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### Describe Your Changes
* `sort` param is unused by the current website engine, and was present only for compatibility
with previous website engine. It is time to remove it as it makes no effect
* re-structure guides content into folders to simplify assets management
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
(cherry picked from commit 35d77a3bed)
278 lines
15 KiB
Markdown
278 lines
15 KiB
Markdown
InfluxDB is a well-known time series database built for
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[IoT](https://en.wikipedia.org/wiki/Internet_of_things) monitoring, Application Performance Monitoring (APM) and
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analytics. It has its query language, unique data model, and rich tooling for collecting and processing metrics.
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Nowadays, the volume of time series data grows constantly, as well as requirements for durable time series storage. And
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sometimes old known solutions just can't keep up with the new expectations.
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VictoriaMetrics is a high-performance opensource time series database specifically designed to deal with huge volumes of
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monitoring data while remaining cost-efficient at the same time. Many companies are choosing to migrate from InfluxDB to
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VictoriaMetrics specifically for performance and scalability reasons. Along them see case studies provided by
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[ARNES](https://docs.victoriametrics.com/casestudies/#arnes)
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and [Brandwatch](https://docs.victoriametrics.com/casestudies/#brandwatch).
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This guide will cover the differences between two solutions, most commonly asked questions, and approaches for migrating
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from InfluxDB to VictoriaMetrics.
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## Data model differences
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While readers are likely familiar
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with [InfluxDB key concepts](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/), the data model of
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VictoriaMetrics is something [new to explore](https://docs.victoriametrics.com/keyconcepts/#data-model). Let's start
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with similarities and differences:
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* both solutions are **schemaless**, which means there is no need to define metrics or their tags in advance;
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* multidimensional data support is implemented
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via [tags](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/data-elements/#tags)
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in InfluxDB and via [labels](https://docs.victoriametrics.com/keyconcepts/#structure-of-a-metric) in
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VictoriaMetrics. However, labels in VictoriaMetrics are always `strings`, while InfluxDB supports multiple data types;
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* timestamps are stored with nanosecond resolution in InfluxDB, while in VictoriaMetrics it is **milliseconds**;
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* in VictoriaMetrics metric value is always `float64`, while InfluxDB supports multiple data types.
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* there are
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no [measurements](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/data-elements/#measurement)
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or [fields](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/data-elements/#field-key) in
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VictoriaMetrics, metric name contains it all. If measurement contains more than 1 field, then for VictoriaMetrics
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it will be multiple metrics;
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* there are no [databases](https://docs.influxdata.com/influxdb/v1.8/concepts/glossary/#database),
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[buckets](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/data-elements/#bucket)
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or [organizations](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/data-elements/#organization). All
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data in VictoriaMetrics is stored in a global namespace or within
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a [tenant](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy).
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See more about multi-tenancy [here](https://docs.victoriametrics.com/keyconcepts/#multi-tenancy).
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Let's consider the
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following [sample data](https://docs.influxdata.com/influxdb/v2.2/reference/key-concepts/data-elements/#sample-data)
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borrowed from InfluxDB docs as an example:
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| _measurement | _field | location | scientist | _value | _time |
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|--------------|--------|----------|-------------|--------|----------------------|
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| census | bees | klamath | anderson | 23 | 2019-08-18T00:00:00Z |
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| census | ants | portland | mullen | 30 | 2019-08-18T00:00:00Z |
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| census | bees | klamath | anderson | 28 | 2019-08-18T00:06:00Z |
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| census | ants | portland | mullen | 32 | 2019-08-18T00:06:00Z |
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In VictoriaMetrics data model this sample will have the following form:
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| metric name | labels | value | time |
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|-------------|:---------------------------------------------|-------|----------------------|
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| census_bees | {location="klamath", scientist="anderson"} | 23 | 2019-08-18T00:00:00Z |
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| census_ants | {location="portland", scientist="mullen"} | 30 | 2019-08-18T00:00:00Z |
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| census_bees | {location="klamath", scientist="anderson"} | 28 | 2019-08-18T00:06:00Z |
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| census_ants | {location="portland", scientist="mullen"} | 32 | 2019-08-18T00:06:00Z |
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Actually, metric name for VictoriaMetrics is also a label with static name `__name__`, and example above can be
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converted to `{__name__="census_bees", location="klamath", scientist="anderson"}`. All labels are indexed by
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VictoriaMetrics, so lookups by names or labels have the same query speed.
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## Write data
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VictoriaMetrics
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supports [InfluxDB line protocol](https://docs.victoriametrics.com/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf)
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for data ingestion. For example, to write a measurement to VictoriaMetrics we need to send an HTTP POST request with
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payload in a line protocol format:
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```sh
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curl -d 'census,location=klamath,scientist=anderson bees=23 1566079200000' -X POST 'http://<victoriametric-addr>:8428/write'
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```
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_hint: timestamp in the example might be out of configured retention for VictoriaMetrics. Consider increasing the
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retention period or changing the timestamp, if that is the case._
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Please note, an arbitrary number of lines delimited by `\n` (aka newline char) can be sent in a single request.
