History[05]

InfluxDB was created by Errplane in late 2013. Backed by Y Combinator, Errplane was initially a SaaS company centered on anomaly detection in data. After discovering a huge gap in the market, the company pivoted to focusing on their open source time series database–this eventually became InfluxDB, and Errplane rebranded to become InfluxData Inc.

Compression[06][07]

Compression strategy varies based on the shape of the data–for instance, with timestamps, run length encoding is used in the best case, Simple8B used in a good case, and raw value encoding is used for the worst case. Snappy, a dictionary compression scheme, is used for strings.

Data Model[08]

Each measurement has a timestamp along with an associated set of tags (tagset) and set of fields (fieldset). Tagset values are represented as strings and are indexed, while fieldset values can be floats, ints, strings, and booleans and cannot be not indexed.

Foreign Keys

InfluxDB does not support foreign keys.

Indexes[07][09]

InfluxDB utilizes TSI (Time Series Index), which is based on the log-structured merge tree.

Joins

Query Compilation

Query Execution[10]

Query Interface[11][10]

The HTTP API is the primary way to query data; a variation of SQL called influxql can be used to query as well. A new custom query language called Flux is currently being developed.

Storage Architecture[09]

The Time Series Index stores index on data on disk.

Storage Model[12]

Storage Organization[13]

InfluxDB uses the Time-Structured Merge Tree (TSM) organizational structure; each TSM file contains compressed and sorted series data.

Stored Procedures

Views[14]

InfluxDB supports continuous queries, which are conceptually similar to materialized views in that expensive query results are precomputed and stored.