VictoriaMetrics: fast, cost-effective monitoring solution and time series database

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VictoriaMetrics

VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database.

VictoriaMetrics is available in binary releases, Docker images, Snap packages and source code.

Play with the demo of Vicky Grafana UI.

The cluster version of VictoriaMetrics is available here.

Learn more about key concepts of VictoriaMetrics and follow the quick start guide for a better experience.

There is also user-friendly database for logs - VictoriaLogs.

If you have questions about VictoriaMetrics, then feel free asking them at VictoriaMetrics community Slack chat.

Contact us if you need enterprise support for VictoriaMetrics. See features available in enterprise package. Enterprise binaries can be downloaded and evaluated for free from the releases page. See how to request a free trial license here.

VictoriaMetrics is developed at a fast pace, so it is recommended periodically checking the CHANGELOG and performing regular upgrades.

VictoriaMetrics has achieved security certifications for Database Software Development and Software-Based Monitoring Services. We apply strict security measures in everything we do. See our Security page for more details.

Prominent features

VictoriaMetrics has the following prominent features:

• It can be used as long-term storage for Prometheus. See these docs for details.
• It can be used as a drop-in replacement for Prometheus in Grafana, because it supports Prometheus querying API.
• It can be used as a drop-in replacement for Graphite in Grafana, because it supports Graphite API. VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see this case study.
• It is easy to setup and operate:
  • VictoriaMetrics consists of a single small executable 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 can be done with vmbackup / vmrestore tools. See this article for more details.
• It implements PromQL-like query language - MetricsQL, which provides improved functionality on top of PromQL.
• It 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.
• It provides high performance and good vertical and horizontal scalability for both data ingestion and data querying. It outperforms InfluxDB and TimescaleDB by up to 20x.
• It uses 10x less RAM than InfluxDB and up to 7x less RAM than Prometheus, Thanos or Cortex when dealing with millions of unique time series (aka high cardinality).
• It is optimized for time series with high churn rate.
• It provides high data compression, so up to 70x more data points may be stored into limited storage comparing to TimescaleDB according to these benchmarks and up to 7x less storage space is required compared to Prometheus, Thanos or Cortex according to this benchmark.
• It is optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc). See disk IO graphs from these benchmarks.
• 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, comparing Thanos to VictoriaMetrics cluster and Remote Write Storage Wars talk from PromCon 2019.
• It protects the storage from data corruption on unclean shutdown (i.e. OOM, hardware reset or kill -9) thanks to the storage architecture.
• It supports metrics' scraping, ingestion and backfilling via the following protocols:
  • Metrics scraping from Prometheus exporters.
  • Prometheus remote write API.
  • Prometheus exposition format.
  • InfluxDB line protocol over HTTP, TCP and UDP.
  • Graphite plaintext protocol with tags.
  • OpenTSDB put message.
  • HTTP OpenTSDB /api/put requests.
  • JSON line format.
  • Arbitrary CSV data.
  • Native binary format.
  • DataDog agent or DogStatsD.
  • NewRelic infrastructure agent.
  • OpenTelemetry metrics format.
• It supports powerful stream aggregation, which can be used as a statsd alternative.
• It supports metrics relabeling.
• It can deal with high cardinality issues and high churn rate issues via series limiter.
• It ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
• It has open source cluster version.
• It can store data on NFS-based storages such as Amazon EFS and Google Filestore.

See also various Articles about VictoriaMetrics.

Case studies and talks

Case studies:

• AbiosGaming
• adidas
• Adsterra
• ARNES
• Brandwatch
• CERN
• COLOPL
• Criteo
• Dig Security
• Fly.io
• German Research Center for Artificial Intelligence
• Grammarly
• Groove X
• Idealo.de
• MHI Vestas Offshore Wind
• Naver
• Razorpay
• Percona
• Roblox
• Sensedia
• Smarkets
• Synthesio
• Wedos.com
• Wix.com
• Zerodha
• zhihu

See also articles and slides about VictoriaMetrics from our users