Database partitioning is an architectural process of subdividing large tables, indexes and data into smaller partitions, enabling database performance and management benefits through parallel processing.
Partitioning breaks up data into smaller, more manageable chunks that can be spread across storage devices or nodes. Each partition forms an independent subset of the overall data that can be queried and manipulated efficiently in parallel. The partitions are consolidated transparently during query execution to produce complete results across the entire dataset.
Partitioning facilitates parallel execution within a server and distributed execution across a cluster. It works closely with the overall query execution engine to improve performance through divide-and-conquer parallelism. Partitioning also aids maintenance operations like backups and insert scalability.
Some ways partitioning is accomplished:
Partitioning provides benefits like:
Partitioning helps for:
Some downsides to partitioning:
Some databases with partitioning capabilities:
Query execution is the process of carrying out the actual steps to retrieve results for a database query as per the generated execution plan.Read more ->
Parallel execution refers to techniques for speeding up database query processing by leveraging multiple CPUs, servers, or resources concurrently.Read more ->
Distributed execution refers to techniques to execute database queries efficiently across clustered servers or nodes, dividing work to utilize parallel resources.Read more ->
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