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https://github.com/VictoriaMetrics/VictoriaMetrics.git
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Merge branch 'public-single-node' into victorialogs-wip
This commit is contained in:
Aliaksandr Valialkin
commit
af646ad1c0
5 changed files with 16 additions and 20 deletions
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@ -16,4 +16,7 @@ issues:
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linters-settings:
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errcheck:
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exclude: ./errcheck_excludes.txt
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exclude-functions:
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- "fmt.Fprintf"
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- "fmt.Fprint"
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- "(net/http.ResponseWriter).Write"
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2
Makefile
2
Makefile
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@ -492,7 +492,7 @@ golangci-lint: install-golangci-lint
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golangci-lint run
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install-golangci-lint:
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which golangci-lint || curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh | sh -s -- -b $(shell go env GOPATH)/bin v1.57.1
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which golangci-lint || curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh | sh -s -- -b $(shell go env GOPATH)/bin v1.59.1
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govulncheck: install-govulncheck
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govulncheck ./...
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@ -88,10 +88,6 @@ Currently, vmanomaly ships with a set of built-in models:
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See [statsmodels.org documentation](https://www.statsmodels.org/dev/examples/notebooks/generated/stl_decomposition.html)
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for LOESS STD.
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1. [**ARIMA**](/anomaly-detection/components/models.html#arima)
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Commonly used forecasting model. See [statsmodels.org documentation](https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima.model.ARIMA.html) for ARIMA.
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1. [**Rolling Quantile**](/anomaly-detection/components/models.html#rolling-quantile)
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A simple moving window of quantiles. Easy to use, easy to understand, but not as powerful as
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@ -135,19 +135,19 @@ Introduced in [1.13.0](/anomaly-detection/CHANGELOG/#1130), `detection_direction
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Here's how default (backward-compatible) behavior looks like - anomalies will be tracked in `both` directions (`y > yhat` or `y < yhat`). This is useful when there is no domain expertise to filter the required direction.
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<img src="schema_detection_direction=both.webp" width="800px" alt="schema_detection_direction=both"/>
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<img src="/anomaly-detection/components/schema_detection_direction=both.webp" width="800px" alt="schema_detection_direction=both"/>
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When set to `above_expected`, anomalies are tracked only when `y > yhat`.
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*Example metrics*: Error rate, response time, page load time, number of failed transactions - metrics where *lower values are better*, so **higher** values are typically tracked.
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<img src="schema_detection_direction=above_expected.webp" width="800px" alt="schema_detection_direction=above_expected"/>
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<img src="/anomaly-detection/components/schema_detection_direction=above_expected.webp" width="800px" alt="schema_detection_direction=above_expected"/>
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When set to `below_expected`, anomalies are tracked only when `y < yhat`.
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*Example metrics*: Service Level Agreement (SLA) compliance, conversion rate, Customer Satisfaction Score (CSAT) - metrics where *higher values are better*, so **lower** values are typically tracked.
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<img src="schema_detection_direction=below_expected.webp" width="800px" alt="schema_detection_direction=below_expected"/>
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<img src="/anomaly-detection/components/schema_detection_direction=below_expected.webp" width="800px" alt="schema_detection_direction=below_expected"/>
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Config with a split example:
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@ -193,11 +193,11 @@ Introduced in [v1.13.0](/anomaly-detection/CHANGELOG/#1130), the `min_dev_from_e
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Visualizations below demonstrate this concept; the green zone defined as the `[yhat - min_dev_from_expected, yhat + min_dev_from_expected]` range excludes actual data points (`y`) from generating anomaly scores if they fall within that range.
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<img src="schema_min_dev_from_expected=0.webp" width="800px" alt="min_dev_from_expected-default"/>
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<img src="/anomaly-detection/components/schema_min_dev_from_expected=0.webp" width="800px" alt="min_dev_from_expected-default"/>
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<img src="schema_min_dev_from_expected=1.0.webp" width="800px" alt="min_dev_from_expected-small"/>
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<img src="/anomaly-detection/components/schema_min_dev_from_expected=1.0.webp" width="800px" alt="min_dev_from_expected-small"/>
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<img src="schema_min_dev_from_expected=5.0.webp" width="800px" alt="min_dev_from_expected-big"/>
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<img src="/anomaly-detection/components/schema_min_dev_from_expected=5.0.webp" width="800px" alt="min_dev_from_expected-big"/>
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## Model types
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@ -224,7 +224,7 @@ If during an inference, you got a series having **new labelset** (not present in
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**Examples:** [Prophet](#prophet), [Holt-Winters](#holt-winters)
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<p></p>
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<img alt="vmanomaly-model-type-univariate" src="model-lifecycle-univariate.webp" width="800px"/>
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<img alt="vmanomaly-model-type-univariate" src="/anomaly-detection/components/model-lifecycle-univariate.webp" width="800px"/>
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### Multivariate Models
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@ -239,7 +239,7 @@ If during an inference, you got a **different amount of series** or some series
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**Examples:** [IsolationForest](#isolation-forest-multivariate)
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<p></p>
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<img alt="vmanomaly-model-type-multivariate" src="model-lifecycle-multivariate.webp" width="800px"/>
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<img alt="vmanomaly-model-type-multivariate" src="/anomaly-detection/components/model-lifecycle-multivariate.webp" width="800px"/>
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### Rolling Models
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@ -256,7 +256,7 @@ Such models put **more pressure** on your reader's source, i.e. if your model sh
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**Examples:** [RollingQuantile](#rolling-quantile)
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<p></p>
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<img alt="vmanomaly-model-type-rolling" src="model-type-rolling.webp" width="800px"/>
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<img alt="vmanomaly-model-type-rolling" src="/anomaly-detection/components/model-type-rolling.webp" width="800px"/>
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### Non-Rolling Models
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@ -273,7 +273,7 @@ Produced model instances are **stored in-memory** between consecutive re-fit cal
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**Examples:** [Prophet](#prophet)
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<p></p>
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<img alt="vmanomaly-model-type-non-rolling" src="model-type-non-rolling.webp" width="800px"/>
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<img alt="vmanomaly-model-type-non-rolling" src="/anomaly-detection/components/model-type-non-rolling.webp" width="800px"/>
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## Built-in Models
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@ -312,7 +312,7 @@ Tuning hyperparameters of a model can be tricky and often requires in-depth know
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- `n_trials` (int) - How many trials to sample from hyperparameter search space. The higher, the longer it takes but the better the results can be. Defaults to 128.
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- `timeout` (float) - How many seconds in total can be spent on each model to tune hyperparameters. The higher, the longer it takes, allowing to test more trials out of defined `n_trials`, but the better the results can be.
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<img alt="vmanomaly-autotune-schema" src="autotune.webp" width="800px"/>
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<img alt="vmanomaly-autotune-schema" src="/anomaly-detection/components/autotune.webp" width="800px"/>
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```yaml
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# ...
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fmt.Fprintf
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fmt.Fprint
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(net/http.ResponseWriter).Write
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