A MySQL 5.5 fork maintained by Twitter with performance optimizations for NUMA, SSDs, and production monitoring.
Twitter MySQL is a specialized fork of MySQL 5.5 maintained by Twitter for internal use. It enhances the standard MySQL with performance optimizations for NUMA architectures and SSDs, adds production monitoring capabilities, and improves reliability features like server-side query timeouts and buffer pool management.
Database administrators and engineers managing large-scale MySQL deployments on modern hardware, particularly those dealing with NUMA systems, SSDs, and requiring deep InnoDB monitoring.
It provides battle-tested, production-focused enhancements not found in upstream MySQL, including NUMA-aware memory allocation, reduced SSD wear, and better tools for monitoring and safe restarts in high-demand environments.
MySQL fork maintained and used at Twitter
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Adds status variables for opened/closed files, deadlock counts, and log sequence numbers (LSN), providing deeper visibility into production workloads as explicitly noted in the README for better troubleshooting.
Includes options like buffer pool pre-allocation, NUMA interleaving via numactl, and OS cache flushing to prevent uneven memory allocation, addressing common inefficiencies in multi-processor systems.
Supports millisecond-granularity timeouts to proactively cancel long-running queries, reducing unnecessary server work and improving resource management in high-demand environments.
Enables safe export and restore of InnoDB buffer pools via prefetch, facilitating rolling restarts with minimal disruption, a key feature for maintaining uptime in production.
Optimizes page-flushing behavior and reduces disk writes to improve performance and lifespan on solid-state drives, tailored for modern hardware deployments.
Based on MySQL 5.5, which lacks modern features, security updates, and performance improvements found in newer versions like MySQL 8.0, limiting long-term viability.
Twitter explicitly states in the README that it provides no guarantees for bug fixes, maintenance, or compatibility outside their internal use, making it a risky choice for adoption.
Optimizations like NUMA memory tuning may add unnecessary setup complexity for systems without NUMA architecture or for teams unfamiliar with low-level database internals.
As an internal fork, detailed documentation beyond the README is sparse, requiring users to rely on source code or community forums for advanced configuration.