{"id":57659,"date":"2022-07-26T15:52:22","date_gmt":"2022-07-26T07:52:22","guid":{"rendered":"https:\/\/version-2.com\/?p=57659"},"modified":"2022-09-05T15:57:49","modified_gmt":"2022-09-05T07:57:49","slug":"understanding-observability-vs-monitoring-part-1","status":"publish","type":"post","link":"https:\/\/version-2.com\/zh\/2022\/07\/understanding-observability-vs-monitoring-part-1\/","title":{"rendered":"UNDERSTANDING OBSERVABILITY VS. MONITORING. PART 1"},"content":{"rendered":"\t\t
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The development of clouds, the DevOps movement, and distributed microservice-based architecture have come together to make observability vital for modern architecture. We\u2019re going to dive into what observability is and how to approach the metrics we need to track.<\/p>

Observability is a way of spotting and troubleshooting the root causes of problems involving software systems whose internals we might not understand. It extends the concept of monitoring, applying it to complex systems with unpredictable and\/or complex failure scenarios.<\/p>

I\u2019ll start with some of the basic principles of observability that I\u2019ve been helping to implement across a growing number of products and teams at Nord Security.<\/p>

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Monitoring vs. Observability<\/b><\/h2>

\u201cMonitoring\u201d and \u201cobservability\u201d are often used interchangeably, but these concepts have a few fundamental differences.<\/p>

Monitoring<\/b> is the process of using telemetry data to understand the health and performance of your application. Monitoring telemetry data is preconfigured, implying that the user has detailed information on their system\u2019s possible failure scenarios and wants to detect them as soon as they happen.<\/p>

In the classical approach to monitoring, we define a set of metrics, collect them from our software system, and react to any changes in the values of these metrics that are of interest to us.<\/p>

For example:<\/p>

Excessive CPU usage can indicate that we need to scale it up to compensate for increasing system loads;<\/p>

A drop in successfully served requests after a fresh release can indicate that the newly released version of the API is malfunctioning;<\/p>

Health checks process binary metrics that represent whether the system is alive at all or not.<\/p>

Observability<\/b> extends this approach. Observability is the ability to understand the state of the system by performing continuous real time analysis of the data it outputs.<\/p>

Instead of just collecting and watching predefined metrics, we continuously collect different output signals. The most common types of signals – the three pillars of observability – are:<\/p>