Distributed Tracing in Micoservices using Zipkin, Sleuth and ELK Stack.

 

What is Distributed Tracing ?

One of the major challenges in microservices is the ability to debug issues and monitor them. A simple action can trigger a chain of microservice calls and it would be tedious to trace these actions across the invoked microservices. This is because each microservice runs in an environment isolated from other microservices so they don’t share resources such as databases or log files. In addition to that, we might also want to track down why a certain microservice call is taking so much time in a given business flow.

The Distributed Tracing pattern addresses the above challenges developers face while building microservices. There are some helpful open-source tools that can be used for distributed tracing, when creating microservices with Spring Boot and Spring Cloud frameworks. This blog walks through the installation steps and implementations of these tools.

Spring Cloud Sleuth: A Spring Cloud library that lets you track the progress of subsequent microservices by adding trace and span id’s on the appropriate HTTP request headers. The library is based on the MDC (Mapped Diagnostic Context) concept, where you can easily extract values put to context and display them in the logs.

Zipkin: A Java-based distributed tracing application that helps gather timing data for every request propagated between independent services. It has a simple management console where we can find a visualization of the time statistics generated by subsequent services.

ELK Stack: Three open source tools — Elasticsearch, Logstash and Kibana form the ELK stack. They are used for searching, analyzing, and visualizing log data in real-time. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a “stash” like Elasticsearch. Kibana lets us visualize this data with charts and graphs.

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