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Inspecting Anomalies

Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. Understanding memory management reduces the possibility of wasting your application's resources and the unexpected effects on performance. According to Sergey Kibish, Anomalies can be illustrated in a simple two-dimensional space (Figure 1).

Figure 1 - Illustration of simple anomalies in two-dimensional space

Abnormal behavior can be identified compared to an established pattern, and anything that deviates from an established baseline pattern is considered an anomaly. Read more here.

Memory Anomaly Detection in N|Solid

Memory Anomalies in the N|Solid Console provides a way to detect early cases of memory miss behavior or upcoming Out of Memory situations before it happens. That way, you can reduce the knowledge problem and view what is being triggered, and definitely, you don't need to be experienced to understand the data. It analyzes the data for you.

Anomaly Detection is helpful for infrastructure to view and for the developer to solve quickly. N|Solid generates anomaly events when the processes exceed the typical percentages of memory. This view helps you see potential memory issues or take a Heapsnap shot from any thread on the fly.

Img 1 - Memory Anomaly Detection - N|Solid

The feature Memory Anomaly Detection allows you:

• Navigate between historical insights and metrics before and after the incident happened.
• Get anomalies at different heap usage levels.
• Detect correlation between sets of memory-specific metrics.
• Filter results by specific processes inside your application.

You can read more about it in NodeSource Documentation.

Demo Video — Memory Anomaly Detection in N|Solid

__NOTE: __ For a better experience, you can activate the closed captions in the video, they are available in English.

• Advantage: View In-depth metrics of each worker thread.
• Benefit: Identify opportunities to improve the performance of CPU-intensive work and identify Memory anomalies taken with a more accurate detection method.

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