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Enhancing RocksDB for Speed & Scale

This blog post was co-authored by Mikhail Bautin and Kannan Muthukkaruppan

As described in our previous post “How We Built a High Performance Document Store on RocksDB?”, YugabyteDB’s distributed document store (DocDB) uses RocksDB as its per-node storage engine. We made multiple performance and data density related enhancements to RocksDB in the course of embedding it into DocDB’s document storage layer (figure below).

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How We Built a High Performance Document Store on RocksDB?

This blog post was co-authored by Mikhail Bautin and Kannan Muthukkaruppan

RocksDB is a popular embeddable persistent key-value store. First open sourced by Facebook in 2012 as a fork of the Google LevelDB project, it has been adapted over the years to a wide range of workloads including database storage engines and application data caching.

In this post, we explain our rationale for selecting RocksDB as a foundational building block for YugabyteDB.

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YugabyteDB Architecture: Diverse Workloads with Operational Simplicity

YugabyteDB is a transactional, high performance, geo-distributed operational database that converges multiple NoSQL and SQL interfaces into an unified solution. The v0.9 public beta of YugabyteDB includes the YCQL and YEDIS APIs that are compatible with Cassandra Query Language (CQL) and Redis APIs respectively. PostgreSQL-compatible YSQL API is under development. A fundamental design goal for YugabyteDB has been to provide the same transactional, performance and operational simplicity guarantees irrespective of the API used.

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