A single application's logs are easy; a distributed system's are a nightmare without aggregation. When a request touches five services across many machines, debugging by SSH-ing into each to grep local files is hopeless. Log aggregation centralises logs from everywhere into one searchable place. The strategy that makes it useful — and affordable — comes down to structure, correlation, retention, and cost, because at volume, logging bills become real.
Centralise everything
The foundation is shipping logs from every service and host to a central system where they can be searched together. This is what lets you follow a request across services and see the whole picture in an incident instead of a fragment. Whatever the backend — an ELK/OpenSearch stack, Loki, or a managed service — the principle is the same: logs must leave the individual machines and land somewhere unified, or they cannot help you when it matters.
Structured logs and correlation ids
Centralised plain-text logs are searchable but hard to query precisely. Structured logging — emitting logs as structured data (JSON) with consistent fields — lets you filter and aggregate on fields like service, level, user, or status, turning logs into queryable data. Pair this with a correlation id: generate an id per request and attach it to every log line across every service that request touches. Now you can pull up the complete story of one request across the whole system with a single query, which is the single most valuable thing aggregation gives you.
Retention and cost
Log volume — and the bill — grows relentlessly, so retention strategy is central, not an afterthought:
- Tier retention — keep recent logs hot and quickly searchable, move older logs to cheaper cold storage, and delete past a defined age.
- Control what you log — high-volume debug logging is expensive; log at appropriate levels and sample where you can.
- Match retention to need — compliance may require long retention for some logs, while most operational logs are only useful for days or weeks.
Logging feels free until the aggregation bill arrives, and it scales with volume that only ever grows. Treat retention tiers, log levels, and sampling as ongoing cost controls, not one-time settings. The team that logs everything at debug level forever discovers that observability has quietly become one of its largest infrastructure costs — manageable, but only if managed deliberately.
Log aggregation centralises logs from every service into one searchable place, made genuinely useful by structured logging and a correlation id that ties a request's story together across services. Layer on tiered retention and active cost control, because log volume and its bill grow without end. Get the structure and the cost strategy right and aggregated logs become the tool that turns a distributed-system incident from guesswork into a query.
Independent software engineer in Nairobi specialising in Acumatica customisations, Laravel backends, and tax fiscalisation integrations across East and Southern Africa.