This article analyzes a poorly performing MES reporting SQL query that scans billions of rows, identifies indexing and partitioning shortcomings, and proposes a redesign using stored procedures, temporary tables, and query‑writing best practices to achieve sub‑second response times.
This article explains the concept, benefits, methods, and practical considerations of partitioning data across multiple Redis instances, covering range and hash partitioning, consistent hashing, implementation options, drawbacks, and recommended tools such as Redis Cluster and Twemproxy.
This article outlines practical strategies for Oracle DBAs to design, automate, and fine‑tune statistics‑gathering processes in large, high‑concurrency environments, covering automatic options, handling skewed data, partitioned tables, bind variables, and ongoing optimization cycles.
This article examines how differing key‑hash algorithms in MySQL 5.1 versus 5.5/5.6 cause data loss in partitioned tables, describes compatibility fixes with ALTER TABLE … ALGORITHM=1, analyzes a MySQL client crash caused by mis‑redirected dump output, explores GTID_PURGED replication issues and replicate‑do‑db filtering gaps, and evaluates the behavior of OPTIMIZE TABLE on TokuDB tables.
This article presents a detailed case study of an Oracle RAC system performance bottleneck, walks through comprehensive monitoring, storage and partitioning analysis, and outlines a step‑by‑step optimization plan—including KEEP pool tuning and partition redesign—that dramatically improved I/O latency and overall throughput.
This article examines why MySQL 5.5/5.6 fails to retrieve non‑null partitioned rows after a physical upgrade, how the ALTER TABLE … PARTITION BY KEY ALGORITHM=1 fix works, the cause of MySQL client crashes when dump output mixes warnings, GTID_PURGED replication bugs, replicate‑filter side effects, and the behavior of TokuDB’s OPTIMIZE TABLE.
This article presents 58.com’s experience with MySQL under massive data volumes, covering core concepts such as single‑instance, sharding, replication and grouping, common availability and read‑write challenges, detailed sharding implementations for user, post, friend and order tables, and post‑sharding business practices including IN queries, non‑partition key queries, and cross‑database pagination.
This article compiles practical discussions on database sharding and partitioning, covering criteria such as user ID, time, and hash, redundancy techniques, caching with Redis, query handling, pagination, consistency, and distributed transaction approaches for scalable backend systems.
This article explains what MySQL table partitioning and sharding (splitting tables) are, compares their implementation, data handling, performance impact, and difficulty, and shows how they can complement each other for high‑traffic, large‑data scenarios.
