What is sharding and how does it work in NoSQL databases?

Sharding is a technique used in NoSQL databases to horizontally partition data across multiple servers or nodes in order to improve scalability, performance, and availability. It involves dividing a large dataset into smaller subsets called shards and distributing them across different machines.

In a sharded NoSQL database, each shard is responsible for storing a specific portion of the data. This distribution is typically based on a shard key, which is a unique identifier or attribute of the data. The shard key is used to determine which shard should store a particular piece of data.

When a client application wants to access or modify data in a sharded NoSQL database, it first sends a request to a coordinator or router node. The coordinator node is responsible for determining which shard(s) contain the requested data and forwarding the request accordingly. It uses the shard key to identify the appropriate shard(s) and routes the request to the corresponding nodes.

Once the request reaches the appropriate shard(s), the data operation is performed locally on that shard. This allows for parallel processing and efficient utilization of resources across multiple nodes. Each shard operates independently and can handle its own subset of data, which enables horizontal scalability as more shards can be added to accommodate increasing data volumes.

Sharding also provides fault tolerance and high availability. If a shard or node fails, the coordinator node can redirect the request to another available shard that contains a replica of the data. Replication is often used in conjunction with sharding to ensure data durability and availability. Each shard can have multiple replicas, which are synchronized to provide redundancy and failover capabilities.

Overall, sharding in NoSQL databases allows for distributing data across multiple machines, enabling horizontal scalability, improved performance, fault tolerance, and high availability. It is a key technique for handling large-scale datasets and accommodating the growing demands of modern applications.