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## VictoriaMetrics
VictoriaMetrics is fast, cost-effective and scalable time-series database. It can be used as long-term remote storage for Prometheus.
It is available in [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases),
[docker images](https://hub.docker.com/r/victoriametrics/victoria-metrics/) and
in [source code](https://github.com/VictoriaMetrics/VictoriaMetrics). Just download VictoriaMetrics and see [how to start it](#how-to-start-victoriametrics).
Cluster version is available [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
## Case studies and talks
* [Adidas](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#adidas)
* [COLOPL](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#colopl)
* [Wix.com](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#wixcom)
* [Wedos.com](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#wedoscom)
* [Dreamteam](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#dreamteam)
## Prominent features
* 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://github.com/VictoriaMetrics/VictoriaMetrics/wiki/ExtendedPromQL) query language, which is inspired by PromQL.
* Supports global query view. Multiple Prometheus instances may write data into VictoriaMetrics. Later this data may be used in 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) when working 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.
* 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` 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://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmbackup/README.md) / [vmrestore](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmrestore/README.md).
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' ingestion and [backfilling](#backfilling) via the following protocols:
* [Prometheus remote write API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write)
* [InfluxDB line protocol](#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf)
* [Graphite plaintext protocol](#how-to-send-data-from-graphite-compatible-agents-such-as-statsd) with [tags](https://graphite.readthedocs.io/en/latest/tags.html#carbon)
if `-graphiteListenAddr` is set.
* [OpenTSDB put message](#sending-data-via-telnet-put-protocol) if `-opentsdbListenAddr` is set.
* [HTTP OpenTSDB /api/put requests](#sending-opentsdb-data-via-http-apiput-requests) if `-opentsdbHTTPListenAddr` is set.
* [/api/v1/import](#how-to-import-time-series-data)
* Ideally works with big amounts of time series data from Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
* Has open source [cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
## Operation
### Table of contents
- [How to start VictoriaMetrics](#how-to-start-victoriametrics)
- [Prometheus setup](#prometheus-setup)
- [Grafana setup](#grafana-setup)
- [How to upgrade VictoriaMetrics?](#how-to-upgrade-victoriametrics)
- [How to apply new config to VictoriaMetrics?](#how-to-apply-new-config-to-victoriametrics)
- [How to send data from InfluxDB-compatible agents such as Telegraf?](#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf)
- [How to send data from Graphite-compatible agents such as StatsD?](#how-to-send-data-from-graphite-compatible-agents-such-as-statsd)
- [Querying Graphite data](#querying-graphite-data)
- [How to send data from OpenTSDB-compatible agents?](#how-to-send-data-from-opentsdb-compatible-agents)
- [Prometheus querying API usage](#prometheus-querying-api-usage)
- [How to build from sources](#how-to-build-from-sources)
- [Development build](#development-build)
- [Production build](#production-build)
- [ARM build](#arm-build)
- [Pure Go build (CGO_ENABLED=0)](#pure-go-build-cgo_enabled0)
- [Building docker images](#building-docker-images)
- [Start with docker-compose](#start-with-docker-compose)
- [Setting up service](#setting-up-service)
- [How to work with snapshots?](#how-to-work-with-snapshots)
- [How to delete time series?](#how-to-delete-time-series)
- [How to export time series?](#how-to-export-time-series)
- [How to import time series data?](#how-to-import-time-series-data)
- [Federation](#federation)
- [Capacity planning](#capacity-planning)
- [High availability](#high-availability)
- [Retention](#retention)
- [Multiple retentions](#multiple-retentions)
- [Downsampling](#downsampling)
- [Multi-tenancy](#multi-tenancy)
- [Scalability and cluster version](#scalability-and-cluster-version)
- [Alerting](#alerting)
- [Security](#security)
- [Tuning](#tuning)
- [Monitoring](#monitoring)
- [Troubleshooting](#troubleshooting)
- [Backfilling](#backfilling)
- [Profiling](#profiling)
- [Integrations](#integrations)
- [Roadmap](#roadmap)
- [Contacts](#contacts)
- [Community and contributions](#community-and-contributions)
- [Third-party contributions](#third-party-contributions)
- [Reporting bugs](#reporting-bugs)
- [Victoria Metrics Logo](#victoria-metrics-logo)
- [Logo Usage Guidelines](#logo-usage-guidelines)
- [Font used:](#font-used)
- [Color Palette:](#color-palette)
- [We kindly ask:](#we-kindly-ask)
### How to start VictoriaMetrics
Just start VictoriaMetrics [executable](https://github.com/VictoriaMetrics/VictoriaMetrics/releases)
or [docker image](https://hub.docker.com/r/victoriametrics/victoria-metrics/) with the desired command-line flags.
The following command-line flags are used the most:
* `-storageDataPath` - path to data directory. VictoriaMetrics stores all the data in this directory. Default path is `victoria-metrics-data` in current working directory.
* `-retentionPeriod` - retention period in months for the data. Older data is automatically deleted. Default period is 1 month.
* `-httpListenAddr` - TCP address to listen to for http requests. By default, it listens port `8428` on all the network interfaces.
* `-graphiteListenAddr` - TCP and UDP address to listen to for Graphite data. By default, it is disabled.
