Complete Guide to Setting Up EFK Stack with Kafka, Redis, Beats, and Spring Boot for Microservices Logging
This diagram illustrates a centralized logging system for microservices using the EFK Stack (Elasticsearch, Fluentd , Kibana) , Kafka, Redis, and Beats. Here's a brief breakdown of the flow: Microservices (Spring Boot) : Each microservice generates logs, which are collected by Beats (e.g., Filebeat or Metricbeat). Beats : Beats agents forward the log data to Kafka . Kafka : Kafka acts as a buffer and ensures reliable delivery of log messages to the next stage. Redis : Redis can act as a caching layer or intermediate queue to handle the log flow efficiently. Fluentd : Fluentd processes, transforms, and enriches log data before forwarding it to Elasticsearch. Elasticsearch : Stores and indexes the log data for search and analysis. Kibana : Provides a user-friendly interface for visualizing and analyzing logs from Elasticsearch. Here’s a step-by-step guide to implementing an end-to-end centralized logging system for Spring Boot microservices using the provided architecture. T...