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Posted to issues@spark.apache.org by "Grant Henke (JIRA)" <ji...@apache.org> on 2019/02/14 16:43:00 UTC
[jira] [Updated] (SPARK-26880) dataDF.queryExecution.toRdd corrupt
rows
[ https://issues.apache.org/jira/browse/SPARK-26880?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Grant Henke updated SPARK-26880:
--------------------------------
Description:
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}
was:
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}
> 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}
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