History[04]

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.

Compression[05]

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

Concurrency Control[06]

Kdb+ uses partition-based timestamp ordering. Each transaction gets their timestamp at the begin. And on each partition, transactions are executed in order of their timestamp.

Data Model[06]

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.

Foreign Keys[06]

Kdb+ supports referential integrity.

Indexes[06]

Kdb+ supports both primary and secondary indexes.

Isolation Levels[07]

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.

Joins[05]

Kdb+ supports sql standard joins. It also supports as-of join and window join.

Logging[05]

Kdb+ uses physical logging and WAL. In-memory event-engine will log new data tolog file to ensure durability.

Query Compilation[05]

Query Execution[05][08]

Kdb+ is written in q language and it's vector-based. Each function/operation in the query plan manipulates array/vector data.

Query Interface[06]

Kdb+ also supports ODBC/JDBC query interfaces.

Storage Architecture[05]

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.

Storage Model[03]

Kdb+ uses DSM both for in-memory and on-disk storage.

Stored Procedures[03]

Kdb+ supports user to write and store UDF in q language in addition to built-in functions.

System Architecture[05]

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.

Views[05]