[jira] [Created] (SPARK-26880) dataDF.queryExecution.toRdd corrupt rows

["Grant Henke (JIRA)" <ji...@apache.org>]

Grant Henke created SPARK-26880:
-----------------------------------

             Summary: dataDF.queryExecution.toRdd corrupt rows
                 Key: SPARK-26880
                 URL: https://issues.apache.org/jira/browse/SPARK-26880
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.4.0
            Reporter: Grant Henke


I have seen a simple case where InternalRows returned by `queryExecution.toRdd` are corrupt. Some rows are duplicated while other are missing. 

This simple test illustrates the issue:
{code:scala}
package org.apache.kudu.spark.kudu
import org.apache.spark.SparkConf
import org.apache.spark.sql.Row
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StructType
import org.junit.Assert._
import org.junit.Test
import org.scalatest.Matchers
import org.scalatest.junit.JUnitSuite
import org.slf4j.Logger
import org.slf4j.LoggerFactory

class SparkTest extends JUnitSuite with Matchers {
  val Log: Logger = LoggerFactory.getLogger(getClass)

  @Test
  def testSparkRowCorruption(): Unit = {
    val conf = new SparkConf()
      .setMaster("local[*]")
      .setAppName("test")
      .set("spark.ui.enabled", "false")
    val ss = SparkSession.builder().config(conf).getOrCreate()

    // Setup a DataFrame for testing.
    val data = Seq(
      Row.fromSeq(Seq(0, "0")),
      Row.fromSeq(Seq(25, "25")),
      Row.fromSeq(Seq(50, "50")),
      Row.fromSeq(Seq(75, "75")),
      Row.fromSeq(Seq(99, "99")),
      Row.fromSeq(Seq(100, "100")),
      Row.fromSeq(Seq(101, "101")),
      Row.fromSeq(Seq(125, "125")),
      Row.fromSeq(Seq(150, "150")),
      Row.fromSeq(Seq(175, "175")),
      Row.fromSeq(Seq(199, "199"))
    )
    val dataRDD = ss.sparkContext.parallelize(data)
    val schema = StructType(
      Seq(
        StructField("key", IntegerType),
        StructField("value", StringType)
      ))
    val dataDF = ss.sqlContext.createDataFrame(dataRDD, schema)

    // Convert to an RDD.
    val rdd = dataDF.queryExecution.toRdd
    
    // Collect the data to compare.
    val resultData = rdd.collect
    resultData.foreach { row =>
      // Log for visualizing the corruption.
      Log.error(s"${row.getInt(0)}")
    }

    // Ensure the keys in the original data and resulting data match.
    val dataKeys = data.map(_.getInt(0)).toSet
    val resultKeys = resultData.map(_.getInt(0)).toSet
    assertEquals(dataKeys, resultKeys)
  }

}
{code}
That test fails with the following: 
{noformat}
10:38:26.967 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 0
10:38:26.967 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 25
10:38:26.967 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 75
10:38:26.968 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 75
10:38:26.968 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 99
10:38:26.968 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 100
10:38:26.969 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 125
10:38:26.969 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 125
10:38:26.969 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 150
10:38:26.970 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 199
10:38:26.970 [ERROR - Test worker] (PartitionByInternalRowTest.scala:57) 199

expected:<Set(101, 0, 25, 125, 150, 50, 199, 175, 99, 75, 100)> but was:<Set(0, 25, 125, 150, 199, 99, 75, 100)>
Expected :Set(101, 0, 25, 125, 150, 50, 199, 175, 99, 75, 100)
Actual   :Set(0, 25, 125, 150, 199, 99, 75, 100)
{noformat}

If I map from and InternalRow to a Row the issue goes away:
{code:scala}
val rdd = dataDF.queryExecution.toRdd.mapPartitions { internalRows =>
      val encoder = RowEncoder.apply(schema).resolveAndBind()
      internalRows.map(encoder.fromRow)
    }
{code}





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