A tablespace consists of one or more datafiles, which are physical structures within the operating system you are using. A datafile is associated with only one tablespace. From a design perspective, tablespaces are containers for physical design structures.

Tablespaces need to be separated by differences. For example, tables should be separated from their indexes and small tables should be separated from large tables. Tablespaces should also represent logical business units if possible. Because a tablespace is the coarsest granularity for backup and recovery or the transportable tablespaces mechanism, the logical business design affects availability and maintenance operations.

You can now use ultralarge data files, a significant improvement in very large databases.

Oracle partitioning is an extremely important functionality for data warehousing, improving manageability, performance and availability. This section presents the key concepts and benefits of partitioning noting special value for data warehousing.

Partitioning allows tables, indexes or index-organized tables to be subdivided into smaller pieces. Each piece of the database object is called a partition. Each partition has its own name, and may optionally have its own storage characteristics. From the perspective of a database administrator, a partitioned object has multiple pieces that can be managed either collectively or individually. This gives the administrator considerable flexibility in managing a partitioned object. However, from the perspective of the user, a partitioned table is identical to a non-partitioned table; no modifications are necessary when accessing a partitioned table using SQL DML commands.

Database objects - tables, indexes, and index-organized tables - are partitioned using a partitioning key, a set of columns that determine in which partition a given row will reside. For example a sales table partitioned on sales date, using a monthly partitioning strategy; the table appears to any application as a single, normal table. However, the DBA can manage and store each monthly partition individually, potentially using different storage tiers, applying table compression to the older data, or store complete ranges of older data in read only tablespaces.

Irrespective of the chosen index partitioning strategy, an index is either coupled or uncoupled with the partitioning strategy of the underlying table. The appropriate index partitioning strategy is chosen based on the business requirements, making partitioning well suited to support any kind of application. Oracle Database 12c differentiates between three types of partitioned indexes.

• Local Indexes

  A local index is an index on a partitioned table that is coupled with the underlying partitioned table, 'inheriting' the partitioning strategy from the table. Consequently, each partition of a local index corresponds to one - and only one - partition of the underlying table. The coupling enables optimized partition maintenance; for example, when a table partition is dropped, Oracle Database simply has to drop the corresponding index partition as well. No costly index maintenance is required. Local indexes are most common in data warehousing environments.

• Global Partitioned Indexes

  A global partitioned index is an index on a partitioned or nonpartitioned table that is partitioned using a different partitioning-key or partitioning strategy than the table. Global-partitioned indexes can be partitioned using range or hash partitioning and are uncoupled from the underlying table. For example, a table could be range-partitioned by month and have twelve partitions, while an index on that table could be hash-partitioned using a different partitioning key and have a different number of partitions. Global partitioned indexes are more common for OLTP than for data warehousing environments.

• Global Non-Partitioned Indexes

  A global non-partitioned index is essentially identical to an index on a non-partitioned table. The index structure is not partitioned and uncoupled from the underlying table. In data warehousing environments, the most common usage of global non-partitioned indexes is to enforce primary key constraints.

A typical usage of partitioning for manageability is to support a 'rolling window' load process in a data warehouse. Suppose that a DBA loads new data into a table on a daily basis. That table could be range partitioned so that each partition contains one day of data. The load process is simply the addition of a new partition. Adding a single partition is much more efficient than modifying the entire table, because the DBA does not need to modify any other partitions. Another advantage of using partitioning is when it is time to remove data. In this situation, an entire partition can be dropped, which is very efficient and fast, compared to deleting each row individually.

By limiting the amount of data to be examined or operated on, partitioning provides a number of performance benefits. Two features specially worth noting are:

• Partitioning pruning: Partitioning pruning is the simplest and also the most substantial means to improve performance using partitioning. Partition pruning can often improve query performance by several orders of magnitude. For example, suppose an application contains an ORDERS table containing an historical record of orders, and that this table has been partitioned by day. A query requesting orders for a single week would only access seven partitions of the ORDERS table. If the table had two years of historical data, this query would access seven partitions instead of 730 partitions. This query could potentially execute 100x faster simply because of partition pruning. Partition pruning works with all of Oracle's other performance features. Oracle Database will utilize partition pruning in conjunction with any indexing technique, join technique, or parallel access method.

• Partition-wise joins: Partitioning can also improve the performance of multi-table joins, by using a technique known as partition-wise joins. Partition-wise joins can be applied when two tables are being joined together, and at least one of these tables is partitioned on the join key. Partition-wise joins break a large join into smaller joins of 'identical' data sets for the joined tables. 'Identical' here is defined as covering exactly the same set of partitioning key values on both sides of the join, thus ensuring that only a join of these 'identical' data sets will produce a result and that other data sets do not have to be considered. Oracle Database is using either the fact of already (physical) equi-partitioned tables for the join or is transparently redistributing ("repartitioning") one table at runtime to create equipartitioned data sets matching the partitioning of the other table, completing the overall join in less time. This offers significant performance benefits both for serial and parallel execution.

Partitioned database objects provide partition independence. This characteristic of partition independence can be an important part of a high-availability strategy. For example, if one partition of a partitioned table is unavailable, all of the other partitions of the table remain online and available. The application can continue to execute queries and transactions against this partitioned table, and these database operations will run successfully if they do not need to access the unavailable partition. The database administrator can specify that each partition be stored in a separate tablespace; this would allow the administrator to do backup and recovery operations on an individual partition or sets of partitions (by virtue of the partition-to-tablespace mapping), independent of the other partitions in the table. Therefore in the event of a disaster, the database could be recovered with just the partitions comprising the active data, and then the inactive data in the other partitions could be recovered at a convenient time, thus decreasing the system down-time.In light of the manageability, performance and availability benefits, it should be part of every data warehouse.

Hierarchies are logical structures that use ordered levels to organize data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure.

Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be composed of more than one hierarchy. For example, in the product dimension, there might be two hierarchies—one for product categories and one for product suppliers.

Dimension hierarchies also group levels from general to granular. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse.

When designing hierarchies, you must consider the relationships in business structures. For example, a divisional multilevel sales organization can have complicated structures.

Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.

Figure 3-2 illustrates a dimension hierarchy based on customers.

Figure 3-2 Typical Levels in a Dimension Hierarchy

Description of "Figure 3-2 Typical Levels in a Dimension Hierarchy"