use of javax.ws.rs.Path 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;
}
use of javax.ws.rs.Path in project pinot by linkedin.
the class AnomalyResource method viewAnomalyFunctions.
/************* CRUD for anomaly functions of collection **********************************************/
// View all anomaly functions
@GET
@Path("/anomaly-function/view")
public List<AnomalyFunctionDTO> viewAnomalyFunctions(@NotNull @QueryParam("dataset") String dataset, @QueryParam("metric") String metric) {
if (StringUtils.isBlank(dataset)) {
throw new IllegalArgumentException("dataset is a required query param");
}
List<AnomalyFunctionDTO> anomalyFunctionSpecs = anomalyFunctionDAO.findAllByCollection(dataset);
List<AnomalyFunctionDTO> anomalyFunctions = anomalyFunctionSpecs;
if (StringUtils.isNotEmpty(metric)) {
anomalyFunctions = new ArrayList<>();
for (AnomalyFunctionDTO anomalyFunctionSpec : anomalyFunctionSpecs) {
if (metric.equals(anomalyFunctionSpec.getTopicMetric())) {
anomalyFunctions.add(anomalyFunctionSpec);
}
}
}
return anomalyFunctions;
}
use of javax.ws.rs.Path in project pinot by linkedin.
the class DatasetConfigResource method viewDatsetConfig.
@GET
@Path("/list")
@Produces(MediaType.APPLICATION_JSON)
public String viewDatsetConfig(@DefaultValue("0") @QueryParam("jtStartIndex") int jtStartIndex, @DefaultValue("100") @QueryParam("jtPageSize") int jtPageSize) {
List<DatasetConfigDTO> datasetConfigDTOs = datasetConfigDao.findAll();
List<DatasetConfigDTO> subList = Utils.sublist(datasetConfigDTOs, jtStartIndex, jtPageSize);
ObjectNode rootNode = JsonResponseUtil.buildResponseJSON(subList);
return rootNode.toString();
}
use of javax.ws.rs.Path in project pinot by linkedin.
the class DatasetConfigResource method createDatasetConfig.
@GET
@Path("/create")
public String createDatasetConfig(@QueryParam("dataset") String dataset, @QueryParam("dimensions") String dimensions, @QueryParam("dimensionsHaveNoPreAggregation") String dimensionsHaveNoPreAggregation, @QueryParam("active") boolean active, @QueryParam("additive") boolean additive, @QueryParam("metricAsDimension") boolean metricAsDimension, @QueryParam("metricValuesColumn") String metricValuesColumn, @QueryParam("metricNamesColumn") String metricNamesColumn, @QueryParam("nonAdditiveBucketSize") Integer nonAdditiveBucketSize, @QueryParam("nonAdditiveBucketUnit") String nonAdditiveBucketUnit, @QueryParam("preAggregatedKeyword") String preAggregatedKeyword, @QueryParam("timeColumn") String timeColumn, @QueryParam("timeDuration") Integer timeDuration, @QueryParam("timeFormat") String timeFormat, @QueryParam("timezone") TimeUnit timeUnit, @QueryParam("timezone") String timezone) {
try {
DatasetConfigDTO datasetConfigDTO = new DatasetConfigDTO();
datasetConfigDTO.setDataset(dataset);
datasetConfigDTO.setDimensions(toList(dimensions));
if (!Strings.isNullOrEmpty(dimensionsHaveNoPreAggregation)) {
datasetConfigDTO.setDimensionsHaveNoPreAggregation(toList(dimensionsHaveNoPreAggregation));
}
datasetConfigDTO.setActive(active);
datasetConfigDTO.setAdditive(additive);
datasetConfigDTO.setMetricAsDimension(metricAsDimension);
datasetConfigDTO.setMetricNamesColumn(metricNamesColumn);
datasetConfigDTO.setMetricValuesColumn(metricValuesColumn);
datasetConfigDTO.setNonAdditiveBucketSize(nonAdditiveBucketSize);
datasetConfigDTO.setNonAdditiveBucketUnit(nonAdditiveBucketUnit);
datasetConfigDTO.setPreAggregatedKeyword(preAggregatedKeyword);
datasetConfigDTO.setTimeColumn(timeColumn);
datasetConfigDTO.setTimeDuration(timeDuration);
datasetConfigDTO.setTimeFormat(timeFormat);
datasetConfigDTO.setTimeUnit(timeUnit);
datasetConfigDTO.setTimezone(timezone);
Long id = datasetConfigDao.save(datasetConfigDTO);
datasetConfigDTO.setId(id);
return JsonResponseUtil.buildResponseJSON(datasetConfigDTO).toString();
} catch (Exception e) {
return JsonResponseUtil.buildErrorResponseJSON("Failed to create dataset:" + dataset).toString();
}
}
use of javax.ws.rs.Path in project pinot by linkedin.
the class EmailResource method removeFunctionFromEmail.
@POST
@Path("{emailId}/delete/{functionId}")
public void removeFunctionFromEmail(@PathParam("emailId") Long emailId, @PathParam("functionId") Long functionId) {
AnomalyFunctionDTO function = functionDAO.findById(functionId);
EmailConfigurationDTO emailConfiguration = emailDAO.findById(emailId);
if (function != null && emailConfiguration != null) {
if (emailConfiguration.getFunctions().contains(function)) {
emailConfiguration.getFunctions().remove(function);
emailDAO.update(emailConfiguration);
}
}
}
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