VictoriaMetrics/docs/Single-server-VictoriaMetrics.md

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![Victoria Metrics logo](logo.png "Victoria Metrics")

## VictoriaMetrics

VictoriaMetrics is fast, cost-effective and scalable 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/) 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).

See our [Wiki](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki) for additional documentation.

[Contact us](mailto:info@victoriametrics.com) if you need paid enterprise support for VictoriaMetrics.
See [features available for enterprise customers](https://github.com/VictoriaMetrics/VictoriaMetrics/issues?q=is%3Aissue+label%3Aenterprise).


## Case studies and talks

* [Adidas](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#adidas)
* [CERN](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#cern)
* [COLOPL](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#colopl)
* [Zerodha](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#zerodha)
* [Wix.com](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#wixcom)
* [Wedos.com](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#wedoscom)
* [Synthesio](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#synthesio)
* [MHI Vestas Offshore Wind](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#mhi-vestas-offshore-wind)
* [Dreamteam](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#dreamteam)
* [Brandwatch](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#brandwatch)
* [Adsterra](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#adsterra)
* [ARNES](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#arnes)


## Prominent features

* VictoriaMetrics can be used as long-term storage for Prometheus or for [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md).
  See [these docs](#prometheus-setup) for details.
* 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/MetricsQL) 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' 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) for details.
  * [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) over HTTP, TCP and UDP.
  * [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).
  * [Arbitrary CSV data](#how-to-import-csv-data).
* Supports metrics' relabeling. See [these docs](#relabeling) for details.
* 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).
* See also technical [Articles about VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/Articles).

## Operation

### Table of contents

* [How to start VictoriaMetrics](#how-to-start-victoriametrics)
  * [Environment variables](#environment-variables)
* [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 scrape Prometheus exporters such as node_exporter](#how-to-scrape-prometheus-exporters-such-as-node-exporter)
* [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)
* [How to import data in Prometheus exposition format](#how-to-import-data-in-prometheus-exposition-format)
* [How to import CSV data](#how-to-import-csv-data)
* [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)
* [Relabeling](#relabeling)
* [Federation](#federation)
* [Capacity planning](#capacity-planning)
* [High availability](#high-availability)
* [Deduplication](#deduplication)
* [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)
* [Replication](#replication)
* [Backups](#backups)
* [Profiling](#profiling)
* [Integrations](#integrations)
* [Third-party contributions](#third-party-contributions)
* [Contacts](#contacts)
* [Community and contributions](#community-and-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.

Other flags have good enough default values, so set them only if you really need this.
VictoriaMetrics accepts [Prometheus querying API requests](#prometheus-querying-api-usage) on port `8428` by default.

Pass `-help` to see all the available flags with description and default values.

It is recommended setting up [monitoring](#monitoring) for VictoriaMetrics.

#### Environment variables

Each flag values can be set thru environment variables by following these rules:

* The `-envflag.enable` flag must be set
* Each `.` in flag names must be substituted by `_` (for example `-insert.maxQueueDuration <duration>` will translate to `insert_maxQueueDuration=<duration>`)
* For repeating flags, an alternative syntax can be used by joining the different values into one using `,` as separator (for example `-storageNode <nodeA> -storageNode <nodeB>` will translate to `storageNode=<nodeA>,<nodeB>`)
* It is possible setting prefix for environment vars with `-envflag.prefix`. For instance, if `-envflag.prefix=VM_`, then env vars must be prepended with `VM_`

### 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:

```bash
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:

```yaml
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`.

Take a look also at [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md),
which can be used as faster and less resource-hungry alternative to Prometheus in certain cases.


### Grafana setup

Create [Prometheus datasource](http://docs.grafana.org/features/datasources/prometheus/) in Grafana with the following Url:

```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/MetricsQL).

### 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 scrape Prometheus exporters such as [node-exporter](https://github.com/prometheus/node_exporter)

VictoriaMetrics can be used as drop-in replacement for Prometheus for scraping targets configured in `prometheus.yml` config file according to [the specification](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#configuration-file).
Just set `-promscrape.config` command-line flag to the path to `prometheus.yml` config - and VictoriaMetrics should start scraping the configured targets.
Currently the following [scrape_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#scrape_config) types are supported:

* [static_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#static_config)
* [file_sd_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#file_sd_config)
* [kubernetes_sd_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#kubernetes_sd_config)
* [ec2_sd_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#ec2_sd_config)
* [gce_sd_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#gce_sd_config)
* [consul_sd_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#consul_sd_config)
* [dns_sd_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#dns_sd_config)

In the future other `*_sd_config` types will be supported.

VictoriaMetrics also supports [importing data in Prometheus exposition format](#how-to-import-data-in-prometheus-exposition-format).

See also [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md), which can be used as drop-in replacement for Prometheus.

### 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:

