Kdb+ is a column-based relational time series database, developed by Kx Systems. Kdb+ database is designed to be used in financial area to store time series data and scale up/out when data increases.
In 1998, Kx Systems released **kdb**. Kx Systems then released **kdb+** as the 64-bit version in 2003. It is written in q language. Kdb+ is built to process large volume of time-series data in areas including finance and IoT.
Kdb+ supports on-disk compression with following algorithms: - kdb+ algorithm: default compression algorithm - gzip: supports different level of compression, larger compression rate needs more computation time - Google Snappy: time performance is better but compression rate is lower compared with previous two algorithms
Kdb+ uses relational model. One big problem to apply relational model in time series database is to handle large data set. Kdb+ supports on-disk compression to hold more data on single machine and data partitioning to distribute data among different machines.
Kdb+ only supports SERIALIZABLE isolation level. This is achieved by using deterministic concurrency control (partition-based). Transactions get their timestamp and execute in order on each partition.
Kdb+ supports sql standard joins. It also supports as-of join and window join.
Kdb+ uses physical logging and WAL. In-memory event-engine will log new data tolog file to ensure durability.
Kdb+ is written in q language and it's vector-based. Each function/operation in the query plan manipulates array/vector data.
Kdb+ has both in-memory and on-disk storage. New data is held in memory and old data is flushed to disk. The flush is controlled by event-engine. By default, event-engine will flush in-memory data to disk at daily basis. Rationale behind this design is the system wants to keep everyday new data in memory for fast query.
Kdb+ uses Lambda architecture on each single node. It has the following properties: - Data currently using stores in memory, while historical data is stored on disk. - New data come in from streaming sources. - Event-engine distribute data to downstream subscribers, including real-time database engine and streaming query engine. - Real-time database projects its content down to on-disk historical database for analytic use at daily basis, controlled by event-engine.
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