Elasticsearch: The Scalable Storage Engine

Elasticsearch stores all logs in the form of JSON documents and makes them searchable instantly.

Key Features:

• Schema-free indexing: it auto-detects fields
• Sharding & replication: horizontal scaling
• Full-text search: use queries like error OR timeout
• Time-based indices: good for log retention policies

Example query:

GET nginx-logs-*/_search
{
  "query": {
    "match": { "response": "500" }
  }
}

Instantly find all logs with HTTP 500 errors from all servers.

Kibana: Visualization and Dashboard

Kibana is the front-end for Elasticsearch — used to explore and visualize logs.

You can:

• Create real-time dashboards
• Filter logs by host, service, status code, or keyword
• Build alerts using Kibana Alerting or Watcher

Example use cases:

• Visualize errors per service
• Monitor API latency distribution
• Track user activity trends

Kibana can also integrate with Machine Learning for anomaly detection in log volume.

Scaling the Stack

As your system grows, log volume can become huge. Here are best practices to manage Elasticsearch at scale:

Layer	Best Practice
Beats	Use multiple Beats agents per server; enable compression
Logstash	Run multiple pipelines; use persistent queues
Elasticsearch	Use ILM (Index Lifecycle Management) to delete old data
Kibana	Create index patterns by service or environment
Architecture	Deploy as cluster: multiple Elasticsearch nodes (data, master, ingest)

Example: Log Flow in Production

Imagine a system with 100 servers and 1,000 containers.

1. Filebeat runs in each container, tailing /var/log/app.log.
2. Filebeat sends logs to Logstash over port 5044.
3. Logstash parses logs (e.g., extract service, level, response_time).
4. Logstash sends structured logs to Elasticsearch.
5. Kibana dashboards show errors, requests per second, and latencies.
6. Alert triggers if error_rate > 5% or response_time > 3s.

Result: full visibility across all systems in seconds.

Security and Reliability

At large scale, logs can contain sensitive data.
Always follow these practices:

• Use TLS encryption between Beats → Logstash → Elasticsearch
• Mask or remove sensitive fields (gsub, mutate filters in Logstash)
• Enable role-based access control in Kibana
• Use Elasticsearch snapshots for backups

Advantages of ELK + Beats

Feature	Benefit
Open source	Free, large community
Modular	Add Beats for any data source
Scalable	Handles terabytes per day
Fast search	Query logs in milliseconds
Real-time	Stream logs as they happen
Visual	Rich dashboards and alerts

Summary

Component	Purpose
Beats	Collect logs and metrics
Logstash	Filter, parse, and enrich logs
Elasticsearch	Store and index data
Kibana	Visualize and alert

Together, they create a powerful, scalable log management system capable of handling millions of events per second.

Conclusion

Elasticsearch + Beats + Logstash + Kibana form the backbone of modern observability.
They let you collect, process, search, and visualize logs in real time, giving full visibility across microservices, servers, and users.

“You can’t fix what you can’t see — observability starts with good logging.”

With Elastic Stack, you can see everything.