mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
synced 2024-11-21 14:44:00 +00:00
d83e05a3e9
* docs/vmanomaly: v1.12.0 & link updates * add autotuned description to model section * - update refs of vmanomaly on enterprise and vmalert pages - add diagrams for model types - update self-monitoring section * - fix typos - remove .index.html from links
118 lines
No EOL
4.1 KiB
Markdown
118 lines
No EOL
4.1 KiB
Markdown
---
|
|
sort: 1
|
|
weight: 1
|
|
title: VictoriaMetrics Anomaly Detection Quick Start
|
|
menu:
|
|
docs:
|
|
parent: "anomaly-detection"
|
|
weight: 1
|
|
title: Quick Start
|
|
aliases:
|
|
- /anomaly-detection/QuickStart.html
|
|
---
|
|
|
|
# VictoriaMetrics Anomaly Detection Quick Start
|
|
|
|
For service introduction visit [README](/anomaly-detection/) page
|
|
and [Overview](/anomaly-detection/overview.html) of how `vmanomaly` works.
|
|
|
|
## How to install and run vmanomaly
|
|
|
|
> To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
|
|
|
|
The following options are available:
|
|
|
|
- [To run Docker image](#docker)
|
|
- [To run in Kubernetes with Helm charts](#kubernetes-with-helm-charts)
|
|
|
|
|
|
### Docker
|
|
|
|
> To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
|
|
|
|
Below are the steps to get `vmanomaly` up and running inside a Docker container:
|
|
|
|
1. Pull Docker image:
|
|
|
|
```sh
|
|
docker pull victoriametrics/vmanomaly:latest
|
|
```
|
|
|
|
2. (Optional step) tag the `vmanomaly` Docker image:
|
|
|
|
```sh
|
|
docker image tag victoriametrics/vmanomaly:latest vmanomaly
|
|
```
|
|
|
|
3. Start the `vmanomaly` Docker container with a *license file*, use the command below.
|
|
**Make sure to replace `YOUR_LICENSE_FILE_PATH`, and `YOUR_CONFIG_FILE_PATH` with your specific details**:
|
|
|
|
```sh
|
|
export YOUR_LICENSE_FILE_PATH=path/to/license/file
|
|
export YOUR_CONFIG_FILE_PATH=path/to/config/file
|
|
docker run -it -v $YOUR_LICENSE_FILE_PATH:/license \
|
|
-v $YOUR_CONFIG_FILE_PATH:/config.yml \
|
|
vmanomaly /config.yml \
|
|
--license-file=/license
|
|
```
|
|
|
|
See also:
|
|
|
|
- You can verify licence online and offline. See the details [here](/anomaly-detection/overview/#licensing).
|
|
- [How to configure `vmanomaly`](#how-to-configure-vmanomaly)
|
|
|
|
### Kubernetes with Helm charts
|
|
|
|
> To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
|
|
|
|
You can run `vmanomaly` in Kubernetes environment
|
|
with [these Helm charts](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/README.md).
|
|
|
|
|
|
## How to configure vmanomaly
|
|
To run `vmanomaly` you need to set up configuration file in `yaml` format.
|
|
|
|
Here is an example of config file that will run [Facebook Prophet](https://facebook.github.io/prophet/) model, that will be retrained every 2 hours on 14 days of previous data. It will generate inference (including `anomaly_score` metric) every 1 minute.
|
|
|
|
|
|
```yaml
|
|
scheduler:
|
|
infer_every: "1m"
|
|
fit_every: "2h"
|
|
fit_window: "14d"
|
|
|
|
models:
|
|
prophet_model:
|
|
class: "model.prophet.ProphetModel"
|
|
args:
|
|
interval_width: 0.98
|
|
|
|
reader:
|
|
datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL]
|
|
sampling_period: "1m"
|
|
queries:
|
|
# define your queries with MetricsQL - https://docs.victoriametrics.com/metricsql/
|
|
cache: "sum(rate(vm_cache_entries))"
|
|
|
|
writer:
|
|
datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL]
|
|
```
|
|
|
|
|
|
Next steps:
|
|
- Define how often to run and make inferences in the [scheduler](/anomaly-detection/components/scheduler/) section of a config file.
|
|
- Setup the datasource to read data from in the [reader](/anomaly-detection/components/reader/) section.
|
|
- Specify where and how to store anomaly detection metrics in the [writer](/anomaly-detection/components/writer/) section.
|
|
- Configure built-in models parameters according to your needs in the [models](/anomaly-detection/components/models/) section.
|
|
- Integrate your [custom models](/anomaly-detection/components/models/#custom-model-guide) with `vmanomaly`.
|
|
- Define queries for input data using [MetricsQL](https://docs.victoriametrics.com/metricsql/).
|
|
|
|
|
|
## Check also
|
|
|
|
Here are other materials that you might find useful:
|
|
|
|
- [Guide: Anomaly Detection and Alerting Setup](/anomaly-detection/guides/guide-vmanomaly-vmalert/)
|
|
- [FAQ](/anomaly-detection/faq/)
|
|
- [Changelog](/anomaly-detection/changelog/)
|
|
- [Anomaly Detection Blog](https://victoriametrics.com/blog/tags/anomaly-detection/) |