),(1536163,'Group B','1 tasks @ 515 MB avg',NULL),(1536163,'Number of tasks','1',NULL),(1536164,'Avg task CPU time (ms)','132240',NULL),(1536164,'Avg task GC time (ms)','6714',NULL),(1536164,'Avg task runtime (ms)','186804',NULL),(1536164,'Number of tasks','1',NULL),(1536164,'Task GC/CPU ratio','0.050771324863883846',NULL),(1536165,'Average task input size','515 MB',NULL),(1536165,'Average task runtime','3 min 6 sec',NULL),(1536165,'Max task runtime','3 min 6 sec',NULL),(1536165,'Min task runtime','3 min 6 sec',NULL),(1536165,'Number of tasks','1',NULL),(1536166,'Median task input size','515 MB',NULL),(1536166,'Median task runtime','3 min 6 sec',NULL),(1536166,'Median task speed','2 MB/s',NULL),(1536166,'Number of tasks','1',NULL),(1536166,'Total input size in MB','515.5552177429199',NULL),(1536167,'Avg output records per task','977313',NULL),(1536167,'Avg spilled records per task','251',NULL),(1536167,'Number of tasks','1',NULL),(1536167,'Ratio of spilled records to output records','2.5682662565626365E-4',NULL),(1536168,'Avg Physical Memory (MB)','968',NULL),(1536168,'Avg task runtime','3 min 6 sec',NULL),(1536168,'Avg Virtual Memory (MB)','2345',NULL),(1536168,'Max Physical Memory (MB)','968',NULL),(1536168,'Min Physical Memory (MB)','968',NULL),(1536168,'Number of tasks','1',NULL),(1536168,'Requested Container Memory','4 GB',NULL),(1536169,'Group A','0 tasks @ 0 bytes avg',NULL),(1536169,'Group B','1 tasks @ 1 KB avg',NULL),(1536169,'Number of tasks','1',NULL),(1536170,'Avg task CPU time (ms)','2830',NULL),(1536170,'Avg task GC time (ms)','73',NULL),(1536170,'Avg task runtime (ms)','8188',NULL),(1536170,'Number of tasks','1',NULL),(1536170,'Task GC/CPU ratio','0.025795053003533568',NULL),(1536171,'Average task runtime','8 sec',NULL),(1536171,'Max task runtime','8 sec',NULL),(1536171,'Min task runtime','8 sec',NULL),(1536171,'Number of tasks','1',NULL),(1536172,'Avg Physical Memory (MB)','301',NULL),(1536172,'Avg task runtime','8 sec',NULL),(1536172,'Avg Virtual Memory (MB)','2338',NULL),(1536172,'Max Physical Memory (MB)','301',NULL),(1536172,'Min Physical Memory (MB)','301',NULL),(1536172,'Number of tasks','1',NULL),(1536172,'Requested Container Memory','4 GB',NULL),(1536173,'Average code runtime','',NULL),(1536173,'Average shuffle time','7 sec (9.03x)',NULL),(1536173,'Average sort time',' (0.04x)',NULL),(1536173,'Number of tasks','1',NULL); +INSERT INTO `yarn_app_heuristic_result_details`(yarn_app_heuristic_result_id,name,value,details) VALUES (1511830,'Group A','1 tasks @ 9 MB avg',NULL),(1511830,'Group B','2 tasks @ 513 MB avg',NULL),(1511830,'Number of tasks','3',NULL),(1511831,'Avg task CPU time (ms)','55903',NULL),(1511831,'Avg task GC time (ms)','316',NULL),(1511831,'Avg task runtime (ms)','69313',NULL),(1511831,'Number of tasks','3',NULL),(1511831,'Task GC/CPU ratio','0.005652648337298535',NULL),(1511832,'Average task input size','345 MB',NULL),(1511832,'Average task runtime','1 min 9 sec',NULL),(1511832,'Max task runtime','1 min 42 sec',NULL),(1511832,'Min