MyRocks

Abstract

Facebook uses MySQL to manage tens of petabytes of data in its main database named the User Database (UDB). UDB serves social activities such as likes, comments, and shares. In the past, Facebook used InnoDB, a B+Tree based storage engine as the backend. The challenge was to find an index structure using less space and write amplification [1]. LSM-tree [2] has the potential to greatly improve these two bottlenecks. RocksDB, an LSM tree-based key/value store was already widely used in variety of applications but had a very low-level key-value interface. To overcome these limitations, MyRocks, a new MySQL storage engine, was built on top of RocksDB by adding relational capabilities. With MyRocks, using the RocksDB API, significant efficiency gains were achieved while still benefiting from all the MySQL features and tools. The transition was mostly transparent to client applications. Facebook completed the UDB migration from InnoDB to MyRocks in 2017. Since then, ongoing improvements in production operations, and additional enhancements to MySQL, MyRocks, and RocksDB, provided even greater efficiency wins. MyRocks also reduced the instance size by 62.3% for UDB data sets and performed fewer I/O operations than InnoDB. Finally, MyRocks consumed less CPU time for serving the same production traffic workload. These gains enabled us to reduce the number of database servers in UDB to less than half, saving significant resources. In this paper, we describe our journey to build and run an OLTP LSM-tree SQL database at scale. We also discuss the features we implemented to keep pace with UDB workloads, what made migrations easier, and what operational and software development challenges we faced during the two years of running MyRocks in production. Among the new features we introduced in RocksDB were transactional support, bulk loading, and prefix bloom filters, all are available for the benefit of all RocksDB users.

ZenFS+: Nurturing Performance and Isolation to ZenFS

“…The authors revealed the locality pattern in searching keys, the distribution of keys and value sizes, and time-varying search patterns with the three representative workloads. Also, Yoshinori et al have reported how RocksDB supports large-scale social graph applications [4].…”

“…RocksDB is an open-source key-value store developed by Facebook [2], [3]. It is widely used in research and industrial projects such as social graph analysis [2], distributed file systems [2], structured/unstructured DB [4], etc. RocksDB is popularly used in large-scale big data systems because it presents improved write performance with concurrent threads.…”

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“…Saugojimo varikliai yra žinomi kaip "lentelių tvarkytojai", iš esmės yra duomenų bazės dalys, kurios interpretuoja ir valdo su duomenų bazių lentelių SQL užklausomis susijusias operacijas. Naujausiose MySQL versijose saugojimo variklius galima projektuoti ir valdyti naudojant "prijungiamą" architektūrą [13]. Yra įvairių saugojimo variklių, tačiau dažniausiai naudojami du: InnoDB ir ManoISAM.…”

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“…RocksDB, pivotal for vector embedding persistence, was chosen for its high-performance characteristics in keyvalue storage and is specially optimized for SSDs. Its data structure handles efficient read and write operations, supported by block-size management, thread pool, and advanced compaction strategies [19], [20]. Moreover, MVCC management at the cluster level makes it ideal for high-speed data processing environments.…”