[GitHub] [incubator-pinot] xiaohui-sun commented on a change in pull request #5398: [TE] Added generic support for configuring Data Quality rules like SLA from Yaml

[GitBox <gi...@apache.org>]

xiaohui-sun commented on a change in pull request #5398:
URL: https://github.com/apache/incubator-pinot/pull/5398#discussion_r427627733



##########
File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/dataquality/DataQualityPipelineJob.java
##########
@@ -26,14 +26,16 @@
 import org.apache.pinot.thirdeye.datalayer.bao.TaskManager;
 import org.apache.pinot.thirdeye.datalayer.dto.TaskDTO;
 import org.apache.pinot.thirdeye.datasource.DAORegistry;
+import org.apache.pinot.thirdeye.detection.DetectionPipelineTaskInfo;
+import org.apache.pinot.thirdeye.detection.TaskUtils;
 import org.quartz.Job;
 import org.quartz.JobExecutionContext;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 
-public class DetectionDataSLAJob implements Job {
-  private static final Logger LOG = LoggerFactory.getLogger(DetectionDataSLAJob.class);
+public class DataQualityPipelineJob implements Job {

Review comment:
       Add some documents on public class.

##########
File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/anomaly/detection/trigger/DataAvailabilityTaskScheduler.java
##########
@@ -122,9 +122,10 @@ public void run() {
             long endtime = System.currentTimeMillis();
             createDetectionTask(detectionConfig, endtime);
 
-            if (DetectionUtils.isDataAvailabilityCheckEnabled(detectionConfig)) {
-              createDataSLACheckTask(detectionConfig, endtime);
-              LOG.info("Scheduling a task for data availability {} due to the fallback mechanism.", detectionConfigId);
+            if (DetectionUtils.isDataQualityCheckEnabled(detectionConfig)) {

Review comment:
       We need to have a unified naming convention for this alert.
   I think DQ is too broad. Availability is overloaded as it is used for triggering as well.
   Let's discuss.

##########
File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/dataquality/spec/DataSlaQualityCheckerSpec.java
##########
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.pinot.thirdeye.detection.dataquality.spec;
+
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import org.apache.pinot.thirdeye.dataframe.util.MetricSlice;
+import org.apache.pinot.thirdeye.detection.spec.AbstractSpec;
+
+
+@JsonIgnoreProperties(ignoreUnknown = true)
+public class DataSlaQualityCheckerSpec extends AbstractSpec {
+  private String sla = "1_DAYS";

Review comment:
       Why it is 1 day here instead of 3 days?

