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docs: vmanomaly - release v1.15.1 (#6782)
### Describe Your Changes vmanomaly - release v1.15.1 updates to docs: - changelog page - reader page (new arguments docs) - typos & fixes ### Checklist The following checks are **mandatory**: - [ ] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/).
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Fred Navruzov
GitHub
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3 changed files with 46 additions and 42 deletions
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@ -13,6 +13,20 @@ Please find the changelog for VictoriaMetrics Anomaly Detection below.
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> **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](./components/scheduler.md#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.**
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## v1.15.1
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Released: 2024-08-08
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- FEATURE: Introduced backward-compatible `data_range` [query-specific parameter](https://docs.victoriametrics.com/anomaly-detection/components/reader/#per-query-parameters) to the [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader). It enables the definition of **valid** data ranges for input per individual query in `queries`, resulting in:
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- **High anomaly scores** (>1) when the *data falls outside the expected range*, indicating a data constraint violation.
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- **Lowest anomaly scores** (=0) when the *model's predictions (`yhat`) fall outside the expected range*, signaling uncertain predictions.
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- For more details, please refer to the [documentation](https://docs.victoriametrics.com/anomaly-detection/components/reader/?highlight=data_range#per-query-parameters).
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- IMPROVEMENT: Added `latency_offset` argument to the [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) to override the default `-search.latencyOffset` [flag of VictoriaMetrics](https://docs.victoriametrics.com/?highlight=search.latencyOffset#list-of-command-line-flags) (30s). The default value is set to 1ms, which should help in cases where `sampling_frequency` is low (10-60s) and `sampling_frequency` equals `infer_every` in the [PeriodicScheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/?highlight=infer_every#periodic-scheduler). This prevents users from receiving `service - WARNING - [Scheduler [scheduler_alias]] No data available for inference.` warnings in logs and allows for consecutive `infer` calls without gaps. To restore the backward compatible behavior, set it equal to your `-search.latencyOffset` value in [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) config section.
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- FIX: Ensure the `use_transform` argument of the [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/
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#online-seasonal-quantile) functions as intended.
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- FIX: Add a docstring for `query_from_last_seen_timestamp` arg of [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader).
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## v1.15.0
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Released: 2024-08-06
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- FEATURE: Introduced models that support [online learning](https://en.wikipedia.org/wiki/Online_machine_learning) for stream-like input. These models significantly reduce the amount of data required for the initial fit stage. For example, they enable reducing `fit_every` from **weeks to hours** and increasing `fit_every` from **hours to weeks** in the [PeriodicScheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#periodic-scheduler), significantly reducing the **peak amount** of data queried from VictoriaMetrics during `fit` stages. The next models were added:
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@ -633,7 +633,7 @@ It uses the `quantiles` triplet to calculate `yhat_lower`, `yhat`, and `yhat_upp
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*Parameters specific for vmanomaly*:
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* `class` (string) - model class name `"model.online.OnlineSeasonalQuantile"` (or `quantile_online` starting from [v1.13.0](../CHANGELOG.md#1130) with class alias support)
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* `class` (string) - model class name `"model.online.OnlineQuantileModel"` (or `quantile_online` starting from [v1.13.0](../CHANGELOG.md#1130) with class alias support)
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* `quantiles` (list[float], optional) - The quantiles to estimate. `yhat_lower`, `yhat`, `yhat_upper` are the quantile order. By default (0.01, 0.5, 0.99).
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* `seasonal_interval` (string, optional) - the interval for the seasonal adjustment. If not set, the model will equal to a simple online quantile model. By default not set.
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* `min_subseason` (str, optional) - the minimum interval to estimate quantiles for. By default not set. Note that the minimum interval should be a multiple of the seasonal interval, i.e. if seasonal_interval='2h', then min_subseason='15m' is valid, but '37m' is not.
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@ -651,7 +651,7 @@ Suppose we have a data with strong intraday (hourly) and intraweek (daily) seaso
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```yaml
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models:
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your_desired_alias_for_a_model:
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class: "quantile_online" # or 'model.online.OnlineSeasonalQuantile'
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class: "quantile_online" # or 'model.online.OnlineQuantileModel'
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quantiles: [0.025, 0.5, 0.975] # lowered to exclude anomalous edges, can be compensated by `scale` param > 1
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seasonal_interval: '7d' # longest seasonality (week, day) = week, starting from `season_starts_from`
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min_subseason: '1h' # smallest seasonality (week, day, hour) = hour, will have its own quantile estimates
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@ -62,6 +62,11 @@ Starting from [v1.13.0](/anomaly-detection/changelog#v1130) there is change of [
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> **Note**: having **different** individual `step` args for queries (i.e. `30s` for `q1` and `2m` for `q2`) is not yet supported for [multivariate model](/anomaly-detection/components/models/index.html#multivariate-models) if you want to run it on several queries simultaneously (i.e. setting [`queries`](/anomaly-detection/components/models/#queries) arg of a model to [`q1`, `q2`]).
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- `data_range` (list[float | string]): Introduced in [v1.15.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1151), it allows defining **valid** data ranges for input per individual query in `queries`, resulting in:
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- **High anomaly scores** (>1) when the *data falls outside the expected range*, indicating a data constraint violation.