* `-opentsdbListenAddr` - TCP and UDP address to listen to for OpenTSDB data over telnet protocol. By default, it is disabled.
* `-opentsdbHTTPListenAddr` - TCP address to listen to for HTTP OpenTSDB data over `/api/put`. By default, it is disabled.
Pass `-help` to see all the available flags with description and default values.
It is recommended setting up [monitoring](#monitoring) for VictoriaMetrics.
### Prometheus setup
Prometheus must be configured with [remote_write](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write)
in order to send data to VictoriaMetrics. Add the following lines
to Prometheus config file (it is usually located at `/etc/prometheus/prometheus.yml`):
```yml
remote_write:
- url: http://<victoriametrics-addr>:8428/api/v1/write
```
Substitute `<victoriametrics-addr>` with the hostname or IP address of VictoriaMetrics.
Then apply the new config via the following command:
```
kill -HUP `pidof prometheus`
```
Prometheus writes incoming data to local storage and replicates it to remote storage in parallel.
This means the data remains available in local storage for `--storage.tsdb.retention.time` duration
even if remote storage is unavailable.
If you plan to send data to VictoriaMetrics from multiple Prometheus instances, then add the following lines into `global` section
of [Prometheus config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#configuration-file):
```yml
global:
external_labels:
datacenter: dc-123
```
This instructs Prometheus to add `datacenter=dc-123` label to each time series sent to remote storage.
The label name may be arbitrary - `datacenter` is just an example. The label value must be unique
across Prometheus instances, so those time series may be filtered and grouped by this label.
For highly loaded Prometheus instances (400k+ samples per second)
the following tuning may be applied:
```
remote_write:
- url: http://<victoriametrics-addr>:8428/api/v1/write
queue_config:
max_samples_per_send: 10000
capacity: 20000
max_shards: 30
```
Using remote write increases memory usage for Prometheus up to ~25%
and depends on the shape of data. If you are experiencing issues with
too high memory consumption try to lower `max_samples_per_send`
and `capacity` params (keep in mind that these two params are tightly connected).
Read more about tuning remote write for Prometheus [here](https://prometheus.io/docs/practices/remote_write).
It is recommended upgrading Prometheus to [v2.12.0](https://github.com/prometheus/prometheus/releases) or newer,
since the previous versions may have issues with `remote_write`.
### Grafana setup
Create [Prometheus datasource](http://docs.grafana.org/features/datasources/prometheus/) in Grafana with the following Url:
```
http://<victoriametrics-addr>:8428
```
Substitute `<victoriametrics-addr>` with the hostname or IP address of VictoriaMetrics.
Then build graphs with the created datasource using [Prometheus query language](https://prometheus.io/docs/prometheus/latest/querying/basics/).
VictoriaMetrics supports native PromQL and [extends it with useful features](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/ExtendedPromQL).
### How to upgrade VictoriaMetrics?
It is safe upgrading VictoriaMetrics to new versions unless [release notes](https://github.com/VictoriaMetrics/VictoriaMetrics/releases)
say otherwise. It is recommended performing regular upgrades to the latest version,
since it may contain important bug fixes, performance optimizations or new features.
Follow the following steps during the upgrade:
1) Send `SIGINT` signal to VictoriaMetrics process in order to gracefully stop it.
2) Wait until the process stops. This can take a few seconds.
3) Start the upgraded VictoriaMetrics.
Prometheus doesn't drop data during VictoriaMetrics restart.
See [this article](https://grafana.com/blog/2019/03/25/whats-new-in-prometheus-2.8-wal-based-remote-write/) for details.
### How to apply new config to VictoriaMetrics?
VictoriaMetrics must be restarted for applying new config:
1) Send `SIGINT` signal to VictoriaMetrics process in order to gracefully stop it.
2) Wait until the process stops. This can take a few seconds.
3) Start VictoriaMetrics with the new config.
Prometheus doesn't drop data during VictoriaMetrics restart.
See [this article](https://grafana.com/blog/2019/03/25/whats-new-in-prometheus-2.8-wal-based-remote-write/) for details.
### How to send data from InfluxDB-compatible agents such as [Telegraf](https://www.influxdata.com/time-series-platform/telegraf/)?
Just use `http://<victoriametric-addr>:8428` url instead of InfluxDB url in agents' configs.
For instance, put the following lines into `Telegraf` config, so it sends data to VictoriaMetrics instead of InfluxDB:
```
[[outputs.influxdb]]
urls = ["http://<victoriametrics-addr>:8428"]
```
Do not forget substituting `<victoriametrics-addr>` with the real address where VictoriaMetrics runs.
VictoriaMetrics maps Influx data using the following rules:
* [`db` query arg](https://docs.influxdata.com/influxdb/v1.7/tools/api/#write-http-endpoint) is mapped into `db` label value
unless `db` tag exists in the Influx line.
* Field names are mapped to time series names prefixed with `{measurement}{separator}` value,
where `{separator}` equals to `_` by default. It can be changed with `-influxMeasurementFieldSeparator` command-line flag.
See also `-influxSkipSingleField` command-line flag. If `{measurement}` is empty, then time series names correspond to field names.
* Field values are mapped to time series values.
* Tags are mapped to Prometheus labels as-is.
