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docs/vmanomaly - release 1.16.0 docs (#7159)

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### Describe Your Changes

doc updates for vmanomaly v1.16.0

### Checklist

The following checks are **mandatory**:

- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).

(cherry picked from commit 0e54cfe350)
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Fred Navruzov 2024-10-02 00:19:14 +02:00 committed by hagen1778
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4
deployment/docker/vmanomaly/vmanomaly-integration/docker-compose.yml
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@ -73,7 +73,7 @@ services:
restart: always
vmanomaly:
container_name: vmanomaly
image: victoriametrics/vmanomaly:v1.11.0
image: victoriametrics/vmanomaly:latest
depends_on:
- "victoriametrics"
ports:
@ -87,7 +87,7 @@ services:
platform: "linux/amd64"
command:
- "/config.yaml"
- "--license-file=/license"
- "--licenseFile=/license"
alertmanager:
container_name: alertmanager
image: prom/alertmanager:v0.27.0

13
docs/anomaly-detection/CHANGELOG.md
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@ -11,7 +11,18 @@ aliases:
---
Please find the changelog for VictoriaMetrics Anomaly Detection below.
> **Important note: Users are strongly encouraged to upgrade to `vmanomaly` [v1.9.2](https://hub.docker.com/repository/docker/victoriametrics/vmanomaly/tags?page=1&ordering=name) or newer for optimal performance and accuracy. <br><br> This recommendation is crucial for configurations with a low `infer_every` parameter [in your scheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#parameters-1), and in scenarios where data exhibits significant high-order seasonality patterns (such as hourly or daily cycles). Previous versions from v1.5.1 to v1.8.0 were identified to contain a critical issue impacting model training, where models were inadvertently trained on limited data subsets, leading to suboptimal fits, affecting the accuracy of anomaly detection. <br><br> Upgrading to v1.9.2 addresses this issue, ensuring proper model training and enhanced reliability. For users utilizing Helm charts, it is recommended to upgrade to version [1.0.0](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/CHANGELOG.md#100) or newer.**
### v1.16.0
Released: 2024-10-01
- FEATURE: Introduced data dumps to a host filesystem for [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader#vm-reader). Resource-intensive setups (multiple queries returning many metrics, bigger `fit_window` arg) will have RAM consumption reduced during fit calls.
- IMPROVEMENT: Added a `groupby` argument for logical grouping in [multivariate models](https://docs.victoriametrics.com/anomaly-detection/components/models#multivariate-models). When specified, a separate multivariate model is trained for each unique combination of label values in the `groupby` columns. For example, to perform multivariate anomaly detection on metrics at the machine level without cross-entity interference, you can use `groupby: [host]` or `groupby: [instance]`, ensuring one model per entity being trained (e.g., per host). Please find more details [here](https://docs.victoriametrics.com/anomaly-detection/components/models/#group-by).
- IMPROVEMENT: Improved performance of [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader#vm-reader) on multicore instances for reading and data processing.
- IMPROVEMENT: Introduced new CLI argument aliases to enhance compatibility with [Helm charts](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/README.md) (i.e. using secrets) and better align with [VictoriaMetrics flags](https://docs.victoriametrics.com/#list-of-command-line-flags):
- `--licenseFile` as an alias for `--license-file`
- `--license.forceOffline` as an alias for `--license-verify-offline`
- `--loggerLevel` as an alias for `--log-level`
- The previous argument format is retained for backward compatibility.
- FIX: The `provide_series` [common argument](https://docs.victoriametrics.com/anomaly-detection/components/models/#provide-series) now correctly filters the written time series in the [IsolationForestMultivariate](https://docs.victoriametrics.com/anomaly-detection/components/models/#isolation-forest-multivariate) model.
## v1.15.9
Released: 2024-08-27

