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I am trying to use Apache Spark to process my large (~230k entries) cassandra dataset, but I am constantly running into different kinds of errors. However I can successfully run applications when running on a dataset ~200 entries. I have a spark setup of 3 nodes with 1 master and 2 workers, and the 2 workers also have a cassandra cluster installed with data indexed with a replication factor of 2. My 2 spark workers show 2.4 and 2.8 GB memory on the web interface and I set spark.executor.memory to 2409 when running an application, to get a combined memory of 4.7 GB. Here is my WebUI Homepage

enter image description here

The environment page of one of the tasks

environment

At this stage, I am simply trying to process data stored in cassandra using spark. Here is the basic code I am using to do this in Java

SparkConf conf = new SparkConf(true)
        .set("spark.cassandra.connection.host", CASSANDRA_HOST)
        .setJars(jars);

SparkContext sc = new SparkContext(HOST, APP_NAME, conf);
SparkContextJavaFunctions context = javaFunctions(sc);

CassandraJavaRDD<CassandraRow> rdd = context.cassandraTable(CASSANDRA_KEYSPACE, CASSANDRA_COLUMN_FAMILY);

System.out.println(rdd.count());

For a successful run, on a small dataset (200 entries), the events interface looks something like this

enter image description here

But when I run the same thing on a large dataset (i.e. I change only the CASSANDRA_COLUMN_FAMILY), the job never terminates inside the terminal, the log looks like this

enter image description here

and after ~2 minutes, the stderr for the executors looks like this

enter image description here

and after ~7 minutes, I get

Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded

in my terminal, and I have to manually kill the SparkSubmit process. However, the large dataset was indexed from a binary file that occupied only 22 MB, and doing nodetool status, I can see that only ~115 MB data is stored in both of my cassandra nodes. I have also tried to use Spark SQL on my dataset, but have got similar results with that too. Where am I going wrong with my setup, and what should I do to successfully process my dataset, for both a Transformation-Action program and a program that uses Spark SQL.

I have already tried the following methods

  • Using -Xms1G -Xmx1G to increase memory, but the program fails with an exception saying that I should instead set spark.executor.memory, which I have.

  • Using spark.cassandra.input.split.size, which fails saying it isn't a valid option, and a similar option is spark.cassandra.input.split.size_in_mb, which I set to 1, with no effect.

EDIT

based on this answer, I have also tried the following methods:

  • set spark.storage.memoryFraction to 0

  • not set spark.storage.memoryFraction to zero and use persist with MEMORY_ONLY, MEMORY_ONLY_SER, MEMORY_AND_DISK and MEMORY_AND_DISK_SER.

Versions:

  • Spark: 1.4.0

  • Cassandra: 2.1.6

  • spark-cassandra-connector: 1.4.0-M1

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suyash
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1 Answers1

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I think there is a issue in the latest spark-cassandra-connector. The parameter spark.cassandra.input.split.size_in_mb is supposed to have a default value of 64 MB which is being interpreted as 64 bytes in the code. This causes too many partitions to be created, which can't be scheduled by spark. Try setting the conf value to 

spark.cassandra.input.split.size_in_mb=67108864
svar29
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  • Indeed, there seems to be a bug. – Piotr Kołaczkowski Jun 25 '15 at 11:34
  • Thanks a lot for this. For now, it works for an input of 230782 records, with a standard program taking ~5 minutes with 3 partitions, while an equivalent spark SQL program made 7 partitions and finished in under 8 seconds ! Cheers ! – suyash Jun 25 '15 at 12:41
  • This is really pathetic. I wasted my 2 days figuring out the problem and here it goes. I was suffering from the same problem. Thanks – Nipun Jul 12 '15 at 06:36