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Difference between Partitioning and Sharding

Partitioning and sharding are two techniques that are often used to scale databases and improve performance. Both involve dividing a large dataset into smaller pieces in order to distribute the workload across multiple servers or nodes. However, there are some key differences between the two approaches.


Partitioning is a technique that is used to divide a table or index into smaller pieces based on a specific criterion, such as a date range or a range of values for a particular column. The goal of partitioning is to improve the performance of queries and index maintenance by limiting the amount of data that needs to be scanned or processed. Partitioning is usually transparent to the application, and the database engine handles the details of mapping rows to partitions and managing the partitions.


Sharding is a technique that is used to horizontally scale a database by distributing the data across multiple servers or nodes. Each shard is a separate database instance that stores a portion of the data. The goal of sharding is to improve the performance and scalability of the database by distributing the workload across multiple servers. Sharding requires more complex application design and integration, since the application needs to be aware of the sharding scheme and send queries to the correct shard.


In general, partitioning is a technique that is used within a single database instance to improve performance and manageability, while sharding is a technique that is used to scale a database across multiple servers. Both approaches have their own strengths and weaknesses, and the best approach for a given situation will depend on the specific requirements of the application.

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