9
docs/anomaly-detection/FAQ.md
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@ -120,7 +120,9 @@ Configuration above will produce N intervals of full length (`fit_window`=14d +
## Resource consumption of vmanomaly
`vmanomaly` itself is a lightweight service, resource usage is primarily dependent on [scheduling](https://docs.victoriametrics.com/anomaly-detection/components/scheduler) (how often and on what data to fit/infer your models), [# and size of timeseries returned by your queries](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader), and the complexity of the employed [models](https://docs.victoriametrics.com/anomaly-detection/components/models). Its resource usage is directly related to these factors, making it adaptable to various operational scales.
> **Note**: Starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130), there is a mode to save anomaly detection models on host filesystem after `fit` stage (instead of keeping them in-memory by default). **Resource-intensive setups** (many models, many metrics, bigger [`fit_window` arg](https://docs.victoriametrics.com/anomaly-detection/components/scheduler#periodic-scheduler-config-example)) and/or 3rd-party models that store fit data (like [ProphetModel](https://docs.victoriametrics.com/anomaly-detection/components/models#prophet) or [HoltWinters](https://docs.victoriametrics.com/anomaly-detection/components/models#holt-winters)) will have RAM consumption greatly reduced at a cost of slightly slower `infer` stage. To enable it, you need to set environment variable `VMANOMALY_MODEL_DUMPS_DIR` to desired location. [Helm charts](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/README.md) are being updated accordingly ([`StatefulSet`](https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/) for persistent storage starting from chart version `1.3.0`).
> **Note**: Starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130), there is an option to save anomaly detection models to the host filesystem after the `fit` stage (instead of keeping them in memory by default). This is particularly useful for **resource-intensive setups** (e.g., many models, many metrics, or larger [`fit_window` argument](https://docs.victoriametrics.com/anomaly-detection/components/scheduler#periodic-scheduler-config-example)) and for 3rd-party models that store fit data (such as [ProphetModel](https://docs.victoriametrics.com/anomaly-detection/components/models#prophet) or [HoltWinters](https://docs.victoriametrics.com/anomaly-detection/components/models#holt-winters)). This reduces RAM consumption significantly, though at the cost of slightly slower `infer` stages. To enable this, set the environment variable `VMANOMALY_MODEL_DUMPS_DIR` to the desired location. If using [Helm charts](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/README.md), starting from chart version `1.3.0` `.persistentVolume.enabled` should be set to `true` in [values.yaml](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/values.yaml).
> **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. To use this, set the environment variable `VMANOMALY_DATA_DUMPS_DIR` to the desired location.
Here's an example of how to set it up in docker-compose using volumes:
```yaml
@ -138,17 +140,20 @@ services:
- ./vmanomaly_license:/license
# map the host directory to the container directory
- vmanomaly_model_dump_dir:/vmanomaly/tmp/models
- vmanomaly_data_dump_dir:/vmanomaly/tmp/data
environment:
# set the environment variable for the model dump directory
- VMANOMALY_MODEL_DUMPS_DIR=/vmanomaly/tmp/models/
VMANOMALY_DATA_DUMPS_DIR=/vmanomaly/tmp/data/
platform: "linux/amd64"
command:
- "/config.yaml"
- "--license-file=/license"
- "--licenseFile=/license"
volumes:
# ...
vmanomaly_model_dump_dir: {}
vmanomaly_data_dump_dir: {}
```
> **Note**: Starting from [v1.15.0](https://docs.victoriametrics.com/anomaly-detection/changelog#v1150) with the introduction of [online models](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models), you can additionally reduce resource consumption (e.g., flatten `fit` stage peaks by querying less data from VictoriaMetrics at once).

