Prometheus is an open-source time series database developed by SoundCloud in Go, and serves as the storage layer for the Prometheus monitoring system. Inspired by the Gorilla system at Facebook, Prometheus is specially designed for monitoring and metric collection. Prometheus contains a user-defined multi-dimensional data model and a query language on multi-dimensional data called PromQL. Apart from local disk storage, Prometheus also has remote storage integrations via Protocol Buffer.
Prometheus was started in 2012 in SoundCloud as an open-source project. The Prometheus time series database has gone through three major versions. Prometheus v1 is a basic implementation, where all time series data and label metadata are stored in LevelDB. V2 addressed several shortcomings of v1 by storing time series data on a per time series basis and adoption of delta-of-delta compression. V3 made further improvements by implementing a write ahead log to handle crashes and better data block compaction.
Similar to many time-series databases, Prometheus is log-structured and is not designed for ACID transactions.
Since the query in Prometheus is quite similar to that in key-value databases, thus the query execution model is tuple-at-a-time model.
Prometheus has an efficient data compression format due to the fact that data samples in the same series often change very little. One of the compression algorithms that Prometheus uses is similar to that of Facebook's Gorilla time-series database, called delta-of-delta compression algorithm. Prometheus also has other customized compression algorithms.
With regard to integration with remote storage engines, Prometheus uses a snappy-compressed protocol buffer encoding over HTTP for both read and write protocols.
Prometheus supports periodic checkpointing, which is done every two hours by default. Checkpointing in Prometheus is done by compacting segments in a given WAL. Therefore when Prometheus recovers from failure, it can replay the WAL to restore its status before crash.
Prometheus ensures data durability by write ahead logging (WAL). The format of how logs are stored on disk in Prometheus is largely borrowed from LevelDB/RocksDB.
A typical data point record in Prometheus's WAL is a triple
(series_id, timestamp, value).
Prometheus stores data as time series. A time series is defined by a metric and a set of key-value labels. A data sample is a data point at a given timestamp, including a
float64 value and a unix timestamp. Therefore a time series can be formally defined as
Prometheus supports the following metric types:
Prometheus supports flexible configuration to choose backend storage service. Prometheus itself maintains a on-disk checkpoint of series data and also supports remote read/write to other storage systems, making Prometheus's integration with other systems much easier. Also, Prometheus supports custom Webhook receivers to send alert notifications, e.g. AWS SNS and IRC bot.
Prometheus has a custom query language called PromQL, which is specially designed to query time-series data. Prometheus query interface also implements math/datetime related functions as well as aggregation. Prometheus also provides a RESTful interface over HTTP.
Prometheus does not have complex data structures for maintaining indexes. Indexes are simply symbol tables that maps metrics/labels to offsets in Prometheus trunk files.
Since the underlying data representation of each series in Prometheus is a list of key-value pair, the storage model of Prometheus is quite similar to normal key-value databases, e.g. LevelDB and RocksDB. Actually, prior to Prometheus 2.0, its storage engine was LevelDB.
Prometheus has two storage architectures: