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https://issues.apache.org/jira/browse/SPARK-13747?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15976580#comment-15976580
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Mousa HAMAD commented on SPARK-13747:
-------------------------------------
I am also running into this issue *sporadically* when collecting the results of
joining two dataframes.
The code that *sporadically* generates this issue is:
{code}
val spark =
SparkSession.builder().appName("application").master("local[*]").getOrCreate()
val itemCountry = spark.read.format("csv")
.option("header", "true")
.schema(StructType(Array(
StructField("itemId", IntegerType, false),
StructField("countryId", IntegerType, false))))
.csv("/item_country.csv") // This file matches the schema provided
val itemPerformance = spark.read.format("csv")
.option("header", "true")
.schema(StructType(Array(
StructField("itemId", IntegerType, false),
StructField("date", TimestampType, false),
StructField("performance", IntegerType, false))))
.csv("/item_performance.csv") // This file matches the schema provided
itemCountry.join(itemPerformance, itemCountry("itemId") ===
itemPerformance("itemId"))
.groupBy("countryId")
.agg(sum(when(to_date(itemPerformance("date")) > to_date(lit("2017-01-01")),
itemPerformance("performance")).otherwise(0)).alias("performance")).show()
{code}
The stack trace is:
{code}
java.lang.IllegalArgumentException: spark.sql.execution.id is already set
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:81)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
at
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
at
org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$collect$1.apply(Dataset.scala:2375)
at
org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$collect$1.apply(Dataset.scala:2375)
at org.apache.spark.sql.Dataset.withCallback(Dataset.scala:2778)
at
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2375)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2351)
at .... [Custom caller functions]
{code}
> Concurrent execution in SQL doesn't work with Scala ForkJoinPool
> ----------------------------------------------------------------
>
> Key: SPARK-13747
> URL: https://issues.apache.org/jira/browse/SPARK-13747
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.0.1
> Reporter: Shixiong Zhu
> Assignee: Shixiong Zhu
> Fix For: 2.2.0
>
>
> Run the following codes may fail
> {code}
> (1 to 100).par.foreach { _ =>
> println(sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count())
> }
> java.lang.IllegalArgumentException: spark.sql.execution.id is already set
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:87)
>
> at
> org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:1904)
> at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:1385)
> {code}
> This is because SparkContext.runJob can be suspended when using a
> ForkJoinPool (e.g.,scala.concurrent.ExecutionContext.Implicits.global) as it
> calls Await.ready (introduced by https://github.com/apache/spark/pull/9264).
> So when SparkContext.runJob is suspended, ForkJoinPool will run another task
> in the same thread, however, the local properties has been polluted.
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