task runtime','9 sec',NULL),(1511832,'Number of tasks','3',NULL),(1511833,'Median task input size','511 MB',NULL),(1511833,'Median task runtime','1 min 36 sec',NULL),(1511833,'Median task speed','5 MB/s',NULL),(1511833,'Number of tasks','3',NULL),(1511833,'Total input size in MB','1035.5147895812988',NULL),(1511834,'Avg output records per task','1094880',NULL),(1511834,'Avg spilled records per task','0',NULL),(1511834,'Number of tasks','3',NULL),(1511834,'Ratio of spilled records to output records','0.0',NULL),(1511835,'Avg Physical Memory (MB)','656',NULL),(1511835,'Avg task runtime','1 min 9 sec',NULL),(1511835,'Avg Virtual Memory (MB)','2354',NULL),(1511835,'Max Physical Memory (MB)','743',NULL),(1511835,'Min Physical Memory (MB)','500',NULL),(1511835,'Number of tasks','3',NULL),(1511835,'Requested Container Memory','2 GB',NULL),(1511836,'Group A','0 tasks @ 0 bytes avg',NULL),(1511836,'Group B','0 tasks @ 0 bytes avg',NULL),(1511836,'Number of tasks','0',NULL),(1511837,'Avg task CPU time (ms)','0',NULL),(1511837,'Avg task GC time (ms)','0',NULL),(1511837,'Avg task runtime (ms)','0',NULL),(1511837,'Number of tasks','0',NULL),(1511837,'Task GC/CPU ratio','0.0',NULL),(1511838,'Average task runtime','0 sec',NULL),(1511838,'Max task runtime','0 sec',NULL),(1511838,'Min task runtime','0 sec',NULL),(1511838,'Number of tasks','0',NULL),(1511839,'Avg Physical Memory (MB)','0',NULL),(1511839,'Avg task runtime','0 sec',NULL),(1511839,'Avg Virtual Memory (MB)','0',NULL),(1511839,'Max Physical Memory (MB)','0',NULL),(1511839,'Min Physical Memory (MB)','0',NULL),(1511839,'Number of tasks','0',NULL),(1511839,'Requested Container Memory','2 GB',NULL),(1511840,'Average code runtime','0 sec',NULL),(1511840,'Average shuffle time','0 sec ',NULL),(1511840,'Average sort time','0 sec ',NULL),(1511840,'Number of tasks','0',NULL),(1511842,'Group A','1 tasks @ 157 MB avg',NULL),(1511842,'Group B','2 tasks @ 504 MB avg',NULL),(1511842,'Number of tasks','3',NULL),(1511843,'Avg task CPU time (ms)','148426',NULL),(1511843,'Avg task GC time (ms)','3774',NULL),(1511843,'Avg task runtime (ms)','161024',NULL),(1511843,'Number of tasks','3',NULL),(1511843,'Task GC/CPU ratio','0.025426812014067615',NULL),(1511844,'Average task input size','388 MB',NULL),(1511844,'Average task runtime','2 min 41 sec',NULL),(1511844,'Max task runtime','3 min 22 sec',NULL),(1511844,'Min task runtime','1 min 25 sec',NULL),(1511844,'Number of tasks','3',NULL),(1511845,'Median task input size','500 MB',NULL),(1511845,'Median task runtime','3 min 14 sec',NULL),(1511845,'Median task speed','2 MB/s',NULL),(1511845,'Number of tasks','3',NULL),(1511845,'Total input size in MB','1165.6459951400757',NULL),(1511846,'Avg output records per task','1157494',NULL),(1511846,'Avg spilled records per task','0',NULL),(1511846,'Number of tasks','3',NULL),(1511846,'Ratio of spilled records to output records','0.0',NULL),(1511847,'Avg Physical Memory (MB)','904',NULL),(1511847,'Avg task runtime','2 min 41 sec',NULL),(1511847,'Avg