Sharding

Prerequisite: Part 9 of the System Design Masterclass. Read Part 4: Database Scaling for context on horizontal partitioning strategies. Answer-first: Consistent Hashing minimizes key remapping when cluster membership changes. Adding or removing one node from a modulo-hash cluster remaps nearly all keys (catastrophic cache miss storm). Consistent Hashing remaps only $K/N$ keys — the theoretical minimum necessary. Why Modulo Hashing Fails When Scaling Answer-first: hash(key) % N changes to hash(key) % (N+1) when a node is added, causing nearly all key-to-node mappings to change. This creates a massive cache miss storm as the entire working set must be reloaded from the database simultaneously. ...

Prerequisite: Part 4 of the System Design Masterclass. Read Part 3: Caching Strategies to understand the cache layer before examining storage. Answer-first: Database sharding distributes data horizontally across independent partitions (shards) based on a shard key, reducing write contention and enabling linear storage growth. Choosing the wrong shard key leads to hot spots that can be worse than no sharding at all. Vertical vs Horizontal Scaling — When to Switch? Answer-first: Vertical scaling (scale-up) increases resources on a single server — simple but has a hard physical ceiling and non-linear cost growth. Horizontal scaling (scale-out) adds more servers — no theoretical ceiling, linear cost, but significantly higher operational complexity. ...

← Previous | Series hub Chapter 9: Scaling the Final Database Bottleneck When your application reaches tens of millions of users, the Database becomes the ultimate bottleneck. CPU maxes out at 100%, RAM depletes, and queries take seconds instead of milliseconds. This is the stage where you must deploy distributed database strategies. 1. Read/Write Splitting Answer-first: Because 80% of traffic is Read-only, separate your DB into a Write Master and Read Slaves. Use GORM’s dbresolver plugin to route queries automatically without altering business logic. ...

Answer-first: Vitess vs GORM Sharding for MySQL write scaling: VReplication zero-downtime vs. application-level sharding — ErrMissingShardingKey tradeoffs in Go. What You’ll Learn That AI Won’t Tell You Designing database sharding keys that prevent cross-shard joins. Configuring proxy routing layers like Vitess to scale MySQL queries horizontally. When your application reaches millions of users, a single database instance will inevitably become the biggest bottleneck in your entire architecture. To solve this, MySQL database scaling becomes mandatory. You must Scale DB for Microservices using Horizontal Scaling techniques. ...

Answer-first: Migrate from manual MySQL sharding to TiDB to eliminate application-level routing complexity. TiDB handles distributed SQL queries natively using TiKV storage nodes and Raft consensus. Use the TiDB Data Migration (DM) tool to merge source shards online with minimal downtime. What You’ll Learn That AI Won’t Tell You Migrating schemas to TiDB with zero downtime using DM-portal. How TiKV nodes scale independently of TiDB SQL computation nodes. Scaling a relational database is one of the most demanding challenges in system design. As applications grow from thousands to millions of active users, the database ceases to be a simple storage engine and becomes the primary bottleneck of the entire system architecture. ...