Partitioning addresses key issues in supporting very large indexes by letting you decompose them into smaller and more manageable pieces called partitions. Partitioning is entirely transparent to applications.
Each partition of an index must have the same logical attributes, such as column names, datatypes, and constraints, but each partition can have separate physical attributes such as pctfree, pctused, 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.
Partitioned indexes allow your data to be broken down into smaller, more manageable pieces called partitions, or subpartitions. Each partition can be managed individually, and can operate independently of the other partitions, thus providing a structure that can be better tuned for availability and performance.
Partitioned indexes improve manageability, availability, performance, and scalability. They can either be partitioned independently (global indexes) or automatically linked to a table's partitioning method (local indexes).
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