Taking Over and Stabilizing a Complex Legacy System: Tencent's Practical Experience

This article shares Tencent's practical experience in taking over and stabilizing a complex legacy content architecture system with 93 services. The content covers four main areas: monitoring construction and alert governance, code documentation (串讲文档), code defect fixing, and R&D process improvement.

Project Background: The content architecture provides content access, computation, and distribution services for QB search. After years of rapid iteration, it comprised 93 services with complex data flows and numerous bugs, causing daily business故障 feedback and service alerts.

Monitoring Construction: The article details platform-built monitoring (CPU, memory, disk, database connections, slow queries) and business custom monitoring approaches including adding business error codes to caller/callee monitoring, injecting business identifiers, and upgrading from single-dimensional to multi-dimensional attribute reporting.

Code Documentation: Explains what code walkthrough documentation is, why it's needed (ensuring code review quality, strengthening understanding, team knowledge accumulation), and how to create it covering module functions, upstream/downstream relationships, architecture, sub-module details, development processes, key metrics, and future optimizations.

Code Quality Improvements: Covers fixing business logic bugs (memory leaks, null pointer access), defensive programming (input validation, array bounds checking, wild pointer prevention, global resource protection), Go-Python memory leak issues, proper external library usage, avoiding infinite retry cascades, real initialization, resource isolation, database pressure optimization (batch fetching, read replicas, connection management, indexing, instance splitting), and mutual exclusion resource management.

Alert Governance: Addresses full-link timeout configuration, business-based SET resource isolation, and using thread pools for high-latency computations.

R&D Process: Implements unified Docker images, code repository configuration with branch protection, BlueCross pipelines (MR pipeline, commit pipeline, XAC release pipeline), and code review mechanisms.

Optimization Results: Alert volume significantly reduced from 159/day to 0/day for core services, business cases dropped from 18/month to 4/month, and on-call manpower reduced from 4+ to 0.8.