Log Aggregation
Collecting logs from all services into a central searchable store. The ELK stack (Elasticsearch, Logstash, Kibana) and Grafana Loki are common solutions.
What is Log Aggregation?
Collecting logs from all services into a central searchable store. The ELK stack (Elasticsearch, Logstash, Kibana) and Grafana Loki are common solutions.
Log Aggregation is a intermediate-level concept that sits in the Observability & Monitoring area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space — not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition — with diagrams, code, and a quiz to lock it in — work through the "Log Aggregation" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Log Aggregation in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
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Related glossary terms you might want to look up next.
Logging
Recording discrete events with timestamps, severity levels, and context. Structured logs (JSON) are searchable; unstructured logs (plaintext) are not. Ship them to a central system.
Elasticsearch
A distributed search and analytics engine built on Apache Lucene. Powers full-text search, log analysis, and real-time analytics at scale.
Observability
The ability to understand a system's internal state from its external outputs. Built on three pillars: metrics, logs, and traces.