Examples with DimensionMap com.linkedin.thirdeye.api.DimensionMap used on opensource projects

Example 11 with DimensionMap

use of com.linkedin.thirdeye.api.DimensionMap in project pinot by linkedin.

the class AnomalyFunctionResource method analyze.

@POST
@Path("/analyze")
@Consumes(MediaType.APPLICATION_JSON)
public Response analyze(AnomalyFunctionDTO anomalyFunctionSpec, @QueryParam("startTime") Long startTime, @QueryParam("endTime") Long endTime) throws Exception {
    // TODO: replace this with Job/Task framework and job tracker page
    BaseAnomalyFunction anomalyFunction = anomalyFunctionFactory.fromSpec(anomalyFunctionSpec);
    List<Pair<Long, Long>> startEndTimeRanges = anomalyFunction.getDataRangeIntervals(startTime, endTime);
    Map<DimensionKey, MetricTimeSeries> dimensionKeyMetricTimeSeriesMap = TimeSeriesUtil.getTimeSeriesForAnomalyDetection(anomalyFunctionSpec, startEndTimeRanges);
    List<RawAnomalyResultDTO> anomalyResults = new ArrayList<>();
    List<RawAnomalyResultDTO> results = new ArrayList<>();
    List<String> collectionDimensions = DAO_REGISTRY.getDatasetConfigDAO().findByDataset(anomalyFunctionSpec.getCollection()).getDimensions();
    for (Map.Entry<DimensionKey, MetricTimeSeries> entry : dimensionKeyMetricTimeSeriesMap.entrySet()) {
        DimensionKey dimensionKey = entry.getKey();
        DimensionMap dimensionMap = DimensionMap.fromDimensionKey(dimensionKey, collectionDimensions);
        if (entry.getValue().getTimeWindowSet().size() < 2) {
            LOG.warn("Insufficient data for {} to run anomaly detection function", dimensionMap);
            continue;
        }
        try {
            // Run algorithm
            MetricTimeSeries metricTimeSeries = entry.getValue();
            LOG.info("Analyzing anomaly function with dimensionKey: {}, windowStart: {}, windowEnd: {}", dimensionMap, startTime, endTime);
            List<RawAnomalyResultDTO> resultsOfAnEntry = anomalyFunction.analyze(dimensionMap, metricTimeSeries, new DateTime(startTime), new DateTime(endTime), new ArrayList<>());
            if (resultsOfAnEntry.size() != 0) {
                results.addAll(resultsOfAnEntry);
            }
            LOG.info("{} has {} anomalies in window {} to {}", dimensionMap, resultsOfAnEntry.size(), new DateTime(startTime), new DateTime(endTime));
        } catch (Exception e) {
            LOG.error("Could not compute for {}", dimensionMap, e);
        }
    }
    if (results.size() > 0) {
        List<RawAnomalyResultDTO> validResults = new ArrayList<>();
        for (RawAnomalyResultDTO anomaly : results) {
            if (!anomaly.isDataMissing()) {
                LOG.info("Found anomaly, sev [{}] start [{}] end [{}]", anomaly.getWeight(), new DateTime(anomaly.getStartTime()), new DateTime(anomaly.getEndTime()));
                validResults.add(anomaly);
            }
        }
        anomalyResults.addAll(validResults);
    }
    return Response.ok(anomalyResults).build();
}

Example 12 with DimensionMap

use of com.linkedin.thirdeye.api.DimensionMap in project pinot by linkedin.

the class AnomalyResource method getAnomalyMergedResultTimeSeries.

