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Parallel Execution or Processing

Oracle RAC Cluster Tips by Burleson Consulting

This is an excerpt from the bestselling book Oracle Grid & Real Application Clusters.  To get immediate access to the code depot of working RAC scripts, buy it directly from the publisher and save more than 30%.

Parallel Processing, a part of the high performance-computing model, involves utilization of large amounts of computing resources to complete a complex task or problem. Originally confined to scientific applications, Parallel Processing has made gradual in-roads into commercial and business applications that need high performance computing facilities like data mining, decision support and risk management applications. Today, Database Applications process huge amounts of data, in terms of loading and updating, and extracting information from very large databases (VLDB) to perform sophisticated analysis.

Parallel Execution or Processing involves dividing a task into several smaller tasks, and working on each of those smaller tasks in parallel. In simple words, if multiple processors engage a computing task, it can be executed faster thereby achieving better response time and increasing throughput. Parallel execution helps systems scale in performance by making optimal use of hardware resources. In a parallel processing system, multiple processes may reside on a single computer or they may be spread across separate computers or nodes as in a cluster. 

Awareness and utility of clusters have gone up tremendously in recent times. In clustered architecture, two or more nodes (hosts) are interconnected and share common storage resources. Each node has its own set of processors. A cluster is usually an aggregation of multiple SMP nodes. Scalability is better achieved in a cluster on a modular basis. As the need arises, additional servers can be added to a cluster.

Opportunities for Parallelism

By employing more resources in terms of processors, large memories and high-speed internal bus technology, many tasks are sped up. Many of the system operations or tasks occur in parallel by utilizing the multiple processors on the server.


Scalability is the ability to maintain higher performance levels as the workload increases by incrementally adding more system capacity in terms of more processors and memory. On a single processor system, it becomes difficult to achieve scalability beyond a certain point. Parallelization, by using multi-processor servers, provides better scalability than single processor systems.

Scalability can be understood from two different perspectives: a speed-up of tasks within the system; and an increase in concurrency in the system, sometime referred to as scale-up.

There are two ways to achieve speed-up of tasks:

* Increasing the execution capacity of the existing hardware components of a server through multiple CPUs.

* Breaking the job into multiple sub-tasks and assigning these components to multiple processors to execute them concurrently.

By using faster components like processors, large memory, low latency messaging mechanism and efficient I/O sub-systems, generally job executions are sped up. However, this is an expensive option. Using the technique of parallelization by utilizing multiple processors helps to achieve speed-up of the jobs.

Scalability is also understood and defined as achieving maximum useful concurrency. In multi-user systems, users share all of the resources. Systems providing concurrency to a large number users should focus on better response times and better synchronization of access mechanisms.

In database applications, scale-up can be either transactional or batch. In batch situations, the user should be able to execute large and complex batch processing without affecting the response time. In transactional environment, there is usually a minimum contention for the same resources and a large number of transactions should be supported without a loss of response time. In both of these cases, response time is usually maintained by the addition of more processors either in a single SMP server or in multi-node cluster.


This is an excerpt from the bestselling book Oracle Grid & Real Application Clusters, Rampant TechPress, by Mike Ault and Madhu Tumma.

You can buy it direct from the publisher for 30%-off and get instant access to the code depot of Oracle tuning scripts.


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