As usage grows and features pile on, applications begin generating more data, often by the hour. That’s a healthy sign for the business. But architecturally, it raises a red flag: the database starts showing strain.
The database sits at the core of nearly every system. Reads, writes, and updates funnel through it. However, unlike stateless services, databases are notoriously hard to scale horizontally. CPUs and memory can be upgraded, but at some point, a single instance, no matter how powerful, becomes the bottleneck. Response times degrade, and queries can time out. Replicas fall behind. Suddenly, what worked at 10,000 users breaks at 10 million.
This is where sharding enters the picture.
Sharding splits a large database into smaller, independent chunks called shards. Each shard handles a subset of the data, allowing traffic and storage to scale out across multiple machines instead of piling onto one.
But sharding is a major shift with real consequences. Application logic often needs to adapt. Query patterns change, and joins become harder. Transactions span physical boundaries. There’s overhead in managing routing, rebalancing, and failover.
This article looks at the fundamentals of database sharding. We cover details like why it matters, how it works, and what trade-offs come with it. We’ll walk through common sharding strategies and practical engineering considerations.