/**
   * Returns the time series for the given anomaly.
   *
   * If viewWindowStartTime and/or viewWindowEndTime is not given, then a window is padded automatically. The padded
   * windows is half of the anomaly window size. For instance, if the anomaly lasts for 4 hours, then the pad window
   * size is 2 hours. The max padding size is 1 day.
   *
   * @param anomalyResultId the id of the given anomaly
   * @param viewWindowStartTime start time of the time series, inclusive
   * @param viewWindowEndTime end time of the time series, inclusive
   * @return the time series of the given anomaly
   * @throws Exception when it fails to retrieve collection, i.e., dataset, information
   */
@GET
@Path("/anomaly-merged-result/timeseries/{anomaly_merged_result_id}")
public AnomalyTimelinesView getAnomalyMergedResultTimeSeries(@NotNull @PathParam("anomaly_merged_result_id") long anomalyResultId, @NotNull @QueryParam("aggTimeGranularity") String aggTimeGranularity, @QueryParam("start") long viewWindowStartTime, @QueryParam("end") long viewWindowEndTime) throws Exception {
    boolean loadRawAnomalies = false;
    MergedAnomalyResultDTO anomalyResult = anomalyMergedResultDAO.findById(anomalyResultId, loadRawAnomalies);
    DimensionMap dimensions = anomalyResult.getDimensions();
    AnomalyFunctionDTO anomalyFunctionSpec = anomalyResult.getFunction();
    BaseAnomalyFunction anomalyFunction = anomalyFunctionFactory.fromSpec(anomalyFunctionSpec);
    // By default, the padding window size is half of the anomaly window.
    if (viewWindowStartTime == 0 || viewWindowEndTime == 0) {
        long anomalyWindowStartTime = anomalyResult.getStartTime();
        long anomalyWindowEndTime = anomalyResult.getEndTime();
        long bucketMillis = TimeUnit.MILLISECONDS.convert(anomalyFunctionSpec.getBucketSize(), anomalyFunctionSpec.getBucketUnit());
        long bucketCount = (anomalyWindowEndTime - anomalyWindowStartTime) / bucketMillis;
        long paddingMillis = Math.max(1, (bucketCount / 2)) * bucketMillis;
        if (paddingMillis > TimeUnit.DAYS.toMillis(1)) {
            paddingMillis = TimeUnit.DAYS.toMillis(1);
        }
        if (viewWindowStartTime == 0) {
            viewWindowStartTime = anomalyWindowStartTime - paddingMillis;
        }
        if (viewWindowEndTime == 0) {
            viewWindowEndTime = anomalyWindowEndTime + paddingMillis;
        }
    }
    TimeGranularity timeGranularity = Utils.getAggregationTimeGranularity(aggTimeGranularity, anomalyFunctionSpec.getCollection());
    long bucketMillis = timeGranularity.toMillis();
    // ThirdEye backend is end time exclusive, so one more bucket is appended to make end time inclusive for frontend.
    viewWindowEndTime += bucketMillis;
    long maxDataTime = collectionMaxDataTimeCache.get(anomalyResult.getCollection());
    if (viewWindowEndTime > maxDataTime) {
        viewWindowEndTime = (anomalyResult.getEndTime() > maxDataTime) ? anomalyResult.getEndTime() : maxDataTime;
    }
    AnomalyDetectionInputContext adInputContext = TimeBasedAnomalyMerger.fetchDataByDimension(viewWindowStartTime, viewWindowEndTime, dimensions, anomalyFunction, anomalyMergedResultDAO, overrideConfigDAO, false);
    MetricTimeSeries metricTimeSeries = adInputContext.getDimensionKeyMetricTimeSeriesMap().get(dimensions);
    if (metricTimeSeries == null) {
        // the timeseries for the given anomaly
        return new AnomalyTimelinesView();
    }
    // Transform time series with scaling factor
    List<ScalingFactor> scalingFactors = adInputContext.getScalingFactors();
    if (CollectionUtils.isNotEmpty(scalingFactors)) {
        Properties properties = anomalyFunction.getProperties();
        MetricTransfer.rescaleMetric(metricTimeSeries, viewWindowStartTime, scalingFactors, anomalyFunctionSpec.getTopicMetric(), properties);
    }
    List<MergedAnomalyResultDTO> knownAnomalies = adInputContext.getKnownMergedAnomalies().get(dimensions);
    // Known anomalies are ignored (the null parameter) because 1. we can reduce users' waiting time and 2. presentation
    // data does not need to be as accurate as the one used for detecting anomalies
    AnomalyTimelinesView anomalyTimelinesView = anomalyFunction.getTimeSeriesView(metricTimeSeries, bucketMillis, anomalyFunctionSpec.getTopicMetric(), viewWindowStartTime, viewWindowEndTime, knownAnomalies);
    // Generate summary for frontend
    List<TimeBucket> timeBuckets = anomalyTimelinesView.getTimeBuckets();
    if (timeBuckets.size() > 0) {
        TimeBucket firstBucket = timeBuckets.get(0);
        anomalyTimelinesView.addSummary("currentStart", Long.toString(firstBucket.getCurrentStart()));
        anomalyTimelinesView.addSummary("baselineStart", Long.toString(firstBucket.getBaselineStart()));
        TimeBucket lastBucket = timeBuckets.get(timeBuckets.size() - 1);
        anomalyTimelinesView.addSummary("currentEnd", Long.toString(lastBucket.getCurrentStart()));
        anomalyTimelinesView.addSummary("baselineEnd", Long.toString(lastBucket.getBaselineEnd()));
    }
    return anomalyTimelinesView;
}