21
docs/anomaly-detection/Overview.md
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@ -250,26 +250,25 @@ docker run -it --net [YOUR_NETWORK] \
-v YOUR_LICENSE_FILE_PATH:/license \
-v YOUR_CONFIG_FILE_PATH:/config.yml \
vmanomaly /config.yml \
--license-file=/license
--licenseFile=/license
```
### Licensing
The license key can be passed via the following command-line flags:
```
--license LICENSE See https://victoriametrics.com/products/enterprise/
for trial license
--license-file LICENSE_FILE
See https://victoriametrics.com/products/enterprise/
for trial license
--license-verify-offline {true,false}
Force offline verification of license code. License is
verified online by default. This flag runs license
verification offline.
--license STRING License key for VictoriaMetrics Enterprise.
See https://victoriametrics.com/products/enterprise/trial/ to obtain a trial license.
--licenseFile STRING Path to file with license key for VictoriaMetrics Enterprise.
See https://victoriametrics.com/products/enterprise/trial/ to obtain a trial license.
--license.forceOffline
Whether to force offline verification for VictoriaMetrics Enterprise license key,
which has been passed either via -license or via -licenseFile command-line flag.
The issued license key must support offline verification feature.
Contact info@victoriametrics.com if you need offline license verification.
```
In order to make it easier to monitor the license expiration date, the following metrics are exposed(see
[Monitoring](#monitoring) section for details on how to scrape them):

6
docs/anomaly-detection/QuickStart.md
View File

@ -50,7 +50,7 @@ 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
--licenseFile=/license
```
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:
@ -62,7 +62,7 @@ docker run -it --user 1000:1000 \
-v $YOUR_LICENSE_FILE_PATH:/license \
-v $YOUR_CONFIG_FILE_PATH:/config.yml \
vmanomaly /config.yml \
--license-file=/license
--licenseFile=/license
```
```yaml
@ -76,7 +76,7 @@ services:
$YOUR_CONFIG_FILE_PATH:/config.yml
command:
- "/config.yml"
- "--license-file=/license"
- "--licenseFile=/license"
# ...
```

47
docs/anomaly-detection/components/models.md
View File

@ -126,7 +126,7 @@ models:
provide_series: ['anomaly_score'] # only `anomaly_score` metric will be available for writing back to the database
```
**Note** If `provide_series` is not specified in model config, the model will produce its default [model-dependent output](#vmanomaly-output). The output can't be less than `['anomaly_score']`. Even if `timestamp` column is omitted, it will be implicitly added to `provide_series` list, as it's required for metrics to be properly written.
> **Note**: If `provide_series` is not specified in model config, the model will produce its default [model-dependent output](#vmanomaly-output). The output can't be less than `['anomaly_score']`. Even if `timestamp` column is omitted, it will be implicitly added to `provide_series` list, as it's required for metrics to be properly written.
### Detection direction
Introduced in [1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#1130), `detection_direction` arg can help in reducing the number of [false positives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-positive) and increasing the accuracy, when domain knowledge suggest to identify anomalies occurring when actual values (`y`) are *above, below, or in both directions* relative to the expected values (`yhat`). Available choices are: `both`, `above_expected`, `below_expected`.
@ -224,7 +224,46 @@ models:
z_threshold: 3
# if not set, equals to setting min_dev_from_expected == 0
queries: ['normal_behavior'] # use the default where it's not needed
```
```
### Group By
> **Note**: The `groupby` argument works only in combination with [multivariate models](#multivariate-models).
Introduced in [v1.16.0](https://docs.victoriametrics.com/anomaly-detection/changelog#v1160), the `groupby` argument (`list[string]`) enables logical grouping within [multivariate models](#multivariate-models). When specified, **a separate multivariate model is trained for each unique combination of label values present in the `groupby` columns**.
For example, to perform multivariate anomaly detection at the machine level while avoiding interference between different entities, you can set `groupby: [host]` or `groupby: [instance]`. This ensures that a **separate multivariate** model is trained for each individual entity (e.g., per host). Below is a simplified example illustrating how to track multivariate anomalies using CPU, RAM, and network data for each host.
```yaml
# other config sections ...
reader:
# other reader params ...
# assume there are M unique hosts identified by the `host` label
queries:
# return one timeseries for each CPU mode per host, total = N*M timeseries
cpu: sum(rate(node_cpu_seconds_total[5m])) by (host, mode)
# return one timeseries per host, total = 1*M timeseries
ram: |
(
(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes)
/ node_memory_MemTotal_bytes
) * 100 by (host)
# return one timeseries per host for both network receive and transmit data, total = 1*M timeseries
network: |
sum(rate(node_network_receive_bytes_total[5m])) by (host)
+ sum(rate(node_network_transmit_bytes_total[5m])) by (host)
models:
iforest: # alias for the model
class: isolation_forest_multivariate
contamination: 0.01
# the multivariate model can be trained on 2+ timeseries returned by 1+ queries
queries: [cpu, ram, network]
# train a distinct multivariate model for each unique value found in the `host` label
# a single multivariate model will be trained on (N + 1 + 1) timeseries, total = M models
groupby: [host]
```
## Model types
@ -260,6 +299,8 @@ For a multivariate type, **one shared model** is fit/used for inference on **all
For example, if you have some **multivariate** model to use 3 [MetricQL queries](https://docs.victoriametrics.com/metricsql/), each returning 5 time series, there will be one shared model created in total. Once fit, this model will expect **exactly 15 time series with exact same labelsets as an input**. This model will produce **one shared [output](#vmanomaly-output)**.
> **Note:** Starting from [v1.16.0](https://docs.victoriametrics.com/anomaly-detection/changelog#v1160), N models — one for each unique combination of label values specified in the `groupby` [common argument](#group-by) — can be trained. This allows for context separation (e.g., one model per host, region, or other relevant grouping label), leading to improved accuracy and faster training. See an example [here](#group-by).
If during an inference, you got a **different amount of series** or some series having a **new labelset** (not present in any of fitted models), the inference will be skipped until you get a model, trained particularly for such labelset during forthcoming re-fit step.
**Implications:** Multivariate models are a go-to default, when your queries returns **fixed** amount of **individual** time series (say, some aggregations), to be used for adding cross-series (and cross-query) context, useful for catching [collective anomalies](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-2/#collective-anomalies) or [novelties](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-2/#novelties) (expanded to multi-input scenario). For example, you may set it up for anomaly detection of CPU usage in different modes (`idle`, `user`, `system`, etc.) and use its cross-dependencies to detect **unseen (in fit data)** behavior.
@ -935,7 +976,7 @@ docker run -it \
-v $(PWD)/custom_model.py:/vmanomaly/model/custom.py \
-v $(PWD)/custom.yaml:/config.yaml \
victoriametrics/vmanomaly:latest /config.yaml \
--license-file=/license
--licenseFile=/license
```
Please find more detailed instructions (license, etc.) [here](https://docs.victoriametrics.com/anomaly-detection/overview/#run-vmanomaly-docker-container)

4
docs/anomaly-detection/guides/guide-vmanomaly-vmalert/README.md
View File

@ -385,7 +385,7 @@ services:
restart: always
vmanomaly:
container_name: vmanomaly
image: victoriametrics/vmanomaly:v1.11.0
image: victoriametrics/vmanomaly:latest
depends_on:
- "victoriametrics"
ports:
@ -399,7 +399,7 @@ services:
platform: "linux/amd64"
command:
- "/config.yaml"
- "--license-file=/license"
- "--licenseFile=/license"
alertmanager:
container_name: alertmanager
image: prom/alertmanager:v0.25.0