shardingpartitioning

Sharding and partitioning are techniques used to divide a data store into multiple pieces in order to scale horizontally, improve performance, and enhance availability. Sharding generally refers to distributing data across multiple machines or nodes, while partitioning is the broader concept of dividing data into parts, which may or may not cross machine boundaries.

Horizontal partitioning, or sharding, splits data by rows into distinct shards. Vertical partitioning divides data by columns, separating frequently accessed attributes from less-used ones. Functional or directory-based partitioning chooses partitions based on data usage or a lookup service that maps keys to partitions. These approaches can be combined to meet specific workload characteristics.

Common partitioning strategies include hash-based, range-based, and list-based methods. Hash-based partitioning uses a hash of the shard key to assign rows to shards, aiming to balance load. Range-based partitioning assigns data to shards according to key value ranges, which can improve locality for range queries. List-based partitioning assigns data to shards based on membership in a predefined set of values. Hybrid or composite strategies may mix methods to address particular needs.

Implementation and operational considerations include rebalancing when nodes are added or removed (resharding), data skew that creates hotspots, and the challenge of performing cross-shard queries or distributed transactions. Transactional guarantees may vary, with some systems offering strong consistency and others relying on eventual consistency or application-level coordination (sagas).

In practice, sharding and partitioning are widely used in large-scale databases and data stores. Relational databases often provide built-in partitioning features (for example, PostgreSQL and MySQL), while NoSQL systems (such as MongoDB and Cassandra) implement explicit sharding. The approach is common in web services, analytics platforms, and search systems that require scalable data management.