spark 2.2 cache() cause the driver OutOfMemoryerror - scala

Question

I'm running Spark 2.2 with Scala on AWS EMR (Zeppling / spark-shell).
I'm trying to calculate very simple calculation: Loading, filtering, caching and counting on a large data set. My data contain 4,500 GB (4.8 TB) ORC format with 51,317,951,565 (51 billion) rows.
first I tried the process it with the following cluster:
1 master node - m4.xlarge - 4 cpu, 16 gb Mem
150 core nodes - r3.xlarge - 4 cpu, 29 gb Mem
150 tasks nodes - r3.xlarge - 4 cpu, 29 gb Mem
but it failed with OutOfMemoryError.
When I looked at Spark UI and Ganglia I saw that after the application load more than 80% of the data, the driver node getting too busy while the executors stop working (CPU usage is very low) until it crashed.
Ganglia CPU usage for master and worker nodes
then I tried to execute the same process just with increasing the driver node to:
1 master node - m4.2xlarge - 8 cpu, 31 gb Mem
and it succeeds.
I don't understand why the Driver node Memory usage is being fulfilled until it crashes. AFAIK only the executors are loading and processing the tasks and the data should not pass to the master. what could be the reason for it?
1) Ganglia Master Node usage for the second scenario
2) Spark UI stages
3) Spark UI DAG visualization
Below you can find the code:
import org.apache.spark.SparkConf
import org.apache.spark.sql.{Dataset, SaveMode, SparkSession, DataFrame}
import org.apache.spark.sql.functions.{concat_ws, expr, lit, udf}
import org.apache.spark.storage.StorageLevel
val df = spark.sql("select * from default.level_1 where date_ >= ('2017-11-08') and date_ <= ('2017-11-27')")
.drop("carrier", "city", "connection_type", "geo_country", "geo_country","geo_lat","geo_lon","geo_lon","geo_type", "ip","keywords","language","lat","lon","store_category","GEO3","GEO4")
.where("GEO4 is not null")
.withColumn("is_away", lit(0))
df.persist(StorageLevel.MEMORY_AND_DISK_SER)
df.count()
Below you can find the error message -
{"Event":"SparkListenerLogStart","Spark Version":"2.2.0"}
{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"driver","Host":"10.44.6.179","Port":44257},"Maximum Memory":6819151872,"Timestamp":1512024674827,"Maximum Onheap Memory":6819151872,"Maximum Offheap Memory":0}
{"Event":"SparkListenerEnvironmentUpdate","JVM Information":{"Java Home":"/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.141-1.b16.32.amzn1.x86_64/jre","Java Version":"1.8.0_141 (Oracle Corporation)","Scala Version":"version 2.11.8"},"Spark Properties":{"spark.sql.warehouse.dir":"hdfs:///user/spark/warehouse","spark.yarn.dist.files":"file:/etc/spark/conf/hive-site.xml","spark.executor.extraJavaOptions":"-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'","spark.driver.host":"10.44.6.179","spark.history.fs.logDirectory":"hdfs:///var/log/spark/apps","spark.eventLog.enabled":"true","spark.driver.port":"33707","spark.shuffle.service.enabled":"true","spark.driver.extraLibraryPath":"/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native","spark.repl.class.uri":"spark://10.44.6.179:33707/classes","spark.jars":"","spark.yarn.historyServer.address":"ip-10-44-6-179.ec2.internal:18080","spark.stage.attempt.ignoreOnDecommissionFetchFailure":"true","spark.repl.class.outputDir":"/mnt/tmp/spark-52cac1b4-614f-43a5-ab9b-5c60c6c1c5a5/repl-9389c888-603e-4988-9593-86e298d2514a","spark.app.name":"Spark shell","spark.scheduler.mode":"FIFO","spark.driver.memory":"11171M","spark.executor.instances":"200","spark.default.parallelism":"3200","spark.resourceManager.cleanupExpiredHost":"true","spark.executor.id":"driver","spark.yarn.appMasterEnv.SPARK_PUBLIC_DNS":"$(hostname -f)","spark.driver.extraJavaOptions":"-XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'","spark.submit.deployMode":"client","spark.master":"yarn","spark.ui.filters":"org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter","spark.blacklist.decommissioning.timeout":"1h","spark.executor.extraLibraryPath":"/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native","spark.sql.hive.metastore.sharedPrefixes":"com.amazonaws.services.dynamodbv2","spark.executor.memory":"20480M","spark.driver.extraClassPath":"/usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar","spark.home":"/usr/lib/spark","spark.eventLog.dir":"hdfs:///var/log/spark/apps","spark.dynamicAllocation.enabled":"true","spark.executor.extraClassPath":"/usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar","spark.sql.catalogImplementation":"hive","spark.executor.cores":"8","spark.history.ui.port":"18080","spark.driver.appUIAddress":"http://ip-10-44-6-179.ec2.internal:4040","spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS":"ip-10-44-6-
Notes -
1) I tried to change the StorageLevel to cache() and DISK_ONLY and it didn't affect the result.
2) I checked the volume of the "scratch space" and I saw that more than 90% of it still not in use.
Thanks!!

Answer 1

I have some assumption that this may be caused by the mechanism inside spark SQL.
In short the spark SQL will collect all the broadcast dataset at driver side so that when you have a big query, the driver must have enough memory to hold the broadcasted data.
Related link to the issue