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Coding July 1, 2024

Spring Boot Logging with ELK: A Configuration Guide

Writen by Mahipalsinh Rana

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It is crucial to highlight that monitoring the right identical logs can significantly improve software development. In a microservices architecture, one operation may involve calling several APIs and thus, when an error is detected, it is difficult to debug the entire application.

Logs act as qualities that assist in carrying out superior analysis and identification of blunders. It helps sysadmins, support teams, and developers to monitor many various things that different services in the system do.  

However, keeping this important data in a decentralized system with a number of applications, services, and systems may pose some challenges. The solution to this problem is provided by the ELK stack, a popular tool for log aggregation and analysis. 

This article will illustrate how you can start using the ELK Stack and use it to gather, analyze, and store logs from your Spring boot application. Further, it will solve the problem of keeping the logs of several services in different windows, even in cases when these services are launched in one application, with the help of Kibana.

Logstash: The dedicated and reliable system that is used to parse and forward Spring Boot logs to Elasticsearch.

Elasticsearch: The most intense layer of the stack consists of an extremely useful search and analytical tool designed specifically for your log data. 

Kibana: The visualization layer that allows us to transform complex log information into easily digestible graphics like dashboards and appealing charts. 

At the end of this blog, you will have a fairly effective system of logs that can help you monitor your Spring Boot applications, obtain valuable information, and make improvements to their performance all thanks to the magic of logs!

Learn More: Spring Boot for WebRTC Signaling Servers

Overview of Kibana and Logstash

Kibana, the open-source jewel of the ELK Stack, transforms data exploration into a visual feast. Designed to work seamlessly with Elasticsearch, it acts as a user-friendly interface, empowering you to visualize, analyze, and comprehend the wealth of information stored within Elasticsearch indexes.se 

Kibana’s arsenal includes the creation of dynamic dashboards, interactive charts, and ad-hoc queries, allowing you to unearth valuable insights from your data. Its intuitive interface and powerful visualization capabilities make it an indispensable tool for diverse applications – from log analysis and infrastructure monitoring to business intelligence.

Learn More: Spring Boot with Microsoft Azure Integration

Kibana

Kibana, the open-source jewel of the ELK Stack, transforms data exploration into a visual feast. Designed to work seamlessly with Elasticsearch, it acts as a user-friendly interface, empowering you to visualize, analyze, and comprehend the wealth of information stored within Elasticsearch indexes.se 

Kibana’s arsenal includes the creation of dynamic dashboards, interactive charts, and ad-hoc queries, allowing you to unearth valuable insights from your data. Its intuitive interface and powerful visualization capabilities make it an indispensable tool for diverse applications – from log analysis and infrastructure monitoring to business intelligence.

Logstash

Logstash, the workhorse of the ELK Stack, acts as a powerful data pipeline. It ingests data from various sources, including logs, databases, and message queues, before refining and enriching it for further analysis. Imagine it as a data refinery, transforming raw materials into a usable format for insightful exploration. 

Logstash boasts a diverse toolkit for data ingestion, supporting various sources like log files, databases, and network streams. It then applies filters and transformations to parse, structure, and enhance the incoming data, ensuring consistency and relevance before routing it to its final destination typically Elasticsearch.

This flexible architecture, coupled with an extensive plugin ecosystem, empowers organizations to seamlessly aggregate, process, and analyze data from disparate sources. With Logstash on board, real-time insights and informed decision-making have become a reality.

Why did we need it?

