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

Example 6 with TimeGranularity

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

the class PqlUtils method getBetweenClause.

static String getBetweenClause(DateTime start, DateTime endExclusive, TimeSpec timeFieldSpec, String collection) throws ExecutionException {
    TimeGranularity dataGranularity = timeFieldSpec.getDataGranularity();
    long startMillis = start.getMillis();
    long endMillis = endExclusive.getMillis();
    long dataGranularityMillis = dataGranularity.toMillis();
    String timeField = timeFieldSpec.getColumnName();
    String timeFormat = timeFieldSpec.getFormat();
    if (timeFormat == null || TimeSpec.SINCE_EPOCH_FORMAT.equals(timeFormat)) {
        // Shrink start and end as per data granularity
        long startAlignmentDelta = startMillis % dataGranularityMillis;
        if (startAlignmentDelta != 0) {
            long startMillisAligned = startMillis + dataGranularityMillis - startAlignmentDelta;
            start = new DateTime(startMillisAligned);
        }
        long endAligmentDelta = endMillis % dataGranularityMillis;
        if (endAligmentDelta != 0) {
            long endMillisAligned = endMillis - endAligmentDelta;
            endExclusive = new DateTime(endMillisAligned);
        }
    }
    String startQueryTime;
    String endQueryTimeExclusive;
    if (timeFormat == null || TimeSpec.SINCE_EPOCH_FORMAT.equals(timeFormat)) {
        long startInConvertedUnits = dataGranularity.convertToUnit(start.getMillis());
        long endInConvertedUnits = dataGranularity.convertToUnit(endExclusive.getMillis());
        startQueryTime = String.valueOf(startInConvertedUnits);
        endQueryTimeExclusive = (endInConvertedUnits == startInConvertedUnits + 1) ? startQueryTime : String.valueOf(endInConvertedUnits);
    } else {
        DateTimeFormatter inputDataDateTimeFormatter = DateTimeFormat.forPattern(timeFormat).withZone(Utils.getDataTimeZone(collection));
        startQueryTime = inputDataDateTimeFormatter.print(start);
        endQueryTimeExclusive = inputDataDateTimeFormatter.print(endExclusive);
    }
    if (startQueryTime.equals(endQueryTimeExclusive)) {
        return String.format(" %s = %s", timeField, startQueryTime);
    } else {
        return String.format(" %s >= %s AND %s < %s", timeField, startQueryTime, timeField, endQueryTimeExclusive);
    }
}

Example 7 with TimeGranularity

use of com.linkedin.thirdeye.api.TimeGranularity 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 8 with TimeGranularity

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

the class Utils method getTimeGranularityFromString.

public static TimeGranularity getTimeGranularityFromString(String aggTimeGranularity) {
    TimeGranularity timeGranularity = null;
    if (aggTimeGranularity.indexOf("_") > -1) {
        String[] split = aggTimeGranularity.split("_");
        timeGranularity = new TimeGranularity(Integer.parseInt(split[0]), TimeUnit.valueOf(split[1]));
    } else {
        timeGranularity = new TimeGranularity(1, TimeUnit.valueOf(aggTimeGranularity));
    }
    return timeGranularity;
}

Example 9 with TimeGranularity

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

the class AnomaliesResource method getAnomalyDataCompareResults.

@GET
@Path("/{anomalyId}")
public AnomalyDataCompare.Response getAnomalyDataCompareResults(@PathParam("anomalyId") Long anomalyId) {
    MergedAnomalyResultDTO anomaly = mergedAnomalyResultDAO.findById(anomalyId);
    if (anomaly == null) {
        LOG.error("Anomaly not found with id " + anomalyId);
        throw new IllegalArgumentException("Anomaly not found with id " + anomalyId);
    }
    AnomalyDataCompare.Response response = new AnomalyDataCompare.Response();
    response.setCurrentStart(anomaly.getStartTime());
    response.setCurrenEnd(anomaly.getEndTime());
    try {
        DatasetConfigDTO dataset = datasetConfigDAO.findByDataset(anomaly.getCollection());
        TimeGranularity granularity = new TimeGranularity(dataset.getTimeDuration(), dataset.getTimeUnit());
        // Lets compute currentTimeRange
        Pair<Long, Long> currentTmeRange = new Pair<>(anomaly.getStartTime(), anomaly.getEndTime());
        MetricTimeSeries ts = TimeSeriesUtil.getTimeSeriesByDimension(anomaly.getFunction(), Arrays.asList(currentTmeRange), anomaly.getDimensions(), granularity, false);
        double currentVal = getTotalFromTimeSeries(ts, dataset.isAdditive());
        response.setCurrentVal(currentVal);
        for (AlertConfigBean.COMPARE_MODE compareMode : AlertConfigBean.COMPARE_MODE.values()) {
            long baselineOffset = EmailHelper.getBaselineOffset(compareMode);
            Pair<Long, Long> baselineTmeRange = new Pair<>(anomaly.getStartTime() - baselineOffset, anomaly.getEndTime() - baselineOffset);
            MetricTimeSeries baselineTs = TimeSeriesUtil.getTimeSeriesByDimension(anomaly.getFunction(), Arrays.asList(baselineTmeRange), anomaly.getDimensions(), granularity, false);
            AnomalyDataCompare.CompareResult cr = new AnomalyDataCompare.CompareResult();
            double baseLineval = getTotalFromTimeSeries(baselineTs, dataset.isAdditive());
            cr.setBaselineValue(baseLineval);
            cr.setCompareMode(compareMode);
            cr.setChange(calculateChange(currentVal, baseLineval));
            response.getCompareResults().add(cr);
        }
    } catch (Exception e) {
        LOG.error("Error fetching the timeseries data from pinot", e);
        throw new RuntimeException(e);
    }
    return response;
}

Example 10 with TimeGranularity

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

the class TimeOnTimeTest method generateGroupByTimeAndDimension.

// CONTRIBUTOR
private static TimeOnTimeComparisonRequest generateGroupByTimeAndDimension() {
    TimeOnTimeComparisonRequest comparisonRequest = new TimeOnTimeComparisonRequest();
    String collection = "thirdeyeAbook";
    comparisonRequest.setCollectionName(collection);
    comparisonRequest.setBaselineStart(new DateTime(2016, 4, 1, 00, 00));
    comparisonRequest.setBaselineEnd(new DateTime(2016, 4, 2, 00, 00));
    comparisonRequest.setCurrentStart(new DateTime(2016, 4, 8, 00, 00));
    comparisonRequest.setCurrentEnd(new DateTime(2016, 4, 9, 00, 00));
    comparisonRequest.setGroupByDimensions(Lists.newArrayList("browserName", "contactsOrigin", "deviceName", "continent", "countryCode", "environment", "locale", "osName", "pageKey", "source", "sourceApp"));
    comparisonRequest.setGroupByDimensions(Lists.newArrayList("environment"));
    List<MetricFunction> metricFunctions = new ArrayList<>();
    metricFunctions.add(new MetricFunction(MetricAggFunction.SUM, "__COUNT"));
    comparisonRequest.setMetricExpressions(Utils.convertToMetricExpressions(metricFunctions));
    comparisonRequest.setAggregationTimeGranularity(new TimeGranularity(1, TimeUnit.HOURS));
    return comparisonRequest;
}