This document lists users of RocksDB and their use cases. If you are using RocksDB, please open a pull request and add yourself to the list.
At Facebook, we use RocksDB as storage engines in multiple data management services and a backend for many different stateful services, including:
- MyRocks -- https://github.com/MySQLOnRocksDB/mysql-5.6
- MongoRocks -- https://github.com/mongodb-partners/mongo-rocks
- ZippyDB -- Facebook's distributed key-value store with Paxos-style replication, built on top of RocksDB.[*] https://www.youtube.com/watch?v=DfiN7pG0D0khtt
- Laser -- Laser is a high query throughput, low (millisecond) latency, key-value storage service built on top of RocksDB.[*]
- Dragon -- a distributed graph query engine. https://code.facebook.com/posts/1737605303120405/dragon-a-distributed-graph-query-engine/
- Stylus -- a low-level stream processing framework writtenin C++.[*]
[*] https://research.facebook.com/publications/realtime-data-processing-at-facebook/
Two different use cases at Linkedin are using RocksDB as a storage engine:
- LinkedIn's follow feed for storing user's activities. Check out the blog post: https://engineering.linkedin.com/blog/2016/03/followfeed--linkedin-s-feed-made-faster-and-smarter
- Apache Samza, open source framework for stream processing
Learn more about those use cases in a Tech Talk by Ankit Gupta and Naveen Somasundaram: http://www.youtube.com/watch?v=plqVp_OnSzg
Yahoo is using RocksDB as a storage engine for their biggest distributed data store Sherpa. Learn more about it here: http://yahooeng.tumblr.com/post/120730204806/sherpa-scales-new-heights
CockroachDB is an open-source geo-replicated transactional database (still in development). They are using RocksDB as their storage engine. Check out their github: https://github.com/cockroachdb/cockroach
DNANexus is using RocksDB to speed up processing of genomics data. You can learn more from this great blog post by Mike Lin: http://devblog.dnanexus.com/faster-bam-sorting-with-samtools-and-rocksdb/
Iron.io is using RocksDB as a storage engine for their distributed queueing system. Learn more from Tech Talk by Reed Allman: http://www.youtube.com/watch?v=HTjt6oj-RL4
Tango is using RocksDB as a graph storage to store all users' connection data and other social activity data.
Turn is using RocksDB as a storage layer for their key/value store, serving at peak 2.4MM QPS out of different datacenters. Check out our RocksDB Protobuf merge operator at: https://github.com/vladb38/rocksdb_protobuf
Check out their blog post: http://blog.cloudera.com/blog/2015/08/inside-santanders-near-real-time-data-ingest-architecture/
Airbnb is using RocksDB as a storage engine for their personalized search service. You can learn more about it here: https://www.youtube.com/watch?v=ASQ6XMtogMs
Pinterest's Object Retrieval System uses RocksDB for storage: https://www.youtube.com/watch?v=MtFEVEs_2Vo
Smyte uses RocksDB as the storage layer for their core key-value storage, high-performance counters and time-windowed HyperLogLog services.
Rakuten Marketing uses RocksDB as the disk cache layer for the real-time bidding service in their Performance DSP.
VWO's Smart Code checker and URL helper uses RocksDB to store all the URLs where VWO's Smart Code is installed.
quasardb is a high-performance, distributed, transactional key-value database that integrates well with in-memory analytics engines such as Apache Spark. quasardb uses a heavily tuned RocksDB as its persistence layer.
Netflix Netflix uses RocksDB on AWS EC2 instances with local SSD drives to cache application data.
TiKV is a GEO-replicated, high-performance, distributed, transactional key-value database. TiKV is powered by Rust and Raft. TiKV uses RocksDB as its persistence layer.
Apache Flink uses RocksDB to store state locally on a machine.
Dgraph is an open-source, scalable, distributed, low latency, high throughput Graph database .They use RocksDB to store state locally on a machine.
Uber uses RocksDB as a durable and scalable task queue.
360 Pika is a nosql compatible with redis. With the huge amount of data stored, redis may suffer for a capacity bottleneck, and pika was born for solving it. It has widely been widely used in many company