ELK Stack

💤0
Lv 10 XP
← 📊 Monitoring & Observability · Logs & Traces

ELK Stack

Intermediate ⭐ 80 XP ⏱ 16 min #observability#elk#logging

Centralize, search, and visualize logs with Elasticsearch, Logstash, and Kibana.

📖Theory

The ELK Stack centralizes logs so you can search across all your systems in one place. Its components:

  • Elasticsearch — a distributed search/analytics engine that stores and indexes logs
  • Logstash — ingests, parses, and transforms logs (often replaced by lightweight Beats/Fluent Bit shippers)
  • Kibana — the UI to search, filter, and visualize

The flow: shippers collect logs → parse into structured fields → index in Elasticsearch → explore in Kibana. Structured (JSON) logs make fields searchable (e.g. status:500 AND service:api), which is the whole point of centralization.

graph LR
  APP["Apps (stdout)"] --> SHIP["Beats / Fluent Bit"]
  SHIP --> LS["Logstash (parse)"]
  LS --> ES["Elasticsearch (index)"]
  ES --> KB["Kibana (search/visualize)"]
Log pipeline into the ELK stack
✍️Hands-On Exercise
  1. Name each ELK component and its responsibility.
  2. Explain why structured (JSON) logging matters for search.
  3. Write a Kibana-style query to find 500 errors for one service.
  4. What role do Beats/Fluent Bit play versus Logstash?
🧾Cheat Sheet▾
ComponentRole
ElasticsearchStore + index logs
LogstashParse/transform logs
KibanaSearch + visualize
Beats / Fluent BitLightweight shippers
IndexSearchable log store
Structured logsJSON fields, queryable
💬Common Interview Questions▾
What does each part of the ELK stack do?

Elasticsearch stores and indexes logs, Logstash (or Beats/Fluent Bit) ingests and parses them, and Kibana provides search and visualization over the indexed data.

Why are structured logs important for centralized logging?

JSON fields are indexed and directly queryable (e.g. status:500 AND service:api), enabling fast, precise search — unlike unstructured text that needs fragile parsing.

📚Official Documentation

📝 My notes on this topic

Auto-saves as you type