Sharding is a technique used in database architecture to horizontally partition data across multiple servers. This is done to improve scalability and performance by dividing the dataset into smaller shards, which are distributed across multiple servers.
The process of sharding typically involves the following steps:
1. Partitioning the dataset: The database is partitioned into smaller subsets, known as shards, based on predefined criteria such as geographical location, time or any other relevant attribute.
1. Distributing the shards: Each shard is then assigned to a different server to ensure that the data is spread evenly across multiple nodes.
1. Data synchronization: Once the data is distributed, each server is responsible for managing and synchronizing the data within its own shard. This ensures that the data remains consistent across all servers.
1. Query processing: When a data query is made, the request is sent to all servers, and each server processes the query for its shard. The results are then combined and returned to the user.
Overall, sharding improves scalability and performance by allowing the database to handle more data and process queries faster. However, it also adds complexity to the database architecture, requiring careful planning and implementation to ensure that data is distributed effectively and efficiently across servers.