For example, the following Influx line:
```
foo,tag1=value1,tag2=value2 field1=12,field2=40
```
is converted into the following Prometheus data points:
```
foo_field1{tag1="value1", tag2="value2"} 12
foo_field2{tag1="value1", tag2="value2"} 40
```
Example for writing data with [Influx line protocol](https://docs.influxdata.com/influxdb/v1.7/write_protocols/line_protocol_tutorial/)
to local VictoriaMetrics using `curl`:
```
curl -d 'measurement,tag1=value1,tag2=value2 field1=123,field2=1.23' -X POST 'http://localhost:8428/write'
```
An arbitrary number of lines delimited by '\n' may be sent in a single request.
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:
```
curl -G 'http://localhost:8428/api/v1/export' -d 'match={__name__=~"measurement_.*"}'
```
The `/api/v1/export` endpoint should return the following response:
```
{"metric":{"__name__":"measurement_field1","tag1":"value1","tag2":"value2"},"values":[123],"timestamps":[1560272508147]}
{"metric":{"__name__":"measurement_field2","tag1":"value1","tag2":"value2"},"values":[1.23],"timestamps":[1560272508147]}
```
Note that Influx line protocol expects [timestamps in *nanoseconds* by default](https://docs.influxdata.com/influxdb/v1.7/write_protocols/line_protocol_tutorial/#timestamp),
while VictoriaMetrics stores them with *milliseconds* precision.
### How to send data from Graphite-compatible agents such as [StatsD](https://github.com/etsy/statsd)?
1) Enable Graphite receiver in VictoriaMetrics by setting `-graphiteListenAddr` command line flag. For instance,
the following command will enable Graphite receiver in VictoriaMetrics on TCP and UDP port `2003`:
```
/path/to/victoria-metrics-prod -graphiteListenAddr=:2003
```
2) Use the configured address in Graphite-compatible agents. For instance, set `graphiteHost`
to the VictoriaMetrics host in `StatsD` configs.
Example for writing data with Graphite plaintext protocol to local VictoriaMetrics using `nc`:
```
echo "foo.bar.baz;tag1=value1;tag2=value2 123 `date +%s`" | nc -N localhost 2003
```
VictoriaMetrics sets the current time if the timestamp is omitted.
An arbitrary number of lines delimited by `\n` may be sent in one go.
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:
```
curl -G 'http://localhost:8428/api/v1/export' -d 'match=foo.bar.baz'
```
The `/api/v1/export` endpoint should return the following response:
```
{"metric":{"__name__":"foo.bar.baz","tag1":"value1","tag2":"value2"},"values":[123],"timestamps":[1560277406000]}
```
### Querying Graphite data
Data sent to VictoriaMetrics via `Graphite plaintext protocol` may be read either via
[Prometheus querying API](#prometheus-querying-api-usage)
or via [go-graphite/carbonapi](https://github.com/go-graphite/carbonapi/blob/master/cmd/carbonapi/carbonapi.example.prometheus.yaml).
### How to send data from OpenTSDB-compatible agents?
VictoriaMetrics supports [telnet put protocol](http://opentsdb.net/docs/build/html/api_telnet/put.html)
and [HTTP /api/put requests](http://opentsdb.net/docs/build/html/api_http/put.html) for ingesting OpenTSDB data.
#### Sending data via `telnet put` protocol
1) Enable OpenTSDB receiver in VictoriaMetrics by setting `-opentsdbListenAddr` command line flag. For instance,
the following command enables OpenTSDB receiver in VictoriaMetrics on TCP and UDP port `4242`:
```
/path/to/victoria-metrics-prod -opentsdbListenAddr=:4242
```
2) Send data to the given address from OpenTSDB-compatible agents.
Example for writing data with OpenTSDB protocol to local VictoriaMetrics using `nc`:
```
echo "put foo.bar.baz `date +%s` 123 tag1=value1 tag2=value2" | nc -N localhost 4242
```
An arbitrary number of lines delimited by `\n` may be sent in one go.
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:
```
curl -G 'http://localhost:8428/api/v1/export' -d 'match=foo.bar.baz'
```
The `/api/v1/export` endpoint should return the following response:
```
{"metric":{"__name__":"foo.bar.baz","tag1":"value1","tag2":"value2"},"values":[123],"timestamps":[1560277292000]}
```
#### Sending OpenTSDB data via HTTP `/api/put` requests
1) Enable HTTP server for OpenTSDB `/api/put` requests by setting `-opentsdbHTTPListenAddr` command line flag. For instance,
the following command enables OpenTSDB HTTP server on port `4242`:
```
/path/to/victoria-metrics-prod -opentsdbHTTPListenAddr=:4242
```
2) Send data to the given address from OpenTSDB-compatible agents.
