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

• Supports Prometheus querying API, so it can be used as Prometheus drop-in replacement in Grafana. Additionally, VictoriaMetrics extends PromQL with opt-in useful features.
• 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 and selects. Outperforms InfluxDB and TimescaleDB by up to 20x.
• Uses 10x less RAM than InfluxDB when working with millions of unique time series (aka high cardinality).
• High data compression, so up to 70x more data points may be crammed into a 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.
• A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, Uber M3, Cortex, InfluxDB or TimescaleDB. See vertical scalability benchmarks and comparing Thanos to VictoriaMetrics cluster.
• Easy operation:
  • VictoriaMetrics consists of a single executable without external dependencies.
  • All the configuration is done via explicit command-line flags with reasonable defaults.
  • All the data is stored in a single directory pointed by -storageDataPath flag.
  • Easy backups from instant snapshots.
• Storage is protected from corruption on unclean shutdown (i.e. hardware reset or kill -9) thanks to the storage architecture.
• Supports metrics' ingestion and backfilling via the following protocols:
  • Prometheus remote write API
  • InfluxDB line protocol
  • Graphite plaintext protocol with tags if -graphiteListenAddr is set.
  • OpenTSDB put message if -opentsdbListenAddr is set.
• Ideally works with big amounts of time series data from Kubernetes, IoT sensors, connected cars and industrial telemetry.
• Has open source cluster version.

Operation

Table of contents

• How to build from sources
  • Development build
  • Production build
  • Building docker images
• How to start VictoriaMetrics
• Start with docker-compose
• Setting up service
• Third-party contributions
• Prometheus setup
• Grafana setup
• How to upgrade VictoriaMetrics?
• How to apply new config to VictoriaMetrics?
• How to send data from InfluxDB-compatible agents such as Telegraf?
• How to send data from Graphite-compatible agents such as StatsD?
• Querying Graphite data
• How to send data from OpenTSDB-compatible agents?
• How to work with snapshots?
• How to delete time series?
• How to export time series?
• Federation
• Capacity planning
• High availability
• Multiple retentions
• Downsampling
• Multi-tenancy
• Scalability and cluster version
• Alerting
• Security
• Tuning
• Monitoring
• Troubleshooting
• Contacts
• Community and contributions
• Reporting bugs
• Victoria Metrics Logo
  • Logo Usage Guidelines
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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.

ARM build

1. Install Go. 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 will build victoria-metrics-arm or victoria-metrics-arm64 binary respectively and put it into the bin folder.

Pure Go (CGO_ENABLED=0)

Pure Go mode builds only Go code without cgo dependencies. This is experimental mode, which may result in lower compression ratio and slower decompression performance. Use it with caution!

1. Install Go. The minimum supported version is Go 1.12.
2. Run make victoria-metrics-pure from the root folder of the repository. It will build victoria-metrica-pure 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.

Start with docker-compose

Docker-compose helps to spin up VictoriaMetrics, Prometheus and Grafana with one command. More details may be found here.

Setting up service

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

Third-party contributions

• Unofficial yum repository (source code)

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
      max_shards: 100

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 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.

It is recommended upgrading Prometheus to v2.10.0 or newer, since the previous versions may have issues with remote_write.

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 upgrade VictoriaMetrics?

It is safe upgrading VictoriaMetrics to new versions unless release notes 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.

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 new config.

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 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.
• 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 to local VictoriaMetrics using curl:

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

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 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

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 timestamp is omitted. Arbitrary number of lines delimited by \n may be sent in one go. After that the 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__":"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 or via go-graphite/carbonapi.

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

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

Arbitrary number of lines delimited by \n may be sent in one go. After that the 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__":"foo.bar.baz","tag1":"value1","tag2":"value2"},"values":[123],"timestamps":[1560277292000]}

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 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 exproted 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. See this article 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 for details.

• Network usage: outbound traffic is negligible. Ingress traffic is ~100 bytes per ingested data point via Prometheus remote_write API. The actual ingress bandwitdh usage depends on the average number of labels per ingested metric and the average size of label values. Higher number of per-metric lables and longer label values mean 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. Higher number of scanned time series and lower step argument results in 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:

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 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.

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.
• VictoriaMetrics has good compression for on-disk data. See this article for details.

These properties reduce the need in downsampling. We plan implementing downsampling in the future. See this issue for details.

Multi-tenancy

Single-node VictoriaMetrics doesn't support multi-tenancy. Use cluster version 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 moderately sized cluster built with competing solutions such as Thanos, Uber M3, InfluxDB or TimescaleDB. See vertical scalability benchmarks.

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.

Alerting

VictoriaMetrics doesn't support rule evaluation and alerting yet, so these actions must be performed either on Prometheus side or on Grafana side.

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:

• -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.
• -deleteAuthKey for protecting /api/v1/admin/tsdb/delete_series endpoint. See how to delete time series.
• -snapshotAuthKey for protecting /snapshot* endpoints. See 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 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.

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.
• vm_rows{type="indexdb"} - the number of rows in inverted index. Each label in each unique time series adds a single row into the inverted index. An approximate number of time series in the database may be calculated as vm_rows{type="indexdb"} / (avg_labels_per_series + 1), where avg_labels_per_series is the average number of labels per each 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

• 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.

• If VictoriaMetrics doesn't work because of certain parts are corrupted due to disk errors, then just remove directoreis 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.

Contacts

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

Community and contributions

Feel free asking any questions regarding VictoriaMetrics:

• slack
• telergam-en
• telergam-ru
• google groups

If you like VictoriaMetrics and want contributing, then we need the following:

• Filing issues and feature requests here.
• 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:

• 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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