Architecture

📖 2 min read 📄 Part 3 of 10

Log Analysis System - Architecture

High-Level Architecture

📋 Log Sources Apps • Servers • Containers • Cloud Services 📡 Log Collectors Filebeat • Fluentd • Logstash 📨 Kafka (Message Queue) ⚙️ Processing Pipeline (Logstash) Parse • Enrich • Transform • Filter 🔍 Elasticsearch (Storage) Hot • Warm • Cold Tiers 📊 Kibana Dashboard • API • Alert Manager
Log Analysis — ELK Stack Pipeline Architecture

Components

1. Log Collectors

  • Filebeat for file-based logs
  • Fluentd for container logs
  • Logstash for processing
  • Cloud-native collectors

2. Message Queue

  • Kafka for buffering
  • Partitioning by source
  • Retention for replay
  • Backpressure handling

3. Processing Pipeline

  • Parse structured/unstructured logs
  • Extract fields
  • Enrich with metadata
  • Filter and transform

4. Storage

  • Elasticsearch for indexing
  • Tiered storage (hot/warm/cold)
  • Index lifecycle management
  • Replication and sharding

5. Query Layer

  • Full-text search
  • Aggregations
  • Real-time streaming
  • Saved searches

6. Visualization

  • Kibana dashboards
  • Custom visualizations
  • Alerting
  • Reporting

Data Flow

Ingestion

1. Collectors read logs
2. Send to Kafka
3. Processors consume
4. Parse and enrich
5. Index in Elasticsearch

Query

1. User submits query
2. Query coordinator
3. Shard-level search
4. Aggregate results
5. Return to user

This architecture provides scalable, reliable log analysis at massive scale.