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update wiki pages

Vika 2024-01-21 21:45:54 +00:00
parent 08d5048fe3
commit 95238f62f6
2 changed files with 2 additions and 2 deletions
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2
CHANGELOG.md

@ -72,7 +72,7 @@ The sandbox cluster installation is running under the constant load generated by
* BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): fix a link for the statistic inaccuracy explanation in the cardinality explorer tool. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5460). * BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): fix a link for the statistic inaccuracy explanation in the cardinality explorer tool. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5460).
* BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): send `step` param for instant queries. The change reverts [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3896) due to reasons explained in [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3896#issuecomment-1896704401). * BUGFIX: [vmui](https://docs.victoriametrics.com/#vmui): send `step` param for instant queries. The change reverts [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3896) due to reasons explained in [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3896#issuecomment-1896704401).
* BUGFIX: all: fix potential panic during components shutdown when [metrics push](https://docs.victoriametrics.com/#push-metrics) is configured. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5548). Thanks to @zhdd99 for the [pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5549). * BUGFIX: all: fix potential panic during components shutdown when [metrics push](https://docs.victoriametrics.com/#push-metrics) is configured. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5548). Thanks to @zhdd99 for the [pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5549).
* BUGFIX: [vmselect](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html): properly determine time range search for instant queries with too big look-behind window like `foo[100y]`. Previously, such queries could return empty responses even if `foo` is present in database. * BUGFIX: [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html): properly process queries with too big lookbehind window such as `foo[100y]`. Previously, such queries could return empty responses even if `foo` is present in database. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5553).
* BUGFIX: [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html): properly handle possible negative results caused by float operations precision error in rollup functions like rate() or increase(). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5571). * BUGFIX: [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html): properly handle possible negative results caused by float operations precision error in rollup functions like rate() or increase(). See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5571).
## [v1.96.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.96.0) ## [v1.96.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.96.0)

2
anomaly-detection/components/models.md

@ -41,7 +41,7 @@ VM Anomaly Detection (`vmanomaly` hereinafter) models support 2 groups of parame
* [Seasonal Trend Decomposition](#seasonal-trend-decomposition) - similarly to Holt-Winters, is best for **data with pronounced [seasonal](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality) and [trend](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) components** * [Seasonal Trend Decomposition](#seasonal-trend-decomposition) - similarly to Holt-Winters, is best for **data with pronounced [seasonal](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality) and [trend](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) components**
* [ARIMA](#arima) - use when your data shows **clear patterns or autocorrelation (the degree of correlation between values of the same series at different periods)**. However, good understanding of machine learning is required to tune. * [ARIMA](#arima) - use when your data shows **clear patterns or autocorrelation (the degree of correlation between values of the same series at different periods)**. However, good understanding of machine learning is required to tune.
* [Isolation forest (Multivariate)](#isolation-forest-multivariate) - useful for **metrics data interaction** (several queries/metrics -> single anomaly score) and **efficient in detecting anomalies in high-dimensional datasets** * [Isolation forest (Multivariate)](#isolation-forest-multivariate) - useful for **metrics data interaction** (several queries/metrics -> single anomaly score) and **efficient in detecting anomalies in high-dimensional datasets**
* [Custom model](#custom-model) - benefit from your own models and expertise to better support your **unique use case**. * [Custom model](#custom-model-guide) - benefit from your own models and expertise to better support your **unique use case**.
### [Prophet](https://facebook.github.io/prophet/) ### [Prophet](https://facebook.github.io/prophet/)