Serialization can save substantial space at the cost of some extra CPU time — and by default, PySpark uses the cPickle serializer. (The following link explains the general internal design of PySpark: PySpark_Internals.)
Prior to PySpark 2.0, the stored objects were always serialized regardless of whether you chose a serialized level. That means, the flag “deserialized” had no effect (as documented below: StorageLevel_configuration.) For that reason, in the StorageLevel options, the following option pairs had the same effects:
- MEMORY_AND_DISK, MEMORY_AND_DISK_SER MEMORY_AND_DISK_2
- MEMORY_AND_DISK_SER_2 MEMORY_ONLY
- MEMORY_ONLY_SER MEMORY_ONLY_2
In the latest master branch (after the release of 1.6.x), the changes in Pull Request #10092 reduce the number of exposed storage levels in PySpark. The following levels have been proposed for deprecation:
Note that all the actual available storage levels in Python include MEMORY_ONLY, MEMORY_ONLY_2, MEMORY_AND_DISK, MEMORY_AND_DISK_2, DISK_ONLY, DISK_ONLY_2 and OFF_HEAP. All these remaining options set “deserialized” to false.
Biography: Dr. Xiao Li is an active Apache Spark committer from IBM Spark Technology Center. His main interests are on Spark, data replication and data integration. He received his Ph.D. from University of Florida in 2011. Xiao has over eight papers and eight patent applications in the field of data management.