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Databases in general, and data warehouses in particular, need to regularly extract data from various sources and transform it into a more useful form. For example, a data warehouse may collect data from the OLTP data sources and transform it according to some business rules to make it useful for management. Traditionally, the way to load a data warehouse has been to first load staging tables with the raw data. Sometimes the data would be transformed outside of the database and loaded directly in one pass to the warehouse tables. Either method is usually very cumbersome, even when you use stateof-the-art extraction and transformation tools or custom scripts. Oracle allows the use of external tables that is, tables that use data that resides in external operating system files. External tables don t need any storage in terms of extents in the Oracle database the definition of an external table merely makes an entry in the data dictionary, which enables you to load data into other Oracle database tables from the external tables. If you drop an external table in Oracle, you ll only be removing its definition from the data dictionary the data itself remains safe in the external source files. External tables are commonly used as intermediate staging tables during data transformations. External tables enable you to view externally stored data as if it were inside a table in the Oracle database. You can perform queries and joins on external tables, but you can t update, insert, or delete from these tables; no DML operations are permissible on external tables.
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14 provides a detailed example of using external tables and discusses them in more depth.
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Oracle databases can be quite large, and it s not uncommon to encounter tables that hold several gigabytes (or even several terabytes) worth of data. Partitioning is a way of logically dividing a large table into smaller chunks to facilitate query processing, DML operations, and database management. All the partitions share the same logical definition, column definitions, and constraints. Improvements in query response times are startling when you partition a multibillion-row table into hundreds or thousands of partitions. In some busy environments, new partitions may be created as often as every hour! Partitioning leads directly to better query performance because the database needs to search only the relevant partitions of the table during a query. This avoidance of unneeded partitions when querying is called partition pruning; the availability of one partition is independent of the availability of the other partitions.
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Data I/O can also be enhanced by using partitions because you can keep the partitions of a heavily accessed table on different disk drives. If you are using the Oracle parallel DML features, partitioned tables provide you with better performance. Partitioning a table also provides partition independence, meaning, among other things, that you can perform your backup and recovery operations, data loading, and index creation on partitions of a large table instead of the whole table. For example, you can copy a single partition s data using the Data Pump Export utility, reducing export and import times dramatically when you only need part of the entire data set. The ability to perform tasks on partitions instead of entire tables means that your database downtime will be reduced drastically.
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Although partitioned tables generally improve query performance in very large tables, they aren t a panacea for poor coding or other design problems in the application. Partitioning also carries a price in terms of additional work to maintain the partitions and their indexes. Of course, you also have to pay for the partitioning capability, as it s an option that you have to license separately from Oracle Corporation.
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Partitioning tables is also an effective way of purging or archiving older data that you don t need right now. It is very common for large data warehouses to archive data that is older than a certain date, and partitioned tables make archiving easy. For example, each quarter you can drop the oldest partition and replace it with a new partition. The partitioned table in this case will end up having roughly the same amount of data, and it will cover the same length of time (a quarterly collection of company data for three years will always have 12 partitions in the table). In addition, large table exports can be performed more quickly when you partition the table into smaller chunks and export each partition separately. Oracle offers six different ways to partition your table data: range partitioning, interval partitioning, hash partitioning, list partitioning, reference partitioning, and system partitioning. In addition, you can use composite partitioning (combine two partitioning methods to divide the data into smaller subpartitions) strategies, which takes the actual number of partitioning methods to over a dozen types. No matter which partitioning method you use, you must specify the following information when creating a partitioned table: Partitioning method: This is one of the six types of partitioning. Partitioning column (or columns): This is the column or columns on the basis of which you want to partition the table (for example, transaction_date). The range or set of values of the partitioning columns are called the partitioning keys. The partition descriptions: These descriptions specify the criteria for the inclusion of the actual partitioning keys in each partition. You use a partition bound for range partitioning and use the clause VALUES LESS THAN to limit the partitioning key values in each partition. In list partitioning, you specify a list of literal values that tell Oracle what partitioning key values qualify for inclusion in a partition. The following sections discuss the different types of partitioning and show how to partition a table.
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