Partitioning addresses key issues in supporting very large tables and indexes by decomposing them into smaller and more manageable pieces called partitions, which are entirely transparent to an application. SQL queries and Data Manipulation Language (DML) statements do not need to be modified to access partitioned tables. However, after partitions are defined, data definition language (DDL) statements can access and manipulate individual partitions rather than entire tables or indexes. This is how partitioning can simplify the manageability of large database objects.
Each partition of a table or index must have the same logical attributes, such as column names, data types, and constraints, but each partition can have separate physical attributes, such as compression enabled or disabled, physical storage settings, and tablespaces.
Partitioning is useful for many different types of applications, particularly applications that manage large volumes of data. OLTP systems often benefit from improvements in manageability and availability, while data warehousing systems benefit from performance and manageability.
It enables data management operations such as data loads, index creation and rebuilding, and backup and recovery at the partition level, rather than on the entire table. This results in significantly reduced times for these operations.
It improves query performance. Often the results of a query can be achieved by accessing a subset of partitions, rather than the entire table. For some queries, this technique (called partition pruning) can provide order-of-magnitude gains in performance.
It significantly reduces the impact of scheduled downtime for maintenance operations.
Partition independence for partition maintenance operations lets you perform concurrent maintenance operations on different partitions of the same table or index. You can also run concurrent
SELECT and DML operations against partitions that are unaffected by maintenance operations.
It increases the availability of mission-critical databases if critical tables and indexes are divided into partitions to reduce the maintenance windows, recovery times, and impact of failures.
Parallel execution provides specific advantages to optimize resource utilization, and minimize execution time. Parallel execution is supported for queries and for DML and DDL.
Partitioning enables faster data access within Oracle Database. Whether a database has 10 GB or 10 TB of data, partitioning can improve data access by orders of magnitude. Partitioning can be implemented without requiring any modifications to your applications. For example, you could convert a nonpartitioned table to a partitioned table without needing to modify any of the
SELECT statements or DML statements that access that table. You do not need to rewrite your application code to take advantage of partitioning.