Example 13 with DimensionMap

use of com.linkedin.thirdeye.api.DimensionMap in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method timeSeriesDataProvider.

@DataProvider(name = "timeSeriesDataProvider")
public Object[][] timeSeriesDataProvider() {
    // The properties for the testing time series
    Properties properties = new Properties();
    long bucketSizeInMS = TimeUnit.SECONDS.toMillis(1);
    // Set up time series key for the testing time series
    TimeSeriesKey timeSeriesKey = new TimeSeriesKey();
    String metric = mainMetric;
    timeSeriesKey.setMetricName(metric);
    DimensionMap dimensionMap = new DimensionMap();
    dimensionMap.put("dimensionName1", "dimensionValue1");
    dimensionMap.put("dimensionName2", "dimensionValue2");
    timeSeriesKey.setDimensionMap(dimensionMap);
    TimeSeries observedTimeSeries = new TimeSeries();
    {
        observedTimeSeries.set(observedStartTime, 10d);
        observedTimeSeries.set(observedStartTime + bucketMillis, 15d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 2, 13d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 3, 27d);
        observedTimeSeries.set(observedStartTime + bucketMillis * 4, 10d);
        Interval observedTimeSeriesInterval = new Interval(observedStartTime, observedStartTime + bucketMillis * 5);
        observedTimeSeries.setTimeSeriesInterval(observedTimeSeriesInterval);
    }
    List<TimeSeries> baselines = new ArrayList<>();
    TimeSeries baseline1TimeSeries = new TimeSeries();
    {
        baseline1TimeSeries.set(baseline1StartTime, 10d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis, 20d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis * 2, 15d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis * 3, 24d);
        baseline1TimeSeries.set(baseline1StartTime + bucketMillis * 4, 14d);
        Interval baseline1Interval = new Interval(baseline1StartTime, baseline1StartTime + bucketMillis * 5);
        baseline1TimeSeries.setTimeSeriesInterval(baseline1Interval);
    }
    baselines.add(baseline1TimeSeries);
    TimeSeries baseline2TimeSeries = new TimeSeries();
    {
        baseline2TimeSeries.set(baseline2StartTime, 10d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis, 10d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis * 2, 5d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis * 3, 20d);
        baseline2TimeSeries.set(baseline2StartTime + bucketMillis * 4, 10d);
        Interval baseline2Interval = new Interval(baseline2StartTime, baseline2StartTime + bucketMillis * 5);
        baseline2TimeSeries.setTimeSeriesInterval(baseline2Interval);
    }
    baselines.add(baseline2TimeSeries);
    return new Object[][] { { properties, timeSeriesKey, bucketSizeInMS, observedTimeSeries, baselines } };
}

Example 14 with DimensionMap

use of com.linkedin.thirdeye.api.DimensionMap in project pinot by linkedin.

the class AnomaliesResource method getAnomalyDetails.

