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What is MPP (Massively Parallel Processing)?

Massively Parallel Processing (MPP), involves the use of numerous processors or computers for the simultaneous execution of tasks. When faced with intricate data problems, the concurrent processing capabilities of Massively Parallel Processing can significantly reduce processing time. This makes MPP especially effective for high-process computing since it can handle large volumes of data and complex queries.

What is MPP?

MPP refers to a type of computing that uses many processors or computers to perform tasks simultaneously. It’s designed to handle large amounts of data and complex queries more efficiently than traditional computing methods.

What is an MPP database?

An MPP database is a data storage system that employs Massively Parallel Processing. An MPP uses numerous processors to carry out tasks concurrently, making it particularly adept at managing large amounts of data and complex queries.

How does massively parallel processing work?

With MPP, data is distributed across multiple nodes, also known as servers, each equipped with its own processor and memory. In essence, each node takes care of a portion of the processing, leading to efficient handling of large volumes of data and complex queries.

The system then runs queries on all nodes simultaneously, significantly reducing processing time. This process is akin to having a team of researchers working on different parts of a problem simultaneously rather than one individual attempting to solve everything.

The localization and storage of data on nodes, whether by rows or columns, differs among different MPP database system providers, but generally, their architecture remains the same. 

Advantages of Massively Parallel Processing

  • Data distribution: One of the hallmark features of Massively Parallel Processing architecture is its ability to distribute data across numerous nodes. This distribution facilitates parallel processing, enabling tasks to be performed concurrently.
  • Scalability: Massively Parallel Processing systems are inherently scalable. As data volumes increase, additional nodes can be incorporated into the system to maintain high-performance levels.
  • Fault tolerance: Even if one node fails, the Massively Parallel Processing system continues to operate, ensuring uninterrupted service. This fault tolerance contributes to the overall reliability of these systems.
  • High performance: By dividing tasks among multiple nodes, Massively Parallel Processing systems can process large amounts of data quickly and efficiently.


While both MPP and Symmetric Multiprocessing (SMP) utilize multiple processors, their approaches to handling tasks differ. In Symmetric Multiprocessing, all processors share a single memory, which can lead to potential bottlenecks. Conversely, each processor has its own memory with Massively Parallel Processing, eliminating this issue and providing faster processing times. 

These features make MPP systems particularly suited for businesses that require real-time insights from large volumes of data. Meanwhile, SMP systems offer simplicity and cost-effectiveness. They are easier to program as all processors share the same memory and operating system. Plus, they provide a cost-effective solution for applications that do not require the high levels of parallelism offered by MPP systems.

The value of MPP for organizations

As businesses continue to generate and depend on vast amounts of data, MPP stands out as an effective solution for the rising costs of physical servers needed to store data and slow response times due to massive datasets being processed. MPP offers a powerful solution for managing large volumes of data. Its ability to process tasks simultaneously enables it to deliver fast, reliable results, making it an excellent choice for businesses seeking real-time insights from their data.

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