This article explains the concept, motivations, design methods, common patterns, and trade‑offs of database sharding (both table and database level), offering practical code examples, scalability criteria, and guidance on avoiding over‑engineering while considering alternatives like TiDB.
Effective MySQL sharding requires sustainable, low‑skew designs, favoring hash‑based methods with proper coprime counts, two‑stage partitioning, routing tables, or consistent hashing, while supporting expansion via doubling or flexible consistent‑hash growth to avoid hot spots and uneven data distribution.
The article reproduces a MongoDB issue where queries using a hashed shard key return no results and a compound unique index silently accepts duplicate documents, explains that the failIndexKeyTooLong parameter allows oversized index entries to be stored without indexing, and shows how to reproduce and fix the problem.
This article explains how to split large-scale database tables using vertical and horizontal partitioning, compares hash-modulo and range-based sharding, and proposes a hybrid approach that leverages both methods to balance data distribution, avoid hotspot issues, and eliminate the need for costly data migrations during scaling.
This article reviews the complete process of database and table sharding, covering when to shard, range and hash strategies, combining both, data migration challenges, business compatibility, and long‑term archiving to keep MySQL workloads manageable.
