Examples with IntervalTimeConfiguration org.opensearch.ad.model.IntervalTimeConfiguration used on opensource projects

Example 11 with IntervalTimeConfiguration

use of org.opensearch.ad.model.IntervalTimeConfiguration in project anomaly-detection by opensearch-project.

the class EntityColdStarterTests method setUp.

@SuppressWarnings("unchecked")
@Override
public void setUp() throws Exception {
    super.setUp();
    numMinSamples = AnomalyDetectorSettings.NUM_MIN_SAMPLES;
    clock = mock(Clock.class);
    when(clock.instant()).thenReturn(Instant.now());
    threadPool = mock(ThreadPool.class);
    setUpADThreadPool(threadPool);
    settings = Settings.EMPTY;
    Client client = mock(Client.class);
    clientUtil = mock(ClientUtil.class);
    detector = TestHelpers.AnomalyDetectorBuilder.newInstance().setDetectionInterval(new IntervalTimeConfiguration(1, ChronoUnit.MINUTES)).setCategoryFields(ImmutableList.of(randomAlphaOfLength(5))).build();
    job = TestHelpers.randomAnomalyDetectorJob(true, Instant.ofEpochMilli(1602401500000L), null);
    doAnswer(invocation -> {
        GetRequest request = invocation.getArgument(0);
        ActionListener<GetResponse> listener = invocation.getArgument(2);
        if (request.index().equals(AnomalyDetectorJob.ANOMALY_DETECTOR_JOB_INDEX)) {
            listener.onResponse(TestHelpers.createGetResponse(job, detectorId, AnomalyDetectorJob.ANOMALY_DETECTOR_JOB_INDEX));
        } else {
            listener.onResponse(TestHelpers.createGetResponse(detector, detectorId, AnomalyDetector.ANOMALY_DETECTORS_INDEX));
        }
        return null;
    }).when(clientUtil).asyncRequest(any(GetRequest.class), any(), any(ActionListener.class));
    Set<Setting<?>> nodestateSetting = new HashSet<>(ClusterSettings.BUILT_IN_CLUSTER_SETTINGS);
    nodestateSetting.add(MAX_RETRY_FOR_UNRESPONSIVE_NODE);
    nodestateSetting.add(BACKOFF_MINUTES);
    ClusterSettings clusterSettings = new ClusterSettings(Settings.EMPTY, nodestateSetting);
    DiscoveryNode discoveryNode = new DiscoveryNode("node1", OpenSearchTestCase.buildNewFakeTransportAddress(), Collections.emptyMap(), DiscoveryNodeRole.BUILT_IN_ROLES, Version.CURRENT);
    ClusterService clusterService = ClusterServiceUtils.createClusterService(threadPool, discoveryNode, clusterSettings);
    stateManager = new NodeStateManager(client, xContentRegistry(), settings, clientUtil, clock, AnomalyDetectorSettings.HOURLY_MAINTENANCE, clusterService);
    SingleFeatureLinearUniformInterpolator singleFeatureLinearUniformInterpolator = new IntegerSensitiveSingleFeatureLinearUniformInterpolator();
    interpolator = new LinearUniformInterpolator(singleFeatureLinearUniformInterpolator);
    searchFeatureDao = mock(SearchFeatureDao.class);
    checkpoint = mock(CheckpointDao.class);
    featureManager = new FeatureManager(searchFeatureDao, interpolator, clock, AnomalyDetectorSettings.MAX_TRAIN_SAMPLE, AnomalyDetectorSettings.MAX_SAMPLE_STRIDE, AnomalyDetectorSettings.TRAIN_SAMPLE_TIME_RANGE_IN_HOURS, AnomalyDetectorSettings.MIN_TRAIN_SAMPLES, AnomalyDetectorSettings.MAX_SHINGLE_PROPORTION_MISSING, AnomalyDetectorSettings.MAX_IMPUTATION_NEIGHBOR_DISTANCE, AnomalyDetectorSettings.PREVIEW_SAMPLE_RATE, AnomalyDetectorSettings.MAX_PREVIEW_SAMPLES, AnomalyDetectorSettings.HOURLY_MAINTENANCE, threadPool, AnomalyDetectorPlugin.AD_THREAD_POOL_NAME);
    checkpointWriteQueue = mock(CheckpointWriteWorker.class);
    rcfSeed = 2051L;
    entityColdStarter = new EntityColdStarter(clock, threadPool, stateManager, AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE, AnomalyDetectorSettings.NUM_TREES, AnomalyDetectorSettings.TIME_DECAY, numMinSamples, AnomalyDetectorSettings.MAX_SAMPLE_STRIDE, AnomalyDetectorSettings.MAX_TRAIN_SAMPLE, interpolator, searchFeatureDao, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, featureManager, settings, AnomalyDetectorSettings.HOURLY_MAINTENANCE, checkpointWriteQueue, rcfSeed, AnomalyDetectorSettings.MAX_COLD_START_ROUNDS);
    detectorId = "123";
    modelId = "123_entity_abc";
    entityName = "abc";
    priority = 0.3f;
    entity = Entity.createSingleAttributeEntity("field", entityName);
    released = new AtomicBoolean();
    inProgressLatch = new CountDownLatch(1);
    releaseSemaphore = () -> {
        released.set(true);
        inProgressLatch.countDown();
    };
    listener = ActionListener.wrap(releaseSemaphore);
    modelManager = new ModelManager(mock(CheckpointDao.class), mock(Clock.class), AnomalyDetectorSettings.NUM_TREES, AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE, AnomalyDetectorSettings.TIME_DECAY, AnomalyDetectorSettings.NUM_MIN_SAMPLES, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, AnomalyDetectorSettings.MIN_PREVIEW_SIZE, AnomalyDetectorSettings.HOURLY_MAINTENANCE, AnomalyDetectorSettings.HOURLY_MAINTENANCE, entityColdStarter, mock(FeatureManager.class), mock(MemoryTracker.class));
}

