gyfora commented on code in PR #502: URL: https://github.com/apache/flink-kubernetes-operator/pull/502#discussion_r1066572069
########## flink-kubernetes-operator/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/JobVertexScaler.java: ########## @@ -0,0 +1,264 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.kubernetes.operator.autoscaler; + +import org.apache.flink.annotation.VisibleForTesting; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.kubernetes.operator.autoscaler.config.AutoScalerOptions; +import org.apache.flink.kubernetes.operator.autoscaler.metrics.EvaluatedScalingMetric; +import org.apache.flink.kubernetes.operator.autoscaler.metrics.ScalingMetric; +import org.apache.flink.kubernetes.operator.autoscaler.utils.AutoScalerUtils; +import org.apache.flink.runtime.jobgraph.JobVertexID; +import org.apache.flink.util.Preconditions; + +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import java.time.Clock; +import java.time.Instant; +import java.time.ZoneId; +import java.util.Map; +import java.util.SortedMap; + +import static org.apache.flink.kubernetes.operator.autoscaler.config.AutoScalerOptions.MAX_SCALE_DOWN_FACTOR; +import static org.apache.flink.kubernetes.operator.autoscaler.config.AutoScalerOptions.SCALE_UP_GRACE_PERIOD; +import static org.apache.flink.kubernetes.operator.autoscaler.config.AutoScalerOptions.TARGET_UTILIZATION; +import static org.apache.flink.kubernetes.operator.autoscaler.config.AutoScalerOptions.VERTEX_MAX_PARALLELISM; +import static org.apache.flink.kubernetes.operator.autoscaler.config.AutoScalerOptions.VERTEX_MIN_PARALLELISM; +import static org.apache.flink.kubernetes.operator.autoscaler.metrics.ScalingMetric.EXPECTED_PROCESSING_RATE; +import static org.apache.flink.kubernetes.operator.autoscaler.metrics.ScalingMetric.MAX_PARALLELISM; +import static org.apache.flink.kubernetes.operator.autoscaler.metrics.ScalingMetric.PARALLELISM; +import static org.apache.flink.kubernetes.operator.autoscaler.metrics.ScalingMetric.TRUE_PROCESSING_RATE; + +/** Component responsible for computing vertex parallelism based on the scaling metrics. */ +public class JobVertexScaler { + + private static final Logger LOG = LoggerFactory.getLogger(JobVertexScaler.class); + + private Clock clock = Clock.system(ZoneId.systemDefault()); + + public int computeScaleTargetParallelism( + Configuration conf, + JobVertexID vertex, + Map<ScalingMetric, EvaluatedScalingMetric> evaluatedMetrics, + SortedMap<Instant, ScalingSummary> history) { + + var currentParallelism = (int) evaluatedMetrics.get(PARALLELISM).getCurrent(); + double averageTrueProcessingRate = evaluatedMetrics.get(TRUE_PROCESSING_RATE).getAverage(); + if (Double.isNaN(averageTrueProcessingRate)) { + LOG.info( + "True processing rate is not available for {}, cannot compute new parallelism", + vertex); + return currentParallelism; + } + + double targetCapacity = + AutoScalerUtils.getTargetProcessingCapacity( + evaluatedMetrics, conf, conf.get(TARGET_UTILIZATION), true); + if (Double.isNaN(targetCapacity)) { + LOG.info( + "Target data rate is not available for {}, cannot compute new parallelism", + vertex); + return currentParallelism; + } + + LOG.info("Target processing capacity for {} is {}", vertex, targetCapacity); + double scaleFactor = targetCapacity / averageTrueProcessingRate; + double minScaleFactor = 1 - conf.get(MAX_SCALE_DOWN_FACTOR); + if (scaleFactor < minScaleFactor) { + LOG.info( + "Computed scale factor of {} for {} is capped by maximum scale down factor to {}", + scaleFactor, + vertex, + minScaleFactor); + scaleFactor = minScaleFactor; + } + + int newParallelism = + scale( + currentParallelism, + (int) evaluatedMetrics.get(MAX_PARALLELISM).getCurrent(), + scaleFactor, + conf.getInteger(VERTEX_MIN_PARALLELISM), + conf.getInteger(VERTEX_MAX_PARALLELISM)); + + if (newParallelism == currentParallelism + || blockScalingBasedOnPastActions( + vertex, + conf, + evaluatedMetrics, + history, + currentParallelism, + newParallelism)) { + return currentParallelism; + } + + // We record our expectations for this scaling operation + evaluatedMetrics.put( + ScalingMetric.EXPECTED_PROCESSING_RATE, EvaluatedScalingMetric.of(targetCapacity)); + return newParallelism; + } + + private boolean blockScalingBasedOnPastActions( + JobVertexID vertex, + Configuration conf, + Map<ScalingMetric, EvaluatedScalingMetric> evaluatedMetrics, + SortedMap<Instant, ScalingSummary> history, + int currentParallelism, + int newParallelism) { + + // If we don't have past scaling actions for this vertex, there is nothing to do + if (history.isEmpty()) { + return false; + } + + boolean scaleUp = currentParallelism < newParallelism; + + return detectImmediateScaleDownAfterScaleUp(vertex, conf, history, scaleUp) + || detectIneffectiveScaleup(vertex, conf, evaluatedMetrics, history, scaleUp); + } + + private boolean detectImmediateScaleDownAfterScaleUp( + JobVertexID vertex, + Configuration conf, + SortedMap<Instant, ScalingSummary> history, + boolean scaleUp) { + + // Does not apply to scale up operations + if (scaleUp) { + return false; + } + + var lastScalingTs = history.lastKey(); + var lastSummary = history.get(lastScalingTs); Review Comment: We have this check earlier before any history based logic on `JobVertexScaler` line 125 -- This is an automated message from the Apache Git Service. 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