in [source code](https://github.com/VictoriaMetrics/VictoriaMetrics). Just download VictoriaMetrics and see [how to start it](#how-to-start-victoriametrics).
* VictoriaMetrics can be used as long-term storage for Prometheus or for [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md).
* 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 inspired by PromQL. MetricsQL is backwards-compatible with PromQL.
* Supports global query view. Multiple Prometheus instances or any other data sources may write data into VictoriaMetrics. Later this data may be queried 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/).
* 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.
* 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).
*`-storageDataPath` - path to data directory. VictoriaMetrics stores all the data in this directory. Default path is `victoria-metrics-data` in the current working directory.
*`-retentionPeriod` - retention for stored data. Older data is automatically deleted. Default retention is 1 month. See [these docs](#retention) for more details.
Other flags have good enough default values, so set them only if you really need this. Pass `-help` to see all the available flags with description and default values.
* Each `.` char in flag name 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 `,` char 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_`
It is recommended upgrading Prometheus to [v2.12.0](https://github.com/prometheus/prometheus/releases) or newer, since previous versions may have issues with `remote_write`.
say otherwise. It is safe skipping multiple versions during the upgrade 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.
### 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:
The file pointed by `-promscrape.config` may contain `%{ENV_VAR}` placeholders, which are substituted by the corresponding `ENV_VAR` environment variable values.
See also [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md), which can be used as drop-in replacement for Prometheus.
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.
* [/api/v1/targets](https://prometheus.io/docs/prometheus/latest/querying/api/#targets) - see [these docs](#how-to-scrape-prometheus-exporters-such-as-node-exporter) for more details.
Additionally to unix timestamps and [RFC3339](https://www.ietf.org/rfc/rfc3339.txt) VictoriaMetrics accepts relative times in `time`, `start` and `end` query args.
For example, the following query would return data for the last 30 minutes: `/api/v1/query_range?start=-30m&query=...`.
By default, VictoriaMetrics returns time series for the last 5 minutes from /api/v1/series, while the Prometheus API defaults to all time. Use `start` and `end` to select a different time range.
*`/api/v1/series/count` - it returns the total number of time series in the database. Some notes:
* the handler scans all the inverted index, so it can be slow if the database contains tens of millions of time series;
* the handler may count [deleted time series](#how-to-delete-time-series) additionally to normal time series due to internal implementation restrictions;
VictoriaMetrics accepts the following additional query args at `/metrics/find` and `/metrics/expand`:
*`label` - for selecting arbitrary label values. By default `label=__name__`, i.e. metric names are selected.
*`delimiter` - for using different delimiters in metric name hierachy. For example, `/metrics/find?delimiter=_&query=node_*` would return all the metric name prefixes
that start with `node_`. By default `delimiter=.`.
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.
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.
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. By default this query will only scan active series in the past 5 minutes, so you may need to
adjust `start` and `end` to a suitable range to achieve match hits.
in order to keep good performance characteristics when accepting new data. These compactions (merges) are performed independently on per-month partitions.
This means that compactions are stopped for per-month partitions if no new data is ingested into these partitions.
Sometimes it is necessary to trigger compactions for old partitions. For instance, in order to free up disk space occupied by [deleted time series](#how-to-delete-time-series).
In this case forced compaction may be initiated on the specified per-month partition by sending request to `/internal/force_merge?partition_prefix=YYYY_MM`,
where `YYYY_MM` is per-month partition name. For example, `http://victoriametrics:8428/internal/force_merge?partition_prefix=2020_08` would initiate forced
merge for August 2020 partition. The call to `/internal/force_merge` returns immediately, while the corresponding forced merge continues running in background.
Forced merges may require additional CPU, disk IO and storage space resources. It is unnecessary to run forced merge under normal conditions,
since VictoriaMetrics automatically performs [optimal merges in background](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282)
Send a request to `http://<victoriametrics-addr>:8428/api/v1/export/native?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.
On large databases you may experience problems with limit on unique timeseries (default value is 300000). In this case you need to adjust `-search.maxUniqueTimeseries` parameter:
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.
The exported data can be imported to VictoriaMetrics via [/api/v1/import/native](#how-to-import-data-in-native-format).
#### How to export data in JSON line format
Consider [exporting data in native format](#how-to-export-data-in-native-format) if big amounts of data must be migrated between VictoriaMetrics instances,
since exporting in native format usually consumes lower amounts of CPU and memory resources, while the resulting exported data occupies lower amounts of disk space.
In order to export data in JSON line format, 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).