Virtual Memory (MB)','2388',NULL),(1511847,'Max Physical Memory (MB)','1109',NULL),(1511847,'Min Physical Memory (MB)','747',NULL),(1511847,'Number of tasks','3',NULL),(1511847,'Requested Container Memory','2 GB',NULL),(1511848,'Group A','0 tasks @ 0 bytes avg',NULL),(1511848,'Group B','0 tasks @ 0 bytes avg',NULL),(1511848,'Number of tasks','0',NULL),(1511849,'Avg task CPU time (ms)','0',NULL),(1511849,'Avg task GC time (ms)','0',NULL),(1511849,'Avg task runtime (ms)','0',NULL),(1511849,'Number of tasks','0',NULL),(1511849,'Task GC/CPU ratio','0.0',NULL),(1511850,'Average task runtime','0 sec',NULL),(1511850,'Max task runtime','0 sec',NULL),(1511850,'Min task runtime','0 sec',NULL),(1511850,'Number of tasks','0',NULL),(1511851,'Avg Physical Memory (MB)','0',NULL),(1511851,'Avg task runtime','0 sec',NULL),(1511851,'Avg Virtual Memory (MB)','0',NULL),(1511851,'Max Physical Memory (MB)','0',NULL),(1511851,'Min Physical Memory (MB)','0',NULL),(1511851,'Number of tasks','0',NULL),(1511851,'Requested Container Memory','2 GB',NULL),(1511852,'Average code runtime','0 sec',NULL),(1511852,'Average shuffle time','0 sec ',NULL),(1511852,'Average sort time','0 sec ',NULL),(1511852,'Number of tasks','0',NULL),(1511866,'Group A','105 tasks @ 302 MB avg',NULL),(1511866,'Group B','99 tasks @ 471 MB avg',NULL),(1511866,'Number of tasks','204',NULL),(1511867,'Avg task CPU time (ms)','69552',NULL),(1511867,'Avg task GC time (ms)','431',NULL),(1511867,'Avg task runtime (ms)','81565',NULL),(1511867,'Number of tasks','204',NULL),(1511867,'Task GC/CPU ratio','0.006196802392454567',NULL),(1511868,'Average task input size','384 MB',NULL),(1511868,'Average task runtime','1 min 21 sec',NULL),(1511868,'Max task runtime','2 min 41 sec',NULL),(1511868,'Min task runtime','41 sec',NULL),(1511868,'Number of tasks','204',NULL),(1511869,'Median task input size','377 MB',NULL),(1511869,'Median task runtime','1 min 19 sec',NULL),(1511869,'Median task speed','4 MB/s',NULL),(1511869,'Number of tasks','204',NULL),(1511869,'Total input size in MB','78404.13675880432',NULL),(1511870,'Avg output records per task','1411730',NULL),(1511870,'Avg spilled records per task','0',NULL),(1511870,'Number of tasks','204',NULL),(1511870,'Ratio of spilled records to output records','0.0',NULL),(1511871,'Avg Physical Memory (MB)','722',NULL),(1511871,'Avg task runtime','1 min 21 sec',NULL),(1511871,'Avg Virtual Memory (MB)','2358',NULL),(1511871,'Max Physical Memory (MB)','768',NULL),(1511871,'Min Physical Memory (MB)','669',NULL),(1511871,'Number of tasks','204',NULL),(1511871,'Requested Container Memory','2 GB',NULL),(1511872,'Group A','0 tasks @ 0 bytes avg',NULL),(1511872,'Group B','0 tasks @ 0 bytes avg',NULL),(1511872,'Number of tasks','0',NULL),(1511873,'Avg task CPU time (ms)','0',NULL),(1511873,'Avg task GC time (ms)','0',NULL),(1511873,'Avg task runtime (ms)','0',NULL),(1511873,'Number of tasks','0',NULL),(1511873,'Task GC/CPU 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tasks','603',NULL),(1511879,'Task