RethinkDB
RethinkDB is an open source scalable JSON database. It was built from scratch with C++, and it is intended for the real-time web. The most significant advantage of RethinkDB is it can continuously push updated query result to applications.
Another advantage of RethinkDB is it offers a flexible query language. It is called ReQL, and it could do nearly anything SQL can do, including table joins and aggregation functions. It could even mix queries with JavaScript expressions and map-reduce.
History
RethinkDB was founded in 2009. The RethinkDB was first released open source version 1.2 in Nov. 2012. It had been developed for 5 years by a team of database experts before the first release. In the first release, it covered the JSON data model, immediate consistency support, Hadoop-style map/reduce, sharing, multi-datacenter replication, and failover. The very first version of RethinkDB was an SSD-optimized storage engine for MySQL. They then switched to build a document DBMS like MongoDB.
In Jun. 2013, it introduced lots of new features for ReQL, like basic access control, regular expression matching, new array operations, random sampling and better error handling. The ReQL is an essential feature for RethinkDB, and this release gave lots of improvement for ReQL.
In Apr. 2015, it released version 2.0.0, and it was the first production-ready release of RethinkDB. In Aug. 2015, it supported automatic failover using a Raft-based protocol. In Nov. 2015, it introduced atomic changefeeds, which include existing values from the database into the changefeed result, and then atomically transition to streaming updates.
In Oct. 2016, RethinkDB company shut down. The reason was they could not build a sustainable business. After one year, the source code was purchased by the Cloud Native Computing Foundation. Moreover, it released a new version with community effort in July 2017.
Concurrency Control
Multi-version Concurrency Control (MVCC)
RethinkDB implements block-level multiversion concurrency control. When a write operation comes while there is an ongoing read operation, RethinkDB takes a snapshot of the B-Tree for each relevant shard. Then it maintains different versions of the blocks in order to execute read and write operations concurrently.
RethinkDB takes exclusive block-level locks when multiple writes are performed on documents when they are close to each other in B-Tree. In the most case, it will not present performance problems because the top levels of B-Tree are cached along with the frequently used blocks.
Indexes
RethinkDB index the data based on the primary key. If the user did not specify the primary key, a random unique is generated for the index automatically. RethinkDB to place the document into an appropriate shard based on primary key, and index it within that shard using a B-Tree data structure.
RethinkDB supports both secondary and compound indexes.
Storage Architecture
The data is stored in a log-structured storage engine built specifically for RethinkDB and inspired by the architecture of BTRFS. It is designed from the ground up for solid state drives.
The storage engine is also used in conjunction with a custom B-Tree-aware caching engine which allows file sizes much greater than the amount of memory.
Logging
The data is stored in a log-structured storage engine built specifically for RethinkDB and inspired by the architecture of BTRFS. The log is implicitly integrated into the storage engine.
For data replication across the replicas, it doesn't require log-shipping. RethinkDB replication is based on B-Tree diff algorithms.
Website
Source Code
https://github.com/rethinkdb/rethinkdb
Tech Docs
https://www.rethinkdb.com/docs/
Developer
RethinkDB
Country of Origin
Start Year
2009
Acquired By
Cloud Native Computing Foundatio
Project Type
Written in
Bash, C++, Java, JavaScript, Python
Supported languages
C#, C++, Clojure, Dart, Delphi, Elixir, Erlang, Go, Haskell, Java, JavaScript, Lua, Nim, Perl, PHP, Python, R, Ruby, Rust, Swift