Send a request to `http://<victoriametrics-addr>:8428/api/v1/export/csv?format=<format>&match=<timeseries_selector_for_export>`,
where:
*`<format>` must contain comma-delimited label names for the exported CSV. The following special label names are supported:
*`__name__` - metric name
*`__value__` - sample value
*`__timestamp__:<ts_format>` - sample timestamp. `<ts_format>` can have the following values:
*`unix_s` - unix seconds
*`unix_ms` - unix milliseconds
*`unix_ns` - unix nanoseconds
*`rfc3339` - [RFC3339](https://www.ietf.org/rfc/rfc3339.txt) time
*`custom:<layout>` - custom layout for time that is supported by [time.Format](https://golang.org/pkg/time/#Time.Format) function from Go.
*`<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.
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.
The exported CSV data can be imported to VictoriaMetrics via [/api/v1/import/csv](#how-to-import-csv-data).
* [Prometheus remote_write API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write). See [these docs](#prometheus-setup) for details.
*`/api/v1/import/native` for importing data obtained from [/api/v1/export/native](#how-to-export-data-in-native-format).
See [these docs](#how-to-import-data-in-native-format) for details.
*`/api/v1/import/csv` for importing arbitrary CSV data. See [these docs](#how-to-import-csv-data) for details.
*`/api/v1/import/prometheus` for importing data in Prometheus exposition format. See [these docs](#how-to-import-data-in-prometheus-exposition-format) for details.
#### How to import data in native format
The most efficient protocol for importing data into VictoriaMetrics is `/api/v1/import/native`.
Example for importing data obtained via [/api/v1/export/native](#how-to-export-data-in-native-format):
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`:
Extra labels may be added to all the imported lines by passing `extra_label=name=value` query args.
For example, `/api/v1/import/csv?extra_label=foo=bar` would add `"foo":"bar"` label to all the imported lines.
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:
Extra labels may be added to all the imported metrics by passing `extra_label=name=value` query args.
For example, `/api/v1/import/prometheus?extra_label=foo=bar` would add `{foo="bar"}` label to all the imported metrics.
If timestamp is missing in `<metric> <value> <timestamp>` Prometheus exposition format line, then the current timestamp is used during data ingestion.
It can be overriden by passing unix timestamp in *milliseconds* via `timestamp` query arg. For example, `/api/v1/import/prometheus?timestamp=1594370496905`.
VictoriaMetrics accepts arbitrary number of lines in a single request to `/api/v1/import/prometheus`, i.e. it supports data streaming.
Note that it could be required to flush response cache after importing historical data. See [these docs](#backfilling) for detail.
VictoriaMetrics also may scrape Prometheus targets - see [these docs](#how-to-scrape-prometheus-exporters-such-as-node-exporter).
*`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).
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
VictoriaMetrics stores various caches in RAM. Memory size for these caches may be limited with `-memory.allowedPercent` or `-memory.allowedBytes` flags.
* 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.
* Install multiple VictoriaMetrics instances in distinct datacenters (availability zones).
* Pass addresses of these instances to [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md) via `-remoteWrite.url` command-line flag:
It is recommended to use [vmagent](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmagent/README.md) instead of Prometheus for highly loaded setups.
on the same time series if they fall within the same discrete 60s bucket. The earliest data point will be kept. In the case of equal timestamps, an arbitrary data point will be kept.
VictoriaMetrics supports retention smaller than 1 month. For example, `-retentionPeriod=5d` would set data retention for 5 days.
Older data is eventually deleted during [background merge](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
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).
* 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)
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.
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/).
Prefer authorizing all the incoming requests from untrusted networks with [vmauth](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/app/vmauth/README.md)
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).
There are officials Grafana dashboards for [single-node VictoriaMetrics](https://grafana.com/dashboards/10229) and [clustered VictoriaMetrics](https://grafana.com/grafana/dashboards/11176).
* 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
* If you store Graphite metrics like `foo.bar.baz` in VictoriaMetrics, then `-search.treatDotsAsIsInRegexps` command-line flag could be useful.
By default `.` chars in regexps match any char. If you need matching only dots, then the `\\.` must be used in regexp filters.
When `-search.treatDotsAsIsInRegexps` option is enabled, then dots in regexps are automatically escaped in order to match only dots instead of arbitrary chars.
This may significantly increase performance when locating time series for the given label filters.
Storage-level replication may be offloaded to durable persistent storage such as [Google Cloud disks](https://cloud.google.com/compute/docs/disks#pdspecs).
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.
* [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.
