Partitioned tables 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.
Each partition of a table or 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.
Tables can be partitioned into up to 64,000 separate partitions. Any table can be partitioned except those tables containing columns with LONG or LONG RAW datatypes.
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. Partitioning enables data management operations such data loads, index creation and rebuilding, and backup/recovery at the partition level, rather than on the entire table. This results in significantly reduced times for these operations. Partitioning improves query performance. In many cases, the results of a query can be achieved by accessing a subset of partitions, rather than the entire table. It can also significantly reduce the impact of scheduled downtime for maintenance operations.
Tables greater than 2GB should always be considered for partitioning along with tables containing historical data, in which new data is added into the newest partition.
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