Parallel DML is useful in a decision support system (DSS) environment where the performance and scalability of accessing large objects are important. Parallel DML complements parallel query in providing you with both querying and updating capabilities for your DSS databases.
Several scenarios where parallel DML is used include:
In a DSS environment, many applications require complex computations that involve constructing and manipulating many large intermediate summary tables. These summary tables are often temporary and frequently do not need to be logged. Parallel DML can speed up the operations against these large intermediate tables. One benefit is that you can put incremental results in the intermediate tables and perform parallel updates.
In addition, the summary tables may contain cumulative or comparative information which has to persist beyond application sessions; thus, temporary tables are not feasible. Parallel DML operations can speed up the changes to these large summary tables.
Many DSS applications score customers periodically based on a set of criteria. The scores are usually stored in large DSS tables. The score information is then used in making a decision, for example, inclusion in a mailing list.
This scoring activity queries and updates a large number of rows in the table. Parallel DML can speed up the operations against these large tables.
Historical tables describe the business transactions of an enterprise over a recent time interval. Periodically, the DBA deletes the set of oldest rows and inserts a set of new rows into the table. Parallel
SELECT and parallel
DELETE operations can speed up this rollover task.
Dropping a partition can also be used to delete old rows. However, the table has to be partitioned by date and with the appropriate time interval.
Batch jobs executed in an OLTP database during off hours have a fixed time during which the jobs must complete. A good way to ensure timely job completion is to execute their operations in parallel. As the workload increases, more computer resources can be added; the scaleup property of parallel operations ensures that the time constraint can be met.