prometheus v2.30 releases: monitoring system and time series database

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 the bridging of third-party data into Prometheus. Examples: system statistics, as well as Docker, HAProxy, StatsD, and JMX metrics.

Changelog v2.30

• [FEATURE] experimental TSDB: Snapshot in-memory chunks on shutdown for faster restarts. Behind --enable-feature=memory-snapshot-on-shutdown flag. #7229
• [FEATURE] experimental Scrape: Configure scrape interval and scrape timeout via relabeling using __scrape_interval__ and __scrape_timeout__ labels respectively. #8911
• [FEATURE] Scrape: Add scrape_timeout_seconds and scrape_sample_limit metric. Behind --enable-feature=extra-scrape-metrics flag to avoid additional cardinality by default. #9247 #9295
• [ENHANCEMENT] Scrape: Add --scrape.timestamp-tolerance flag to adjust scrape timestamp tolerance when enabled via --scrape.adjust-timestamps. #9283
• [ENHANCEMENT] Remote Write: Improve throughput when sending exemplars. #8921
• [ENHANCEMENT] TSDB: Optimise WAL loading by removing extra map and caching min-time #9160
• [ENHANCEMENT] promtool: Speed up checking for duplicate rules. #9262/#9306
• [ENHANCEMENT] Scrape: Reduce allocations when parsing the metrics. #9299
• [ENHANCEMENT] docker_sd: Support host network mode #9125
• [BUGFIX] Exemplars: Fix panic when resizing exemplar storage from 0 to a non-zero size. #9286
• [BUGFIX] TSDB: Correctly decrement prometheus_tsdb_head_active_appenders when the append has no samples. #9230
• [BUGFIX] promtool rules backfill: Return 1 if backfill was unsuccessful. #9303
• [BUGFIX] promtool rules backfill: Avoid creation of overlapping blocks. #9324
• [BUGFIX] config: Fix a panic when reloading configuration with a null relabel action. #9224

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