Unify and Unleash Insights: The Power of ELK for Spring Boot Logs 

The ELK Stack (Elasticsearch, Logstash, and Kibana) offers a compelling solution for managing and analyzing logs from your Spring Boot applications. Here’s how it empowers you: 

  • Centralized Hub: ELK consolidates logs from various sources, including Spring Boot, into a single, searchable platform. This simplifies management, allowing you to effortlessly search, analyze, and troubleshoot across distributed systems. 
  • Real-Time Visibility: Elasticsearch provides real-time indexing and search capabilities. Logs are available for analysis as soon as they’re ingested, enabling faster issue detection and resolution, leading to better system reliability and performance. 
  • Effortless Scaling: Elasticsearch seamlessly scales with your Spring Boot application’s growth. It effortlessly handles increasing log volumes without compromising performance. 
  • Flexible Retention: ELK offers customizable data retention policies, allowing you to define how long to store log data. This ensures historical logs are available for compliance, auditing, and trend analysis purposes. 
  • Advanced Analytics: Elasticsearch’s powerful search features (full-text search, filtering, aggregation) combined with Kibana’s visualization capabilities empower you to create custom dashboards and charts. Gain valuable insights into application performance, user behavior, and system trends. 
  • Proactive Monitoring: Set up alerts and monitoring in the ELK Stack based on predefined thresholds or patterns in your log data. This proactive approach allows you to identify and address issues before they escalate, minimizing downtime and ensuring a seamless user experience. 

Overall, the ELK Stack provides a comprehensive solution for log management and analysis. It empowers organizations to optimize Spring Boot applications, improve troubleshooting efficiency, and gain unmatched operational visibility.

Learn More: Elasticsearch Integration with Spring Boot

Optimizing Log Management Workflow with ELK Stack

In the intricate landscape of modern software development, efficient log management is paramount. Imagine a scenario where all the logs from your Spring Boot applications are consolidated into a single file, awaiting analysis. This setup is the foundation of a streamlined log management workflow, empowered by the ELK Stack – Elasticsearch, Logstash, and Kibana.

Workflow Overview:

  • Aggregation of Spring Boot Logs: All the logs generated by your Spring Boot applications are channeled into a single log file. This consolidation simplifies the subsequent steps of log processing and analysis, ensuring a cohesive approach to log management. 
  • Ingestion via Logstash: Enter Logstash, the data processing powerhouse of the ELK Stack. Configured to watch over the designated log file, Logstash springs into action, parsing, transforming, and enriching the raw log data before whisking it away to Elasticsearch for storage. 
  • Elasticsearch Storage: Elasticsearch, the heart of the ELK Stack, eagerly awaits the influx of log data from Logstash. As logs are ingested, Elasticsearch diligently indexes them, making them readily searchable and available for analysis. Its scalable architecture ensures that no log is left behind, even as your Spring Boot applications scale and generate more data. 
  • Monitoring and Analysis with Kibana: With the logs securely stored in Elasticsearch, it’s time to unleash the power of visualization and analysis through Kibana. Armed with a rich set of visualization tools and dashboards, Kibana transforms raw log data into actionable insights. From performance metrics to error trends, Kibana illuminates the inner workings of your Spring Boot applications, empowering you to make informed decisions and optimize system performance.

Flow diagram

spring-boot-logging-with-elk-a-configuration-guide

Step 1: Application.Properties for Logging and Elasticsearch Configuration

Step 2: Create Logs.conf File for Configuring Logstash With Spring Boot Logs

Step 3: Run the Below Command With Logstash.conf File Inside Bin Directory of Logstash<<version>>

View in Kibana Dashboard

In most cases, logs are known to be an essential resource that any developer has to deal with especially when it comes to debugging applications. The Logstash enables delivering of logs in a centralized and scalable manner through the simplified ELK Stack. From this article, we commenced the debate on how ELK Stack functions and offered a guide on how to transmit Spring Boot application logs to the Elastic Stack (ELK). 

Meet the idealistic leader behind Inexture Solutions – Mahipalsinh Rana! With over 15 years of experience in Enterprise software design and development, Mahipalsinh Rana brings a wealth of technical knowledge and expertise to his role as CTO. He is also a liferay consultant with over a decade of experience in the industry. Apart from all he has technical background spans more than 15 years, making him a go-to authority for all things enterprise software development.

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