Sharding is a database architecture pattern that involves dividing a large dataset into smaller, more manageable parts called shards. Each shard is a horizontal partition of data in a database or search engine, and is held on a separate database server instance, effectively spreading the load[12]. This technique is used to scale databases horizontally, enabling them to handle more requests and store more data by distributing the workload across multiple machines[1][12].
In the context of databases, sharding allows for the distribution of data across multiple servers, where each server hosts a subset of the data. This subset, or shard, contains a portion of the total data, making each shard an independent database. Collectively, these shards represent the entire dataset[6][7]. Sharding is particularly useful for applications that require high throughput and large volumes of data, as it helps in reducing the index size, improving search performance, and enabling the database to grow beyond the limitations of a single server[12].
Sharding can be implemented in various ways, including key-based (or hash-based) sharding and range-based sharding. Key-based sharding involves using a shard key to distribute data across shards. The shard key is a field or set of fields that determines how data is partitioned and distributed. In hash-based sharding, a hash function is applied to the shard key to evenly distribute data across shards. Range-based sharding, on the other hand, divides data into shards based on ranges of shard key values, allowing for efficient range queries[1][17].
While sharding offers significant benefits in terms of scalability and performance, it also introduces complexity to the system. Choosing an appropriate shard key, managing data distributio...
