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FAQ
What is the main purpose of VictoriaMetrics?
To provide the best monitoring solution.
Who uses VictoriaMetrics?
See case studies.
Which features does VictoriaMetrics have?
See these docs.
How to start using VictoriaMetrics?
See these docs.
Is it safe to enable remote write in Prometheus?
Yes. Prometheus continues writing data to local storage after enabling remote write, so all the existing local storage data and new data is available for querying via Prometheus as usual.
It is recommended using vmagent for scraping Prometheus targets and writing data to VictoriaMetrics.
How does VictoriaMetrics compare to other remote storage solutions for Prometheus such as M3 from Uber, Thanos, Cortex, etc.?
VictoriaMetrics is simpler, faster, more cost-effective and it provides MetricsQL query language based on PromQL. The simplicity is twofold:
- It is simpler to configure and operate. There is no need in configuring sidecars, fighting gossip protocol or setting up third-party systems such as Consul, Cassandra, DynamoDB or Memcached.
- VictoriaMetrics has simpler architecture. This means less bugs and more useful features in the long run comparing to competing TSDBs.
See comparing Thanos to VictoriaMetrics cluster and Remote Write Storage Wars talk from PromCon 2019.
VictoriaMetrics also uses less RAM than Thanos components.
What is the difference between VictoriaMetrics and Cortex?
VictoriaMetrics is similar to Cortex in the following aspects:
- Both systems accept data from vmagent or Prometheus via standard remote_write API, i.e. there is no need in running sidecars unlike in Thanos case.
- Both systems support multi-tenancy out of the box. See the corresponding docs for VictoriaMetrics.
- Both systems support data replication. See replication in Cortex and replication in VictoriaMetrics.
- Both systems scale horizontally to multiple nodes. See these docs for details.
- Both systems support alerting and recording rules via the corresponding tools such as vmalert.
The main differences between Cortex and VictoriaMetrics:
- Cortex re-uses Prometheus source code, while VictoriaMetrics is written from scratch.
- Cortex heavily relies on third-party services such as Consul, Memcache, DynamoDB, BigTable, Cassandra, etc. This may increase operational complexity and reduce system reliability comparing to VictoriaMetrics' case, which doesn't use any external services. Compare Cortex Architecture to VictoriaMetrics architecture.
- VictoriaMetrics provides production-ready single-node solution, which is much easier to setup and operate than Cortex cluster.
- Cortex may lose up to 12 hours of recent data on Ingestor failure - see the corresponding docs. VictoriaMetrics may lose only a few seconds of recent data, which isn't synced to persistent storage yet. See this article for details.
- Cortex is usually slower and requires more CPU and RAM than VictoriaMetrics. See this talk from Adidas at PromCon 2019 and other case studies.
- VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to Prometheus remote_write protocol - InfluxDB, OpenTSDB, Graphite, CSV. See these docs for details.
What is the difference between VictoriaMetrics and Thanos?
- Thanos re-uses Prometheus source code, while VictoriaMetrics is written from scratch.
- VictoriaMetrics accepts data via standard remote_write API for Prometheus, while Thanos uses non-standard Sidecar, which must run alongside each Prometheus instance.
- Thanos Sidecar requires disabling data compaction in Prometheus, which may hurt Prometheus performance and increase RAM usage. See these docs for more details.
- Thanos stores data in object storage (Amazon S3 or Google GCS), while VictoriaMetrics stores data in block storage (GCP persistent disks, Amazon EBS or bare metal HDD). While object storage is usually less expensive, block storage provides much lower latencies and higher throughput. VictoriaMetrics works perfectly with HDD-based block storage - there is no need in using more expensive SSD or NVMe disks in most cases.
- Thanos may lose up to 2 hours of recent data, which wasn't uploaded yet to object storage. VictoriaMetrics may lose only a few seconds of recent data, which isn't synced to persistent storage yet. See this article for details.
- VictoriaMetrics provides production-ready single-node solution, which is much easier to setup and operate than Thanos components.