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To get the written data back let's export all series matching the `location="klamath"` filter:
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```sh
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curl -G 'http://<victoriametric-addr>:8428/api/v1/export' -d 'match={location="klamath"}'
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```
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The expected response is the following:
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```json
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{
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"metric": {
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"__name__": "census_bees",
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"location": "klamath",
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"scientist": "anderson"
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},
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"values": [
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23
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],
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"timestamps": [
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1566079200000
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]
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}
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```
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Please note, VictoriaMetrics performed additional
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[data mapping](https://docs.victoriametrics.com/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf)
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to the data ingested via InfluxDB line protocol.
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Support of InfluxDB line protocol also means VictoriaMetrics is compatible with
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[Telegraf](https://github.com/influxdata/telegraf). To configure Telegraf, simply
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add `http://<victoriametric-addr>:8428` URL to Telegraf configs:
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```
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[[outputs.influxdb]]
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urls = ["http://<victoriametrics-addr>:8428"]
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```
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In addition to InfluxDB line protocol, VictoriaMetrics supports many other ways for
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[metrics collection](https://docs.victoriametrics.com/keyconcepts/#write-data).
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## Query data
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VictoriaMetrics does not have a command-line interface (CLI). Instead, it provides
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an [HTTP API](https://docs.victoriametrics.com/single-server-victoriametrics/#prometheus-querying-api-usage)
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for serving read queries. This API is used in various integrations such as
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[Grafana](https://docs.victoriametrics.com/single-server-victoriametrics/#grafana-setup). The same API is also used
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by [VMUI](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui) - a graphical User Interface for
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querying and visualizing metrics:
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See more about [how to query data in VictoriaMetrics](https://docs.victoriametrics.com/keyconcepts/#query-data).
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### Basic concepts
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Let's take a closer look at querying specific with the following data sample:
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```sql
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foo
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,instance=localhost bar=1.00 1652169600000000000
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foo,instance=localhost bar=2.00 1652169660000000000
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foo,instance=localhost bar=3.00 1652169720000000000
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foo,instance=localhost bar=5.00 1652169840000000000
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foo,instance=localhost bar=5.50 1652169960000000000
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foo,instance=localhost bar=5.50 1652170020000000000
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foo,instance=localhost bar=4.00 1652170080000000000
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foo,instance=localhost bar=3.50 1652170260000000000
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foo,instance=localhost bar=3.25 1652170320000000000
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foo,instance=localhost bar=3.00 1652170380000000000
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foo,instance=localhost bar=2.00 1652170440000000000
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foo,instance=localhost bar=1.00 1652170500000000000
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foo,instance=localhost bar=4.00 1652170560000000000
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```
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The data sample consists data points for a measurement `foo`
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and a field `bar` with additional tag `instance=localhost`. If we would like plot this data as a time series in Grafana
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it might have the following look:
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The query used for this panel is written in
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[InfluxQL](https://docs.influxdata.com/influxdb/v1.8/query_language/):
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```sql
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SELECT last ("bar")
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FROM "foo"
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WHERE ("instance" = 'localhost')
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AND $timeFilter
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GROUP BY time (1m)
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```
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Having this, let's import the same data sample in VictoriaMetrics and plot it in Grafana as well. To understand how the
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InfluxQL query might be translated to MetricsQL let's break it into components first:
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* `SELECT last("bar") FROM "foo"` - all requests
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to [instant](https://docs.victoriametrics.com/keyconcepts/#instant-query)
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or [range](https://docs.victoriametrics.com/keyconcepts/#range-query) VictoriaMetrics APIs are reads, so no need
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to specify the `SELECT` statement. There are no `measurements` or `fields` in VictoriaMetrics, so the whole expression
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can be replaced with `foo_bar` in MetricsQL;
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* `WHERE ("instance" = 'localhost')`- [filtering by labels](https://docs.victoriametrics.com/keyconcepts/#filtering)
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in MetricsQL requires specifying the filter in curly braces next to the metric name. So in MetricsQL filter expression
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will be translated to `{instance="localhost"}`;
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* `WHERE $timeFilter` - filtering by time is done via request params sent along with query, so in MetricsQL no need to
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specify this filter;
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* `GROUP BY time(1m)` - grouping by time is done by default
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in [range](https://docs.victoriametrics.com/keyconcepts/#range-query) API according to specified `step` param.
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This param is also a part of params sent along with request. See how to perform additional
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[aggregations and grouping via MetricsQL](https://docs.victoriametrics.com/keyconcepts/#aggregation-and-grouping-functions)
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.
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In result, executing the `foo_bar{instance="localhost"}` MetricsQL expression with `step=1m` for the same set of data in
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Grafana will have the following form:
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Visualizations from both databases are a bit different - VictoriaMetrics shows some extra points
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filling the gaps in the graph. This behavior is described in more
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detail [here](https://docs.victoriametrics.com/keyconcepts/#range-query). In InfluxDB, we can achieve a similar
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behavior by adding `fill(previous)` to the query.
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VictoriaMetrics fills the gaps on the graph assuming time series are always continuous and not discrete.
