What is Log Aggregation?

Log aggregation is the process of gathering log data from every source in your environment — application containers, VMs, load balancers, databases, cloud services, network devices — and centralizing it in a single system where it can be searched and correlated. It solves the fundamental problem of distributed systems: the information you need during an incident is scattered across hundreds of machines that you can’t SSH into one by one.

Why aggregate logs?

• Correlation — a user-facing error might involve an ALB, three microservices, and a database; only centralized logs let you follow it across all of them
• Ephemeral infrastructure — containers and serverless functions disappear along with their local logs; aggregation is the only way those logs survive
• Access & security — engineers get one audited place to search instead of shell access to production
• Alerting & analytics — you can only alert on patterns you can see in one place

How log aggregation works

A collection agent runs close to each source — as a DaemonSet on Kubernetes nodes, a sidecar, or a host daemon. Popular agents include Fluent Bit, Fluentd, Vector, and the OpenTelemetry Collector. The agent tails files or container stdout, attaches metadata (pod, namespace, host), optionally buffers against network failures, and forwards to a central backend — often through a telemetry pipeline that filters and transforms in flight.

Key design concerns: backpressure handling (what happens when the backend is slow), metadata enrichment (Kubernetes labels turn anonymous text into searchable context), and noise reduction (health checks and debug logs can be most of your volume and none of your value).

Log aggregation with OpenObserve

OpenObserve ingests from all standard agents — see the Fluent Bit on Kubernetes guide — plus syslog, OTLP, and an Elasticsearch-compatible bulk API. Aggregated logs land on object storage, so centralizing everything stays affordable even at Kubernetes scale.