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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2021-03-31 12:58:11 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2021-03-31 12:58:11 +0000 |
commit | f99c4526d94d3e04124c5c48ab4a3da6ca53a458 (patch) | |
tree | a2ed8860030cc49f492b09b3222d593c65619800 /docs/guides/monitor/anomaly-detection.md | |
parent | Adding upstream version 1.29.3. (diff) | |
download | netdata-c0ecfccbffe7a59dafa73d092f44c3a9333256e4.tar.xz netdata-c0ecfccbffe7a59dafa73d092f44c3a9333256e4.zip |
Adding upstream version 1.30.0.upstream/1.30.0
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to '')
-rw-r--r-- | docs/guides/monitor/anomaly-detection.md | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/docs/guides/monitor/anomaly-detection.md b/docs/guides/monitor/anomaly-detection.md index bb9dbc829..2fa4896c6 100644 --- a/docs/guides/monitor/anomaly-detection.md +++ b/docs/guides/monitor/anomaly-detection.md @@ -79,9 +79,10 @@ yourself if it doesn't already exist. Either way, the final result should look l anomalies: yes ``` -[Restart the Agent](/docs/configure/start-stop-restart.md) with `sudo systemctl restart netdata` to start up the -anomalies collector. By default, the model training process runs every 30 minutes, and uses the previous 4 hours of -metrics to establish a baseline for health and performance across the default included charts. +[Restart the Agent](/docs/configure/start-stop-restart.md) with `sudo systemctl restart netdata`, or the [appropriate +method](/docs/configure/start-stop-restart.md) for your system, to start up the anomalies collector. By default, the +model training process runs every 30 minutes, and uses the previous 4 hours of metrics to establish a baseline for +health and performance across the default included charts. > ๐ก The anomaly collector may need 30-60 seconds to finish its initial training and have enough data to start > generating anomaly scores. You may need to refresh your browser tab for the **Anomalies** section to appear in menus @@ -106,7 +107,7 @@ involve tweaking the behavior of the ML training itself. doesn't have historical metrics going back that far, consider [changing the metrics retention policy](/docs/store/change-metrics-storage.md) or reducing this window. - `custom_models`: A way to define custom models that you want anomaly probabilities for, including multi-node or - streaming setups. More on custom models in part 3 of this guide series. + streaming setups. > โ ๏ธ Setting `charts_regex` with many charts or `train_n_secs` to a very large number will have an impact on the > resources and time required to train a model for every chart. The actual performance implications depend on the @@ -172,20 +173,19 @@ example, it's time to apply that knowledge to other mission-critical parts of yo what to monitor next, check out our list of [collectors](/collectors/COLLECTORS.md) to see what kind of metrics Netdata can collect from your systems, containers, and applications. -For a more user-friendly anomaly detection experience, try out the [Metric -Correlations](https://learn.netdata.cloud/docs/cloud/insights/metric-correlations) feature in Netdata Cloud. Metric -Correlations runs only at your requests, removing unrelated charts from the dashboard to help you focus on root cause -analysis. +Keep on moving to [part 2](/docs/guides/monitor/visualize-monitor-anomalies.md), which covers the charts and alarms +Netdata creates for unsupervised anomaly detection. -Stay tuned for the next two parts of this guide, which provide more real-world context for the anomalies collector. -First, maximize the immediate value you get from anomaly detection by tracking preconfigured alarms, visualizing -anomalies in charts, and building a new dashboard tailored to your applications. Then, learn about creating custom ML -models, which help you holistically monitor an application or service by monitoring anomalies across a _cluster of -charts_. +For a different troubleshooting experience, try out the [Metric +Correlations](https://learn.netdata.cloud/docs/cloud/insights/metric-correlations) feature in Netdata Cloud. Metric +Correlations helps you perform faster root cause analysis by narrowing a dashboard to only the charts most likely to be +related to an anomaly. ### Related reference documentation - [Netdata Agent ยท Anomalies collector](/collectors/python.d.plugin/anomalies/README.md) +- [Netdata Agent ยท Nginx collector](https://learn.netdata.cloud/docs/agent/collectors/go.d.plugin/modules/nginx) +- [Netdata Agent ยท web log collector](https://learn.netdata.cloud/docs/agent/collectors/go.d.plugin/modules/weblog) - [Netdata Cloud ยท Metric Correlations](https://learn.netdata.cloud/docs/cloud/insights/metric-correlations) [![analytics](https://www.google-analytics.com/collect?v=1&aip=1&t=pageview&_s=1&ds=github&dr=https%3A%2F%2Fgithub.com%2Fnetdata%2Fnetdata&dl=https%3A%2F%2Fmy-netdata.io%2Fgithub%2Fdocs%2Fguides%2Fmonitor%2Fanomaly-detectionl&_u=MAC~&cid=5792dfd7-8dc4-476b-af31-da2fdb9f93d2&tid=UA-64295674-3)](<>) |