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by Burleson Consulting

The Data Warehouse Development Life Cycle

Parallelism And Oracle Data Warehousing
SMP And MPP Processing

SMP, or symmetrical multiprocessing, describes an architecture where many CPUs share a common memory area and I/O buffer. This type of architecture is not scaleable, as additional processors must compete for the shared memory and I/O resources. On the other hand, MPP, or massively parallel processors, describes an architecture where many independent processors share nothing, operating via a common I/O bus. An MPP system can add processors without impeding performance, and performance will actually increase as processors are added.

Some tasks are naturally suited for parallel execution. But more common are tasks that have components that can be parallelized while also containing some serial operations. One of the most common examples of highly parallel operations is the text search of a very large database. In this case, thousands of concurrent processes can search portions of the data, and when all processes have completed, the query manager can merge and sort the results for presentation.

The most confounding tasks for parallel processing are those that have many steps that rely on the output from previous steps. But even with these types of processes, we can find some tasks that have components that can be run in parallel. For example, consider the tasks involved in placing an order for a product.

1. Check customer profile.

2. Check customer credit rating.

3. Check customer payment history.

4. Check inventory levels.

5. Calculate the costs for the items.

6. Add sales tax.

7. Decrement the inventory on hand.

8. Prepare a shipping order.

9. Print customer bill.

Now, which of these processes can be parallelized? It appears that there are some operations that can be parallel while others must be serial (see Figure 7.5).

Figure 7.5 Parallelism of dependent tasks.

Here, we can see that there are three phases to the process, where the output of one phase serves as input to the next phase. Within each phase, we can see a number of tasks that can be run in parallel. In essence, the process of parallelism requires the restructuring of linear tasks to identify those tasks that can run concurrently, while preserving the sequence of tasks that must be serialized.

This is an excerpt from "High Performance Data Warehousing". To learn more about Oracle, try "Oracle Tuning: The Definitive Reference", by Donald K. Burleson.  You can buy it direct from the publisher at 30% off here:


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