Examples with OperationProgress com.linkedin.kafka.cruisecontrol.async.progress.OperationProgress used on opensource projects

Example 11 with OperationProgress

use of com.linkedin.kafka.cruisecontrol.async.progress.OperationProgress in project cruise-control by linkedin.

the class LoadMonitorTest method testStateWithOnlyActiveSnapshotWindow.

@Test
public void testStateWithOnlyActiveSnapshotWindow() {
    TestContext context = prepareContext();
    LoadMonitor loadMonitor = context.loadmonitor();
    KafkaMetricSampleAggregator aggregator = context.aggregator();
    // populate the metrics aggregator.
    // four samples for each partition
    CruiseControlUnitTestUtils.populateSampleAggregator(1, 4, aggregator, PE_T0P0, 0, WINDOW_MS, METRIC_DEF);
    CruiseControlUnitTestUtils.populateSampleAggregator(1, 4, aggregator, PE_T0P1, 0, WINDOW_MS, METRIC_DEF);
    CruiseControlUnitTestUtils.populateSampleAggregator(1, 4, aggregator, PE_T1P0, 0, WINDOW_MS, METRIC_DEF);
    CruiseControlUnitTestUtils.populateSampleAggregator(1, 4, aggregator, PE_T1P1, 0, WINDOW_MS, METRIC_DEF);
    LoadMonitorState state = loadMonitor.state(new OperationProgress());
    // The load monitor only has an active window. There is no stable window.
    assertEquals(0, state.numValidPartitions());
    assertEquals(0, state.numValidWindows());
    assertTrue(state.monitoredWindows().isEmpty());
}

Example 12 with OperationProgress

use of com.linkedin.kafka.cruisecontrol.async.progress.OperationProgress in project cruise-control by linkedin.

the class OfflineProposalGenerator method main.

public static void main(String[] argv) throws Exception {
    // TODO: probably need to save this in the original model file
    Properties props = KafkaCruiseControlUnitTestUtils.getKafkaCruiseControlProperties();
    KafkaCruiseControlConfig config = new KafkaCruiseControlConfig(props);
    ModelUtils.init(config);
    ModelParameters.init(config);
    BalancingConstraint balancingConstraint = new BalancingConstraint(config);
    long start = System.currentTimeMillis();
    ClusterModel clusterModel = clusterModelFromFile(argv[0]);
    long end = System.currentTimeMillis();
    double duration = (end - start) / 1000.0;
    System.out.println("Model loaded in " + duration + "s.");
    ClusterModelStats origStats = clusterModel.getClusterStats(balancingConstraint);
    String loadBeforeOptimization = clusterModel.brokerStats().toString();
    // Instantiate the components.
    GoalOptimizer goalOptimizer = new GoalOptimizer(config, null, new SystemTime(), new MetricRegistry());
    start = System.currentTimeMillis();
    GoalOptimizer.OptimizerResult optimizerResult = goalOptimizer.optimizations(clusterModel, new OperationProgress());
    end = System.currentTimeMillis();
    duration = (end - start) / 1000.0;
    String loadAfterOptimization = clusterModel.brokerStats().toString();
    System.out.println("Optimize goals in " + duration + "s.");
    System.out.println(optimizerResult.goalProposals().size());
    System.out.println(loadBeforeOptimization);
    System.out.println(loadAfterOptimization);
    ClusterModelStats optimizedStats = clusterModel.getClusterStats(balancingConstraint);
    double[] testStatistics = AnalyzerUtils.testDifference(origStats.utilizationMatrix(), optimizedStats.utilizationMatrix());
    System.out.println(Arrays.stream(RawAndDerivedResource.values()).map(x -> x.toString()).collect(Collectors.joining(", ")));
    System.out.println(Arrays.stream(testStatistics).boxed().map(pValue -> Double.toString(pValue)).collect(Collectors.joining(", ")));
}

Example 13 with OperationProgress

use of com.linkedin.kafka.cruisecontrol.async.progress.OperationProgress in project cruise-control by linkedin.

the class OptimizationVerifier method executeGoalsFor.