Example for writing a single data point:
```
curl -H 'Content-Type: application/json' -d '{"metric":"x.y.z","value":45.34,"tags":{"t1":"v1","t2":"v2"}}' http://localhost:4242/api/put
```
Example for writing multiple data points in a single request:
```
curl -H 'Content-Type: application/json' -d '[{"metric":"foo","value":45.34},{"metric":"bar","value":43}]' http://localhost:4242/api/put
```
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:
```
curl -G 'http://localhost:8428/api/v1/export' -d 'match[]=x.y.z' -d 'match[]=foo' -d 'match[]=bar'
```
The `/api/v1/export` endpoint should return the following response:
```
{"metric":{"__name__":"foo"},"values":[45.34],"timestamps":[1566464846000]}
{"metric":{"__name__":"bar"},"values":[43],"timestamps":[1566464846000]}
{"metric":{"__name__":"x.y.z","t1":"v1","t2":"v2"},"values":[45.34],"timestamps":[1566464763000]}
```
### Prometheus querying API usage
VictoriaMetrics supports the following handlers from [Prometheus querying API](https://prometheus.io/docs/prometheus/latest/querying/api/):
* [/api/v1/query](https://prometheus.io/docs/prometheus/latest/querying/api/#instant-queries)
* [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries)
* [/api/v1/series](https://prometheus.io/docs/prometheus/latest/querying/api/#finding-series-by-label-matchers)
* [/api/v1/labels](https://prometheus.io/docs/prometheus/latest/querying/api/#getting-label-names)
* [/api/v1/label/.../values](https://prometheus.io/docs/prometheus/latest/querying/api/#querying-label-values)
These handlers can be queried from Prometheus-compatible clients such as Grafana or curl.
VictoriaMetrics accepts additional args for `/api/v1/labels` and `/api/v1/label/.../values` handlers.
See [this feature request](https://github.com/prometheus/prometheus/issues/6178) for details:
* Any number [time series selectors](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors) via `match[]` query arg.
* Optional `start` and `end` query args for limiting the time range for the selected labels or label values.
Additionally VictoriaMetrics provides the following handlers:
* `/api/v1/series/count` - it returns the total number of time series in the database. Note that this handler scans all the inverted index,
so it can be slow if the database contains tens of millions of time series.
* `/api/v1/labels/count` - it returns a list of `label: values_count` entries. It can be used for determining labels with the maximum number of values.
### How to build from sources
We recommend using either [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases) or
[docker images](https://hub.docker.com/r/victoriametrics/victoria-metrics/) instead of building VictoriaMetrics
from sources. Building from sources is reasonable when developing additional features specific
to your needs.
#### Development build
1. [Install Go](https://golang.org/doc/install). The minimum supported version is Go 1.12.
2. Run `make victoria-metrics` from the root folder of the repository.
It builds `victoria-metrics` binary and puts it into the `bin` folder.
#### Production build
1. [Install docker](https://docs.docker.com/install/).
2. Run `make victoria-metrics-prod` from the root folder of the repository.
It builds `victoria-metrics-prod` binary and puts it into the `bin` folder.
#### ARM build
ARM build may run on Raspberry Pi or on [energy-efficient ARM servers](https://blog.cloudflare.com/arm-takes-wing/).
#### Development ARM build
1. [Install Go](https://golang.org/doc/install). The minimum supported version is Go 1.12.
2. Run `make victoria-metrics-arm` or `make victoria-metrics-arm64` from the root folder of the repository.
It builds `victoria-metrics-arm` or `victoria-metrics-arm64` binary respectively and puts it into the `bin` folder.
#### Production ARM build
1. [Install docker](https://docs.docker.com/install/).
2. Run `make victoria-metrics-arm-prod` or `make victoria-metrics-arm64-prod` from the root folder of the repository.
It builds `victoria-metrics-arm-prod` or `victoria-metrics-arm64-prod` binary respectively and puts it into the `bin` folder.
#### Pure Go build (CGO_ENABLED=0)
`Pure Go` mode builds only Go code without [cgo](https://golang.org/cmd/cgo/) dependencies.
This is an experimental mode, which may result in a lower compression ratio and slower decompression performance.
Use it with caution!
1. [Install Go](https://golang.org/doc/install). The minimum supported version is Go 1.12.
2. Run `make victoria-metrics-pure` from the root folder of the repository.
It builds `victoria-metrics-pure` binary and puts it into the `bin` folder.
#### Building docker images
Run `make package-victoria-metrics`. It builds `victoriametrics/victoria-metrics:<PKG_TAG>` docker image locally.
`<PKG_TAG>` is auto-generated image tag, which depends on source code in the repository.
The `<PKG_TAG>` may be manually set via `PKG_TAG=foobar make package-victoria-metrics`.
### Start with docker-compose
[Docker-compose](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/docker-compose.yml)
helps to spin up VictoriaMetrics, Prometheus and Grafana with one command.
More details may be found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker#folder-contains-basic-images-and-tools-for-building-and-running-victoria-metrics-in-docker).
### Setting up service
Read [these instructions](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/43) on how to set up VictoriaMetrics as a service in your OS.
### How to work with snapshots?
VictoriaMetrics can create [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282)
for all the data stored under `-storageDataPath` directory.
Navigate to `http://<victoriametrics-addr>:8428/snapshot/create` in order to create an instant snapshot.
The page will return the following JSON response:
```
{"status":"ok","snapshot":"<snapshot-name>"}
```
Snapshots are created under `<-storageDataPath>/snapshots` directory, where `<-storageDataPath>`
is the command-line flag value. Snapshots can be archived to backup storage at any time
with [vmbackup](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmbackup/README.md).
The `http://<victoriametrics-addr>:8428/snapshot/list` page contains the list of available snapshots.
