org.apache.flink.runtime.healthmanager.plugins.detectors.LargeTimerCountDetector Maven / Gradle / Ivy
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package org.apache.flink.runtime.healthmanager.plugins.detectors;
import org.apache.flink.api.common.JobID;
import org.apache.flink.configuration.ConfigOption;
import org.apache.flink.configuration.ConfigOptions;
import org.apache.flink.runtime.healthmanager.HealthMonitor;
import org.apache.flink.runtime.healthmanager.metrics.MetricAggType;
import org.apache.flink.runtime.healthmanager.metrics.MetricProvider;
import org.apache.flink.runtime.healthmanager.metrics.TaskMetricSubscription;
import org.apache.flink.runtime.healthmanager.metrics.timeline.TimelineAggType;
import org.apache.flink.runtime.healthmanager.plugins.Detector;
import org.apache.flink.runtime.healthmanager.plugins.Symptom;
import org.apache.flink.runtime.healthmanager.plugins.symptoms.JobVertexLargeTimerCount;
import org.apache.flink.runtime.healthmanager.plugins.utils.MetricUtils;
import org.apache.flink.runtime.jobgraph.JobVertexID;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.apache.flink.runtime.healthmanager.plugins.utils.MetricNames.TASK_TIMER_COUNT;
/**
* The LargeTimerCountDetector detects the timer count of a job.
* Detects {@link JobVertexLargeTimerCount} if the max avg timer count
* of tasks of the same vertex is higher then the threshold.
* Generally, this detector is used to detect heap memory usage of window operator.
*/
public class LargeTimerCountDetector implements Detector {
private static final Logger LOGGER = LoggerFactory.getLogger(LargeTimerCountDetector.class);
public static final ConfigOption LARGE_TIMER_COUNT_THRESHOLD =
ConfigOptions.key("healthmonitor.timer-count.threshold").defaultValue(6000000L);
private JobID jobID;
private MetricProvider metricProvider;
private HealthMonitor monitor;
private final long timerCountCheckInterval = 1;
private long largeTimerCountThreshold;
private Map timerCountSubs;
@Override
public void open(HealthMonitor monitor) {
jobID = monitor.getJobID();
metricProvider = monitor.getMetricProvider();
this.monitor = monitor;
//timerCountCheckInterval = monitor.getConfig().getLong(HealthMonitorOptions.PARALLELISM_SCALE_INTERVAL);
largeTimerCountThreshold = monitor.getConfig().getLong(LARGE_TIMER_COUNT_THRESHOLD);
timerCountSubs = new HashMap<>();
for (JobVertexID vertexId : monitor.getJobConfig().getVertexConfigs().keySet()) {
TaskMetricSubscription timerCountSub = metricProvider.subscribeTaskMetric(
jobID, vertexId, TASK_TIMER_COUNT, MetricAggType.MAX, timerCountCheckInterval, TimelineAggType.LATEST);
timerCountSubs.put(vertexId, timerCountSub);
}
}
@Override
public void close() {
if (metricProvider != null && timerCountSubs != null) {
for (TaskMetricSubscription timerCountSub : timerCountSubs.values()) {
if (timerCountSub != null) {
metricProvider.unsubscribe(timerCountSub);
}
}
}
}
@Override
public Symptom detect() throws Exception {
LOGGER.debug("Start detecting.");
List jobVertexIDs = new ArrayList<>();
for (JobVertexID vertexId : timerCountSubs.keySet()) {
TaskMetricSubscription timerCountSub = timerCountSubs.get(vertexId);
if (!MetricUtils.validateTmMetric(
monitor, timerCountCheckInterval, timerCountSub.getValue())) {
LOGGER.debug("Skip vertex {} metric {}, metrics missing.", vertexId, timerCountSub.getValue());
continue;
}
LOGGER.debug("Vertex {} timer count {}", vertexId, timerCountSub.getValue());
if (timerCountSub.getValue().f1 > largeTimerCountThreshold) {
jobVertexIDs.add(vertexId);
}
}
if (!jobVertexIDs.isEmpty()) {
LOGGER.info("Large timer count detected for vertices {}.", jobVertexIDs);
return new JobVertexLargeTimerCount(jobID, jobVertexIDs);
}
return null;
}
}