Spark excels at processing in-memory data.  We are going to look at various caching options and their effects, and (hopefully) provide some tips for optimizing Spark memory caching.

When caching in Spark, there are two options

1. Raw storage

2. Serialized

Here are some differences between the two options

So what are the trade offs?

Here is a quick experiment.  I cache a bunch of RDDs using both options and measure memory footprint and processing time.  My RDDs range in size from 100MB to 1GB.

Testing environment:

3 node spark cluster running on Amazon EC2 (m1.large type with 8G memory per node)

Reading data files from S3 bucket

Testing method:

$   ./bin/spark-shell  --driver-memory 8g> val f = sc.textFile("s3n://bucket_path/1G.data")> f.persist(org.apache.spark.storage.StorageLevel.MEMORY_ONLY) // specify the cache option> f.count()  // do this a few times and measure times// also look at RDD memory size from Spark application UI, under 'Storage' tab

On to the results:

Conclusions

raw caching consumes has a bigger footprint in  in memory – about 2x – 4x (e.g. 100MB RDD becomes 370MB)

Serialized caching consumes almost the same amount of memory as RDD (plus some overhead)

Raw cache is very fast to process, and it scales pretty well

Processing serialized cached data takes longer

So what does all this mean?

For small data sets (few hundred megs) we can use raw caching.  Even though this will consume more memory, the small size won’t put too much pressure on Java garbage collection.

Raw caching is also good for iterative work loads (say we are doing a bunch of iterations over data).  Because the processing is very fast

For medium / large data sets (10s of Gigs or 100s of Gigs) serialized caching would be helpful.  Because this will not consume too much memory.  And garbage collecting gigs of memory can be taxing