GC/CPU ratio','0.010255996123718001',NULL),(1511880,'Average task input size','358 MB',NULL),(1511880,'Average task runtime','1 min 24 sec',NULL),(1511880,'Max task runtime','2 min 53 sec',NULL),(1511880,'Min task runtime','39 sec',NULL),(1511880,'Number of tasks','603',NULL),(1511881,'Median task input size','336 MB',NULL),(1511881,'Median task runtime','1 min 21 sec',NULL),(1511881,'Median task speed','4 MB/s',NULL),(1511881,'Number of tasks','603',NULL),(1511881,'Total input size in MB','216403.73294448853',NULL),(1511882,'Avg output records per task','327606',NULL),(1511882,'Avg spilled records per task','0',NULL),(1511882,'Number of tasks','603',NULL),(1511882,'Ratio of spilled records to output records','0.0',NULL),(1511883,'Avg Physical Memory (MB)','714',NULL),(1511883,'Avg task runtime','1 min 24 sec',NULL),(1511883,'Avg Virtual Memory (MB)','2352',NULL),(1511883,'Max Physical Memory (MB)','854',NULL),(1511883,'Min Physical Memory (MB)','650',NULL),(1511883,'Number of tasks','603',NULL),(1511883,'Requested Container Memory','2 GB',NULL),(1511884,'Group A','0 tasks @ 0 bytes avg',NULL),(1511884,'Group B','0 tasks @ 0 bytes avg',NULL),(1511884,'Number of tasks','0',NULL),(1511885,'Avg task CPU time (ms)','0',NULL),(1511885,'Avg task GC time (ms)','0',NULL),(1511885,'Avg task runtime (ms)','0',NULL),(1511885,'Number of tasks','0',NULL),(1511885,'Task GC/CPU ratio','0.0',NULL),(1511886,'Average task runtime','0 sec',NULL),(1511886,'Max task runtime','0 sec',NULL),(1511886,'Min task runtime','0 sec',NULL),(1511886,'Number of tasks','0',NULL),(1511887,'Avg Physical Memory (MB)','0',NULL),(1511887,'Avg task runtime','0 sec',NULL),(1511887,'Avg Virtual Memory (MB)','0',NULL),(1511887,'Max Physical Memory (MB)','0',NULL),(1511887,'Min Physical Memory (MB)','0',NULL),(1511887,'Number of tasks','0',NULL),(1511887,'Requested Container Memory','2 GB',NULL),(1511888,'Average code runtime','0 sec',NULL),(1511888,'Average shuffle time','0 sec ',NULL),(1511888,'Average sort time','0 sec ',NULL),(1511888,'Number of tasks','0',NULL),(1511902,'Group A','959 tasks @ 318 MB avg',NULL),(1511902,'Group B','986 tasks @ 467 MB avg',NULL),(1511902,'Number of tasks','1945',NULL),(1511903,'Avg task CPU time (ms)','76812',NULL),(1511903,'Avg task GC time (ms)','876',NULL),(1511903,'Avg task runtime (ms)','88276',NULL),(1511903,'Number of tasks','1945',NULL),(1511903,'Task GC/CPU ratio','0.011404468051866895',NULL),(1511904,'Average task input size','393 MB',NULL),(1511904,'Average task runtime','1 min 28 sec',NULL),(1511904,'Max task runtime','4 min 28 sec',NULL),(1511904,'Min task runtime','34 sec',NULL),(1511904,'Number of tasks','1945',NULL),(1511905,'Median task input size','396 MB',NULL),(1511905,'Median task runtime','1 min 24 sec',NULL),(1511905,'Median task speed','4 MB/s',NULL),(1511905,'Number of tasks','1945',NULL),(1511905,'Total input size in MB','766235.623585701',NULL),(1511906,'Avg output records per 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