What Are Structured Logs?

Structured logs turn log lines from prose you grep into data you can query and correlate.

Structured logs are log records emitted as machine-readable key-value data (often JSON) rather than free-form text, so they can be filtered, aggregated and correlated, for example, linked to a trace by trace ID.

Structured vs unstructured

An unstructured log is a line of text that a human reads and a machine struggles to parse reliably. A structured log carries named fields, timestamp, level, service, trace_id, and any relevant context, so you can filter by field, aggregate counts, and join logs to the traces and metrics they relate to.

Why it matters at scale

At volume, free-text logs are hard to search precisely and impossible to aggregate reliably. Structured fields let you answer questions (how many errors of this type, for this customer, on this version) and, crucially, correlate a log line to the exact trace it belongs to, which is where logs become part of observability rather than a separate silo.

How it fits Opstral

Opstral ingests structured logs alongside metrics and traces, and correlation on shared fields (like trace and resource identifiers) is what lets Sentinel AI assemble a complete picture of an incident.