Massively Parallel Processing (MPP)


What is Massively Parallel Processing (MPP)

Massively Parallel Processing (MPP) is a computing architecture designed to manage large datasets and perform tasks simultaneously. It uses multiple processing units, or nodes. Each node in an MPP database works independently, with its own operating system and dedicated memory. This approach allows MPP databases to handle significant amounts of data and provide faster analytics based on these large datasets.
MPP databases are used in various applications and industries, including business intelligence. In this context, MPP databases enable more people within an organization to run their own data analyses and queries simultaneously without experiencing lag or longer response times. This flexibility improves decision-making processes.
Additionally, the scalability of MPP databases makes them useful for centralizing data in a single location. Instead of breaking up large datasets, MPP allows them to be stored in one place and accessed from different points. This centralization is valuable for organizations with substantial amounts of data from various sources, such as marketing, web, operational, logistics, and HR data. It makes it easier to uncover insights and build comprehensive dashboards.


How MPP Databases Work

MPP databases distribute the processing of data across multiple nodes, each with its own operating system and memory. MPP can be configured with shared-nothing or shared-disk architectures. Shared-nothing offers high scalability by partitioning data across independent nodes without shared resources, while shared-disk allows for efficient data sharing through common disk storage as well as providing elasticity.

In an MPP system, a leader node is responsible for coordinating and managing the tasks assigned to the compute nodes. The leader node receives queries and breaks them down into smaller tasks, which are then assigned to the compute nodes for processing. Each compute node works on its assigned task, processing a portion of the data, and returns the results to the leader node and the leader nodes return the result to users. This highly distributed architecture ensures that there is no single point of bottleneck in the system.


The Advantages of MPP Databases

The main advantage of MPP databases is their ability to scale horizontally, allowing organizations to add more compute nodes as needed to handle growing data volumes and processing demands. This is in contrast to traditional databases, which often require upgrading to more powerful servers (scaling vertically) to handle increased workloads.
MPP databases provide an efficient solution for processing and analyzing large amounts of data. Their ability to scale horizontally and distribute workloads across multiple nodes results in faster data analysis and insights.

StarRocks MPP Architecture

StarRocks has a simple MPP architecture that only has two kinds of processes, Front End and Back End.
StarRocks MPP Architecture
In this figure, the frontend (FE) nodes are responsible for metadata management while the backend (BE) nodes are responsible for data storage and local data computing. As the FE makes query plans and schedules tasks across all of the nodes, the BE nodes can continue to run data import jobs and schema changes.
In the preceding figure, StarRocks logically splits one query into multiple query fragments. Each query fragment can have one or more fragment instances, and each instance will be scheduled to one BE. One query fragment can contain one or more operators. In the preceding figure, the query fragment has three operators: Scan, Filter, and Agg. Different fragments can be executed with different parallelism.
 As shown in the figure above, multiple query fragments are executed in parallel in different pipelines in the memory, rather than stage-by-stage execution like a batch processing engine. The Shuffle operation (data redistribution) plays a key role in improving query performance. It is also important for achieving high-cardinality aggregation and large table joins.


In conclusion, Massively Parallel Processing (MPP) marks a significant advancement in data processing, catalyzing a shift towards more streamlined and efficient methods of handling substantial data volumes. It essentially functions as a powerhouse, capable of dissecting enormous datasets swiftly and adeptly through the concurrent efforts of multiple nodes. The StarRocks MPP system exemplifies this innovative approach, fragmenting complex queries into smaller segments, thus facilitating a rapid analysis procedure. This development heralds a new era of data management, wherein businesses can navigate the complexities of big data with an enhanced strategic perspective and analytical depth. As a plethora of industries transition to a data-centric operational model, MPP emerges as a pioneering tool, poised to elevate organizations to unprecedented levels of analytical precision and strategic foresight.