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diff --git a/docs/guides/monitor/visualize-monitor-anomalies.md b/docs/guides/monitor/visualize-monitor-anomalies.md deleted file mode 100644 index 90ce20a4..00000000 --- a/docs/guides/monitor/visualize-monitor-anomalies.md +++ /dev/null @@ -1,142 +0,0 @@ ---- -title: "Monitor and visualize anomalies with Netdata (part 2)" -description: "Using unsupervised anomaly detection and machine learning, get notified " -image: /img/seo/guides/monitor/visualize-monitor-anomalies.png -author: "Joel Hans" -author_title: "Editorial Director, Technical & Educational Resources" -author_img: "/img/authors/joel-hans.jpg" -custom_edit_url: https://github.com/netdata/netdata/edit/master/docs/guides/monitor/visualize-monitor-anomalies.md ---- - -Welcome to part 2 of our series of guides on using _unsupervised anomaly detection_ to detect issues with your systems, -containers, and applications using the open-source Netdata Agent. For an introduction to detecting anomalies and -monitoring associated metrics, see [part 1](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/anomaly-detection-python.md), which covers prerequisites and -configuration basics. - -With anomaly detection in the Netdata Agent set up, you will now want to visualize and monitor which charts have -anomalous data, when, and where to look next. - -> 💡 In certain cases, the anomalies collector doesn't start immediately after restarting the Netdata Agent. If this -> happens, you won't see the dashboard section or the relevant [charts](#visualize-anomalies-in-charts) right away. Wait -> a minute or two, refresh, and look again. If the anomalies charts and alarms are still not present, investigate the -> error log with `less /var/log/netdata/error.log | grep anomalies`. - -## Test anomaly detection - -Time to see the Netdata Agent's unsupervised anomaly detection in action. To trigger anomalies on the Nginx web server, -use `ab`, otherwise known as [Apache Bench](https://httpd.apache.org/docs/2.4/programs/ab.html). Despite its name, it -works just as well with Nginx web servers. Install it on Ubuntu/Debian systems with `sudo apt install apache2-utils`. - -> 💡 If you haven't followed the guide's example of using Nginx, an easy way to test anomaly detection on your node is -> to use the `stress-ng` command, which is available on most Linux distributions. Run `stress-ng --cpu 0` to create CPU -> stress or `stress-ng --vm 0` for RAM stress. Each test will cause some "collateral damage," in that you may see CPU -> utilization rise when running the RAM test, and vice versa. - -The following test creates a minimum of 10,000,000 requests for Nginx to handle, with a maximum of 10 at any given time, -with a run time of 60 seconds. If your system can handle those 10,000,000 in less than 60 seconds, `ab` will keep -sending requests until the timer runs out. - -```bash -ab -k -c 10 -t 60 -n 10000000 http://127.0.0.1/ -``` - -Let's see how Netdata detects this anomalous behavior and propagates information to you through preconfigured alarms and -dashboards that automatically organize anomaly detection metrics into meaningful charts to help you begin root cause -analysis (RCA). - -## Monitor anomalies with alarms - -The anomalies collector creates two "classes" of alarms for each chart captured by the `charts_regex` setting. All these -alarms are preconfigured based on your [configuration in -`anomalies.conf`](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/anomaly-detection-python.md#configure-the-anomalies-collector). With the `charts_regex` -and `charts_to_exclude` settings from [part 1](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/anomaly-detection-python.md) of this guide series, the -Netdata Agent creates 32 alarms driven by unsupervised anomaly detection. - -The first class triggers warning alarms when the average anomaly probability for a given chart has stayed above 50% for -at least the last two minutes. - - - -The second class triggers warning alarms when the number of anomalies in the last two minutes hits 10 or higher. - - - -If you see either of these alarms in Netdata Cloud, the local Agent dashboard, or on your preferred notification -platform, it's a safe bet that the node's current metrics have deviated from normal. That doesn't necessarily mean -there's a full-blown incident, depending on what application/service you're using anomaly detection on, but it's worth -further investigation. - -As you use the anomalies collector, you may find that the default settings provide too many or too few genuine alarms. -In this case, [configure the alarm](https://github.com/netdata/netdata/blob/master/docs/monitor/configure-alarms.md) with `sudo ./edit-config -health.d/anomalies.conf`. Take a look at the `lookup` line syntax in the [health -reference](https://github.com/netdata/netdata/blob/master/health/REFERENCE.md#alarm-line-lookup) to understand how the anomalies collector automatically creates -alarms for any dimension on the `anomalies_local.probability` and `anomalies_local.anomaly` charts. - -## Visualize anomalies in charts - -In either [Netdata Cloud](https://app.netdata.cloud) or the local Agent dashboard at `http://NODE:19999`, click on the -**Anomalies** [section](https://github.com/netdata/netdata/blob/master/web/gui/README.md#sections) to see the pair of anomaly detection charts, which are -preconfigured to visualize per-second anomaly metrics based on your [configuration in -`anomalies.conf`](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/anomaly-detection-python.md#configure-the-anomalies-collector). - -These charts have the contexts `anomalies.probability` and `anomalies.anomaly`. Together, these charts -create meaningful visualizations for immediately recognizing not only that something is going wrong on your node, but -give context as to where to look next. - -The `anomalies_local.probability` chart shows the probability that the latest observed data is anomalous, based on the -trained model. The `anomalies_local.anomaly` chart visualizes 0→1 predictions based on whether the latest observed -data is anomalous based on the trained model. Both charts share the same dimensions, which you configured via -`charts_regex` and `charts_to_exclude` in [part 1](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/anomaly-detection-python.md). - -In other words, the `probability` chart shows the amplitude of the anomaly, whereas the `anomaly` chart provides quick -yes/no context. - - - -Before `08:32:00`, both charts show little in the way of verified anomalies. Based on the metrics the anomalies -collector has trained on, a certain percentage of anomaly probability score is normal, as seen in the -`web_log_nginx_requests_prob` dimension and a few others. What you're looking for is large deviations from the "noise" -in the `anomalies.probability` chart, or any increments to the `anomalies.anomaly` chart. - -Unsurprisingly, the stress test that began at `08:32:00` caused significant changes to these charts. The three -dimensions that immediately shot to 100% anomaly probability, and remained there during the test, were -`web_log_nginx.requests_prob`, `nginx_local.connections_accepted_handled_prob`, and `system.cpu_pressure_prob`. - -## Build an anomaly detection dashboard - -[Netdata Cloud](https://app.netdata.cloud) features a drag-and-drop [dashboard -editor](https://github.com/netdata/netdata/blob/master/docs/visualize/create-dashboards.md) that helps you create entirely new dashboards with charts targeted for -your specific applications. - -For example, here's a dashboard designed for visualizing anomalies present in an Nginx web server, including -documentation about why the dashboard exists and where to look next based on what you're seeing: - - - -Use the anomaly charts for instant visual identification of potential anomalies, and then Nginx-specific charts, in the -right column, to validate whether the probability and anomaly counters are showing a valid incident worth further -investigation using [Metric Correlations](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/metric-correlations.md) to narrow -the dashboard into only the charts relevant to what you're seeing from the anomalies collector. - -## What's next? - -Between this guide and [part 1](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/anomaly-detection-python.md), which covered setup and configuration, you -now have a fundamental understanding of how unsupervised anomaly detection in Netdata works, from root cause to alarms -to preconfigured or custom dashboards. - -We'd love to hear your feedback on the anomalies collector. Hop over to the [community -forum](https://community.netdata.cloud/t/anomalies-collector-feedback-megathread/767), and let us know if you're already getting value from -unsupervised anomaly detection, or would like to see something added to it. You might even post a custom configuration -that works well for monitoring some other popular application, like MySQL, PostgreSQL, Redis, or anything else we -[support through collectors](https://github.com/netdata/netdata/blob/master/collectors/COLLECTORS.md). - -### Related reference documentation - -- [Netdata Agent · Anomalies collector](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/anomalies/README.md) -- [Netdata Cloud · Build new dashboards](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/dashboards.md) - - |