Navigate to `http://<victoriametrics-addr>:8428/snapshot/delete?snapshot=<snapshot-name>` in order
to delete `<snapshot-name>` snapshot.
Navigate to `http://<victoriametrics-addr>:8428/snapshot/delete_all` in order to delete all the snapshots.
Steps for restoring from a snapshot:
1. Stop VictoriaMetrics with `kill -INT`.
2. Restore snapshot contents from backup with [vmrestore](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmrestore/README.md)
to the directory pointed by `-storageDataPath`.
3. Start VictoriaMetrics.
### How to delete time series?
Send a request to `http://<victoriametrics-addr>:8428/api/v1/admin/tsdb/delete_series?match[]=<timeseries_selector_for_delete>`,
where `<timeseries_selector_for_delete>` may contain any [time series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors)
for metrics to delete. After that all the time series matching the given selector are deleted. Storage space for
the deleted time series isn't freed instantly - it is freed during subsequent merges of data files.
It is recommended verifying which metrics will be deleted with the call to `http://<victoria-metrics-addr>:8428/api/v1/series?match[]=<timeseries_selector_for_delete>`
before actually deleting the metrics.
The delete API is intended mainly for the following cases:
- One-off deleting of accidentally written invalid (or undesired) time series.
- One-off deleting of user data due to [GDPR](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation).
It isn't recommended using delete API for the following cases, since it brings non-zero overhead:
- Regular cleanups for unneded data. Just prevent writing unneeded data into VictoriaMetrics.
- Reducing disk space usage by deleting unneded time series. This doesn't work as expected, since the deleted
time series occupy disk space until the next merge operation, which can never occur.
It is better using `-retentionPeriod` command-line flag for efficient pruning of old data.
### How to export time series?
Send a request to `http://<victoriametrics-addr>:8428/api/v1/export?match[]=<timeseries_selector_for_export>`,
where `<timeseries_selector_for_export>` may contain any [time series selector](https://prometheus.io/docs/prometheus/latest/querying/basics/#time-series-selectors)
for metrics to export. Use `{__name__!=""}` selector for fetching all the time series.
The response would contain all the data for the selected time series in [JSON streaming format](https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON).
Each JSON line would contain data for a single time series. An example output:
```
{"metric":{"__name__":"up","job":"node_exporter","instance":"localhost:9100"},"values":[0,0,0],"timestamps":[1549891472010,1549891487724,1549891503438]}
{"metric":{"__name__":"up","job":"prometheus","instance":"localhost:9090"},"values":[1,1,1],"timestamps":[1549891461511,1549891476511,1549891491511]}
```
Optional `start` and `end` args may be added to the request in order to limit the time frame for the exported data. These args may contain either
unix timestamp in seconds or [RFC3339](https://www.ietf.org/rfc/rfc3339.txt) values.
Pass `Accept-Encoding: gzip` HTTP header in the request to `/api/v1/export` in order to reduce network bandwidth during exporing big amounts
of time series data. This enables gzip compression for the exported data. Example for exporting gzipped data:
```
curl -H 'Accept-Encoding: gzip' http://localhost:8428/api/v1/export -d 'match[]={__name__!=""}' > data.jsonl.gz
```
The maximum duration for each request to `/api/v1/export` is limited by `-search.maxExportDuration` command-line flag.
Exported data can be imported via POST'ing it to [/api/v1/import](#how-to-import-time-series-data).
### How to import time series data?
Time series data can be imported via any supported ingestion protocol:
* [Prometheus remote_write API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write)
* [Influx line protocol](#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf)
* [Graphite plaintext protocol](#how-to-send-data-from-graphite-compatible-agents-such-as-statsd)
* [OpenTSDB telnet put protocol](#sending-data-via-telnet-put-protocol)
* [OpenTSDB http /api/put](#sending-opentsdb-data-via-http-apiput-requests)
* `/api/v1/import` http POST handler, which accepts data from [/api/v1/export](#how-to-export-time-series).
The most efficient protocol for importing data into VictoriaMetrics is `/api/v1/import`. Example for importing data obtained via `/api/v1/export`:
```
# Export the data from <source-victoriametrics>:
curl http://source-victoriametrics:8428/api/v1/export -d 'match={__name__!=""}' > exported_data.jsonl
# Import the data to <destination-victoriametrics>:
curl -X POST http://destination-victoriametrics:8428/api/v1/import -T exported_data.jsonl
```
Pass `Content-Encoding: gzip` HTTP request header to `/api/v1/import` for importing gzipped data:
```
# Export gzipped data from <source-victoriametrics>:
curl -H 'Accept-Encoding: gzip' http://source-victoriametrics:8428/api/v1/export -d 'match={__name__!=""}' > exported_data.jsonl.gz
# Import gzipped data to <destination-victoriametrics>:
curl -X POST -H 'Content-Encoding: gzip' http://destination-victoriametrics:8428/api/v1/import -T exported_data.jsonl.gz
```
Each request to `/api/v1/import` can load up to a single vCPU core on VictoriaMetrics. Import speed can be improved by splitting the original file into smaller parts
and importing them concurrently. Note that the original file must be split on newlines.
