Sharding Introduction
Sharding is a method for storing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations.
Purpose of Sharding
Database systems with large data sets and high throughput applications can challenge the capacity of a single server. High query rates can exhaust the CPU capacity of the server. Larger data sets exceed the storage capacity of a single machine. Finally, working set sizes larger than the system’s RAM stress the I/O capacity of disk drives.
To address these issues of scales, database systems have two basic approaches: vertical scaling andsharding.
Vertical scaling adds more CPU and storage resources to increase capacity. Scaling by adding capacity has limitations: high performance systems with large numbers of CPUs and large amount of RAM are disproportionately more expensive than smaller systems. Additionally, cloud-based providers may only allow users to provision smaller instances. As a result there is apractical maximum capability for vertical scaling.
Sharding, or horizontal scaling, by contrast, divides the data set and distributes the data over multiple servers, or shards. Each shard is an independent database, and collectively, the shards make up a single logical database.
Diagram of a large collection with data distributed across 4 shards.
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