Example 12 with IntervalTimeConfiguration

use of org.opensearch.ad.model.IntervalTimeConfiguration in project anomaly-detection by opensearch-project.

the class ADBatchTaskRunner method getDateRangeOfSourceData.

private void getDateRangeOfSourceData(ADTask adTask, BiConsumer<Long, Long> consumer, ActionListener<String> internalListener) {
    String taskId = adTask.getTaskId();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder().aggregation(AggregationBuilders.min(AGG_NAME_MIN_TIME).field(adTask.getDetector().getTimeField())).aggregation(AggregationBuilders.max(AGG_NAME_MAX_TIME).field(adTask.getDetector().getTimeField())).size(0);
    if (adTask.getEntity() != null && adTask.getEntity().getAttributes().size() > 0) {
        BoolQueryBuilder query = new BoolQueryBuilder();
        adTask.getEntity().getAttributes().entrySet().forEach(entity -> query.filter(new TermQueryBuilder(entity.getKey(), entity.getValue())));
        searchSourceBuilder.query(query);
    }
    SearchRequest request = new SearchRequest().indices(adTask.getDetector().getIndices().toArray(new String[0])).source(searchSourceBuilder);
    client.search(request, ActionListener.wrap(r -> {
        InternalMin minAgg = r.getAggregations().get(AGG_NAME_MIN_TIME);
        InternalMax maxAgg = r.getAggregations().get(AGG_NAME_MAX_TIME);
        double minValue = minAgg.getValue();
        double maxValue = maxAgg.getValue();
        // If time field not exist or there is no value, will return infinity value
        if (minValue == Double.POSITIVE_INFINITY) {
            internalListener.onFailure(new ResourceNotFoundException(adTask.getDetectorId(), "There is no data in the time field"));
            return;
        }
        long interval = ((IntervalTimeConfiguration) adTask.getDetector().getDetectionInterval()).toDuration().toMillis();
        DetectionDateRange detectionDateRange = adTask.getDetectionDateRange();
        long dataStartTime = detectionDateRange.getStartTime().toEpochMilli();
        long dataEndTime = detectionDateRange.getEndTime().toEpochMilli();
        long minDate = (long) minValue;
        long maxDate = (long) maxValue;
        if (minDate >= dataEndTime || maxDate <= dataStartTime) {
            internalListener.onFailure(new ResourceNotFoundException(adTask.getDetectorId(), "There is no data in the detection date range"));
            return;
        }
        if (minDate > dataStartTime) {
            dataStartTime = minDate;
        }
        if (maxDate < dataEndTime) {
            dataEndTime = maxDate;
        }
        // normalize start/end time to make it consistent with feature data agg result
        dataStartTime = dataStartTime - dataStartTime % interval;
        dataEndTime = dataEndTime - dataEndTime % interval;
        logger.debug("adjusted date range: start: {}, end: {}, taskId: {}", dataStartTime, dataEndTime, taskId);
        if ((dataEndTime - dataStartTime) < NUM_MIN_SAMPLES * interval) {
            internalListener.onFailure(new AnomalyDetectionException("There is not enough data to train model").countedInStats(false));
            return;
        }
        consumer.accept(dataStartTime, dataEndTime);
    }, e -> {
        internalListener.onFailure(e);
    }));
}

Example 13 with IntervalTimeConfiguration

use of org.opensearch.ad.model.IntervalTimeConfiguration in project anomaly-detection by opensearch-project.

the class IndexAnomalyDetectorJobActionHandler method createJob.