### Federation
VictoriaMetrics exports [Prometheus-compatible federation data](https://prometheus.io/docs/prometheus/latest/federation/)
at `http://<victoriametrics-addr>:8428/federate?match[]=<timeseries_selector_for_federation>`.
Optional `start` and `end` args may be added to the request in order to scrape the last point for each selected time series on the `[start ... end]` interval.
`start` and `end` may contain either unix timestamp in seconds or [RFC3339](https://www.ietf.org/rfc/rfc3339.txt) values. By default, the last point
on the interval `[now - max_lookback ... now]` is scraped for each time series. The default value for `max_lookback` is `5m` (5 minutes), but it can be overridden.
For instance, `/federate?match[]=up&max_lookback=1h` would return last points on the `[now - 1h ... now]` interval. This may be useful for time series federation
with scrape intervals exceeding `5m`.
### Capacity planning
A rough estimation of the required resources for ingestion path:
* RAM size: less than 1KB per active time series. So, ~1GB of RAM is required for 1M active time series.
Time series is considered active if new data points have been added to it recently or if it has been recently queried.
The number of active time series may be obtained from `vm_cache_entries{type="storage/hour_metric_ids"}` metric
exported on the `/metrics` page.
VictoriaMetrics stores various caches in RAM. Memory size for these caches may be limited by `-memory.allowedPercent` flag.
* CPU cores: a CPU core per 300K inserted data points per second. So, ~4 CPU cores are required for processing
the insert stream of 1M data points per second. The ingestion rate may be lower for high cardinality data or for time series with high number of labels.
See [this article](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) for details.
If you see lower numbers per CPU core, then it is likely active time series info doesn't fit caches,
so you need more RAM for lowering CPU usage.
* Storage space: less than a byte per data point on average. So, ~260GB is required for storing a month-long insert stream
of 100K data points per second.
The actual storage size heavily depends on data randomness (entropy). Higher randomness means higher storage size requirements.
Read [this article](https://medium.com/faun/victoriametrics-achieving-better-compression-for-time-series-data-than-gorilla-317bc1f95932)
for details.
* Network usage: outbound traffic is negligible. Ingress traffic is ~100 bytes per ingested data point via
[Prometheus remote_write API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write).
The actual ingress bandwidth usage depends on the average number of labels per ingested metric and the average size
of label values. The higher number of per-metric labels and longer label values mean the higher ingress bandwidth.
The required resources for query path:
* RAM size: depends on the number of time series to scan in each query and the `step`
argument passed to [/api/v1/query_range](https://prometheus.io/docs/prometheus/latest/querying/api/#range-queries).
The higher number of scanned time series and lower `step` argument results in the higher RAM usage.
* CPU cores: a CPU core per 30 millions of scanned data points per second.
* Network usage: depends on the frequency and the type of incoming requests. Typical Grafana dashboards usually
require negligible network bandwidth.
### High availability
1) Install multiple VictoriaMetrics instances in distinct datacenters (availability zones).
2) Add addresses of these instances to `remote_write` section in Prometheus config:
```yml
remote_write:
- url: http://<victoriametrics-addr-1>:8428/api/v1/write
queue_config:
max_samples_per_send: 10000
# ...
- url: http://<victoriametrics-addr-N>:8428/api/v1/write
queue_config:
max_samples_per_send: 10000
```
3) Apply the updated config:
```
kill -HUP `pidof prometheus`
```
4) Now Prometheus should write data into all the configured `remote_write` urls in parallel.
5) Set up [Promxy](https://github.com/jacksontj/promxy) in front of all the VictoriaMetrics replicas.
6) Set up Prometheus datasource in Grafana that points to Promxy.
If you have Prometheus HA pairs with replicas `r1` and `r2` in each pair, then configure each `r1`
to write data to `victoriametrics-addr-1`, while each `r2` should write data to `victoriametrics-addr-2`.
### Retention
Retention is configured with `-retentionPeriod` command-line flag. For instance, `-retentionPeriod=3` means
that the data will be stored for 3 months and then deleted.
Data is split in per-month subdirectories inside `<-storageDataPath>/data/small` and `<-storageDataPath>/data/big` folders.
Directories for months outside the configured retention are deleted on the first day of new month.
In order to keep data according to `-retentionPeriod` max disk space usage is going to be `-retentionPeriod` + 1 month.
For example if `-retentionPeriod` is set to 1, data for January is deleted on March 1st.
### Multiple retentions
Just start multiple VictoriaMetrics instances with distinct values for the following flags:
* `-retentionPeriod`
* `-storageDataPath`, so the data for each retention period is saved in a separate directory
* `-httpListenAddr`, so clients may reach VictoriaMetrics instance with proper retention
### Downsampling
There is no downsampling support at the moment, but:
- VictoriaMetrics is optimized for querying big amounts of raw data. See benchmark results for heavy queries
in [this article](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
- VictoriaMetrics has good compression for on-disk data. See [this article](https://medium.com/@valyala/victoriametrics-achieving-better-compression-for-time-series-data-than-gorilla-317bc1f95932)
for details.
These properties reduce the need of downsampling. We plan to implement downsampling in the future.
See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/36) for details.
### Multi-tenancy
Single-node VictoriaMetrics doesn't support multi-tenancy. Use [cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster) instead.
