What is Sharding or Data Partitioning?
Let’s understand sharding with the help of an example:
You get the pizza in different slices and you share these slices with your friends. Sharding which is also known as data partitioning works on the same concept of sharing the Pizza slices.
It is basically a database architecture pattern in which we split a large dataset into smaller chunks (logical shards) and we store/distribute these chunks in different machines/database nodes (physical shards).
- Each chunk/partition is known as a “shard” and each shard has the same database schema as the original database.
- We distribute the data in such a way that each row appears in exactly one shard.
- It’s a good mechanism to improve the scalability of an application.
- Database shards are autonomous, they don’t share any of the same data or computing resources. In some cases, though, it may make sense to replicate certain tables into each shard to serve as reference tables.
Database Sharding | System Design
Database sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database.
Important Topics for the Database Sharding
- What is Sharding or Data Partitioning?
- Sharding Architectures
- Key Based Sharding
- Horizontal or Range Based Sharding
- Vertical Sharding
- Directory-Based Sharding
- Advantages of Sharding in System Design
- Disadvantages of Sharding in System Design
- Conclusion
When designing a sharded database, the following key considerations should be taken into account:
- Data distribution: How the data will be split across the shards, either based on a specific key such as the user ID or by using a hash function.
- Shard rebalancing: How the data will be balanced across the shards as the amount of data changes over time.
- Query routing: How queries will be directed to the correct shard, either by using a dedicated routing layer or by including the shard information in the query.
- Data consistency: How data consistency will be maintained across the shards, for example by using transaction logs or by employing a distributed database system.
- Failure handling: How the system will handle the failure of one or more shards, including data recovery and data redistribution.
- Performance: How the sharded database will perform in terms of read and write speed, as well as overall system performance and scalability.
In summary, Database Sharding is a complex but important concept in system design that can help to improve the scalability and performance of a database-driven system. A strong understanding of database sharding is often viewed as a key requirement for successful system design.