---
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.

![An example anomaly probability
alarm](https://user-images.githubusercontent.com/1153921/104225767-0a0a9480-5404-11eb-9bfd-e29592397203.png)

The second class triggers warning alarms when the number of anomalies in the last two minutes hits 10 or higher.

![An example anomaly count
alarm](https://user-images.githubusercontent.com/1153921/104225769-0aa32b00-5404-11eb-95f3-7309f9429fe1.png)

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.

![Two charts created by the anomalies
collector](https://user-images.githubusercontent.com/1153921/104226380-ef84eb00-5404-11eb-9faf-9e64c43b95ff.png)

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:

![An example anomaly detection
dashboard](https://user-images.githubusercontent.com/1153921/104226915-c6188f00-5405-11eb-9bb4-559a18016fa7.png)

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)