Persistent Data Access
Pervasive MPP delivered
The challenges involved in processing large amounts of data are similar to those of any large-scale project. What is the best method for accomplishing a task that is too large for any one person, piece of equipment, or facility to handle? The answer is simple—split it up. The hard part is what comes next. How do you break it down, and more importantly, how do you bring it all back together?
Massively parallel processing (MPP) is a class of architectures aimed specifically at addressing the processing requirements of very large databases.
The technical underpinnings of all MPP approaches are a shared nothing environment. The less that is shared, the more work can be parallelized. The essential difference between MPP and other classes of architecture is the decision to implement parallelization at every level, thus the term "massively parallel."
In the specific context of working with very large data, the approach to the first challenge is to do as much of the work in parallel as possible. All MPP architectures share this approach--the difference lies in how successful they are at accomplishing the goal.
The Dataupia Satori Server leverages the most advanced degree of parallelism, pervasive MPP. Pervasive MPP provides each node within the system complete processing and assembly capabilities so that it is self-sufficient. This ensures that incoming queries can always be assured of adequate aggregation services to maximize performance even at large scale.
In this video, Dataupia's Chief Technology Officer, John O'Brien, explains the Three Degrees of Parallelism and outlines the benefits and challenges of each model.
Degrees of MPP
All MPP approaches improve upon the scalability offered by nonparallel systems, especially in terms of amounts of data they can handle. Where they differ most is in the degree of flexibility they offer in the numbers of queries they can process in parallel and the types of queries (cross node or single node) they can process.
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