Single-node version [scales perfectly](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae)
with the number of CPU cores, RAM and available storage space.
Single-node version is easier to configure and operate comparing to cluster version, so think twice before sticking to cluster version.
Join [our Slack](http://slack.victoriametrics.com/) or [contact us](mailto:info@victoriametrics.com) with consulting and support questions.
## Prominent features
- Supports all the features of [single-node version](https://github.com/VictoriaMetrics/VictoriaMetrics).
Compiled binaries for cluster version are available in the `assets` section of [releases page](https://github.com/VictoriaMetrics/VictoriaMetrics/releases).
See archives containing `cluster` word.
Docker images for cluster version are available here:
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_`
All the cluster components expose various metrics in Prometheus-compatible format at `/metrics` page on the TCP port set in `-httpListenAddr` command-line flag.
-`<accountID>` is an arbitrary 32-bit integer identifying namespace for data ingestion (aka tenant). It is possible to set it as `accountID:projectID`,
where `projectID` is also arbitrary 32-bit integer. If `projectID` isn't set, then it equals to `0`.
-`prometheus` and `prometheus/api/v1/write` - for inserting data with [Prometheus remote write API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write)
-`influx/write` and `influx/api/v2/write` - for inserting data with [Influx line protocol](https://docs.influxdata.com/influxdb/v1.7/write_protocols/line_protocol_tutorial/).
-`prometheus/api/v1/import/csv` - for importing arbitrary CSV data. See [these docs](https://victoriametrics.github.io/Single-server-VictoriaMetrics.html#how-to-import-csv-data) for details.
-`prometheus/api/v1/import/prometheus` - for importing data in [Prometheus text exposition format](https://github.com/prometheus/docs/blob/master/content/docs/instrumenting/exposition_formats.md#text-based-format) and in [OpenMetrics format](https://github.com/OpenObservability/OpenMetrics/blob/master/specification/OpenMetrics.md). See [these docs](https://victoriametrics.github.io/Single-server-VictoriaMetrics.html#how-to-import-data-in-prometheus-exposition-format) for details.
-`api/v1/labels` - returns a [list of label names](https://prometheus.io/docs/prometheus/latest/querying/api/#getting-label-names).
-`api/v1/label/<label_name>/values` - returns values for the given `<label_name>` according [to API](https://prometheus.io/docs/prometheus/latest/querying/api/#querying-label-values).
-`api/v1/export` - exports raw data in JSON line format. See [this article](https://medium.com/@valyala/analyzing-prometheus-data-with-external-tools-5f3e5e147639) for details.
-`api/v1/export/native` - exports raw data in native binary format. It may be imported into another VictoriaMetrics via `api/v1/import/native` (see above).
VictoriaMetrics accepts optional `topN=N` and `date=YYYY-MM-DD` query args for this handler, where `N` is the number of top entries to return in the response
and `YYYY-MM-DD` is the date for collecting the stats. By default the stats is collected for the current day.
-`render` - implements Graphite Render API. See [these docs](https://graphite.readthedocs.io/en/stable/render_api.html). This functionality is available in [Enterprise package](https://victoriametrics.com/enterprise.html).
-`tags/tagSeries` - registers time series. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#adding-series-to-the-tagdb).
-`tags/tagMultiSeries` - register multiple time series. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#adding-series-to-the-tagdb).
-`tags` - returns tag names. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#exploring-tags).
-`tags/<tag_name>` - returns tag values for the given `<tag_name>`. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#exploring-tags).
-`tags/autoComplete/tags` - returns tags matching the given `tagPrefix` and/or `expr`. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#auto-complete-support).
-`tags/autoComplete/values` - returns tag values matching the given `valuePrefix` and/or `expr`. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#auto-complete-support).
-`tags/delSeries` - deletes series matching the given `path`. See [these docs](https://graphite.readthedocs.io/en/stable/tags.html#removing-series-from-the-tagdb).
* URL for query stats across all tenants: `http://<vmselect>:8481/api/v1/status/top_queries`. It lists with the most frequently executed queries and queries taking the most duration.
* URL for time series deletion: `http://<vmselect>:8481/delete/<accountID>/prometheus/api/v1/admin/tsdb/delete_series?match[]=<timeseries_selector_for_delete>`.
Note that the `delete_series` handler should be used only in exceptional cases such as deletion of accidentally ingested incorrect time series. It shouldn't
be used on a regular basis, since it carries non-zero overhead.
*`vmstorage` nodes provide the following HTTP endpoints on `8482` port:
which can be used for backups in background. Snapshots are created in `<storageDataPath>/snapshots` folder, where `<storageDataPath>` is the corresponding
command-line flag value.
