diff --git a/docs/anomaly-detection/guides/guide-vmanomaly-vmalert.md b/docs/anomaly-detection/guides/guide-vmanomaly-vmalert.md index 239d254ec0..c9e9425844 100644 --- a/docs/anomaly-detection/guides/guide-vmanomaly-vmalert.md +++ b/docs/anomaly-detection/guides/guide-vmanomaly-vmalert.md @@ -26,7 +26,7 @@ aliases: - [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/) - [Node exporter](https://github.com/prometheus/node_exporter#node-exporter) (v1.7.0) and [Alertmanager](https://prometheus.io/docs/alerting/latest/alertmanager/) (v0.27.0) -vmanomaly typical setup diagram +vmanomaly typical setup diagram > **Note: Configurations used throughout this guide can be found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-integration/)** @@ -108,7 +108,7 @@ In this context, the metric `node_cpu_seconds_total` provides a comprehensive br The `node_cpu_seconds_total` metric is classified as a [counter](https://docs.victoriametrics.com/keyconcepts/#counter) type. To analyze the duration each CPU core spends in these modes, it is necessary to compute the rate of change per second using the [rate function](https://docs.victoriametrics.com/metricsql/#rate): `rate(node_cpu_seconds_total)`. For a more refined and smoother aggregation of data by mode, we apply the sum function. The resulting query is formulated as follows: `sum(rate(node_cpu_seconds_total[5m])) by (mode, instance, job)`. Below is an illustrative example of how this query might be visualized in Grafana: -node_cpu_rate_graph +node_cpu_rate_graph This query will yield a total of eight time series, each corresponding to a CPU mode. The number of series is unaffected by the number of CPU cores, due to the `by` aggregation applied. These series serve as the input for `vmanomaly`, where the service independently fits a separate instance of the configured model type to each of time series. @@ -452,7 +452,7 @@ networks: Before running our docker-compose make sure that your directory contains all required files: -all files +all files This docker-compose file will pull docker images, set up each service and run them all together with the command: @@ -489,7 +489,7 @@ Each of these metrics will contain same labels our query `sum(rate(node_cpu_seco ### Anomaly scores for each metric with its according labels. Query: `anomaly_score` -Anomaly score graph +Anomaly score graph
Check out if the anomaly score is high for datapoints you think are anomalies. If not, you can try other parameters in the config file or try other model type. @@ -500,7 +500,7 @@ As you may notice a lot of data shows anomaly score greater than 1. It is expect Queries: `yhat_lower`, `yhat_upper` and `yhat` -yhat lower and yhat upper +yhat lower and yhat upper Boundaries of 'normal' metric values according to model inference. @@ -508,10 +508,10 @@ Boundaries of 'normal' metric values according to model inference. On the page `http://localhost:8880/vmalert/groups` you can find our configured Alerting rule: -alert rule +alert rule According to the rule configured for vmalert we will see Alert when anomaly score exceed 1. You will see an alert on Alert tab. `http://localhost:8880/vmalert/alerts`: -alerts firing +alerts firing ## 10. Conclusion