/**
   * Generates Anomaly Details for each merged anomaly
   * @param mergedAnomaly
   * @param datasetConfig
   * @param timeSeriesDateFormatter
   * @param startEndDateFormatterHours
   * @param startEndDateFormatterDays
   * @param externalUrl
   * @return
   */
private AnomalyDetails getAnomalyDetails(MergedAnomalyResultDTO mergedAnomaly, DatasetConfigDTO datasetConfig, DateTimeFormatter timeSeriesDateFormatter, DateTimeFormatter startEndDateFormatterHours, DateTimeFormatter startEndDateFormatterDays, String externalUrl) throws Exception {
    String dataset = datasetConfig.getDataset();
    String metricName = mergedAnomaly.getMetric();
    AnomalyFunctionDTO anomalyFunctionSpec = anomalyFunctionDAO.findById(mergedAnomaly.getFunctionId());
    BaseAnomalyFunction anomalyFunction = anomalyFunctionFactory.fromSpec(anomalyFunctionSpec);
    String aggGranularity = constructAggGranularity(datasetConfig);
    long anomalyStartTime = mergedAnomaly.getStartTime();
    long anomalyEndTime = mergedAnomaly.getEndTime();
    TimeRange range = getTimeseriesOffsetedTimes(anomalyStartTime, anomalyEndTime, datasetConfig);
    long currentStartTime = range.getStart();
    long currentEndTime = range.getEnd();
    DimensionMap dimensions = mergedAnomaly.getDimensions();
    TimeGranularity timeGranularity = Utils.getAggregationTimeGranularity(aggGranularity, anomalyFunctionSpec.getCollection());
    long bucketMillis = timeGranularity.toMillis();
    AnomalyDetails anomalyDetails = null;
    try {
        AnomalyDetectionInputContext adInputContext = TimeBasedAnomalyMerger.fetchDataByDimension(currentStartTime, currentEndTime, dimensions, anomalyFunction, mergedAnomalyResultDAO, overrideConfigDAO, true);
        MetricTimeSeries metricTimeSeries = adInputContext.getDimensionKeyMetricTimeSeriesMap().get(dimensions);
        // Transform time series with scaling factor
        List<ScalingFactor> scalingFactors = adInputContext.getScalingFactors();
        if (CollectionUtils.isNotEmpty(scalingFactors)) {
            Properties properties = anomalyFunction.getProperties();
            MetricTransfer.rescaleMetric(metricTimeSeries, currentStartTime, scalingFactors, anomalyFunctionSpec.getTopicMetric(), properties);
        }
        List<MergedAnomalyResultDTO> knownAnomalies = adInputContext.getKnownMergedAnomalies().get(dimensions);
        // Known anomalies are ignored (the null parameter) because 1. we can reduce users' waiting time and 2. presentation
        // data does not need to be as accurate as the one used for detecting anomalies
        AnomalyTimelinesView anomalyTimelinesView = anomalyFunction.getTimeSeriesView(metricTimeSeries, bucketMillis, anomalyFunctionSpec.getTopicMetric(), currentStartTime, currentEndTime, knownAnomalies);
        anomalyDetails = constructAnomalyDetails(metricName, dataset, datasetConfig, mergedAnomaly, anomalyFunctionSpec, currentStartTime, currentEndTime, anomalyTimelinesView, timeSeriesDateFormatter, startEndDateFormatterHours, startEndDateFormatterDays, externalUrl);
    } catch (Exception e) {
        LOG.error("Exception in constructing anomaly wrapper for anomaly {}", mergedAnomaly.getId(), e);
    }
    return anomalyDetails;
}

Example 15 with DimensionMap

use of com.linkedin.thirdeye.api.DimensionMap in project pinot by linkedin.

the class AbstractManagerTestBase method getAnomalyResult.

protected RawAnomalyResultDTO getAnomalyResult() {
    RawAnomalyResultDTO anomalyResult = new RawAnomalyResultDTO();
    anomalyResult.setScore(1.1);
    anomalyResult.setStartTime(System.currentTimeMillis());
    anomalyResult.setEndTime(System.currentTimeMillis());
    anomalyResult.setWeight(10.1);
    DimensionMap dimensionMap = new DimensionMap();
    dimensionMap.put("dimensionName", "dimensionValue");
    anomalyResult.setDimensions(dimensionMap);
    anomalyResult.setCreationTimeUtc(System.currentTimeMillis());
    return anomalyResult;
}