Hi, I'm using Spark 2.2, and have a big batch job, using dataframes (with built-in, basic types). It references the same intermediate dataframe multiple times, so I wanted to try to cache() that and see if it helps, both in memory footprint and performance.
Now, the Spark 2.2 tuning page ( http://spark.apache.org/docs/latest/tuning.html) clearly says: 1. The default Spark serialization is Java serialization. 2. It is recommended to switch to Kyro serialization. 3. "Since Spark 2.0.0, we internally use Kryo serializer when shuffling RDDs with simple types, arrays of simple types, or string type". Now, I remember that in 2.0 launch, there were discussion of a third serialization format that is much more performant and compact. (Encoder?), but it is not referenced in the tuning guide and its Scala doc is not very clear to me. Specifically, Databricks shared some graphs etc of how much it is better than Kyro and Java serialization - see Encoders here: https://databricks.com/blog/2016/01/04/introducing-apache-spark-datasets.html So, is that relevant to cache()? If so, how can I enable it - and is it for MEMORY_AND_DISK_ONLY or MEMORY_AND_DISK_SER? I tried to play with some other variations, like enabling Kyro by the tuning guide instructions, but didn't see any impact on the cached dataframe size (same tens of GBs in the UI). So any tips around that? Thanks. Ofir Manor Co-Founder & CTO | Equalum Mobile: +972-54-7801286 | Email: ofir.ma...@equalum.io
