---
title: "A Crash Course on Database Sharding"
description: "Learn database sharding: concepts, techniques, and implementation."
image: "https://assets.bytebytego.com/diagrams/0065-a-crash-course-on-database-sharding.png"
createdAt: "2024-03-04"
draft: false
categories:
  - database-and-storage
tags:
  - "database"
  - "sharding"
---

![](https://assets.bytebytego.com/diagrams/0065-a-crash-course-on-database-sharding.png)

## What is Database Sharding?

Database sharding is a type of database partitioning that separates very large databases into smaller, faster, more easily managed parts called data shards. The word shard means a small piece of something bigger.

Sharding is also known as horizontal partitioning. Each shard contains a portion of the data, and all shards together contain all of the data.

## Why Sharding?

Sharding is implemented to solve these problems:

*   **Too much data on one machine**: A single database server can only handle so much data.

*   **Too many requests on one machine**: A single database server can only handle so many requests.

*   **High Latency**: As data grows, query latency increases.

## How Sharding Works

Sharding involves splitting a database into multiple, independent parts (shards) and distributing them across different servers or machines. Each shard contains a subset of the data, and all shards collectively hold the entire dataset.

### Sharding Key

A sharding key is a column in the database table that determines how the data is distributed across the shards. The sharding key is used by the sharding algorithm to determine which shard a particular row of data should be stored in.

### Sharding Algorithm

The sharding algorithm is the logic that determines which shard a particular row of data should be stored in. The sharding algorithm uses the sharding key to make this determination.

Here are some common sharding algorithms:

*   **Range-based sharding**: Data is divided into ranges based on the sharding key. For example, users with IDs from 1 to 1000 might be stored in shard 1, users with IDs from 1001 to 2000 in shard 2, and so on.

*   **Hash-based sharding**: A hash function is applied to the sharding key to determine the shard. For example, `shard_id = hash(user_id) % num_shards`.

*   **Directory-based sharding**: A lookup table (or directory) is used to map sharding keys to shard locations.

## Sharding Approaches

*   **Application-level sharding**: The application is responsible for determining which shard to use for each query.

*   **Middleware sharding**: A middleware layer sits between the application and the database and handles the sharding logic.

*   **Database-native sharding**: The database system itself provides sharding capabilities.

## Sharding Challenges

*   **Increased Complexity**: Sharding adds complexity to the database infrastructure and application code.

*   **Data Distribution**: Choosing the right sharding key and algorithm is crucial for even data distribution and performance.

*   **Joins and Transactions**: Performing joins and transactions across shards can be challenging and may require distributed transaction management.

*   **Resharding**: Changing the sharding scheme after the database has been sharded can be a complex and time-consuming process.