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153 lines
5.6 KiB
Markdown
153 lines
5.6 KiB
Markdown
---
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sort: 1
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weight: 1
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title: VictoriaMetrics Anomaly Detection Quick Start
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menu:
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docs:
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parent: "anomaly-detection"
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weight: 1
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title: Quick Start
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aliases:
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- /anomaly-detection/QuickStart.html
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---
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For service introduction visit [README](./README.md) page
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and [Overview](./Overview.md) of how `vmanomaly` works.
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## How to install and run vmanomaly
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> To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
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The following options are available:
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- [To run Docker image](#docker)
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- [To run in Kubernetes with Helm charts](#kubernetes-with-helm-charts)
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> **Note**: Starting from [v1.13.0](./CHANGELOG.md#v1130) there is a mode to keep anomaly detection models on host filesystem after `fit` stage (instead of keeping them in-memory by default); This may lead to **noticeable reduction of RAM used** on bigger setups. See instructions [here](./FAQ.md#resource-consumption-of-vmanomaly).
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### Docker
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> To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
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Below are the steps to get `vmanomaly` up and running inside a Docker container:
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1. Pull Docker image:
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```sh
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docker pull victoriametrics/vmanomaly:latest
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```
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2. (Optional step) tag the `vmanomaly` Docker image:
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```sh
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docker image tag victoriametrics/vmanomaly:latest vmanomaly
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```
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3. Start the `vmanomaly` Docker container with a *license file*, use the command below.
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**Make sure to replace `YOUR_LICENSE_FILE_PATH`, and `YOUR_CONFIG_FILE_PATH` with your specific details**:
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```sh
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export YOUR_LICENSE_FILE_PATH=path/to/license/file
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export YOUR_CONFIG_FILE_PATH=path/to/config/file
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docker run -it -v $YOUR_LICENSE_FILE_PATH:/license \
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-v $YOUR_CONFIG_FILE_PATH:/config.yml \
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vmanomaly /config.yml \
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--license-file=/license
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```
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In case you found `PermissionError: [Errno 13] Permission denied:` in `vmanomaly` logs, set user/user group to 1000 in the run command above / in a docker-compose file:
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```sh
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export YOUR_LICENSE_FILE_PATH=path/to/license/file
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export YOUR_CONFIG_FILE_PATH=path/to/config/file
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docker run -it --user 1000:1000 \
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-v $YOUR_LICENSE_FILE_PATH:/license \
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-v $YOUR_CONFIG_FILE_PATH:/config.yml \
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vmanomaly /config.yml \
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--license-file=/license
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```
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```yaml
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# docker-compose file
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services:
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# ...
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vmanomaly:
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image: victoriametrics/vmanomaly:latest
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volumes:
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$YOUR_LICENSE_FILE_PATH:/license
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$YOUR_CONFIG_FILE_PATH:/config.yml
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command:
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- "/config.yml"
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- "--license-file=/license"
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# ...
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```
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For a complete docker-compose example please refer to [our alerting guide](./guides/guide-vmanomaly-vmalert/README.md), chapter [docker-compose](./guides/guide-vmanomaly-vmalert/README.md#docker-compose)
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See also:
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- Verify the license online OR offline. See the details [here](./Overview.md#licensing).
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- [How to configure `vmanomaly`](#how-to-configure-vmanomaly)
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### Kubernetes with Helm charts
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> To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
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You can run `vmanomaly` in Kubernetes environment
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with [these Helm charts](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/README.md).
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## How to configure vmanomaly
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To run `vmanomaly` you need to set up configuration file in `yaml` format.
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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.
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```yaml
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schedulers:
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2h_1m:
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# https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#periodic-scheduler
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class: 'periodic'
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infer_every: '1m'
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fit_every: '2h'
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fit_window: '2w'
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models:
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# https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet
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prophet_model:
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class: "prophet" # or "model.prophet.ProphetModel" until v1.13.0
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args:
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interval_width: 0.98
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reader:
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# https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader
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datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL]
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sampling_period: "1m"
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queries:
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# define your queries with MetricsQL - https://docs.victoriametrics.com/metricsql/
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cache: "sum(rate(vm_cache_entries))"
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writer:
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# https://docs.victoriametrics.com/anomaly-detection/components/writer/#vm-writer
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datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL]
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```
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Next steps:
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- Define how often to run and make inferences in the [scheduler](./components/scheduler.md) section of a config file.
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- Setup the datasource to read data from in the [reader](./components/reader.md) section.
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- Specify where and how to store anomaly detection metrics in the [writer](./components/writer.md) section.
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- Configure built-in models parameters according to your needs in the [models](./components/models.md) section.
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- Integrate your [custom models](./components/models.md#custom-model-guide) with `vmanomaly`.
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- Define queries for input data using [MetricsQL](../../MetricsQL.md).
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## Check also
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Here are other materials that you might find useful:
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- [Guide: Anomaly Detection and Alerting Setup](./guides/guide-vmanomaly-vmalert/README.md)
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- [FAQ](./FAQ.md)
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- [Changelog](./CHANGELOG.md)
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- [Anomaly Detection Blog](https://victoriametrics.com/blog/tags/anomaly-detection/)
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