Jamie Hutton created SPARK-15673:
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Summary: Indefinite hanging issue with combination of cache, sort
and unionAll
Key: SPARK-15673
URL: https://issues.apache.org/jira/browse/SPARK-15673
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 1.6.1, 1.6.0
Environment: I am running the test code on both a hortonworks sandbox
and also on AWS EMR / EC2.
Reporter: Jamie Hutton
I have raised a couple of bugs to do with spark hanging. One of the previous
ones (https://issues.apache.org/jira/browse/SPARK-15000) has been resolved in
1.6.1 but the following example is still an issue in 1.6.1.
The code below is a self-contained test case which generates some data and will
lead to the hanging behaviour when run in spark-submit in 1.6.0 or 1.6.1.
Strangely the code also hangs in spark-shell in 1.6.0 but it doesnt seem to in
1.6.1 (hence providing the main method test below). I run this using:
spark-submit --class HangingTest --master local <path-to-compiled-jar>
The hanging doesnt occur if you remove either of the first two cache steps OR
the sort steps (I have added comments to this affect below). We have hit quite
a few indefinite hanging issues with spark (another is this:
https://issues.apache.org/jira/browse/SPARK-15002). There seems to be a rather
fundamental issue with chaining steps together and using the cache call.
The bug seems to be confined to reading data out of hadoop - if we put the data
onto a local drive (using file://) then the hanging stops happening.
This may seem rather a convoluted test case but that is mainly because I have
stripped the code back to the simplest possible code that causes the issue.
*CODE BELOW*
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.functions.count
import org.apache.spark.sql.functions.desc
object HangingTest {
def main(args: Array[String]): Unit = {
val conf = new SparkConf()
val sc = new SparkContext(conf)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
/*Generate some data*/
val r = scala.util.Random
val list = (0L to 500L).map(i=>(i,r.nextInt(500).asInstanceOf[Long]))
val distData = sc.parallelize(list)
import sqlContext.implicits._
val df=distData.toDF("var1","var2")
df.write.format("parquet").mode("overwrite").save("/tmp/df_hanging_test1.parquet")
df.select("var2").write.format("parquet").mode("overwrite").save("/tmp/df_hanging_test2.parquet")
val df1 = sqlContext.read.load("/tmp/df_hanging_test1.parquet")
/*Removing this step stops the hanging*/
df1.cache
/*Removing the sort part of this step stops the hanging*/
val freq=df1.select("var1").groupBy("var1").agg(count("var1") as
"var1_cnt").sort(desc("var1_cnt"))
/*Removing this step stops the hanging*/
freq.cache
val df2 = sqlContext.read.load("/tmp/df_hanging_test2.parquet")
val var2=df1.select("var2").unionAll(df2.select("var2"))
/*Removing the sort part of this step stops the hanging*/
val freq2=var2.select("var2").groupBy("var2").agg(count("var2") as
"var2_cnt").sort(desc("var2_cnt"))
/*this cache step hangs indefinitely*/
freq2.cache
/*the .show never happens - it gets stuck on the .cache above*/
freq2.show
}
}
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