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- **Lowest anomaly scores** (=0) when the *model's predictions (`yhat`) fall outside the expected range*, meaning uncertain predictions.
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### Per-query config example
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```yaml
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reader:
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@ -72,6 +77,7 @@ reader:
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ingestion_rate:
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expr: 'sum(rate(vm_rows_inserted_total[5m])) by (type) > 0'
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step: '2m' # overrides global `sampling_period` of 1m
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data_range: [10, 'inf'] # meaning only positive values > 10 are expected, i.e. a value `y` < 10 will trigger anomaly score > 1
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```
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### Config parameters
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@ -91,182 +97,166 @@ reader:
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`class`
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</td>
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<td>
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`reader.vm.VmReader` (or `vm` starting from [v1.13.0](../CHANGELOG.md#v1130))
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</td>
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<td>
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Name of the class needed to enable reading from VictoriaMetrics or Prometheus. VmReader is the default option, if not specified.
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</td>
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</tr>
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<tr>
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<td>
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`queries`
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</td>
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<td>
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See [per-query config example](#per-query-config-example) above
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See [per-query config example](#per-query-config-example) above
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</td>
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<td>
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<td>
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See [per-query config section](#per-query-parameters) above
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</td>
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</tr>
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<tr>
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<td>
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`datasource_url`
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</td>
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<td>
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`http://localhost:8481/`
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</td>
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<td>
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Datasource URL address
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</td>
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</tr>
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<tr>
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<td>
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`tenant_id`
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</td>
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<td>
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`0:0`
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</td>
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<td>
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For VictoriaMetrics Cluster version only, tenants are identified by accountID or accountID:projectID. See VictoriaMetrics Cluster [multitenancy docs](../../Cluster-VictoriaMetrics.md#multitenancy)
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</td>
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</tr>
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<tr>
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<td>
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`sampling_period`
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</td>
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<td>
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`1h`
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</td>
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<td>
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Frequency of the points returned. Will be converted to `/query_range?step=%s` param (in seconds). **Required** since [v1.9.0](../CHANGELOG.md#v190).
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</td>
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</tr>
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<tr>
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<td>
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`query_range_path`
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</td>
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<td>
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`/api/v1/query_range`
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</td>
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<td>
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Performs PromQL/MetricsQL range query
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</td>
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</tr>
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<tr>
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<td>
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`health_path`
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</td>
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<td>
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`health`
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</td>
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<td>
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Absolute or relative URL address where to check availability of the datasource.
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</td>
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</tr>
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<tr>
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<td>
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`user`
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</td>
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<td>
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`USERNAME`
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</td>
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<td>
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BasicAuth username
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</td>
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</tr>
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<tr>
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<td>
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`password`
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</td>
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<td>
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`PASSWORD`
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</td>
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<td>
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BasicAuth password
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</td>
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</tr>
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<tr>
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<td>
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`timeout`
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</td>
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<td>
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`30s`
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</td>
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<td>
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Timeout for the requests, passed as a string
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</td>
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</tr>
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<tr>
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<td>
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`verify_tls`
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</td>
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<td>
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`false`
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</td>
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<td>
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Allows disabling TLS verification of the remote certificate.
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</td>
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</tr>
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<tr>
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<td>
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`bearer_token`
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</td>
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<td>
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`token`
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</td>
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<td>
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Token is passed in the standard format with header: `Authorization: bearer {token}`
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</td>
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</tr>
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<tr>
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<td>
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`extra_filters`
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</td>
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<td>
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`[]`
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</td>
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<td>
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List of strings with series selector. See: [Prometheus querying API enhancements](../../README.md##prometheus-querying-api-enhancements)
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</td>
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</tr>
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<tr>
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<td>
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`query_from_last_seen_timestamp`
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</td>
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<td>
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`True`
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</td>
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<td>
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If True, then query will be performed from the last seen timestamp for a given series. If False, then query will be performed from the start timestamp, based on a schedule period. Defaults to `True`. (`False` prior to [v1.15.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1151)). Useful for `infer` stages in case there were skipped `infer` calls prior to given.
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</td>
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</tr>
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<tr>
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<td>
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`latency_offset`
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</td>
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<td>
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`1ms`
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</td>
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<td>
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Introduced in [v1.15.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1151), it allows overriding the default `-search.latencyOffset` [flag of VictoriaMetrics](https://docs.victoriametrics.com/?highlight=search.latencyOffset#list-of-command-line-flags) (30s). The default value is set to 1ms, which should help in cases where `sampling_frequency` is low (10-60s) and `sampling_frequency` equals `infer_every` in the [PeriodicScheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/?highlight=infer_every#periodic-scheduler). This prevents users from receiving `service - WARNING - [Scheduler [scheduler_alias]] No data available for inference.` warnings in logs and allows for consecutive `infer` calls without gaps. To restore the old behavior, set it equal to your `-search.latencyOffset` [flag value]((https://docs.victoriametrics.com/?highlight=search.latencyOffset#list-of-command-line-flags)).
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</td>
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</tr>
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</tbody>
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</table>
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