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Anomaly detection with applied intelligence

With applied intelligence's anomaly detection, New Relic alerts your team of any unusual behavior instantly. New Relic uses applied intelligence to constantly observe your applications. We use this information to determine your application's baseline, or expected, performance. Whenever behavior deviates from the baseline, we know right away and alert your team so you can address any errors promptly and efficiently.

There are two types of anomaly detection at New Relic: custom and automatic. Learn about which anomaly detection is right for each situation your team would like to monitor and how to implement anomaly detection in your system.

The anomaly dashboard where your team can monitor any unusual behavior in your system.

How we use anomalies

At New Relic, our own developers know how important it's to monitor the health of our applications. We want our customers to access to the data they need whenever they need it so our team needs to be alerted if there are any outliers in our system's performance. New Relic's anomaly detection uses applied intelligence to monitor three key golden signals: throughput, error rate, and latency. With anomaly detection, our developers monitor the baseline performance for these metrics.

So, let's say that one afternoon there's a spike in response time and it's taking longer than usual for our customers to access the homepage. Anomaly detection will flag this anomalous behavior because our latency metric data has deviated from its baseline. This doesn't necessarily mean there is a problem, it just indicates that AI has registered something out of the ordinary in our system and we should take a deeper look.

We monitor this unusual behavior in a few ways. First, our team uses the anomaly dashboard so we can see what changed and when.

Explore any anomaly in your system's performance to better understand what errors you're receiving and why.

We also set up notifications for anomalies to be delivered in Slack, and we set up a webhook to deliver messages when we need them.

How to set up slack or webhook notifications for anomalies in the New Relic UI.

These events are also available for querying, creating custom dashboards, and alerting. After we set up an anomaly detection configuration (a group of apps we're interested in), we can add this configuration as a source. Then the anomalies will be automatically correlated with other data sources via incident intelligence.

There are two types of anomaly detection: automatic and custom

Automatic anomalies are the most efficient way for your team to learn about unusual behavior in your APM-monitored applications. Automatic anomaly detection is a hands-off tool your team can implement to ensure that you're notified the moment behavior in your application deviates from baseline. You can use automatic anomalies to identify the source of the problem and take the appropriate steps to get your system running smoothly again.

Custom anomalies allow increased configurability for your team. Custom anomalies provide your team with the capability to alert on any NQRL condition and to adjust and optimize your thresholds. Custom anomalies also use the same advanced tuning settings as static alerting so you can ensure your team sees only the anomaly incidents important to you.

Option

Automation Level

When To Use

Coverage

Static

Entirely configurable

When you need to set a single threshold for all your data.

All entities, all signals

Anomaly (Configurable)

Semi-automated

When you want to automatically learn trends in your data but have control over the threshold

All entities, all signals

Automatic anomaly

Completely automatic

When you want a broad understanding of changes in key metrics on your applications and services with no configuration needed. Data trends and thresholds are automatically determined through our machine learning engine.

APM entities, golden signals

Anomaly set-up

Once you choose to monitor anomalous behavior in your system using either our custom or automatic anomaly detection, you will need to make sure that your team is notified of any unusual behavior and that you can query and understand your data. It doesn't matter if you choose custom or automatic anomaly detection, the set-up is the same.

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