What is RocksDB?
RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.
RocksDB is a tool in the Databases category of a tech stack.
RocksDB is an open source tool with 25.4K GitHub stars and 5.8K GitHub forks. Here’s a link to RocksDB's open source repository on GitHub
Who uses RocksDB?
12 companies reportedly use RocksDB in their tech stacks, including Facebook, LinkedIn, and Keevo.
92 developers on StackShare have stated that they use RocksDB.
dbForge Studio for MySQL, Akutan, Crux, LedisDB, and FastoNoSQL are some of the popular tools that integrate with RocksDB. Here's a list of all 5 tools that integrate with RocksDB.
Pros of RocksDB
Made by Facebook
Ability to add logic to the database layer where needed
Decisions about RocksDB
Here are some stack decisions, common use cases and reviews by companies and developers who chose RocksDB in their tech stack.
I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!
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- Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM
- Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory
- Scales linearly with number of CPUs so that it works well on ARM processors
RocksDB Alternatives & Comparisons
What are some alternatives to RocksDB?
See all alternatives
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
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Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.