### Scalability and cluster version
Though single-node VictoriaMetrics cannot scale to multiple nodes, it is optimized for resource usage - storage size / bandwidth / IOPS, RAM, CPU.
This means that a single-node VictoriaMetrics may scale vertically and substitute a moderately sized cluster built with competing solutions
such as Thanos, Uber M3, InfluxDB or TimescaleDB. See [vertical scalability benchmarks](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
So try single-node VictoriaMetrics at first and then [switch to cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster) if you still need
horizontally scalable long-term remote storage for really large Prometheus deployments.
[Contact us](mailto:info@victoriametrics.com) for paid support.
### Alerting
VictoriaMetrics doesn't support rule evaluation and alerting yet, so these actions can be performed at the following places:
* At Prometheus - see [the corresponding docs](https://prometheus.io/docs/alerting/overview/).
* At Promxy - see [the corresponding docs](https://github.com/jacksontj/promxy/blob/master/README.md#how-do-i-use-alertingrecording-rules-in-promxy).
* At Grafana - see [the corresponding docs](https://grafana.com/docs/alerting/rules/).
### Security
Do not forget protecting sensitive endpoints in VictoriaMetrics when exposing it to untrusted networks such as the internet.
Consider setting the following command-line flags:
* `-tls`, `-tlsCertFile` and `-tlsKeyFile` for switching from HTTP to HTTPS.
* `-httpAuth.username` and `-httpAuth.password` for protecting all the HTTP endpoints
with [HTTP Basic Authentication](https://en.wikipedia.org/wiki/Basic_access_authentication).
* `-deleteAuthKey` for protecting `/api/v1/admin/tsdb/delete_series` endpoint. See [how to delete time series](#how-to-delete-time-series).
* `-snapshotAuthKey` for protecting `/snapshot*` endpoints. See [how to work with snapshots](#how-to-work-with-snapshots).
Explicitly set internal network interface for TCP and UDP ports for data ingestion with Graphite and OpenTSDB formats.
For example, substitute `-graphiteListenAddr=:2003` with `-graphiteListenAddr=<internal_iface_ip>:2003`.
### Tuning
* There is no need for VictoriaMetrics tuning since it uses reasonable defaults for command-line flags,
which are automatically adjusted for the available CPU and RAM resources.
* There is no need for Operating System tuning since VictoriaMetrics is optimized for default OS settings.
The only option is increasing the limit on [the number of open files in the OS](https://medium.com/@muhammadtriwibowo/set-permanently-ulimit-n-open-files-in-ubuntu-4d61064429a),
so Prometheus instances could establish more connections to VictoriaMetrics.
* The recommended filesystem is `ext4`, the recommended persistent storage is [persistent HDD-based disk on GCP](https://cloud.google.com/compute/docs/disks/#pdspecs),
since it is protected from hardware failures via internal replication and it can be [resized on the fly](https://cloud.google.com/compute/docs/disks/add-persistent-disk#resize_pd).
If you plan to store more than 1TB of data on `ext4` partition or plan extending it to more than 16TB,
then the following options are recommended to pass to `mkfs.ext4`:
```
mkfs.ext4 ... -O 64bit,huge_file,extent -T huge
```
### Monitoring
VictoriaMetrics exports internal metrics in Prometheus format at `/metrics` page.
These metrics may be collected either via Prometheus by adding the corresponding scrape config to it.
Alternatively they can be self-scraped by setting `-selfScrapeInterval` command-line flag to duration greater than 0.
For example, `-scrapeInterval=10s` would enable self-scraping of `/metrics` page with 10 seconds interval.
There are officials Grafana dashboards for [single-node VictoriaMetrics](https://grafana.com/dashboards/10229) and [clustered VictoriaMetrics](https://grafana.com/grafana/dashboards/11176).
The most interesting metrics are:
* `vm_cache_entries{type="storage/hour_metric_ids"}` - the number of time series with new data points during the last hour
aka active time series.
* `rate(vm_new_timeseries_created_total[5m])` - time series churn rate.
* `vm_rows{type="indexdb"}` - the number of rows in inverted index. High value for this number usually mean high churn rate for time series.
* Sum of `vm_rows{type="storage/big"}` and `vm_rows{type="storage/small"}` - total number of `(timestamp, value)` data points
in the database.
* Sum of all the `vm_cache_size_bytes` metrics - the total size of all the caches in the database.
* `vm_allowed_memory_bytes` - the maximum allowed size for caches in the database. It is calculated as `system_memory * <-memory.allowedPercent> / 100`,
where `system_memory` is the amount of system memory and `-memory.allowedPercent` is the corresponding flag value.
* `vm_rows_inserted_total` - the total number of inserted rows since VictoriaMetrics start.
### Troubleshooting
* It is recommended to use default command-line flag values (i.e. don't set them explicitly) until the need
of tweaking these flag values arises.
* If VictoriaMetrics works slowly and eats more than a CPU core per 100K ingested data points per second,
then it is likely you have too many active time series for the current amount of RAM.
It is recommended increasing the amount of RAM on the node with VictoriaMetrics in order to improve
ingestion performance.
Another option is to increase `-memory.allowedPercent` command-line flag value. Be careful with this
option, since too big value for `-memory.allowedPercent` may result in high I/O usage.