```toml
[[outputs.influxdb]]
  urls = ["http://<victoriametrics-addr>:8428"]
```

Do not forget substituting `<victoriametrics-addr>` with the real address where VictoriaMetrics runs.

Another option is to enable TCP and UDP receiver for Influx line protocol via `-influxListenAddr` command-line flag
and stream plain Influx line protocol data to the configured TCP and/or UDP addresses.

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 or `-influxSkipMeasurement` command-line flag is set, 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:

```raw
foo,tag1=value1,tag2=value2 field1=12,field2=40
```

is converted into the following Prometheus data points:

```raw
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`:

```bash
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' (aka newline char) may be sent in a single request.
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:

```bash
curl -G 'http://localhost:8428/api/v1/export' -d 'match={__name__=~"measurement_.*"}'
```

The `/api/v1/export` endpoint should return the following response:

```jsonl
{"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`:

```bash
/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`:

```bash
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` (aka newline char) may be sent in one go.
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:

```bash
curl -G 'http://localhost:8428/api/v1/export' -d 'match=foo.bar.baz'
```

The `/api/v1/export` endpoint should return the following response:

```bash
{"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`:

```bash
/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`:

```bash
echo "put foo.bar.baz `date +%s` 123 tag1=value1 tag2=value2" | nc -N localhost 4242
```

An arbitrary number of lines delimited by `\n` (aka newline char) may be sent in one go.
After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:

```bash
curl -G 'http://localhost:8428/api/v1/export' -d 'match=foo.bar.baz'
```

The `/api/v1/export` endpoint should return the following response:

```bash
{"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`:

```bash
/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:

```bash
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:

```bash
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:

```bash
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:

```bash
{"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]}
```


### How to import CSV data

Arbitrary CSV data can be imported via `/api/v1/import/csv`. The CSV data is imported according to the provided `format` query arg.
The `format` query arg must contain comma-separated list of parsing rules for CSV fields. Each rule consists of three parts delimited by a colon:

```
<column_pos>:<type>:<context>
```

* `<column_pos>` is the position of the CSV column (field). Column numbering starts from 1. The order of parsing rules may be arbitrary.
* `<type>` describes the column type. Supported types are:
  * `metric` - the corresponding CSV column at `<column_pos>` contains metric value, which must be integer or floating-point number.
    The metric name is read from the `<context>`. CSV line must have at least a single metric field. Multiple metric fields per CSV line is OK.
  * `label` - the corresponding CSV column at `<column_pos>` contains label value. The label name is read from the `<context>`.
    CSV line may have arbitrary number of label fields. All these labels are attached to all the configured metrics.
  * `time` - the corresponding CSV column at `<column_pos>` contains metric time. CSV line may contain either one or zero columns with time.
    If CSV line has no time, then the current time is used. The time is applied to all the configured metrics.
    The format of the time is configured via `<context>`. Supported time formats are:
    * `unix_s` - unix timestamp in seconds.
    * `unix_ms` - unix timestamp in milliseconds.
    * `unix_ns` - unix timestamp in nanoseconds. Note that VictoriaMetrics rounds the timestamp to milliseconds.
    * `rfc3339` - timestamp in [RFC3339](https://tools.ietf.org/html/rfc3339) format, i.e. `2006-01-02T15:04:05Z`.
    * `custom:<layout>` - custom layout for the timestamp. The `<layout>` may contain arbitrary time layout according to [time.Parse rules in Go](https://golang.org/pkg/time/#Parse).

Each request to `/api/v1/import/csv` may contain arbitrary number of CSV lines.

Example for importing CSV data via `/api/v1/import/csv`:

```bash
curl -d "GOOG,1.23,4.56,NYSE" 'http://localhost:8428/api/v1/import/csv?format=2:metric:ask,3:metric:bid,1:label:ticker,4:label:market'
curl -d "MSFT,3.21,1.67,NASDAQ" 'http://localhost:8428/api/v1/import/csv?format=2:metric:ask,3:metric:bid,1:label:ticker,4:label:market'
```

After that the data may be read via [/api/v1/export](#how-to-export-time-series) endpoint:

