As microservices architecture has become more popular, there has been a growing need for API observability. This is because microservices applications are made up of many small, independent services that communicate with each other through APIs. This can make it difficult to track the performance and health of an application, as well as identify and troubleshoot problems.
API observability is a broader term than API monitoring. Monitoring focuses on tracking known metrics, such as request latency and response time. Observability, on the other hand, also includes tracking unknown metrics, such as error rates and resource utilization. This allows developers to get a more complete picture of how an API is performing, and to identify problems before they impact users.
There are a number of tools that can be used to implement API observability. Some popular options include:
Prometheus: A popular open-source monitoring system that can be used to collect metrics from APIs.
Grafana: A visualization tool that can be used to display Prometheus metrics.
Jaeger: A distributed tracing tool that can be used to track requests as they flow through an application.
By implementing API observability, developers can gain a deeper understanding of how their applications are performing. This can help them to identify and troubleshoot problems more quickly, and to improve the overall performance and reliability of their applications.
Continue reading to learn more about:
-The advantages of observability compared to monitoring
-The pillars of API observability (functional test automation, performance management, security and analytics)
-Metrics (API dependencies, API stats, Api spec
Benefits and examples
Use cases
-Evaluating 3rd party tools
-API observability with openTelemetry and distributed tracing
-How to make it actionable: Visualization, granular error data and insights
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