Building for scale is a common problem in programming.

Here’s one solution you should know:

There are a handful of common growing pains that systems face.

Inefficient data storage and slow queries are two of them.

Database sharding is a popular solution that aims to solve this.

With database sharding, a large database is separated out into smaller parts called data shards.

These shards are distributed across multiple servers.

This allows the system to scale horizontally (add more servers) as needed.

Each shard operates independently from other shards.

This means that a problem in one shard doesn't propagate to other shards which boosts overall reliability & availability

The queries that run on the database determine how the database is split up

Some common approaches are:

🔹 Range-based: a set range of a value. E.g. IDs 1-1000

🔹 Hash-based: running a value (like ID) through a hash function and using the hash to select a shard

🔹 Geographically: picking a shard that is geographically closer to where the data was generated

By basing your sharding strategy on the queries that you need to run, you can ensure you only need to query a single shard which drastically boosts performance.

While it may seem like a great solution to future-proof your database, it isn’t wise to implement it prematurely.

This is because it adds a lot of complexity to your database management system.

Not only is it a lot of work to set up, the maintenance required is also significant

You’ll need to ensure that data is distributed evenly across the shards, which can be challenging.

There will also be cases where you need data across multiple shards, which can lead to very complex queries.

Like most scaling solutions, database sharding is a powerful solution that requires careful consideration as to whether it is the best approach for your system right now.