- Thanos may be harder to setup and operate comparing to VictoriaMetrics, since it has more moving parts, which can be connected with less reliable networks. See this article for details.
- Thanos is usually slower and requires more CPU and RAM than VictoriaMetrics. See this talk from Adidas at PromCon 2019.
- VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to Prometheus remote_write protocol - InfluxDB, OpenTSDB, Graphite, CSV. See these docs for details.
How does VictoriaMetrics compare to InfluxDB?
- VictoriaMetrics requires 10x less RAM and it works faster.
- VictoriaMetrics provides better query language than InfluxQL or Flux.
- VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to InfluxDB - Prometheus remote_write, OpenTSDB, Graphite, CSV. See these docs for details.
How does VictoriaMetrics compare to TimescaleDB?
- TimescaleDB insists on using SQL as a query language. While SQL is more powerful than PromQL, this power is rarely required during typical TSDB usage. Real-world queries usually look clearer and simpler when written in PromQL than in SQL.
- VictoriaMetrics requires up to 70x less storage space comparing to TimescaleDB for storing the same amount of time series data. The gap in storage space usage can be lowered from 70x to 3x if compression in TimescaleDB is properly configured (it isn't an easy task in general case :)).
- VictoriaMetrics accepts data in multiple popular data ingestion protocols - InfluxDB, OpenTSDB, Graphite, CSV, while TimescaleDB supports only SQL inserts.
Does VictoriaMetrics use Prometheus technologies like other clustered TSDBs built on top of Prometheus such as Thanos or Cortex?
No. VictoriaMetrics core is written in Go from scratch by fasthttp author. The architecture is optimized for storing and querying large amounts of time series data with high cardinality. VictoriaMetrics storage uses certain ideas from ClickHouse. Special thanks to Alexey Milovidov.
Are there performance comparisons with other solutions?
Yes:
- Benchmarking time series workloads on Apache Kudu using TSBS
- Billy: how VictoriaMetrics deals with more than 500 billion rows
- Measuring vertical scalability for time series databases: VictoriaMetrics vs InfluxDB vs TimescaleDB.
- Measuring insert performance on high-cardinality time series: VictoriaMetrics vs InfluxDB
- TSBS benchmark on high-cardinality time series: VictoriaMetrics vs InfluxDB vs TimescaleDB
- Standard TSBS benchmark: VictoriaMetrics vs InfluxDB vs TimescaleDB
See also other articles about VictoriaMetrics.
What is the pricing for VictoriaMetrics?
The following versions are open source and free:
We provide commercial support for both versions. Contact us for the pricing.
The following commercial versions of VictoriaMetrics are planned:
- Managed cluster in the Cloud.
- SaaS version.
Contact us for more information on our plans.
Why VictoriaMetrics doesn't support Prometheus remote read API?
Remote read API requires transferring all the raw data for all the requested metrics over the given time range. For instance,
if a query covers 1000 metrics with 10K values each, then the remote read API had to return 1000*10K=10M metric values to Prometheus.
This is slow and expensive.
Prometheus remote read API isn't intended for querying foreign data aka global query view. See this issue for details.
So just query VictoriaMetrics directly via Prometheus Querying API or via Prometheus datasource in Grafana.
Does VictoriaMetrics deduplicate data from Prometheus instances scraping the same targets (aka HA pairs)?
Yes. See these docs for details.
Does VictoriaMetrics support replication?
Yes. See these docs for details.
Where is the source code of VictoriaMetrics?
Source code for the following versions is available in the following places:
Does VictoriaMetrics fit for data from IoT sensors and industrial sensors?
VictoriaMetrics is able to handle data from hundreds of millions of IoT sensors and industrial sensors. It supports high cardinality data, perfectly scales up on a single node and scales horizontally to multiple nodes.
Where can I ask questions about VictoriaMetrics?
Questions about VictoriaMetrics can be asked via the following channels:
Where can I file bugs and feature requests regarding VictoriaMetrics?
File bugs and feature requests here.
Are you looking for investors?
Yes. Mail us if you are interested in.