Introduction to Parallel Execution

Introduction to Parallel Execution

When Oracle runs SQL statements in parallel, multiple processes work together simultaneously to run a single SQL statement. By dividing the work necessary to run a statement among multiple processes, Oracle can run the statement more quickly than if only a single process ran it. This is called parallel execution or parallel processing.

Parallel execution dramatically reduces response time for data-intensive operations on large databases typically associated with decision support systems (DSS) and data warehouses. Symmetric multiprocessing (SMP), clustered systems, and massively parallel systems (MPP) gain the largest performance benefits from parallel execution because statement processing can be split up among many CPUs on a single Oracle system. You can also implement parallel execution on certain types of online transaction processing (OLTP) and hybrid systems.

Parallelism is the idea of breaking down a task so that, instead of one process doing all of the work in a query, many processes do part of the work at the same time. An example of this is when 12 processes handle 12 different months in a year instead of one process handling all 12 months by itself. The improvement in performance can be quite high.

Parallel execution helps systems scale in performance by making optimal use of hardware resources. If your system’s CPUs and disk controllers are already heavily loaded, you need to alleviate the system’s load or increase these hardware resources before using parallel execution to improve performance.

Some tasks are not well-suited for parallel execution. For example, many OLTP operations are relatively fast, completing in mere seconds or fractions of seconds, and the overhead of utilizing parallel execution would be large, relative to the overall execution time.

See Also: Oracle9i Data Warehousing Guide for specific information on tuning your parameter files and database to take full advantage of parallel execution

When to Implement Parallel Execution

During business hours, most OLTP systems should probably not use parallel execution. During off-hours, however, parallel execution can effectively process high-volume batch operations. For example, a bank can use parallelized batch programs to perform the millions of updates required to apply interest to accounts.

18-2 Oracle9i Database Concepts

Introduction to Parallel Execution

The most common example of using parallel execution is for DSS. Complex queries, such as those involving joins or searches of very large tables, are often best run in parallel.

Parallel execution is useful for many types of operations that access significant amounts of data. Parallel execution improves performance for:

Parallel execution benefits systems that have all of the following characteristics:

Symmetric multiprocessors (SMP), clusters, or massively parallel systems (for example, multiple CPUs)

Sufficient I/O bandwidth

Under-utilized or intermittently used CPUs (for example, systems where CPU use is typically less than 30%)

Sufficient memory to support additional memory-intensive processes such as sorts, hashing, and I/O buffers

If your system lacks any of these characteristics, parallel execution might not significantly improve performance. In fact, parallel execution can reduce system performance on overutilized systems or systems with insufficient I/O bandwidth.

See Also: Oracle9i Data Warehousing Guide for further information regarding when to implement parallel execution

When Not to Implement Parallel Execution

Parallel execution is not normally useful for:

Environments in which the typical query or transaction is very short (a few seconds or less). This includes most online transaction systems. Parallel execution is not useful in these environments because there is a cost associated with coordinating the parallel execution servers; for short transactions, the cost of this coordination may outweigh the benefits of parallelism.

Environments in which the CPU, memory, or I/O resources are already heavily utilized. Parallel execution is designed to exploit additional available hardware

Parallel Execution of SQL Statements 18-3