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Spark SQL Bucketing at Facebook
Spark SQL Bucketing at Facebook

Spark SQL Bucketing at Facebook

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Bucketing is a popular data partitioning technique to pre-shuffle and (optionally) pre-sort data during writes. This is ideal for a variety of write-once and read-many datasets at Facebook, where Spark can automatically avoid expensive shuffles/sorts (when the underlying data is joined/aggregated on its bucketed keys) resulting in substantial savings in both CPU and IO.

Over the last year, we’ve added a series of optimizations in Apache Spark as a means towards achieving feature parity with Hive and Spark. These include avoiding shuffle/sort when joining/aggregating/inserting on tables with mismatching buckets, allowing user to skip shuffle/sort when writing to bucketed tables, adding data validators before writing bucketed data, among many others. As a direct consequence of these efforts, we’ve witnessed over 10x growth (spanning 40% of total compute) in queries that read one or more bucketed tables across the entire data warehouse at Facebook.

In this talk, we’ll take a deep dive into the internals of bucketing support in SparkSQL, describe use-cases where bucketing is useful, touch upon some of the on-going work to automatically suggest bucketing tables based on query column lineage, and summarize the lessons learned from developing bucketing support in Spark at Facebook over the last 2 years

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