How do you optimize the storage of a PySpark DataFrame?
Instruction: Describe the considerations and techniques for efficiently storing DataFrames in PySpark to minimize memory usage and processing time.
Updated
Separate durable file layout, cached DataFrame storage and serialization between processes. They are different optimization targets.
For durable analytical data, compare Parquet or ORC with an appropriate codec, column types and file sizes. Column pruning and suitable predicate pushdown can reduce I/O, but compression ratio and speed depend on the workload. Measure scan bytes, file count, read/write time and total storage while preserving schema, nulls and numeric precision.
Choose directory partition columns from common selective filters and manageable cardinality. Partitioning by every customer can create many tiny directories/files. File partition pruning does not promise that a later join or aggregation will avoid a shuffle. Bucketing has separate table/API requirements, and the optimizer can use it only when compatible plans and settings permit.
Persist a DataFrame only when reuse justifies its materialization and memory/disk cost. Select a supported storage level, inspect the Storage tab, force the intended action and release it when finished. Reducing columns and avoiding duplicated cached versions can matter more than changing a serializer. Cache eviction and recomputation remain possible.
Kryo is a JVM serialization option for applicable Spark objects; it is not a universal replacement for Spark SQL's internal row/columnar representation or Python serialization. Changing the serializer setting does not automatically shrink every PySpark DataFrame. Likewise, a broadcast join changes execution and replicates a build side; it is not a durable storage format or a general memory-saving technique.
Test the actual read/write and repeated-query workload with representative data, including small-file and skew behavior. Treat overwrite, compaction and table replacement as publication operations requiring the chosen storage system's commit and recovery guarantees.