📚 Learn how you can integrate #ELK stack with your Spring Boot application for logs aggregation and monitoring in a centralized way.
TL;DR: Logs are the most critical way for debugging. They can help us follow the different operations carried out by the various services of our system.
This article will demonstrate how to integrate ELK Stack with a Spring Boot application for logs aggregation and monitoring in a centralized and scalable way.
You will also learn how to create custom index patterns through a Filebeat configuration and separate logs of different services of the same application by different indexes.
The application's source code used in this tutorial is available in this GitHub repository.
Having a good log monitoring infrastructure is a key feature to have while developing any software. For instance, in a microservices architecture, any single operation triggers a chain of API calls making it challenging to debug the entire application in case an error comes.
This is where Logs act as essential information that allows us to investigate better and diagnose the errors. They can help sysadmins, support teams, and even developers to follow the different operations carried out by the different services of the system.
But it becomes very complex to maintain this critical data in a distributed environment where many applications, services, and systems are running. As a solution for this problem, we're going to look at the ELK stack, a useful tool for centralized log aggregation and analysis.
This article will demonstrate how you can integrate ELK Stack with a Spring Boot application to collect, process, store, and view the logs.
Apart from this, while working with the ELK stack, the default action is to show all the logs of a particular application at a single place inside Kibana.
In this article, you will also tackle this problem and learn how you can view the logs of multiple services separately that are running in parallel inside one single application.
ELK is a collection of three open-source applications - Elasticsearch, Logstash, and Kibana from Elastic that accepts data from any source or format, on which you can then perform search, analysis, and visualize that data.
- Elasticsearch — Elasticsearch stores and indexes the data. It is a NoSQL database based on Lucene's open-source search engine. Since Elasticsearch is developed using Java, therefore, it can run on different platforms. One particular aspect where it excels is indexing streams of data such as logs.
- Logstash — Logstash is a tool that integrates with a variety of deployments. It is used to collect, parse, transform, and buffer data from a variety of sources. The data collected by Logstash can be shipped to one or more targets like Elasticsearch.
- Kibana — Kibana acts as an analytics and visualization layer on top of Elasticsearch. Kibana can be used to search, view, and interpret the data stored in Elasticsearch.