/**
 * Execute given goals in the given cluster enforcing the given constraint. Return pass / fail status of a test.
 * A test fails if:
 * 1) Rebalance: During the optimization process, optimization of a goal leads to a worse cluster state (in terms of
 * the requirements of the same goal) than the cluster state just before starting the optimization.
 * 2) Self Healing: There are replicas on dead brokers after self healing.
 * 3) Adding a new broker causes the replicas to move among old brokers.
 *
 * @param constraint         Balancing constraint for the given cluster.
 * @param clusterModel       The state of the cluster.
 * @param goalNameByPriority Name of goals by the order of execution priority.
 * @param excludedTopics     The excluded topics.
 * @param verifications      The verifications to make after the optimization.
 * @return Pass / fail status of a test.
 */
static boolean executeGoalsFor(BalancingConstraint constraint, ClusterModel clusterModel, Map<Integer, String> goalNameByPriority, Collection<String> excludedTopics, List<Verification> verifications) throws Exception {
    // Get the initial stats from the cluster.
    ClusterModelStats preOptimizedStats = clusterModel.getClusterStats(constraint);
    // Set goals by their priority.
    SortedMap<Integer, Goal> goalByPriority = new TreeMap<>();
    for (Map.Entry<Integer, String> goalEntry : goalNameByPriority.entrySet()) {
        Integer priority = goalEntry.getKey();
        String goalClassName = goalEntry.getValue();
        Class<? extends Goal> goalClass = (Class<? extends Goal>) Class.forName(goalClassName);
        try {
            Constructor<? extends Goal> constructor = goalClass.getDeclaredConstructor(BalancingConstraint.class);
            constructor.setAccessible(true);
            goalByPriority.put(priority, constructor.newInstance(constraint));
        } catch (NoSuchMethodException badConstructor) {
            // Try default constructor
            goalByPriority.put(priority, goalClass.newInstance());
        }
    }
    // Generate the goalOptimizer and optimize given goals.
    long startTime = System.currentTimeMillis();
    Properties props = KafkaCruiseControlUnitTestUtils.getKafkaCruiseControlProperties();
    StringJoiner stringJoiner = new StringJoiner(",");
    excludedTopics.forEach(stringJoiner::add);
    props.setProperty(KafkaCruiseControlConfig.TOPICS_EXCLUDED_FROM_PARTITION_MOVEMENT_CONFIG, stringJoiner.toString());
    GoalOptimizer goalOptimizer = new GoalOptimizer(new KafkaCruiseControlConfig(constraint.setProps(props)), null, new SystemTime(), new MetricRegistry());
    GoalOptimizer.OptimizerResult optimizerResult = goalOptimizer.optimizations(clusterModel, goalByPriority, new OperationProgress());
    LOG.trace("Took {} ms to execute {} to generate {} proposals.", System.currentTimeMillis() - startTime, goalByPriority, optimizerResult.goalProposals().size());
    for (Verification verification : verifications) {
        switch(verification) {
            case GOAL_VIOLATION:
                if (!verifyGoalViolations(optimizerResult)) {
                    return false;
                }
                break;
            case NEW_BROKERS:
                if (!clusterModel.newBrokers().isEmpty() && !verifyNewBrokers(clusterModel, constraint)) {
                    return false;
                }
                break;
            case DEAD_BROKERS:
                if (!clusterModel.deadBrokers().isEmpty() && !verifyDeadBrokers(clusterModel)) {
                    return false;
                }
                break;
            case REGRESSION:
                if (clusterModel.selfHealingEligibleReplicas().isEmpty() && !verifyRegression(optimizerResult, preOptimizedStats)) {
                    return false;
                }
                break;
            default:
                throw new IllegalStateException("Invalid verification " + verification);
        }
    }
    return true;
}

Example 14 with OperationProgress

use of com.linkedin.kafka.cruisecontrol.async.progress.OperationProgress in project cruise-control by linkedin.

the class KafkaMetricSampleAggregatorTest method testNotEnoughSnapshots.