```bash
curl -G 'http://localhost:8428/api/v1/export' -d 'match[]={ticker!=""}'
```

The following response should be returned:
```bash
{"metric":{"__name__":"bid","market":"NASDAQ","ticker":"MSFT"},"values":[1.67],"timestamps":[1583865146520]}
{"metric":{"__name__":"bid","market":"NYSE","ticker":"GOOG"},"values":[4.56],"timestamps":[1583865146495]}
{"metric":{"__name__":"ask","market":"NASDAQ","ticker":"MSFT"},"values":[3.21],"timestamps":[1583865146520]}
{"metric":{"__name__":"ask","market":"NYSE","ticker":"GOOG"},"values":[1.23],"timestamps":[1583865146495]}
```

Note that it could be required to flush response cache after importing historical data. See [these docs](#backfilling) for detail.


### How to import data in Prometheus exposition format

VictoriaMetrics accepts data in [Prometheus exposition format](https://github.com/prometheus/docs/blob/master/content/docs/instrumenting/exposition_formats.md#text-based-format)
via `/api/v1/import/prometheus` path. For example, the following line imports a single line in Prometheus exposition format into VictoriaMetrics:

```bash
curl -d 'foo{bar="baz"} 123' -X POST 'http://localhost:8428/api/v1/import/prometheus'
```

The following command may be used for verifying the imported data:

```bash
curl -G 'http://localhost:8428/api/v1/export' -d 'match={__name__=~"foo"}'
```

It should return somethins like the following:

```
{"metric":{"__name__":"foo","bar":"baz"},"values":[123],"timestamps":[1594370496905]}
```

VictoriaMetrics accepts arbitrary number of lines in a single request to `/api/v1/import/prometheus`, i.e. it supports data streaming.

VictoriaMetrics also may scrape Prometheus targets - see [these docs](#how-to-scrape-prometheus-exporters-such-as-node-exporter).


### 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)
* [/api/v1/status/tsdb](https://prometheus.io/docs/prometheus/latest/querying/api/#tsdb-stats)

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.
* `/api/v1/status/active_queries` - it returns a list of currently running queries.

### 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 or when testing bugfixes.

#### Development build

1. [Install Go](https://golang.org/doc/install). The minimum supported version is Go 1.13.
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 1.13.
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 1.13.
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`.

By default the image is built on top of [alpine](https://hub.docker.com/_/alpine) image for improved debuggability.
It is possible to build the package on top of any other base image by setting it via `<ROOT_IMAGE>` environment variable.
For example, the following command builds the image on top of [scratch](https://hub.docker.com/_/scratch) image:

```bash
ROOT_IMAGE=scratch 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, [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md) 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:

```json
{"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 [background merges of data files](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).

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 `/api/v1/admin/tsdb/delete_series` handler may be protected with `authKey` if `-deleteAuthKey` command-line flag is set.

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 unneeded data. Just prevent writing unneeded data into VictoriaMetrics.
  This can be done with [relabeling](#relabeling).
  See [this article](https://www.robustperception.io/relabelling-can-discard-targets-timeseries-and-alerts) for details.
* Reducing disk space usage by deleting unneeded 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 when deleting too old data.

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:

```jsonl
{"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.

Optional `max_rows_per_line` arg may be added to the request in order to limit the maximum number of rows exported per each JSON line.
By default each JSON line contains all the rows for a single time series.

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:

```bash
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).
* `/api/v1/import/csv` http POST handler, which accepts CSV data. See [these docs](#how-to-import-csv-data) for details.
* `/api/v1/import/prometheus` http POST handler, which accepts data in Prometheus exposition format. See [these docs](#how-to-import-data-in-prometheus-exposition-format) for details.

The most efficient protocol for importing data into VictoriaMetrics is `/api/v1/import`. Example for importing data obtained via `/api/v1/export`:

```bash
# 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:

```bash
# 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
```

Note that it could be required to flush response cache after importing historical data. See [these docs](#backfilling) for detail.

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.


### Relabeling

VictoriaMetrics supports Prometheus-compatible relabeling for all the ingested metrics if `-relabelConfig` command-line flag points
to a file containing a list of [relabel_config](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#relabel_config) entries.