-`/snapshot/list` - list available snasphots.
-`/snapshot/delete?snapshot=<id>` - delete the given snapshot.
-`/snapshot/delete_all` - delete all the snapshots.
Snapshots may be created independently on each `vmstorage` node. There is no need in synchronizing snapshots' creation
Cluster performance and capacity scales with adding new nodes.
*`vminsert` and `vmselect` nodes are stateless and may be added / removed at any time.
Do not forget updating the list of these nodes on http load balancer.
Adding more `vminsert` nodes scales data ingestion rate. See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/175#issuecomment-536925841)
about ingestion rate scalability.
Adding more `vmselect` nodes scales select queries rate.
*`vmstorage` nodes own the ingested data, so they cannot be removed without data loss.
Adding more `vmstorage` nodes scales cluster capacity.
Steps to add `vmstorage` node:
1. Start new `vmstorage` node with the same `-retentionPeriod` as existing nodes in the cluster.
2. Gradually restart all the `vmselect` nodes with new `-storageNode` arg containing `<new_vmstorage_host>:8401`.
3. Gradually restart all the `vminsert` nodes with new `-storageNode` arg containing `<new_vmstorage_host>:8400`.
-`vmselect` continues serving partial responses if at least a single `vmstorage` node is available. If consistency over availability is preferred, then either pass `-search.denyPartialResponse` command-line flag to `vmselect` or pass `deny_partial_response=1` query arg in requests to `vmselect`.
* The recommended total number of vCPU cores for all the `vminsert` instances can be calculated from the ingestion rate: `vCPUs = ingestion_rate / 150K`.
* The recommended number of vCPU cores per each `vminsert` instance should equal to the number of `vmstorage` instances in the cluster.
* The amount of RAM per each `vminsert` instance should be 1GB or more. RAM is used as a buffer for spikes in ingestion rate.
* The recommended total number of vCPU cores for all the `vmstorage` instances can be calculated from the ingestion rate: `vCPUs = ingestion_rate / 150K`.
* The recommended total amount of RAM for all the `vmstorage` instances can be calculated from the number of active time series: `RAM = 2 * active_time_series * 1KB`.
The recommended hardware for `vmselect` instances highly depends on the type of queries. Lightweight queries over small number of time series usually require
small number of vCPU cores and small amount of RAM on `vmselect`, while heavy queries over big number of time series (>10K) usually require
bigger number of vCPU cores and bigger amounts of RAM.
It is recommended to run all the components for a single cluster in the same subnetwork with high bandwidth, low latency and low error rates.
This improves cluster performance and availability.
It isn't recommended spreading components for a single cluster across multiple availability zones, since cross-AZ network usually has lower bandwidth, higher latency
and higher error rates comparing the network inside AZ.
If you need multi-AZ setup, then it is recommended running independed clusters in each AZ and setting up
The cluster must contain at least `2*N-1``vmstorage` nodes, where `N`
is replication factor, in order to maintain the given replication factor for newly ingested data when `N-1` of storage nodes are lost.
For example, when `-replicationFactor=3` is passed to `vminsert`, then it replicates all the ingested data to 3 distinct `vmstorage` nodes,
so up to 2 `vmstorage` nodes can be lost without data loss. The minimum number of `vmstorage` nodes should be equal to `2*3-1 = 5`, so when 2 `vmstorage` nodes are lost,
the remaining 3 `vmstorage` nodes could provide the `-replicationFactor=3` for newly ingested data.
Note that the replication increases resource usage - CPU, RAM, disk space, network bandwidth - by up to `-replicationFactor` times. So it may be worth
offloading the replication to underlying storage pointed by `-storageDataPath` such as [Google Compute Engine persistent disk](https://cloud.google.com/compute/docs/disks/#pdspecs),
which is protected from data loss and data corruption. It also provide consistently high performance
It is recommended performing periodical backups from [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282)
for protecting from user errors such as accidental data deletion.
The following steps must be performed for each `vmstorage` node for creating a backup:
1. Create an instant snapshot by navigating to `/snapshot/create` HTTP handler. It will create snapshot and return its name.
2. Archive the created snapshot from `<-storageDataPath>/snapshots/<snapshot_name>` folder using [vmbackup](https://victoriametrics.github.io/vmbackup.html).
- 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.
Due to `KISS` cluster version of VictoriaMetrics has no the following "features" popular in distributed computing world:
- Fragile gossip protocols. See [failed attempt in Thanos](https://github.com/improbable-eng/thanos/blob/030bc345c12c446962225221795f4973848caab5/docs/proposals/completed/201809_gossip-removal.md).
