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

["Dilip Biswal (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-26880?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16768754#comment-16768754 ] 

Dilip Biswal commented on SPARK-26880:
--------------------------------------

Hello,

Shouldn't we use `dataDF.rdd` as opposed to ` dataDF.queryExecution.toRdd` to get RDD[Rows] ? The former one
 returns the rdd after converting the rows from internal format to external one. The later one is
 the internal version of RDD i.e RDD[InternalRow].

cc [~srowen@scient.com] [~cloud_fan]

Regards,

-- Dilip

> 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
>            Priority: Major
>
> 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}
> 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}
> val rdd = dataDF.queryExecution.toRdd.mapPartitions { internalRows =>
>    val encoder = RowEncoder.apply(schema).resolveAndBind()
>    internalRows.map(encoder.fromRow)
> }
> {code}
> Converting with CatalystTypeConverters also appears to resolve the issue:
> {code}
> val rdd = dataDF.queryExecution.toRdd.mapPartitions { internalRows =>
>    val typeConverter = CatalystTypeConverters.createToScalaConverter(schema)
>    internalRows.map(ir => typeConverter(ir).asInstanceOf[Row])
> }
> {code}



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