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MetricsRepository using Spark tables as the data source (#518)
* spark table repository * review comments --------- Co-authored-by: vpenikalapati <[email protected]>
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src/main/scala/com/amazon/deequ/repository/sparktable/SparkMetricsRepository.scala
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| /** | ||
| * Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"). You may not | ||
| * use this file except in compliance with the License. A copy of the License | ||
| * is located at | ||
| * | ||
| * http://aws.amazon.com/apache2.0/ | ||
| * | ||
| * or in the "license" file accompanying this file. This file 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 com.amazon.deequ.repository.sparktable | ||
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| import com.amazon.deequ.analyzers.Analyzer | ||
| import com.amazon.deequ.analyzers.runners.AnalyzerContext | ||
| import com.amazon.deequ.metrics.Metric | ||
| import com.amazon.deequ.repository._ | ||
| import org.apache.spark.sql.functions._ | ||
| import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession} | ||
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| class SparkTableMetricsRepository(session: SparkSession, tableName: String) extends MetricsRepository { | ||
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| import session.implicits._ | ||
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| override def save(resultKey: ResultKey, analyzerContext: AnalyzerContext): Unit = { | ||
| val serializedContext = AnalysisResultSerde.serialize(Seq(AnalysisResult(resultKey, analyzerContext))) | ||
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| val successfulMetrics = analyzerContext.metricMap | ||
| .filter { case (_, metric) => metric.value.isSuccess } | ||
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| val metricDF = successfulMetrics.map { case (analyzer, metric) => | ||
| SparkTableMetric(resultKey.toString, analyzer.toString, metric.value.toString, | ||
| resultKey.dataSetDate, serializedContext) | ||
| }.toSeq.toDF() | ||
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| metricDF.write | ||
| .mode(SaveMode.Append) | ||
| .saveAsTable(tableName) | ||
| } | ||
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| override def loadByKey(resultKey: ResultKey): Option[AnalyzerContext] = { | ||
| val df: DataFrame = session.table(tableName) | ||
| val matchingRows = df.filter(col("resultKey") === resultKey.toString).collect() | ||
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| if (matchingRows.isEmpty) { | ||
| None | ||
| } else { | ||
| val serializedContext = matchingRows(0).getAs[String]("serializedContext") | ||
| AnalysisResultSerde.deserialize(serializedContext).headOption.map(_.analyzerContext) | ||
| } | ||
| } | ||
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| override def load(): MetricsRepositoryMultipleResultsLoader = { | ||
| SparkTableMetricsRepositoryMultipleResultsLoader(session, tableName) | ||
| } | ||
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| } | ||
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| case class SparkTableMetric(resultKey: String, metricName: String, metricValue: String, resultTimestamp: Long, | ||
| serializedContext: String) | ||
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| case class SparkTableMetricsRepositoryMultipleResultsLoader(session: SparkSession, | ||
| tableName: String, | ||
| private val tagValues: Option[Map[String, String]] = None, | ||
| private val analyzers: Option[Seq[Analyzer[_, Metric[_]]]] | ||
| = None, | ||
| private val timeAfter: Option[Long] = None, | ||
| private val timeBefore: Option[Long] = None | ||
| ) extends MetricsRepositoryMultipleResultsLoader { | ||
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| override def withTagValues(tagValues: Map[String, String]): MetricsRepositoryMultipleResultsLoader = | ||
| this.copy(tagValues = Some(tagValues)) | ||
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| override def forAnalyzers(analyzers: Seq[Analyzer[_, Metric[_]]]): MetricsRepositoryMultipleResultsLoader = | ||
| this.copy(analyzers = Some(analyzers)) | ||
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| override def after(dateTime: Long): MetricsRepositoryMultipleResultsLoader = | ||
| this.copy(timeAfter = Some(dateTime)) | ||
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| override def before(dateTime: Long): MetricsRepositoryMultipleResultsLoader = | ||
| this.copy(timeBefore = Some(dateTime)) | ||
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| override def get(): Seq[AnalysisResult] = { | ||
| val initialDF: DataFrame = session.table(tableName) | ||
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| val tagValuesFilter: DataFrame => DataFrame = df => { | ||
| tagValues.map { tags => | ||
| tags.foldLeft(df) { (currentDF, tag) => | ||
| currentDF.filter(row => { | ||
| val ser = row.getAs[String]("serializedContext") | ||
| AnalysisResultSerde.deserialize(ser).exists(ar => { | ||
| val tags = ar.resultKey.tags | ||
| tags.contains(tag._1) && tags(tag._1) == tag._2 | ||
| }) | ||
| }) | ||
| } | ||
| }.getOrElse(df) | ||
| } | ||
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| val specificAnalyzersFilter: DataFrame => DataFrame = df => { | ||
| analyzers.map(analyzers => df.filter(col("metricName").isin(analyzers.map(_.toString): _*))) | ||
| .getOrElse(df) | ||
| } | ||
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| val timeAfterFilter: DataFrame => DataFrame = df => { | ||
| timeAfter.map(time => df.filter(col("resultTimestamp") > time.toString)).getOrElse(df) | ||
| } | ||
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| val timeBeforeFilter: DataFrame => DataFrame = df => { | ||
| timeBefore.map(time => df.filter(col("resultTimestamp") < time.toString)).getOrElse(df) | ||
| } | ||
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| val filteredDF = Seq(tagValuesFilter, specificAnalyzersFilter, timeAfterFilter, timeBeforeFilter) | ||
| .foldLeft(initialDF) { | ||
| (df, filter) => filter(df) | ||
| } | ||
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| // Convert the final DataFrame to the desired output format | ||
| filteredDF.collect().flatMap(row => { | ||
| val serializedContext = row.getAs[String]("serializedContext") | ||
| AnalysisResultSerde.deserialize(serializedContext) | ||
| }).toSeq | ||
| } | ||
|