private void createJob(AnomalyDetector detector) {
    try {
        IntervalTimeConfiguration interval = (IntervalTimeConfiguration) detector.getDetectionInterval();
        Schedule schedule = new IntervalSchedule(Instant.now(), (int) interval.getInterval(), interval.getUnit());
        Duration duration = Duration.of(interval.getInterval(), interval.getUnit());
        AnomalyDetectorJob job = new AnomalyDetectorJob(detector.getDetectorId(), schedule, detector.getWindowDelay(), true, Instant.now(), null, Instant.now(), duration.getSeconds(), detector.getUser(), detector.getResultIndex());
        getAnomalyDetectorJobForWrite(detector, job);
    } catch (Exception e) {
        String message = "Failed to parse anomaly detector job " + detectorId;
        logger.error(message, e);
        listener.onFailure(new OpenSearchStatusException(message, RestStatus.INTERNAL_SERVER_ERROR));
    }
}

Example 14 with IntervalTimeConfiguration

use of org.opensearch.ad.model.IntervalTimeConfiguration in project anomaly-detection by opensearch-project.

the class ModelValidationActionHandler method checkDataFilterSparsity.

private void checkDataFilterSparsity(long latestTime) {
    AggregationBuilder aggregation = getBucketAggregation(latestTime, (IntervalTimeConfiguration) anomalyDetector.getDetectionInterval());
    BoolQueryBuilder query = QueryBuilders.boolQuery().filter(anomalyDetector.getFilterQuery());
    SearchSourceBuilder searchSourceBuilder = getSearchSourceBuilder(query, aggregation);
    SearchRequest searchRequest = new SearchRequest(anomalyDetector.getIndices().toArray(new String[0])).source(searchSourceBuilder);
    client.search(searchRequest, ActionListener.wrap(response -> processDataFilterResults(response, latestTime), listener::onFailure));
}

Example 15 with IntervalTimeConfiguration

use of org.opensearch.ad.model.IntervalTimeConfiguration in project anomaly-detection by opensearch-project.

the class ModelValidationActionHandler method checkFeatureQueryDelegate.

private void checkFeatureQueryDelegate(long latestTime) throws IOException {
    ActionListener<MergeableList<double[]>> validateFeatureQueriesListener = ActionListener.wrap(response -> {
        windowDelayRecommendation(latestTime);
    }, exception -> {
        listener.onFailure(new ADValidationException(exception.getMessage(), DetectorValidationIssueType.FEATURE_ATTRIBUTES, ValidationAspect.MODEL));
    });
    MultiResponsesDelegateActionListener<MergeableList<double[]>> multiFeatureQueriesResponseListener = new MultiResponsesDelegateActionListener<>(validateFeatureQueriesListener, anomalyDetector.getFeatureAttributes().size(), CommonErrorMessages.FEATURE_QUERY_TOO_SPARSE, false);
    for (Feature feature : anomalyDetector.getFeatureAttributes()) {
        AggregationBuilder aggregation = getBucketAggregation(latestTime, (IntervalTimeConfiguration) anomalyDetector.getDetectionInterval());
        BoolQueryBuilder query = QueryBuilders.boolQuery().filter(anomalyDetector.getFilterQuery());
        List<String> featureFields = ParseUtils.getFieldNamesForFeature(feature, xContentRegistry);
        for (String featureField : featureFields) {
            query.filter(QueryBuilders.existsQuery(featureField));
        }
        SearchSourceBuilder searchSourceBuilder = getSearchSourceBuilder(query, aggregation);
        SearchRequest searchRequest = new SearchRequest(anomalyDetector.getIndices().toArray(new String[0])).source(searchSourceBuilder);
        client.search(searchRequest, ActionListener.wrap(response -> {
            Histogram aggregate = checkBucketResultErrors(response);
            if (aggregate == null) {
                return;
            }
            double fullBucketRate = processBucketAggregationResults(aggregate);
            if (fullBucketRate < CONFIG_BUCKET_MINIMUM_SUCCESS_RATE) {
                multiFeatureQueriesResponseListener.onFailure(new ADValidationException(CommonErrorMessages.FEATURE_QUERY_TOO_SPARSE, DetectorValidationIssueType.FEATURE_ATTRIBUTES, ValidationAspect.MODEL));
            } else {
                multiFeatureQueriesResponseListener.onResponse(new MergeableList<>(new ArrayList<>(Collections.singletonList(new double[] { fullBucketRate }))));
            }
        }, e -> {
            logger.error(e);
            multiFeatureQueriesResponseListener.onFailure(new OpenSearchStatusException(CommonErrorMessages.FEATURE_QUERY_TOO_SPARSE, RestStatus.BAD_REQUEST, e));
        }));
    }
}