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### Describe Your Changes
docs/vmanomaly - release 1.18.0
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
(cherry picked from commit 5d73b8b866)
181 lines
8.2 KiB
Markdown
181 lines
8.2 KiB
Markdown
---
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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](https://docs.victoriametrics.com/anomaly-detection/) page
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and [Overview](https://docs.victoriametrics.com/anomaly-detection/overview/) 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](https://docs.victoriametrics.com/anomaly-detection/changelog/#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](https://docs.victoriametrics.com/anomaly-detection/faq/#on-disk-mode).
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> **Note**: Starting from [v1.16.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1160), a similar optimization is available for data read from VictoriaMetrics TSDB. See instructions [here](https://docs.victoriametrics.com/anomaly-detection/faq/#on-disk-mode).
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### Command-line arguments
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The `vmanomaly` service supports several command-line arguments to configure its behavior, including options for licensing, logging levels, and more. These arguments can be passed when starting the service via Docker or any other setup. Below is the list of available options:
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```shellhelp
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VictoriaMetrics Anomaly Detection Service
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positional arguments:
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config YAML config file. Multiple files will override each other's top level values (aka shallow merge), so multiple configs can be combined.
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options:
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-h show this help message and exit
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--license STRING License key for VictoriaMetrics Enterprise. See https://victoriametrics.com/products/enterprise/trial/ to obtain a trial license.
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--licenseFile PATH Path to file with license key for VictoriaMetrics Enterprise. See https://victoriametrics.com/products/enterprise/trial/ to obtain a trial license.
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--license.forceOffline
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Whether to force offline verification for VictoriaMetrics Enterprise license key, which has been passed either via -license or via -licenseFile command-line flag.
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The issued license key must support offline verification feature. Contact info@victoriametrics.com if you need offline license verification.
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--loggerLevel {FATAL,WARNING,ERROR,DEBUG,INFO}
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Minimum level to log. Possible values: DEBUG, INFO, WARNING, ERROR, FATAL.
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```
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You can specify these options when running `vmanomaly` to fine-tune logging levels or handle licensing configurations, as per your requirements.
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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:v1.18.0
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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:v1.18.0 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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--licenseFile=/license \
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--loggerLevel=INFO
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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 $YOUR_CONFIG_FILE_PATH:/config.yml \
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vmanomaly /config.yml \
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--licenseFile=/license \
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--loggerLevel=INFO
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```
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```yaml
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# docker-compose file
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services:
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# ...
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vmanomaly:
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image: victoriametrics/vmanomaly:v1.18.0
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volumes:
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$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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- "--licenseFile=/license"
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- "--loggerLevel=INFO"
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# ...
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```
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For a complete docker-compose example please refer to [our alerting guide](https://docs.victoriametrics.com/anomaly-detection/guides/guide-vmanomaly-vmalert/), chapter [docker-compose](https://docs.victoriametrics.com/anomaly-detection/guides/guide-vmanomaly-vmalert/#docker-compose)
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See also:
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- Verify the license online OR offline. See the details [here](https://docs.victoriametrics.com/anomaly-detection/overview/#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](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/) section of a config file.
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- Setup the datasource to read data from in the [reader](https://docs.victoriametrics.com/anomaly-detection/components/reader/) section.
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- Specify where and how to store anomaly detection metrics in the [writer](https://docs.victoriametrics.com/anomaly-detection/components/writer/) section.
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- Configure built-in models parameters according to your needs in the [models](https://docs.victoriametrics.com/anomaly-detection/components/models/) section.
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- Integrate your [custom models](https://docs.victoriametrics.com/anomaly-detection/components/models/#custom-model-guide) with `vmanomaly`.
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- Define queries for input data using [MetricsQL](https://docs.victoriametrics.com/metricsql/).
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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](https://docs.victoriametrics.com/anomaly-detection/guides/guide-vmanomaly-vmalert/)
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- [FAQ](https://docs.victoriametrics.com/anomaly-detection/faq/)
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- [Changelog](https://docs.victoriametrics.com/anomaly-detection/changelog/)
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- [Anomaly Detection Blog](https://victoriametrics.com/blog/tags/anomaly-detection/)
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