Additionally VictoriaMetrics provides the following extra actions for relabeling rules:

* `replace_all`: replaces all the occurences of `regex` in the values of `source_labels` with the `replacement` and stores the result in the `target_label`.
* `labelmap_all`: replaces all the occurences of `regex` in all the label names with the `replacement`.
* `keep_if_equal`: keeps the entry if all label values from `source_labels` are equal.
* `drop_if_equal`: drops the entry if all the label values from `source_labels` are equal.

See also [relabeling in vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md#relabeling).


### 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.
  This means that heavy queries that touch big number of time series (over 10K) and/or big number data points (over 100M)
  usually require more CPU resources than tiny queries that touch a few time series with small number of data points.

* 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) Pass addresses of these instances to [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md) via `-remoteWrite.url` command-line flag:

```bash
/path/to/vmagent -remoteWrite.url=http://<victoriametrics-addr-1>:8428/api/v1/write -remoteWrite.url=http://<victoriametrics-addr-2>:8428/api/v1/write
```

Alternatively these addresses may be passed 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:

```bash
kill -HUP `pidof prometheus`
```

It is recommended to use [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md) instead of Prometheus for highly loaded setups.

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`.

Another option is to write data simultaneously from Prometheus HA pair to a pair of VictoriaMetrics instances
with the enabled de-duplication. See [this section](#deduplication) for details.


### Deduplication

VictoriaMetrics de-duplicates data points if `-dedup.minScrapeInterval` command-line flag
is set to positive duration. For example, `-dedup.minScrapeInterval=60s` would de-duplicate data points
on the same time series if they are located closer than 60s to each other.
The de-duplication reduces disk space usage if multiple identically configured Prometheus instances in HA pair
write data to the same VictoriaMetrics instance. Note that these Prometheus instances must have identical
`external_labels` section in their configs, so they write data to the same time series.

### 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.
It is safe to extend `-retentionPeriod` on existing data. If `-retentionPeriod` is set to lower
value than before then data outside the configured period will be eventually deleted.

### 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

Then set up [vmauth](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmauth/README.md) in front of VictoriaMetrics instances,
so it could route requests from particular user to VictoriaMetrics with the desired retention.
The same scheme could be implemented for multiple tenants in [VictoriaMetrics cluster](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/cluster/README.md).


### 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.

It is possible to (ab)use [-dedup.minScrapeInterval](#deduplication) for basic downsampling.
For instance, if interval between the ingested data points is 15s, then `-dedup.minScrapeInterval=5m` will leave
only a single data point out of 20 initial data points per each 5m interval.

### 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

It is recommended using [vmalert](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmalert/README.md) for alerting.

Additionally, alerting can be set up with the following tools:

* With Prometheus - see [the corresponding docs](https://prometheus.io/docs/alerting/overview/).
* With Promxy - see [the corresponding docs](https://github.com/jacksontj/promxy/blob/master/README.md#how-do-i-use-alertingrecording-rules-in-promxy).
* With 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).
* `-search.resetCacheAuthKey` for protecting `/internal/resetRollupResultCache` endpoint. See [backfilling](#backfilling) for more details.

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`.

Prefer authorizing all the incoming requests from untrusted networks with [vmauth](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmauth/README.md)
or similar auth proxy.


