Video Lesson
Structured Logging Best Practices
Structured logging means emitting logs as key-value pairs (typically JSON) rather than free-form text. This makes logs machine-parseable and queryable. Essential fields: timestamp, level, service, trace_id, user_id, message, and relevant domain data.
We will implement structured logging in a sample application using popular libraries (Winston for Node.js, structlog for Python, slog for Go) and ensure consistent field naming across services.
Log Aggregation and Search
Centralized log aggregation (ELK Stack, Loki, Datadog) collects logs from all services into a searchable store. We will set up a Loki + Grafana stack as a lightweight alternative to Elasticsearch.
LogQL (Loki’s query language) and AI-generated queries let you find needles in haystacks of log data. We will practice writing queries that correlate events across services using trace IDs.
AI-Powered Log Analysis
AI transforms log analysis from manual searching to intelligent discovery. Modern tools can automatically cluster similar log patterns, surface anomalies, identify root causes, and generate summaries of incidents from log data.
We will use AI chatbots to analyze log samples — paste in log output and get instant analysis of error patterns, suggested root causes, and recommended next debugging steps.
Hands-On Exercises
- Refactor a sample application’s logging from unstructured to structured JSON format — ensure all recommended fields are present
- Write 5 LogQL or Elasticsearch queries that would help debug common production issues (high latency, auth failures, data sync errors)
- Paste a sample error log sequence into an AI tool and evaluate the quality of its root cause analysis