Tien-Dung LE created SPARK-12837:
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Summary: Spark driver requires large memory space for serialized
results even there are no data collected to the driver
Key: SPARK-12837
URL: https://issues.apache.org/jira/browse/SPARK-12837
Project: Spark
Issue Type: Question
Components: SQL
Affects Versions: 1.6.0, 1.5.2
Reporter: Tien-Dung LE
Executing a sql statement with a large number of partitions requires a high
memory space for the driver even there are no requests to collect data back to
the driver.
Here are steps to re-produce the issue.
1. Start spark shell with a spark.driver.maxResultSize setting
{code:shell}
bin/spark-shell --driver-memory=1g --conf spark.driver.maxResultSize=1m
{code}
2. Execute the code
{code:scala}
case class Toto( a: Int, b: Int)
val df = sc.parallelize( 1 to 1e6.toInt).map( i => Toto( i, i)).toDF
sqlContext.setConf( "spark.sql.shuffle.partitions", "200" )
df.groupBy("a").count().saveAsParquetFile( "toto1" ) // OK
sqlContext.setConf( "spark.sql.shuffle.partitions", 1e3.toInt.toString )
df.repartition(1e3.toInt).groupBy("a").count().repartition(1e3.toInt).saveAsParquetFile(
"toto2" ) // ERROR
{code}
The error message is
{code:scala}
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
Total size of serialized results of 393 tasks (1025.9 KB) is bigger than
spark.driver.maxResultSize (1024.0 KB)
{code}
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