VictoriaMetrics：VictoriaMetrics：快速，经济高效的监控解决方案和时间序列数据库

[](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest) [](https://hub.docker.com/r/victoriametrics/victoria-metrics) [](http://slack.victoriametrics.com/) [](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/LICENSE) [](https://goreportcard.com/report/github.com/VictoriaMetrics/VictoriaMetrics) [](https://github.com/VictoriaMetrics/VictoriaMetrics/actions) [](https://codecov.io/gh/VictoriaMetrics/VictoriaMetrics)  ## VictoriaMetrics VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database. It is available in [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases), [docker images](https://hub.docker.com/r/victoriametrics/victoria-metrics/), [Snap package](https://snapcraft.io/victoriametrics) and in [source code](https://github.com/VictoriaMetrics/VictoriaMetrics). Just download VictoriaMetrics and see [how to start it](#how-to-start-victoriametrics). If you use Ubuntu, then just run `snap install victoriametrics` in order to install and run it. Then read [Prometheus setup](#prometheus-setup) and [Grafana setup](#grafana-setup) docs. Cluster version is available [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster). See additional docs at our [Wiki](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki). [Contact us](mailto:info@victoriametrics.com) if you need paid enterprise support for VictoriaMetrics. See [features available for enterprise customers](https://victoriametrics.com/enterprise.html). ## Case studies and talks Alphabetically sorted links to case studies: * [adidas](https://victoriametrics.github.io/CaseStudies.html#adidas) * [Adsterra](https://victoriametrics.github.io/CaseStudies.html#adsterra) * [ARNES](https://victoriametrics.github.io/CaseStudies.html#arnes) * [Brandwatch](https://victoriametrics.github.io/CaseStudies.html#brandwatch) * [CERN](https://victoriametrics.github.io/CaseStudies.html#cern) * [COLOPL](https://victoriametrics.github.io/CaseStudies.html#colopl) * [Dreamteam](https://victoriametrics.github.io/CaseStudies.html#dreamteam) * [Idealo.de](https://victoriametrics.github.io/CaseStudies.html#idealode) * [MHI Vestas Offshore Wind](https://victoriametrics.github.io/CaseStudies.html#mhi-vestas-offshore-wind) * [Synthesio](https://victoriametrics.github.io/CaseStudies.html#synthesio) * [Wedos.com](https://victoriametrics.github.io/CaseStudies.html#wedoscom) * [Wix.com](https://victoriametrics.github.io/CaseStudies.html#wixcom) * [Zerodha](https://victoriametrics.github.io/CaseStudies.html#zerodha) * [zhihu](https://victoriametrics.github.io/CaseStudies.html#zhihu) ## Prominent features * VictoriaMetrics can be used as long-term storage for Prometheus or for [vmagent](https://victoriametrics.github.io/vmagent.html). See [these docs](#prometheus-setup) for details. * VictoriaMetrics supports [Prometheus querying API](https://prometheus.io/docs/prometheus/latest/querying/api/), so it can be used as Prometheus drop-in replacement in Grafana. * VictoriaMetrics implements [MetricsQL](https://victoriametrics.github.io/MetricsQL.html) query language backwards compatible with PromQL. * VictoriaMetrics provides global query view. Multiple Prometheus instances or any other data sources may ingest data into VictoriaMetrics. Later this data may be queried via a single query. * High performance and good scalability for both [inserts](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b) and [selects](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4). [Outperforms InfluxDB and TimescaleDB by up to 20x](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae). * [Uses 10x less RAM than InfluxDB](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) and [up to 7x less RAM than Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f) when dealing with millions of unique time series (aka high cardinality). * Optimized for time series with high churn rate. Think about [prometheus-operator](https://github.com/coreos/prometheus-operator) metrics from frequent deployments in Kubernetes. * High data compression, so [up to 70x more data points](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4) may be crammed into limited storage comparing to TimescaleDB and [up to 7x less storage space is required comparing to Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f). * Optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc). See [graphs from these benchmarks](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b). * A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB. See [vertical scalability benchmarks](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae), [comparing Thanos to VictoriaMetrics cluster](https://medium.com/@valyala/comparing-thanos-to-victoriametrics-cluster-b193bea1683) and [Remote Write Storage Wars](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) talk from [PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/). * Easy operation: * VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d) without external dependencies. * All the configuration is done via explicit command-line flags with reasonable defaults. * All the data is stored in a single directory pointed by `-storageDataPath` command-line flag. * Easy and fast backups from [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282) to S3 or GCS with [vmbackup](https://victoriametrics.github.io/vmbackup.html) / [vmrestore](https://victoriametrics.github.io/vmrestore.html). See [this article](https://medium.com/@valyala/speeding-up-backups-for-big-time-series-databases-533c1a927883) for more details. * Storage is protected from corruption on unclean shutdown (i.e. OOM, hardware reset or `kill -9`) thanks to [the storage architecture](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282). * Supports metrics' scraping, ingestion and [backfilling](#backfilling) via the following protocols: * [Metrics from Prometheus exporters](https://github.com/prometheus/docs/blob/master/content/docs/instrumenting/exposition_formats.md#text-based-format) such as [node_exporter](https://github.com/prometheus/node_exporter). See [these docs](#how-to-scrape-prometheus-exporters-such-as-node-exporter