Practice : Log Correlation for RCA

Purpose and Strategic Importance

Log Correlation for Root Cause Analysis (RCA) enables engineers to trace complex events across distributed systems, helping them understand incident causes quickly and accurately. By stitching together log events across services, environments, and components, teams gain a holistic view of what happened - when, where, and why.

This practice accelerates incident diagnosis, reduces MTTR, and builds shared understanding of system behaviours, improving both technical reliability and team confidence.

Description of the Practice

  • Log correlation involves connecting related events using common identifiers (e.g. trace IDs, user IDs, session tokens, transaction IDs).
  • Logs from different services are ingested into a central platform (e.g. ELK, Datadog, Splunk, Loki) and linked using metadata or structured formats.
  • Engineers query and visualise correlated events to reconstruct timelines, detect anomalies, and verify hypotheses.
  • Often integrated with tracing and metrics for full observability.

How to Practise It (Playbook)

1. Getting Started

  • Implement structured logging across services using JSON or logfmt.
  • Inject consistent identifiers into log context (e.g. trace ID, request ID, correlation ID).
  • Ingest logs into a searchable platform and ensure log fields are indexed.
  • Develop basic queries to follow a request lifecycle across services.

2. Scaling and Maturing

  • Build saved searches and dashboards for common incident scenarios.
  • Link logs to traces and metrics in observability tooling to enhance RCA workflows.
  • Train engineers on how to build timeline views and pivot between logs.
  • Use log correlation in postmortems to verify sequences and contributing factors.
  • Enrich logs with domain-specific metadata (e.g. customer segment, environment, feature flag state).

3. Team Behaviours to Encourage

  • Ask “how would we trace this through logs?” during development.
  • Review logging quality and correlation coverage in architecture and readiness reviews.
  • Treat logging as part of operational design - not an afterthought.
  • Share learnings and reusable queries from incident reviews.

4. Watch Out For…

  • Logs missing IDs or key fields needed for correlation.
  • Inconsistent log schemas across services or teams.
  • Overly verbose or unstructured logs that hinder analysis.
  • Platform limits on query performance or retention impacting visibility.

5. Signals of Success

  • Engineers quickly reconstruct full request or failure journeys.
  • RCA timelines are clear, shared, and trusted across teams.
  • Log correlation improves detection and understanding of complex or multi-service failures.
  • MTTR drops and confidence in system observability rises.
  • Logging becomes a proactive design consideration in new services.