##########
File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/dataquality/components/DataSlaQualityChecker.java
##########
@@ -0,0 +1,215 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.pinot.thirdeye.detection.dataquality.components;
+
+import com.google.common.collect.ArrayListMultimap;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.commons.lang3.StringUtils;
+import org.apache.pinot.thirdeye.anomaly.AnomalyType;
+import org.apache.pinot.thirdeye.common.time.TimeGranularity;
+import org.apache.pinot.thirdeye.dataframe.DataFrame;
+import org.apache.pinot.thirdeye.dataframe.util.MetricSlice;
+import org.apache.pinot.thirdeye.datalayer.dto.DatasetConfigDTO;
+import org.apache.pinot.thirdeye.datalayer.dto.MergedAnomalyResultDTO;
+import org.apache.pinot.thirdeye.detection.DetectionUtils;
+import org.apache.pinot.thirdeye.detection.InputDataFetcher;
+import org.apache.pinot.thirdeye.detection.annotation.Components;
+import org.apache.pinot.thirdeye.detection.annotation.DetectionTag;
+import org.apache.pinot.thirdeye.detection.annotation.Param;
+import org.apache.pinot.thirdeye.detection.annotation.PresentationOption;
+import org.apache.pinot.thirdeye.detection.dataquality.spec.DataSlaQualityCheckerSpec;
+import org.apache.pinot.thirdeye.detection.spi.components.AnomalyDetector;
+import org.apache.pinot.thirdeye.detection.spi.components.BaselineProvider;
+import org.apache.pinot.thirdeye.detection.spi.model.DetectionResult;
+import org.apache.pinot.thirdeye.detection.spi.model.InputData;
+import org.apache.pinot.thirdeye.detection.spi.model.InputDataSpec;
+import org.apache.pinot.thirdeye.detection.spi.model.TimeSeries;
+import org.apache.pinot.thirdeye.rootcause.impl.MetricEntity;
+import org.joda.time.DateTime;
+import org.joda.time.DateTimeZone;
+import org.joda.time.Interval;
+import org.joda.time.Period;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+
+/**
+ * Performs data sla checks for the window and generates DATA_MISSING anomalies.
+ */
+@Components(title = "Data Sla Quality Checker",
+    type = "DATA_SLA",
+    tags = {DetectionTag.RULE_DETECTION},
+    description = "Checks if data is missing or not based on the configured sla",
+    presentation = {
+        @PresentationOption(name = "data sla", template = "is ${sla}")
+    },
+    params = {
+        @Param(name = "sla", placeholder = "value")
+    })
+public class DataSlaQualityChecker implements AnomalyDetector<DataSlaQualityCheckerSpec>, BaselineProvider<DataSlaQualityCheckerSpec> {
+  private static final Logger LOG = LoggerFactory.getLogger(DataSlaQualityChecker.class);
+
+  private String sla;
+  private InputDataFetcher dataFetcher;
+  private final String DEFAULT_DATA_SLA = "3_DAYS";
+
+  @Override
+  public DetectionResult runDetection(Interval window, String metricUrn) {
+    return DetectionResult.from(runSLACheck(MetricEntity.fromURN(metricUrn), window));
+  }
+
+  @Override
+  public TimeSeries computePredictedTimeSeries(MetricSlice slice) {
+    return TimeSeries.empty();
+  }
+
+  @Override
+  public void init(DataSlaQualityCheckerSpec spec, InputDataFetcher dataFetcher) {
+    this.sla = spec.getSla();
+    this.dataFetcher = dataFetcher;
+  }
+
+  /**
+   * Runs the data sla check for the window on the given metric
+   */
+  private List<MergedAnomalyResultDTO> runSLACheck(MetricEntity me, Interval window) {
+    List<MergedAnomalyResultDTO> anomalies = new ArrayList<>();
+
+    // We want to measure the overall dataset availability (filters can be ignored)
+    long startTime = window.getStart().getMillis();
+    long endTime = window.getEnd().getMillis();
+    MetricSlice metricSlice = MetricSlice.from(me.getId(), startTime, endTime, ArrayListMultimap.<String, String>create());
+    InputData data = this.dataFetcher.fetchData(new InputDataSpec()
+        .withTimeseriesSlices(Collections.singletonList(metricSlice))
+        .withMetricIdsForDataset(Collections.singletonList(me.getId()))
+        .withMetricIds(Collections.singletonList(me.getId())));
+    DatasetConfigDTO datasetConfig = data.getDatasetForMetricId().get(me.getId());
+
+    try {
+      long datasetLastRefreshTime = datasetConfig.getLastRefreshTime();
+      if (datasetLastRefreshTime <= 0) {
+        // no availability event -> assume we have processed data till the current detection start
+        datasetLastRefreshTime = startTime - 1;
+      }
+
+      MetricSlice slice = MetricSlice.from(me.getId(), datasetLastRefreshTime + 1, endTime);
+      if (isMissingData(datasetLastRefreshTime, startTime)) {
+        // Double check with data source as 2 things are possible.
+        // 1. This dataset/source may not support availability events
+        // 2. The data availability event pipeline has some issue.
+
+        DataFrame dataFrame = data.getTimeseries().get(metricSlice);
+        if (dataFrame == null || dataFrame.isEmpty()) {
+          // no data
+          if (hasMissedSLA(datasetLastRefreshTime, endTime)) {
+            anomalies.add(createDataSLAAnomaly(slice, datasetConfig));
+          }
+        } else {
+          datasetLastRefreshTime = dataFrame.getDoubles("timestamp").max().longValue();
+          if (isPartialData(datasetLastRefreshTime, endTime, datasetConfig)) {
+            if (hasMissedSLA(datasetLastRefreshTime, endTime)) {
+              slice = MetricSlice.from(me.getId(), datasetLastRefreshTime + 1, endTime);
+              anomalies.add(createDataSLAAnomaly(slice, datasetConfig));
+            }
+          }
+        }
+      } else if (isPartialData(datasetLastRefreshTime, endTime, datasetConfig)) {
+        // Optimize for the common case - the common case is that the data availability events are arriving
+        // correctly and we need not re-fetch the data to double check.
+        if (hasMissedSLA(datasetLastRefreshTime, endTime)) {
+          anomalies.add(createDataSLAAnomaly(slice, datasetConfig));
+        }
+      }
+    } catch (Exception e) {
+      LOG.error(String.format("Failed to run sla check on metric URN %s", me.getUrn()), e);
+    }

Review comment:
       Why the logic is so complicated?
   Can we just compare the data last time vs the expected refresh time?




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