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Document database systems store self-describing semi-structured records, suchas JSON, "as-is" without requiring the users to pre-define a schema. Thisprovides users with the flexibility to change the structure of incoming recordswithout worrying about taking the system offline or hindering the performanceof currently running queries. However, the flexibility of such systems does notfree. The large amount of redundancy in the records can introduce anunnecessary storage overhead and impact query performance.  Our focus in this paper is to address the storage overhead issue byintroducing a tuple compactor framework that infers and extracts the schemafrom self-describing semi-structured records during the data ingestion. As manyprominent document stores, such as MongoDB and Couchbase, adopt Log StructuredMerge (LSM) trees in their storage engines, our framework exploits LSMlifecycle events to piggyback the schema inference and extraction operations.We have implemented and empirically evaluated our approach to measure itsimpact on storage, data ingestion, and query performance in the context ofApache AsterixDB.

An LSM-based Tuple Compaction Framework for Apache AsterixDB (Extended Version)