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

Example 16 with MetricTimeSeries

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

the class TimeSeriesResponseConverter method toMap.

/**
   * Convert the response to a Map<DimensionKey, MetricTimeSeries>. DimensionKey is generated based
   * off of schemaDimensions, while the MetricTimeSeries objects are generated based on the rows
   * within the response input. The metrics returned in the MetricTimeSeries instances correspond to
   * the metric names as opposed to the full metric function (eg __COUNT instead of SUM(__COUNT))
   */
public static Map<DimensionKey, MetricTimeSeries> toMap(TimeSeriesResponse response, List<String> schemaDimensions) {
    DimensionKeyGenerator dimensionKeyGenerator = new DimensionKeyGenerator(schemaDimensions);
    List<String> metrics = new ArrayList<>(response.getMetrics());
    Set<String> metricSet = new HashSet<>(metrics);
    List<MetricType> types = Collections.nCopies(metrics.size(), MetricType.DOUBLE);
    MetricSchema metricSchema = new MetricSchema(metrics, types);
    SetMultimap<DimensionKey, TimeSeriesRow> dimensionKeyToRows = HashMultimap.create();
    // group the rows by their dimension key
    for (int i = 0; i < response.getNumRows(); i++) {
        TimeSeriesRow row = response.getRow(i);
        DimensionKey dimensionKey = dimensionKeyGenerator.get(row.getDimensionNames(), row.getDimensionValues());
        dimensionKeyToRows.put(dimensionKey, row);
    }
    Map<DimensionKey, MetricTimeSeries> result = new HashMap<>();
    for (Entry<DimensionKey, Collection<TimeSeriesRow>> entry : dimensionKeyToRows.asMap().entrySet()) {
        DimensionKey key = entry.getKey();
        MetricTimeSeries metricTimeSeries = new MetricTimeSeries(metricSchema);
        result.put(key, metricTimeSeries);
        for (TimeSeriesRow timeSeriesRow : entry.getValue()) {
            long timestamp = timeSeriesRow.getStart();
            for (TimeSeriesMetric metric : timeSeriesRow.getMetrics()) {
                String metricName = metric.getMetricName();
                // contain additional info, eg the raw metrics required for calculating derived ones.
                if (metricSet.contains(metricName)) {
                    Double value = metric.getValue();
                    metricTimeSeries.increment(timestamp, metricName, value);
                }
            }
        }
    }
    return result;
}

Example 17 with MetricTimeSeries

use of com.linkedin.thirdeye.api.MetricTimeSeries 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;
}