Prometheus

Prometheus, a Cloud Native Computing Foundation project, is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Prometheus’ main distinguishing features as compared to other monitoring systems are:

• a multi-dimensional data model (timeseries defined by metric name and set of key/value dimensions)
• a flexible query language to leverage this dimensionality
• no dependency on distributed storage; single server nodes are autonomous
• timeseries collection happens via a pull model over HTTP
• pushing timeseries is supported via an intermediary gateway
• targets are discovered via service discovery or static configuration
• multiple modes of graphing and dashboarding support
• support for hierarchical and horizontal federation

Feature

Dimensional data
Prometheus implements a highly dimensional data model. Time series are identified by a metric name and a set of key-value pairs.

Powerful queries
PromQL allows slicing and dicing of collected time series data in order to generate ad-hoc graphs, tables, and alerts.

Great visualization
Prometheus has multiple modes for visualizing data: a built-in expression browser, Grafana integration, and a console template language.

Efficient storage
Prometheus stores time series in memory and on local disk in an efficient custom format. Scaling is achieved by functional sharding and federation.

Simple operation
Each server is independent for reliability, relying only on local storage. Written in Go, all binaries are statically linked and easy to deploy.

Precise alerting
Alerts are defined based on Prometheus’s flexible PromQL and maintain dimensional information. An alertmanager handles notifications and silencing.

Many client libraries
Client libraries allow easy instrumentation of services. Over ten languages are supported already and custom libraries are easy to implement.

Many integrations
Existing exporters allow bridging of third-party data into Prometheus. Examples: system statistics, as well as Docker, HAProxy, StatsD, and JMX metrics.

Changelog v2.12.0

• [FEATURE] Track currently active PromQL queries in a log file. #5794
• [FEATURE] Enable and provide binaries for mips64 / mips64le architectures. #5792
• [ENHANCEMENT] Improve responsiveness of targets web UI and API endpoint. #5740
• [ENHANCEMENT] Improve remote write desired shards calculation. #5763
• [ENHANCEMENT] Flush TSDB pages more precisely. tsdb#660
• [ENHANCEMENT] Add prometheus_tsdb_retention_limit_bytes metric. tsdb#667
• [ENHANCEMENT] Add logging during TSDB WAL replay on startup. tsdb#662
• [ENHANCEMENT] Improve TSDB memory usage. tsdb#653, tsdb#643, tsdb#654, tsdb#642, tsdb#627
• [BUGFIX] Check for duplicate label names in remote read. #5829
• [BUGFIX] Mark deleted rules’ series as stale on next evaluation. #5759
• [BUGFIX] Fix JavaScript error when showing warning about out-of-sync server time. #5833
• [BUGFIX] Fix promtool test rules panic when providing empty exp_labels. #5774
• [BUGFIX] Only check last directory when discovering checkpoint number. #5756
• [BUGFIX] Fix error propagation in WAL watcher helper functions. #5741
• [BUGFIX] Correctly handle empty labels from alert templates. #5845

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© Prometheus Authors 2014-2019