[GitHub] spark pull request #19092: [SPARK-21878] [SQL] [TEST] Create SQLMetricsTestU...

[gatorsmile <gi...@git.apache.org>]

Github user gatorsmile commented on a diff in the pull request:

    https://github.com/apache/spark/pull/19092#discussion_r136251106
  
    --- Diff: sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLMetricsSuite.scala ---
    @@ -17,112 +17,10 @@
     
     package org.apache.spark.sql.hive.execution
     
    -import java.io.File
    -
    -import org.apache.spark.sql.catalyst.TableIdentifier
    -import org.apache.spark.sql.functions._
    +import org.apache.spark.sql.execution.metric.SQLMetricsTestUtils
     import org.apache.spark.sql.hive.test.TestHiveSingleton
    -import org.apache.spark.sql.test.SQLTestUtils
    -import org.apache.spark.util.Utils
    -
    -class SQLMetricsSuite extends SQLTestUtils with TestHiveSingleton {
    -  import spark.implicits._
    -
    -  /**
    -   * Get execution metrics for the SQL execution and verify metrics values.
    -   *
    -   * @param metricsValues the expected metric values (numFiles, numPartitions, numOutputRows).
    -   * @param func the function can produce execution id after running.
    -   */
    -  private def verifyWriteDataMetrics(metricsValues: Seq[Int])(func: => Unit): Unit = {
    -    val previousExecutionIds = spark.sharedState.listener.executionIdToData.keySet
    -    // Run the given function to trigger query execution.
    -    func
    -    spark.sparkContext.listenerBus.waitUntilEmpty(10000)
    -    val executionIds =
    -      spark.sharedState.listener.executionIdToData.keySet.diff(previousExecutionIds)
    -    assert(executionIds.size == 1)
    -    val executionId = executionIds.head
    -
    -    val executionData = spark.sharedState.listener.getExecution(executionId).get
    -    val executedNode = executionData.physicalPlanGraph.nodes.head
    -
    -    val metricsNames = Seq(
    -      "number of written files",
    -      "number of dynamic part",
    -      "number of output rows")
    -
    -    val metrics = spark.sharedState.listener.getExecutionMetrics(executionId)
    -
    -    metricsNames.zip(metricsValues).foreach { case (metricsName, expected) =>
    -      val sqlMetric = executedNode.metrics.find(_.name == metricsName)
    -      assert(sqlMetric.isDefined)
    -      val accumulatorId = sqlMetric.get.accumulatorId
    -      val metricValue = metrics(accumulatorId).replaceAll(",", "").toInt
    -      assert(metricValue == expected)
    -    }
    -
    -    val totalNumBytesMetric = executedNode.metrics.find(_.name == "bytes of written output").get
    -    val totalNumBytes = metrics(totalNumBytesMetric.accumulatorId).replaceAll(",", "").toInt
    -    assert(totalNumBytes > 0)
    -  }
    -
    -  private def testMetricsNonDynamicPartition(
    -      dataFormat: String,
    -      tableName: String): Unit = {
    -    withTable(tableName) {
    -      Seq((1, 2)).toDF("i", "j")
    -        .write.format(dataFormat).mode("overwrite").saveAsTable(tableName)
    -
    -      val tableLocation =
    -        new File(spark.sessionState.catalog.getTableMetadata(TableIdentifier(tableName)).location)
     
    -      // 2 files, 100 rows, 0 dynamic partition.
    -      verifyWriteDataMetrics(Seq(2, 0, 100)) {
    -        (0 until 100).map(i => (i, i + 1)).toDF("i", "j").repartition(2)
    -          .write.format(dataFormat).mode("overwrite").insertInto(tableName)
    -      }
    -      assert(Utils.recursiveList(tableLocation).count(_.getName.startsWith("part-")) == 2)
    -    }
    -  }
    -
    -  private def testMetricsDynamicPartition(
    -      provider: String,
    -      dataFormat: String,
    -      tableName: String): Unit = {
    -    withTempPath { dir =>
    -      spark.sql(
    -        s"""
    -           |CREATE TABLE $tableName(a int, b int)
    -           |USING $provider
    -           |PARTITIONED BY(a)
    -           |LOCATION '${dir.toURI}'
    -         """.stripMargin)
    -      val table = spark.sessionState.catalog.getTableMetadata(TableIdentifier(tableName))
    -      assert(table.location == makeQualifiedPath(dir.getAbsolutePath))
    -
    -      val df = spark.range(start = 0, end = 40, step = 1, numPartitions = 1)
    -        .selectExpr("id a", "id b")
    -
    -      // 40 files, 80 rows, 40 dynamic partitions.
    -      verifyWriteDataMetrics(Seq(40, 40, 80)) {
    -        df.union(df).repartition(2, $"a")
    -          .write
    -          .format(dataFormat)
    -          .mode("overwrite")
    -          .insertInto(tableName)
    -      }
    -      assert(Utils.recursiveList(dir).count(_.getName.startsWith("part-")) == 40)
    -    }
    -  }
    -
    -  test("writing data out metrics: parquet") {
    -    testMetricsNonDynamicPartition("parquet", "t1")
    -  }
    -
    -  test("writing data out metrics with dynamic partition: parquet") {
    --- End diff --
    
    The above two test cases are moved to `sql/execution/metric/SQLMetricsSuite.scala`


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