I figure out the second question, because if I don't pass in the num of
partition for the test data, it will by default assume has max executors
(although I don't know what is this default max num).
val lines = sc.parallelize(List("-240990|161327,9051480,0,2,30.48,75",
"-240990|161324,9051480,0,2,30.48,75"),2)
will only trigger 2 executors.
So I think the default executors num will be decided by the first RDD
operation need to send to executors. This give me a weird way to control the
num of executors (a fake/test code piece run to kick off the executors
first, then run the real behavior - because executor will run the whole
lifecycle of the applications? Although this may not have any real value in
practice J
But I still need help for my first question.
Thanks a lot.
Regards,
Shuai
From: Shuai Zheng [mailto:szheng.c...@gmail.com]
Sent: Thursday, January 15, 2015 4:03 PM
To: user@spark.apache.org
Subject: RE: Executor parameter doesn't work for Spark-shell on EMR Yarn
Forget to mention, I use EMR AMI 3.3.1, Spark 1.2.0. Yarn 2.4. The spark is
setup by the standard script:
s3://support.elasticmapreduce/spark/install-spark
From: Shuai Zheng [mailto:szheng.c...@gmail.com]
Sent: Thursday, January 15, 2015 3:52 PM
To: user@spark.apache.org
Subject: Executor parameter doesn't work for Spark-shell on EMR Yarn
Hi All,
I am testing Spark on EMR cluster. Env is a one node cluster r3.8xlarge. Has
32 vCore and 244G memory.
But the command line I use to start up spark-shell, it can't work. For
example:
~/spark/bin/spark-shell --jars
/home/hadoop/vrisc-lib/aws-java-sdk-1.9.14/lib/*.jar --num-executors 6
--executor-memory 10G
Neither num-executors nor memory setup works.
And more interesting, if I use test code:
val lines = sc.parallelize(List("-240990|161327,9051480,0,2,30.48,75",
"-240990|161324,9051480,0,2,30.48,75"))
var count = lines.mapPartitions(dynamoDBBatchWriteFunc).collect.sum
It will start 32 executors (then I assume it try to start all executors for
every vCore).
But if I use some real data to do it (the file size is 200M):
val lines = sc.textFile("s3://.../part-r-00000")
var count = lines.mapPartitions(dynamoDBBatchWriteFunc).collect.sum
It will only start 4 executors, which map to the number of HDFS split (200M
will have 4 splits).
So I have two questions:
1, Why the setup parameter is ignored by Yarn? How can I limit the number of
executors I can run?
2, Why my much smaller test data set will trigger 32 executors but my real
200M data set will only have 4 executors?
So how should I control the executor setup on the spark-shell? And I print
the sparkConf, it looks like much less than I expect, and I don't see my
pass in parameter show there.
scala> sc.getConf.getAll.foreach(println)
(spark.tachyonStore.folderName,spark-af0c4d42-fe4d-40b0-a3cf-25b6a9e16fa0)
(spark.app.id,local-1421353031552)
(spark.eventLog.enabled,true)
(spark.executor.id,driver)
(spark.repl.class.uri,http://10.181.82.38:58415)
(spark.driver.host,ip-10-181-82-38.ec2.internal)
(spark.executor.extraJavaOptions,-verbose:gc -XX:+PrintGCDetails
-XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC
-XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70)
(spark.app.name,Spark shell)
(spark.fileserver.uri,http://10.181.82.38:54666)
(spark.jars,file:/home/hadoop/vrisc-lib/aws-java-sdk-1.9.14/lib/aws-java-sdk
-1.9.14.jar)
(spark.eventLog.dir,hdfs:///spark-logs)
(spark.executor.extraClassPath,/home/hadoop/spark/classpath/emr/*:/home/hado
op/spark/classpath/emrfs/*:/home/hadoop/share/hadoop/common/lib/*:/home/hado
op/.versions/2.4.0/share/hadoop/common/lib/hadoop-lzo.jar)
(spark.master,local[*])
(spark.driver.port,54191)
(spark.driver.extraClassPath,/home/hadoop/spark/classpath/emr/*:/home/hadoop
/spark/classpath/emrfs/*:/home/hadoop/share/hadoop/common/lib/*:/home/hadoop
/.versions/2.4.0/share/hadoop/common/lib/hadoop-lzo.jar)
I search the old threads, attached email answer the question about why vCore
setup doesn't work. But I think this is not same issue as me. Otherwise then
default Yarn Spark setup can't do any adjustment?
Regards,
Shuai
