Overview

Partitioning can improve performance and help manage data. In particular, partitioning can help manage large tables and indexes by dividing them into smaller, more manageable pieces. Partitions, like a large-scale index, provide faster and easier access to data.

Each partition can reside on a separate segment. Partitions are database objects and can be managed independently. You can, for example, load data and create indexes at the partition level. Yet partitions are transparent to the end user, who can select, insert, and delete data using the same DML commands whether the table is partitioned or not.

Partitioning is the basis for parallel processing, which can significantly improve performance.

Adaptive Server supports horizontal partitioning, in which a selection of table rows can be distributed among disk devices. Individual table or index rows are assigned to a partition according to a partitioning strategy. There are several ways to partition a table:

• Round-robin – by random assignment.

• Hash – using a hashing function to determine row assignment (semantics-based partitioning).

• Range – according to values in the data row falling within a range of specified values (semantics-based partitioning).

• List – according to values in the data row matching specified values (semantics-based partitioning).

Partitioning provides these benefits:

• Improved scalability.

• Improved performance – concurrent multiple I/O on different partitions, and multiple threads on multiple CPUs working concurrently on multiple partitions.

• Faster response time.

• Partition transparency to applications.

• Very large database (VLDB) support – concurrent scanning of multiple partitions of very large tables.

• Range partitioning to manage historical data.