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Victoria Metrics

Single-node VictoriaMetrics

VictoriaMetrics is fast, cost-effective and scalable time series database. It can be used as a long-term remote storage for Prometheus. It is available in binary releases, docker images and in source code.

Cluster version is available here.

Prominent features

Operation

Table of contents

How to build from sources

We recommend using either binary releases or docker images instead of building VictoriaMetrics from sources. Building from sources is reasonable when developing an additional features specific to your needs.

Development build

  1. Install Go. The minimum supported version is Go 1.12.
  2. Run make victoria-metrics from the root folder of the repository. It will build victoria-metrics binary and put it into the bin folder.

Production build

  1. Install docker.
  2. Run make victoria-metrics-prod from the root folder of the repository. It will build victoria-metrics-prod binary and put it into the bin folder.

Building docker images

Run make package-victoria-metrics. It will build 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.

How to start VictoriaMetrics

Just start VictoriaMetrics executable or docker image 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.
  • -retentionPeriod - retention period in months for the data. Older data is automatically deleted.
  • -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. By default it is disabled.

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

Setting up service

Read these instructions on how to set up VictoriaMetrics as a service in your OS.

Third-party contributions

Prometheus setup

Add the following lines to Prometheus config file (it is usually located at /etc/prometheus/prometheus.yml):

remote_write:
  - url: http://<victoriametrics-addr>:8428/api/v1/write
    queue_config:
      max_samples_per_send: 10000

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 to remote storage in parallel. This means the data remains available in local storage for --storage.tsdb.retention.time duration if remote storage stops working.

If you plan sending data to VictoriaMetrics from multiple Prometheus instances, then add the following lines into global section of Prometheus config:

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 time series may be filtered and grouped by this label.

Grafana setup

Create Prometheus datasource 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. VictoriaMetrics supports native PromQL and extends it with useful features.

How to send data from InfluxDB-compatible agents such as 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 is mapped into db label value
  • Field names are mapped to time series names prefixed by {measurement}. value
  • 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 on how to write data with Influx line protocol to local VictoriaMetrics using curl:

curl -d 'measurement,tag1=value1,tag2=value2 field1=123,field2=1.23' -X POST 'http://localhost:8428/write'

After that this data may be read via /api/v1/export endpoint:

curl -G 'http://localhost:8428/api/v1/export' --data-urlencode 'match={__name__!=""}'

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]}

How to send data from Graphite-compatible agents such as 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
  1. Use the configured address in Graphite-compatible agents. For instance, set graphiteHost to the VictoriaMetrics host in StatsD configs.

How to send data from OpenTSDB-compatible agents?

  1. Enable OpenTSDB receiver in VictoriaMetrics by setting -opentsdbListenAddr command line flag. For instance, the following command will enable OpenTSDB receiver in VictoriaMetrics on TCP and UDP port 4242:
/path/to/victoria-metrics-prod ... -opentsdbListenAddr=:4242
  1. Send data to the given address from OpenTSDB-compatible agents.

How to apply new config / upgrade VictoriaMetrics?

VictoriaMetrics must be restarted in order to upgrade or apply 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 the upgraded VictoriaMetrics with new config.

How to work with snapshots?

VictoriaMetrics is able to create instant snapshots 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 via cp -L, rsync -L, scp -r or any similar tool that follows symlinks during copying.

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. Remove the entire contents of the directory pointed by -storageDataPath command-line flag.
  3. Copy snapshot contents to the directory pointed by -storageDataPath.
  4. 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 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.

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 for metrics to export. The response would contain all the data for the selected time series in JSON streaming format. 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 values.

Federation

VictoriaMetrics exports Prometheus-compatible federation data 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 values. By default the last point on the interval [now - max_lookback ... now] is scraped for each time series. Default value for max_lookback is 5m (5 minutes), but 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

Rough estimation of the required resources:

  • 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. VictoriaMetrics stores various caches in RAM. Memory size for these caches may be limited with -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. 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 size: 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.

High availability

  1. Install multiple VictoriaMetrics instances in distinct datacenters.
  2. Add addresses of these instances to remote_write section in Prometheus config:
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
  1. Apply the updated config:
kill -HUP `pidof prometheus`
  1. Now Prometheus should write data into all the configured remote_write urls in parallel.
  2. Set up Promxy in front of all the VictoriaMetrics replicas.
  3. Set up Prometheus datasource in Grafana that points to Promxy.

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

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 moderately sized cluster built with competing solutions such as Thanos, Uber M3, InfluxDB or TimescaleDB.

So try single-node VictoriaMetrics at first and then switch to cluster version if you still need horizontally scalable long-term remote storage for really large Prometheus deployments. Contact us for paid support.

Security

Do not forget protecting sensitive endpoints in VictoriaMetrics when exposing it to untrusted networks such as internet. Consider setting the following command-line flags:

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 in 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 in Operating System tuning, since VictoriaMetrics is optimized for default OS settings. The only option is increasing the limit on the number open files in the OS, so Prometheus instances could establish more connections to VictoriaMetrics.

Monitoring

VictoriaMetrics exports internal metrics in Prometheus format on the /metrics page. Add this page to Prometheus' scrape config in order to collect VictoriaMetrics metrics. There is an official Grafana dashboard for single-node VictoriaMetrics.

Troubleshooting

  • 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.

Contacts

Contact us with any questions regarding VictoriaMetrics at info@victoriametrics.com.

Community and contributions

Feel free asking any questions regarding VictoriaMetrics:

We are open to third-party pull requests provided they follow KISS design principle:

  • Prefer simple code and architecture.
  • Avoid complex abstractions.
  • Avoid magic code and fancy algorithms.
  • Avoid big external dependencies.
  • 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.

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