CODEWITHSUNDEEP
javahadoopapache-sparkhiveapache-spark-sql
Reena Upadhyay
Reena Upadhyay

Reputation: 2017

spark-1.5.1 throwing out of memory error for hive 1.2.0 using HiveContext in java code

I have a spark-1.5.1 for HADOOP 2.6 running in stand alone mode on my local machine. I am trying to run a hive query from a sample java application, pointing spark.master to (spark://impetus-i0248u:7077) spark master running on my local machine. Here is the piece of java code:

 SparkConf sparkconf = new SparkConf().set("spark.master", "spark://impetus-i0248u:7077").set("spark.app.name", "sparkhivesqltest")
        .set("spark.cores.max", "2").set("spark.executor.memory", "2g").set("worker_max_heapsize","2g").set("spark.driver.memory", "2g");

 SparkContext sc = new SparkContext(sparkconf);

HiveContext sqlContext = new HiveContext(sc);
DataFrame jdbcDF = sqlContext.sql("select * from bm.rutest");

List<Row> employeeFullNameRows = jdbcDF.collectAsList();

HiveContext is getting initialized properly as it is able to establish connection with hive metastore. I am getting exception at jdbcDF.collectAsList();

Here is the error coming when spark tries to submit the job:

Submitting 15/12/10 20:00:42 INFO DAGScheduler: Submitting 2 missing tasks from ResultStage 0 (MapPartitionsRDD[3] at collectAsList at HiveJdbcTest.java:30) 15/12/10 20:00:42 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks 15/12/10 20:00:42 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, 172.26.52.54, ANY, 2181 bytes) 15/12/10 20:00:42 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, 172.26.52.54, ANY, 2181 bytes)

Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "sparkDriver-akka.remote.default-remote-dispatcher-5" Exception in thread "shuffle-server-1" Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "shuffle-server-1" Exception in thread "threadDeathWatcher-2-1" Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "threadDeathWatcher-2-1"

Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "sparkDriver-akka.remote.default-remote-dispatcher-6" Exception in thread "qtp1003369013-56" Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "qtp1003369013-56"

Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "sparkDriver-akka.remote.default-remote-dispatcher-21"

Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "sparkDriver-akka.actor.default-dispatcher-17"

Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "sparkDriver-akka.remote.default-remote-dispatcher-23" Exception in thread "shuffle-server-2" Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "shuffle-server-2"

Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "sparkDriver-akka.actor.default-dispatcher-2"

Below is the configuration added in spark-env.sh

SPARK_EXECUTOR_CORES=2
SPARK_EXECUTOR_MEMORY=3G
SPARK_WORKER_CORES=2
SPARK_WORKER_MEMORY=2G
SPARK_EXECUTOR_INSTANCES=2
SPARK_WORKER_INSTANCES=1

If I set, spark.master to local[*], it works fine but when I point it to master running on my machine, I get this above mentioned exception. If I try connecting to mysql db, with the same configuration, it works fine.

PS: The table has only single row.

Please help..!

Upvotes: 1

Views: 1361

Answers (1)

Sumit
Sumit

Reputation: 1420

Here is the explanations to the concepts in your question: -

  1. local[*] = The execution is multi threaded and not distributed. Good for development when jobs are tested on Single Machine. It works in your case because data is not Shuffled or moved from Executors to driver...All is in one Single JVM and local.
  2. collectAsList - this method will collect all data from Executors on Driver Node, which causes Shuffling and Shuffling is a memory intensive process as it requires serialization, network and Disk IO.
  3. javaRDD().toLocalIterator() = Produce the same results as collect() but works on each partition sequentially and does not involve shuffling. Take care that we use this only order of partitions in RDDs and the order of items in partitions is well defined.

So considering above, as you are using local box, there is quite possibility that local(*) or collectAsList() may not give any OOM but collect() may produce memory exceptions.

Upvotes: 2

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