Log Governance and Mining Solution for Distributed Systems

Logs are essential for troubleshooting, performance tuning, and data analysis, but challenges such as correlating logs across requests, GC overhead, missing core logs, and resource consumption hinder their effective use.

The article proposes a log governance and mining solution that standardizes, normalizes, and unifies logs to unlock their hidden value.

Four primary log usage scenarios are identified: metric monitoring, trace debugging, performance analysis, and data analysis/reporting.

3.1 Distributed System Log Integration – Generate a globally unique traceId at the start of each request, propagate it through all components via context, and include it in every log entry to achieve end‑to‑end traceability.

3.2 Front‑Back Log Integration – Create a traceId at the entry point (e.g., API gateway), return it in the response header, let the front‑end store and resend it on subsequent requests, enabling seamless correlation between client‑side and server‑side logs.

3.3 Unified Standard Log Management – Define layered log levels, a unified log format (timestamp, traceId, level, source, request/response details, stack trace), a consistent ingestion pipeline, analysis tools, and security/compression measures.

3.4 Large Payload Log Handling – Detect oversized log messages based on memory thresholds, offload them to an asynchronous compression and sending task pool, and use efficient algorithms such as Gzip or ZSTD.

3.5 Efficient Log Cleaning and Multidimensional Analysis – Process raw logs, extract key fields via an Aviator script, aggregate dimensions into ClickHouse, and visualize results through dashboards for rapid insight.

Future plans include componentized, zero‑intrusion integration, configurable modules, sampling of core logs to reduce storage costs, and resource‑saving techniques like custom serialization and batch transmission.