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|
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| } |
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121
src/test/scala/com/amazon/deequ/repository/sparktable/SparkTableMetricsRepositoryTest.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| /** | ||
| * Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"). You may not | ||
| * use this file except in compliance with the License. A copy of the License | ||
| * is located at | ||
| * | ||
| * http://aws.amazon.com/apache2.0/ | ||
| * | ||
| * or in the "license" file accompanying this file. This file 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. | ||
| * | ||
| */ | ||
|
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| package com.amazon.deequ.repository.sparktable | ||
|
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| import com.amazon.deequ.SparkContextSpec | ||
| import com.amazon.deequ.analyzers.Size | ||
| import com.amazon.deequ.analyzers.runners.AnalyzerContext | ||
| import com.amazon.deequ.metrics.{DoubleMetric, Entity} | ||
| import com.amazon.deequ.repository.ResultKey | ||
| import com.amazon.deequ.utils.FixtureSupport | ||
| import org.scalatest.wordspec.AnyWordSpec | ||
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| import scala.util.Try | ||
|
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| class SparkTableMetricsRepositoryTest extends AnyWordSpec | ||
| with SparkContextSpec | ||
| with FixtureSupport { | ||
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| // private var spark: SparkSession = _ | ||
| // private var repository: SparkTableMetricsRepository = _ | ||
| private val analyzer = Size() | ||
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| "spark table metrics repository " should { | ||
| "save and load a single metric" in withSparkSessionCustomWareHouse { spark => | ||
| val resultKey = ResultKey(System.currentTimeMillis(), Map("tag" -> "value")) | ||
| val metric = DoubleMetric(Entity.Column, "m1", "", Try(100)) | ||
| val context = AnalyzerContext(Map(analyzer -> metric)) | ||
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| val repository = new SparkTableMetricsRepository(spark, "metrics_table") | ||
| // Save the metric | ||
| repository.save(resultKey, context) | ||
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| // Load the metric | ||
| val loadedContext = repository.loadByKey(resultKey) | ||
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| assert(loadedContext.isDefined) | ||
| assert(loadedContext.get.metric(analyzer).contains(metric)) | ||
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| } | ||
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| "save multiple metrics and load them" in withSparkSessionCustomWareHouse { spark => | ||
| val repository = new SparkTableMetricsRepository(spark, "metrics_table") | ||
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| val resultKey1 = ResultKey(System.currentTimeMillis(), Map("tag" -> "tagValue1")) | ||
| val metric = DoubleMetric(Entity.Column, "m1", "", Try(100)) | ||
| val context1 = AnalyzerContext(Map(analyzer -> metric)) | ||
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| val resultKey2 = ResultKey(System.currentTimeMillis(), Map("tag" -> "tagValue2")) | ||
| val metric2 = DoubleMetric(Entity.Column, "m2", "", Try(101)) | ||
| val context2 = AnalyzerContext(Map(analyzer -> metric2)) | ||
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| repository.save(resultKey1, context1) | ||
| repository.save(resultKey2, context2) | ||
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| val loadedMetrics = repository.load().get() | ||
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| assert(loadedMetrics.length == 2) | ||
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| loadedMetrics.flatMap(_.resultKey.tags) | ||
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| } | ||
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| "save and load metrics with tag" in withSparkSessionCustomWareHouse { spark => | ||
| val repository = new SparkTableMetricsRepository(spark, "metrics_table") | ||
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| val resultKey1 = ResultKey(System.currentTimeMillis(), Map("tag" -> "A")) | ||
| val metric = DoubleMetric(Entity.Column, "m1", "", Try(100)) | ||
| val context1 = AnalyzerContext(Map(analyzer -> metric)) | ||
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| val resultKey2 = ResultKey(System.currentTimeMillis(), Map("tag" -> "B")) | ||
| val metric2 = DoubleMetric(Entity.Column, "m2", "", Try(101)) | ||
| val context2 = AnalyzerContext(Map(analyzer -> metric2)) | ||
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| repository.save(resultKey1, context1) | ||
| repository.save(resultKey2, context2) | ||
| val loadedMetricsForTagA = repository.load().withTagValues(Map("tag" -> "A")).get() | ||
| assert(loadedMetricsForTagA.length == 1) | ||
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| val tagsMapA = loadedMetricsForTagA.flatMap(_.resultKey.tags).toMap | ||
| assert(tagsMapA.size == 1, "should have 1 result") | ||
| assert(tagsMapA.contains("tag"), "should contain tag") | ||
| assert(tagsMapA("tag") == "A", "tag should be A") | ||
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| val loadedMetricsForAllMetrics = repository.load().forAnalyzers(Seq(analyzer)).get() | ||
| assert(loadedMetricsForAllMetrics.length == 2, "should have 2 results") | ||
|
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| } | ||
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| "save and load to iceberg a single metric" in withSparkSessionIcebergCatalog { spark => { | ||
| val resultKey = ResultKey(System.currentTimeMillis(), Map("tag" -> "value")) | ||
| val metric = DoubleMetric(Entity.Column, "m1", "", Try(100)) | ||
| val context = AnalyzerContext(Map(analyzer -> metric)) | ||
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| val repository = new SparkTableMetricsRepository(spark, "local.metrics_table") | ||
| // Save the metric | ||
| repository.save(resultKey, context) | ||
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| // Load the metric | ||
| val loadedContext = repository.loadByKey(resultKey) | ||
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| assert(loadedContext.isDefined) | ||
| assert(loadedContext.get.metric(analyzer).contains(metric)) | ||
| } | ||
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| } | ||
| } | ||
| } |