### 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`:

```bash
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 by [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md)
or 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, `-selfScrapeInterval=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).
There is also an [alternative dashboard for clustered VictoriaMetrics](https://grafana.com/grafana/dashboards/11831).

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.
* `increase(vm_new_timeseries_created_total[1h])` - time series churn rate during the previous hour.
* `sum(vm_rows{type=~"storage/.*"})` - total number of `(timestamp, value)` data points in the database.
* `sum(rate(vm_rows_inserted_total[5m]))` - ingestion rate, i.e. how many samples are inserted int the database per second.
* `vm_free_disk_space_bytes` - free space left at `-storageDataPath`.
* `sum(vm_data_size_bytes)` - the total size of data on disk.
* `increase(vm_slow_row_inserts_total[5m])` - the number of slow inserts during the last 5 minutes.
  If this number remains high during extended periods of time, then it is likely more RAM is needed for optimal handling
  of the current number of active time series.
* `increase(vm_slow_metric_name_loads_total[5m])` - the number of slow loads of metric names during the last 5 minutes.
  If this number remains high during extended periods of time, then it is likely more RAM is needed for optimal handling
  of the current number of active time series.

VictoriaMetrics also exposes currently running queries with their execution times at `/api/v1/status/active_queries` page.


### 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.

* It is recommended upgrading to the latest available release from [this page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases),
  since the encountered issue could be already fixed there.

* It is recommended inspecting logs during troubleshooting, since they may contain useful information.

* 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.
  VictoriaMetrics [exposes](#monitoring) `vm_slow_*` metrics, which could be used as an indicator of low amounts of RAM.
  It is recommended increasing the amount of RAM on the node with VictoriaMetrics in order to improve
  ingestion and query performance in this case.
  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 prioritizes data ingestion over data querying. So if it has no enough resources for data ingestion,
  then data querying may slow down significantly.

* 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. The remaining amount of free space
  can be [monitored](#monitoring) via `vm_free_disk_space_bytes` metric. The total size of data
  stored on the disk can be monitored via sum of `vm_data_size_bytes` metrics.

* 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.

* If you see gaps on the graphs, try resetting the cache by sending request to `/internal/resetRollupResultCache`.
  If this removes gaps on the graphs, then it is likely data with timestamps older than `-search.cacheTimestampOffset`
  is ingested into VictoriaMetrics. Make sure that data sources have synchronized time with VictoriaMetrics.

  If the gaps are related to irregular intervals between samples, then try adjusting `-search.minStalenessInterval` command-line flag
  to value close to the maximum interval between samples.

* If you are switching from InfluxDB or TimescaleDB, then take a look at `-search.maxStalenessInterval` command-line flag.
  It may be needed in order to suppress default gap filling algorithm used by VictoriaMetrics - by default it assumes
  each time series is continuous instead of discrete, so it fills gaps between real samples with regular intervals.

* Metrics and labels leading to high cardinality or high churn rate can be determined at `/api/v1/status/tsdb` page.
  See [these docs](https://prometheus.io/docs/prometheus/latest/querying/api/#tsdb-stats) for details.
  VictoriaMetrics accepts optional `date=YYYY-MM-DD` and `topN=42` args on this page. By default `date` equals to the current date,
  while `topN` equals to 10.

* VictoriaMetrics limits the number of labels per each metric with `-maxLabelsPerTimeseries` command-line flag.
  This prevents from ingesting metrics with too many labels. It is recommended [monitoring](#monitoring) `vm_metrics_with_dropped_labels_total`
  metric in order to determine whether `-maxLabelsPerTimeseries` must be adjusted for your workload.


### Backfilling

VictoriaMetrics accepts historical data in arbitrary order of time via [any supported ingestion method](#how-to-import-time-series-data).
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.

An alternative solution is to query `/internal/resetRollupResultCache` url after backfilling is complete. This will reset
the query cache, which could contain incomplete data cached during the backfilling.

Yet another solution is to increase `-search.cacheTimestampOffset` flag value in order to disable caching
for data with timestamps close to the current time.


### Replication

Single-node VictoriaMetrics doesn't support application-level replication. Use cluster version instead.
See [these docs](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/cluster/README.md#replication-and-data-safety) for details.

Storage-level replication may be offloaded to durable persistent storage such as [Google Cloud disks](https://cloud.google.com/compute/docs/disks#pdspecs).

See also [high availability docs](#high-availability) and [backup docs](#backups).


### Backups

VictoriaMetrics supports backups via [vmbackup](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmbackup/README.md)
and [vmrestore](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmrestore/README.md) tools.
We also provide provide `vmbackuper` tool for paid enterprise subscribers - see [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/466) for details.


### 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:

```bash
curl -s http://<victoria-metrics-host>:8428/debug/pprof/heap > mem.pprof
```

* CPU profile. It can be collected with the following command:

```bash
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](https://github.com/go-graphite/carbonapi/blob/master/cmd/carbonapi/carbonapi.example.prometheus.yaml).
* [Ansible role for installing single-node VictoriaMetrics](https://github.com/dreamteam-gg/ansible-victoriametrics-role).
* [Ansible role for installing cluster VictoriaMetrics](https://github.com/Slapper/ansible-victoriametrics-cluster-role).

## 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)
* [Prometheus Oauth proxy](https://gitlab.com/optima_public/prometheus_oauth_proxy) - see [this article](https://medium.com/@richard.holly/powerful-saas-solution-for-detection-metrics-c67b9208d362) for details.

## 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.

## 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.