* VictoriaMetrics requires free disk space for [merging data files to bigger ones](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
It may slow down when there is no enough free space left. So make sure `-storageDataPath` directory
has at least 20% of free space comparing to disk size.
* If VictoriaMetrics doesn't work because of certain parts are corrupted due to disk errors,
then just remove directories with broken parts. This will recover VictoriaMetrics at the cost
of data loss stored in the broken parts. In the future, `vmrecover` tool will be created
for automatic recovering from such errors.
### Backfilling
VictoriaMetrics accepts historical data in arbitrary order of time.
Make sure that configured `-retentionPeriod` covers timestamps for the backfilled data.
It is recommended disabling query cache with `-search.disableCache` command-line flag when writing
historical data with timestamps from the past, since the cache assumes that the data is written with
the current timestamps. Query cache can be enabled after the backfilling is complete.
### Profiling
VictoriaMetrics provides handlers for collecting the following [Go profiles](https://blog.golang.org/profiling-go-programs):
- Memory profile. It can be collected with the following command:
```
curl -s http://<victoria-metrics-host>:8428/debug/pprof/heap > mem.pprof
```
- CPU profile. It can be collected with the following command:
```
curl -s http://<victoria-metrics-host>:8428/debug/pprof/profile > cpu.pprof
```
The command for collecting CPU profile waits for 30 seconds before returning.
The collected profiles may be analyzed with [go tool pprof](https://github.com/google/pprof).
## Integrations
* [netdata](https://github.com/netdata/netdata) can push data into VictoriaMetrics via `Prometheus remote_write API`.
See [these docs](https://github.com/netdata/netdata#integrations).
* [go-graphite/carbonapi](https://github.com/go-graphite/carbonapi) can use VictoriaMetrics as time series backend.
See [this example](/blob/master/cmd/carbonapi/carbonapi.example.prometheus.yaml).
* [Ansible role for installing VictoriaMetrics](https://github.com/dreamteam-gg/ansible-victoriametrics-role).
## Roadmap
- [ ] Replication [#118](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/118)
- [ ] Support of Object Storages (GCS, S3, Azure Storage) [#38](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/38)
- [ ] Data downsampling [#36](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/36)
- [ ] Alert Manager Integration [#119](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/119)
- [ ] CLI tool for data migration, re-balancing and adding/removing nodes [#103](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/103)
The discussion happens [here](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/129). Feel free to comment on any item or add you own one.
## Contacts
Contact us with any questions regarding VictoriaMetrics at [info@victoriametrics.com](mailto:info@victoriametrics.com).
## Community and contributions
Feel free asking any questions regarding VictoriaMetrics:
- [slack](http://slack.victoriametrics.com/)
- [reddit](https://www.reddit.com/r/VictoriaMetrics/)
- [telegram-en](https://t.me/VictoriaMetrics_en)
- [telegram-ru](https://t.me/VictoriaMetrics_ru1)
- [google groups](https://groups.google.com/forum/#!forum/victorametrics-users)
If you like VictoriaMetrics and want to contribute, then we need the following:
- Filing issues and feature requests [here](https://github.com/VictoriaMetrics/VictoriaMetrics/issues).
- Spreading a word about VictoriaMetrics: conference talks, articles, comments, experience sharing with colleagues.
- Updating documentation.
We are open to third-party pull requests provided they follow [KISS design principle](https://en.wikipedia.org/wiki/KISS_principle):
- Prefer simple code and architecture.
- Avoid complex abstractions.
- Avoid magic code and fancy algorithms.
- Avoid [big external dependencies](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d).
- Minimize the number of moving parts in the distributed system.
- Avoid automated decisions, which may hurt cluster availability, consistency or performance.
Adhering `KISS` principle simplifies the resulting code and architecture, so it can be reviewed, understood and verified by many people.
## Third-party contributions
* [Unofficial yum repository](https://copr.fedorainfracloud.org/coprs/antonpatsev/VictoriaMetrics/) ([source code](https://github.com/patsevanton/victoriametrics-rpm))
* [Prometheus -> VictoriaMetrics exporter #1](https://github.com/ryotarai/prometheus-tsdb-dump)
* [Prometheus -> VictoriaMetrics exporter #2](https://github.com/AnchorFree/tsdb-remote-write)
## Reporting bugs
Report bugs and propose new features [here](https://github.com/VictoriaMetrics/VictoriaMetrics/issues).
## Victoria Metrics Logo
[Zip](VM_logo.zip) contains three folders with different image orientations (main color and inverted version).
Files included in each folder:
* 2 JPEG Preview files
* 2 PNG Preview files with transparent background
* 2 EPS Adobe Illustrator EPS10 files
### Logo Usage Guidelines
#### Font used:
* Lato Black
* Lato Regular
#### Color Palette:
* HEX [#110f0f](https://www.color-hex.com/color/110f0f)
* HEX [#ffffff](https://www.color-hex.com/color/ffffff)
### We kindly ask:
- Please don't use any other font instead of suggested.
- There should be sufficient clear space around the logo.
- Do not change spacing, alignment, or relative locations of the design elements.
- Do not change the proportions of any of the design elements or the design itself. You may resize as needed but must retain all proportions.
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