@Test
public void testNotEnoughSnapshots() {
    KafkaCruiseControlConfig config = new KafkaCruiseControlConfig(getLoadMonitorProperties());
    Metadata metadata = getMetadata(Collections.singleton(TP));
    KafkaMetricSampleAggregator metricSampleAggregator = new KafkaMetricSampleAggregator(config, metadata);
    populateSampleAggregator(NUM_WINDOWS + 1, MIN_SAMPLES_PER_WINDOW, metricSampleAggregator);
    try {
        // Only 4 snapshots has smaller timestamp than the timestamp we passed in.
        ModelCompletenessRequirements requirements = new ModelCompletenessRequirements(NUM_WINDOWS, 0.0, false);
        metricSampleAggregator.aggregate(clusterAndGeneration(metadata.fetch()), -1L, (NUM_WINDOWS - 1) * WINDOW_MS - 1, requirements, new OperationProgress());
        fail("Should throw NotEnoughValidWindowsException");
    } catch (NotEnoughValidWindowsException nse) {
    // let it go
    }
}

Example 15 with OperationProgress

use of com.linkedin.kafka.cruisecontrol.async.progress.OperationProgress in project cruise-control by linkedin.

the class KafkaMetricSampleAggregatorTest method testExcludeInvalidMetricSample.

@Test
public void testExcludeInvalidMetricSample() throws NotEnoughValidWindowsException {
    KafkaCruiseControlConfig config = new KafkaCruiseControlConfig(getLoadMonitorProperties());
    Metadata metadata = getMetadata(Collections.singleton(TP));
    KafkaMetricSampleAggregator metricSampleAggregator = new KafkaMetricSampleAggregator(config, metadata);
    MetricDef metricDef = KafkaCruiseControlMetricDef.metricDef();
    populateSampleAggregator(NUM_WINDOWS + 1, MIN_SAMPLES_PER_WINDOW, metricSampleAggregator);
    // Set the leader to be node 1, which is different from the leader in the metadata.
    PartitionMetricSample sampleWithDifferentLeader = new PartitionMetricSample(1, TP);
    sampleWithDifferentLeader.record(metricDef.metricInfo(DISK_USAGE.name()), 10000);
    sampleWithDifferentLeader.record(metricDef.metricInfo(CPU_USAGE.name()), 10000);
    sampleWithDifferentLeader.record(metricDef.metricInfo(LEADER_BYTES_IN.name()), 10000);
    sampleWithDifferentLeader.record(metricDef.metricInfo(LEADER_BYTES_OUT.name()), 10000);
    sampleWithDifferentLeader.close(0);
    // Only populate the CPU metric
    PartitionMetricSample incompletePartitionMetricSample = new PartitionMetricSample(0, TP);
    incompletePartitionMetricSample.record(metricDef.metricInfo(CPU_USAGE.name()), 10000);
    incompletePartitionMetricSample.close(0);
    metricSampleAggregator.addSample(sampleWithDifferentLeader);
    metricSampleAggregator.addSample(incompletePartitionMetricSample);
    // Check the snapshot value and make sure the metric samples above are excluded.
    Map<PartitionEntity, ValuesAndExtrapolations> snapshotsForPartition = metricSampleAggregator.aggregate(clusterAndGeneration(metadata.fetch()), NUM_WINDOWS * WINDOW_MS, new OperationProgress()).valuesAndExtrapolations();
    ValuesAndExtrapolations snapshots = snapshotsForPartition.get(PE);
    for (Resource resource : Resource.values()) {
        int metricId = KafkaCruiseControlMetricDef.resourceToMetricId(resource);
        double expectedValue = resource == Resource.DISK ? MIN_SAMPLES_PER_WINDOW - 1 : (MIN_SAMPLES_PER_WINDOW - 1) / 2.0;
        assertEquals("The utilization for " + resource + " should be " + expectedValue, expectedValue, snapshots.metricValues().valuesFor(metricId).get(NUM_WINDOWS - 1), 0);
    }
}