ELK 스택을 사용한 중앙 집중식 로그 기록

In a microservices world, applications don't run as one big piece. Instead, they're many small, independent services.

This means logs are scattered across many servers and containers. Finding issues becomes a huge challenge without a central place to view them all.

• Debugging: Hard to trace requests across services.
• Monitoring: Difficult to spot trends or errors.
• Maintenance: Manually checking logs is time-consuming.

The ELK Stack is a popular solution for centralized logging. It's a powerful collection of three open-source tools:

• Elasticsearch: Stores and indexes logs.
• Logstash: Processes and transforms logs.
• Kibana: Visualizes and analyzes logs.

Together, they create a robust pipeline for all your application logs.

Elasticsearch is a highly scalable search engine. It's built for speed and can handle vast amounts of data, like all your application logs.

• Indexing: Organizes logs for fast searching.
• Scalability: Easily handles growing log volumes.
• Powerful Search: Allows complex queries to find specific events or patterns.

Think of it as the super-efficient database for your logs.

Logstash is the data processing engine of the ELK stack. It collects logs from various sources, transforms them, and then sends them to Elasticsearch.

It works like a funnel with three main stages:

• Inputs: Where logs are collected from (e.g., files, network, message queues).
• Filters: Where logs are parsed, enriched, or modified (e.g., extract data, remove sensitive info).
• Outputs: Where processed logs are sent (e.g., Elasticsearch).

Kibana is the user interface that sits on top of Elasticsearch. It lets you explore, visualize, and build dashboards from your log data.

With Kibana, you can:

• Search and filter logs in real-time.
• Create interactive charts and graphs.
• Build custom dashboards to monitor application health.
• Identify trends and troubleshoot issues quickly.

Spring Boot applications use SLF4J as a logging facade and Logback as the default implementation.

By default, Spring Boot logs to the console. These logs include timestamps, log levels (INFO, DEBUG, WARN, ERROR), thread names, and the actual log message.

This console output is a common source for Logstash or other agents to pick up.

Here's a minimal Spring Boot application that demonstrates basic logging at different levels. Run it and observe the console output.

How do logs from your Spring Boot app reach the ELK stack?

A common approach is to use Filebeat. Filebeat is a lightweight shipper that runs on your application servers. It monitors log files or console output and forwards them to Logstash or Elasticsearch.

This makes sure logs are collected reliably without burdening your application.

Let's summarize the typical flow for centralized logging with ELK:

1. Your Spring Boot Application generates logs (e.g., to console or a file).
2. Filebeat collects these logs from the source.
3. Logstash receives logs from Filebeat, processes them (parses, filters).
4. Logstash sends processed logs to Elasticsearch for storage and indexing.
5. Kibana queries Elasticsearch to visualize and analyze the log data.

Each component in the ELK stack has a distinct and crucial role. Understanding these roles is key to effective logging.

We've explored the importance of centralized logging for microservices and introduced the ELK (Elasticsearch, Logstash, Kibana) stack.

• Elasticsearch: Stores and indexes logs for fast searching.
• Logstash: Processes and transforms logs from various sources.
• Kibana: Provides powerful visualization and analysis tools.
• Filebeat: A common agent for collecting logs from applications.

This powerful combination ensures you can effectively monitor and troubleshoot your distributed applications.