Knowledge Factory

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...

Architecture diagram: Components Breakdown: Microservices 1, 2, and 3: These are the core applications responsible for generating logs. Logstash: Acts as a central log processor. It collects logs from the microservices, transforms them into a common format (usually JSON), and sends them to Elasticsearch. Elasticsearch: A powerful search engine that stores and indexes the logs received from Logstash. It allows for fast retrieval and analysis of log data. Kafka: A distributed streaming platform used to buffer and distribute logs from the microservices to Logstash. Kafka ensures reliability and fault tolerance in log handling. Redis: An in-memory data store used for caching, session management, and other purposes. Redis can help improve the performance and scalability of the application. Beats: A lightweight data shipper that can forward logs from the microservices to Logstash or Kafka. Beats is typically used for collecting logs from application files or other sources. Kibana: A data vis...