Log Aggregation

Log Aggregation

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Build comprehensive log aggregation systems to collect, parse, and analyze logs from multiple sources, enabling centralized monitoring, debugging, and compliance auditing.

When to Use

• Centralized log collection
• Distributed system debugging
• Compliance and audit logging
• Security event monitoring
• Application performance analysis
• Error tracking and alerting
• Historical log retention
• Real-time log searching

Quick Start

Minimal working example:

# docker-compose.yml - ELK Stack setup
version: "3.8"

services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.5.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
    ports:
      - "9200:9200"
    volumes:
      - elasticsearch_data:/usr/share/elasticsearch/data
    healthcheck:
      test: curl -s http://localhost:9200 >/dev/null || exit 1
      interval: 10s
      timeout: 5s
      retries: 5

  logstash:
    image: docker.elastic.co/logstash/logstash:8.5.0
    volumes:
      - ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
    ports:
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Parse and structure log data
• Use appropriate log levels
• Add contextual information
• Implement log retention policies
• Set up log-based alerting
• Index important fields
• Use consistent timestamp formats
• Implement access controls

❌ DON'T

• Store sensitive data in logs
• Log at DEBUG level in production
• Send raw unstructured logs
• Ignore storage costs
• Skip log parsing
• Lack monitoring of log systems
• Store logs forever
• Log PII without encryption