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:

Guide	Contents
ELK Stack Configuration	ELK Stack Configuration
Logstash Pipeline Configuration	Logstash Pipeline Configuration
Filebeat Configuration	Filebeat Configuration
Kibana Dashboard and Alerts	Kibana Dashboard and Alerts
Loki Configuration (Kubernetes)	Loki Configuration (Kubernetes)
Log Aggregation Deployment Script	Log Aggregation Deployment Script

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