ELK Stack (Elasticsearch, Logstash, Kibana): The Classic Stack for Log Collection and Analysis

After establishing that centralized logging is not just a convenience but a necessity, it’s time to dive into specific tools. Let’s start with one of the most well-known and widely adopted solutions in the logging world — the ELK Stack.

The ELK Stack is a set of three open-source components developed by Elastic. It provides a powerful, flexible, and scalable solution for log collection, processing, storage, search, and visualization.

What Is the ELK Stack and Its Components?

The name ELK comes from its three core components:

1. Elasticsearch — a distributed search and analytics engine built on Apache Lucene. It’s the “heart” of ELK, storing log events in indices, supporting full-text search and aggregations, and scaling horizontally.

2. Logstash — a log processing tool responsible for collecting, parsing, filtering, and enriching logs before sending them to Elasticsearch. Supports dozens of input/output plugins.

3. Kibana — a web interface for visualizing data in Elasticsearch. Used for building dashboards, querying logs, and real-time exploration.

How Does the ELK Stack Work?

A typical data flow looks like this:

1. Collection — agents (Beats, like Filebeat) send logs to Logstash or directly to Elasticsearch.
2. Processing — Logstash parses, filters, and enriches the logs.
3. Storage — data is indexed in Elasticsearch.
4. Visualization — Kibana displays logs, charts, and dashboards.

Advantages of the ELK Stack

• Fast and flexible search — Elasticsearch provides near-instant full-text search across gigabytes or terabytes of logs.
• Powerful visualization — Kibana offers interactive charts, tables, maps, filters, and dashboards.
• Flexible log processing — Logstash can ingest data from many sources and convert it into structured formats.
• Scalability — Each component can be scaled independently.
• Strong community — Active user base, plenty of resources, and well-maintained documentation.

Drawbacks and Limitations

• Resource-intensive — Logstash and Elasticsearch can consume significant CPU, RAM, and I/O, especially under heavy load.
• Operational complexity — Setup, configuration, and maintenance require expertise. Monitoring indices, shards, backups, and security is essential.
• Licensing — Since 2021, Elastic uses SSPL and Elastic License. Some features (Security, Alerting, ML, SIEM, Graph) are only available in the commercial edition.
• High storage footprint — Indexing logs increases the volume of stored data.

Use Cases and Who It’s For

ELK Stack is ideal for:

• Large enterprises — with strict logging, scalability, and security requirements.
• DevOps and SRE teams — needing a flexible, powerful system for log and performance analysis.
• Security event analysis — especially with SIEM and alerting (commercial version).

Unique Features of the ELK Stack

• PPL (Pipeline Processing Language) — a SQL-like query language (part of Elastic Observability).
• Alerting — trigger rules and notifications (in paid versions).
• Machine Learning — anomaly and pattern detection (commercial).
• SIEM — Security Information and Event Management (Elastic Security, paid).
• Beats — lightweight agents for collecting logs, metrics, traffic, and more.

Conclusion

The ELK Stack remains the de facto standard for logging in large-scale IT infrastructures. Its power, flexibility, and maturity make it highly sought after. Despite its resource demands and licensing changes, it continues to evolve and is used in thousands of projects around the world.

In the next article, we’ll take a look at OpenSearch — an open fork of Elasticsearch and Kibana that has preserved openness and offers a complete alternative to the commercial Elastic Stack.