How API observability solves troubleshooting misery

By Eli Cohen

Microservices have gained popularity as an architecture for developing distributed applications that can scale and evolve rapidly

Nevertheless, as microservices are embraced, observability becomes a critical element for guaranteeing the efficiency of troubleshooting and ongoing maintenance aPi observability enables the monitoring and analysis of the interactions between microservices through their aPis Naturally, in a microservices architecture, the app is divided into multiple, independently deployable services, and communication between them happens through aPis, from traditional ones such as resTful or soaP to newer ones such as grPC, graphQL, or even asynchronous ones like Kafka aPi observability allows for the tracking of aPi calls, response times, errors, and gives granular visibility into the behavior of a single aPi, which can help identify performance issues and ensure the reliability of the system Without aPi observability, it can be challenging or even impossible to identify the root cause of issues and troubleshoot them accordingly

How is observability different from monitoring?

observability is the ability to understand the internal state of a system by analyzing the data it produces, such as logs, metrics, and traces it allows developers to gain insights into the performance, behavior, and health of a system, even in distributed and dynamic environments like in microservices it provides a comprehensive view of a system by allowing developers to track requests as they move through the various components of a microservices architecture

Monitoring, on the other hand, uses logs, metrics, and alerts as well, but it is often limited to predefined metrics and thresholds, and can miss issues that are not covered by the defined metrics. in contrast, observability is a broader concept that encompasses monitoring but goes beyond it, hence allowing faster troubleshooting, improved collaboration, debugging, and solving issues before they become problems in production

To summarize, observability enables teams to quickly identify and diagnose issues, reducing MTTr (Mean Time to resolve) and improving overall quality

The challenge of API observability in microservices

one critical component of observability in microservices is aPi observability, which is the ability to monitor and analyze aPi behavior. While critical, aPi observability can also pose significant challenges:

• aPi proliferation is a major challenge in aPi observability

• developers and product owners lose track and control due to too many aPis

• developers don’t have access to organized data needed to resolve issues such as high error rates or latency

• aPis behavior is constantly changing, which adds to the complexity

• Third-party aPis also add to the complexity

• different types of aPis, including synchronous (HTTP/grPC) and asynchronous, leading to a difference in data flows that can make it more challenging to trace requests and understand the behavior of the system

• Not specific to aPis but relevant still, abundance of data generated by multiple observability tools can cause data overload, making it challenging to extract useful insights, and the manual maintenance of data collection is time-consuming and error-prone as a result, cloud-native developers struggle to lower MTTr, improve developer experience, and maintain app quality in production in addition, traditional aPM tooling may fall short in providing sufficient insights for complex, cloud-native environments as they weren’t built from the ground up for monitoring aPis

A day-to-day challenge explained: Root cause analysis for an API latency issue

Let’s demonstrate some of the above-mentioned challenges in microservices aPi observability and troubleshooting.