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To limit the interval on which VictoriaMetrics will try to fill the gaps, set `-search.setLookbackToStep`
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command-line flag. This limits the gap filling to a single `step` interval passed to
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[/api/v1/query_range](https://docs.victoriametrics.com/keyconcepts/#range-query).
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This behavior is close to InfluxDB data model.
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### Advanced usage
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The good thing is that knowing the basics and some aggregation functions is often enough for using MetricsQL or PromQL.
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Let's consider one of the most popular Grafana
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dashboards [Node Exporter Full](https://grafana.com/grafana/dashboards/1860). It has almost 15 million downloads and
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about 230 PromQL queries in it! But a closer look at those queries shows the following:
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* ~120 queries are just selecting a metric with label filters,
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e.g. `node_textfile_scrape_error{instance="$node",job="$job"}`;
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* ~80 queries are using [rate](https://docs.victoriametrics.com/metricsql/#rate) function for selected metric,
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e.g. `rate(node_netstat_Tcp_InSegs{instance=\"$node\",job=\"$job\"})`
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* and the rest
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are [aggregation functions](https://docs.victoriametrics.com/keyconcepts/#aggregation-and-grouping-functions)
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like [sum](https://docs.victoriametrics.com/metricsql/#sum)
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or [count](https://docs.victoriametrics.com/metricsql/#count).
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To get a better understanding of how MetricsQL works, see the following resources:
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* [MetricsQL concepts](https://docs.victoriametrics.com/keyconcepts/#metricsql);
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* [MetricsQL functions](https://docs.victoriametrics.com/metricsql/);
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* [PromQL tutorial for beginners](https://valyala.medium.com/promql-tutorial-for-beginners-9ab455142085).
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## How to migrate current data from InfluxDB to VictoriaMetrics
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Migrating data from other TSDBs to VictoriaMetrics is as simple as importing data via any of
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[supported formats](https://docs.victoriametrics.com/keyconcepts/#push-model).
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But migration from InfluxDB might get easier when using [vmctl](https://docs.victoriametrics.com/vmctl/) -
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VictoriaMetrics command-line tool. See more about
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migrating [from InfluxDB v1.x versions](https://docs.victoriametrics.com/vmctl/#migrating-data-from-influxdb-1x).
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Migrating data from InfluxDB v2.x is not supported yet. But there is
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useful [3rd party solution](https://docs.victoriametrics.com/vmctl/#migrating-data-from-influxdb-2x) for this.
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Please note, that data migration is a backfilling process. So, please
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consider [backfilling tips](https://docs.victoriametrics.com/single-server-victoriametrics/#backfilling).
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## Frequently asked questions
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* How does VictoriaMetrics compare to InfluxDB?
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* _[Answer](https://docs.victoriametrics.com/faq/#how-does-victoriametrics-compare-to-influxdb)_
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* Why don't VictoriaMetrics support Remote Read API, so I don't need to learn MetricsQL?
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* _[Answer](https://docs.victoriametrics.com/faq/#why-doesnt-victoriametrics-support-the-prometheus-remote-read-api)_
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* The PromQL and MetricsQL are often mentioned together - why is that?
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* _MetricsQL - query language inspired by PromQL. MetricsQL is backward-compatible with PromQL, so Grafana
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dashboards backed by Prometheus datasource should work the same after switching from Prometheus to
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VictoriaMetrics. Both languages mostly share the same concepts with slight differences._
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* Query returns more data points than expected - why?
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* _VictoriaMetrics may return non-existing data points if `step` param is lower than the actual data resolution. See
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more about this [here](https://docs.victoriametrics.com/keyconcepts/#range-query)._
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* How do I get the `real` last data point, not `ephemeral`?
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* _[last_over_time](https://docs.victoriametrics.com/metricsql/#last_over_time) function can be used for
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limiting the lookbehind window for calculated data. For example, `last_over_time(metric[10s])` would return
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calculated samples only if the real samples are located closer than 10 seconds to the calculated timestamps
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according to
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`start`, `end` and `step` query args passed
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to [range query](https://docs.victoriametrics.com/keyconcepts/#range-query)._
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* How do I get raw data points with MetricsQL?
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* _For getting raw data points specify the interval at which you want them in square brackets and send
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as [instant query](https://docs.victoriametrics.com/keyconcepts/#instant-query). For
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example, `GET api/v1/query?query=my_metric[5m]&time=<time>` will return raw samples for `my_metric` in interval
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from `<time>` to `<time>-5m`._
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* Can you have multiple aggregators in a MetricsQL query, e.g. `SELECT MAX(field), MIN(field) ...`?
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* _Yes, try the following query `( alias(max(field), "max"), alias(min(field), "min") )`._
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* How to translate Influx `percentile` function to MetricsQL?
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* _[Answer](https://stackoverflow.com/questions/66431990/translate-influx-percentile-function-to-promqlb)_
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* How to translate Influx `stddev` function to MetricsQL?
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* _[Answer](https://stackoverflow.com/questions/66433143/translate-influx-stddev-to-promql)_
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