Sharding in MongoDB is a method for distributing data across multiple machines, allowing the database to support deployments with very large data sets and high throughput operations. Sharding allows for horizontal scaling of storage and workloads by splitting them across multiple machines, thus increasing read/write throughput, storage capacity, and high availability. It involves distributing a single dataset across multiple databases, which are then stored on multiple machines, allowing for larger datasets to be split into smaller chunks and stored in multiple data nodes, thereby increasing the total storage capacity of the system.
In a sharded cluster, data is distributed across multiple shards, with each shard containing a subset of the sharded data. Each shard can be deployed as a replica set, providing high availability and data consistency. The mongos acts as a query router, providing an interface between client applications and the sharded cluster. Config servers store metadata and configuration settings for the cluster. The balancer is a background process that automatically migrates chunks across the shards to ensure that each shard always has the same number of chunks. MongoDB supports two sharding strategies for distributing data across sharded clusters: ranged sharding and hashed sharding.
Ultimately, sharding is a valuable tool for developers and a cost-effective way to scale out database capacity. While it may seem complicated in practice, sharding, and working effectively with sharded data, can be very intuitive with MongoDB.
In summary, sharding in MongoDB is a powerful technique for distributing data across multiple machines, enabling the database to handle large data sets and high throughput operations, and providing increased read/write throughput, storage capacity, and high availability.
