What is Massively Parallel Processing (MPP)?
The amount of data created, collected, stored, and consumed worldwide is staggering, reaching a record of more than 64 zettabytes in 2020. By the end of 2021, analysts are forecasting that number to grow by 12.5% to reach 72 zettabytes.
In today’s data-intensive world, companies collect and store massive amounts of data. This requires an ever-growing storage capacity and computing power to process big data.
That’s where massively parallel processing (MPP) comes into play.
Take the example of a large chain of hospitals with locations across the country that manages healthcare data for hundreds of thousands of patients. As part of the treatment, they collect hundreds of data points on patients, creating huge datasets.
In its database, the hospital group may have 10 million rows. Sorting through that data to find the information you need can take a significant amount of time when one server has to do it alone. Using an MPP system with 1,000 nodes, the work is distributed so that each node only has to handle 1/100th of the computing load.
Slow speeds and lengthy searches can frustrate even the most diligent employees. It’s one of the reasons a Forrester study shows that between 60% and 73% of data collected by an enterprise goes unused in analytics. Data silos, a lack of a centralized data warehouse, and slow query speeds make data difficult to use.
What Is Massively Parallel Processing?
Massively parallel processing (MPP) is a collaborative processing of the same program using two or more processors. By using different processors, speed can be dramatically increased.
Since the computers running the processing nodes are independent and do not share memory, each processor handles a different part of the data program and has its own operating system. Companies use a messaging interface so that the different MPP processors can arrange thread handling for faster analytics for business intelligence in large volumes of data.
MPP databases are shared among processors. The MPP architecture allows relevant information to be communicated between processors. Large datasets in data warehouses connect independent processing nodes.
A Quick History of Big Data
Companies such as Teradata developed the database technology that was dubbed massively parallel processing. MPP architecture was a significant technology leap forward when it came to handling large datasets.
Before Teradata, computers took a long time to process big data. Limited to a single operating system and memory stores, an organization needed high-performing and expensive CPUs for research and other work. MPP solves this problem for organizations by increasing speed, especially for big data.
MPP databases solve the speed problem by allocating the required processing power across multiple nodes for efficiency.
What Is an MPP Database?
An MPP database is a data warehouse or type of database where processing is split among servers or nodes.
A leader node handles communication with each of the individual nodes. The computer nodes carry out the process requested by dividing up the work into units and more manageable tasks. An MPP process can scale horizontally by adding additional computing nodes rather than having to scale vertically by adding more servers.
The more processors attached to the data warehouse and MPP databases, the faster the data can be sifted and sorted to respond to queries. This eliminates the long time required for complex searches on large datasets.
Data warehouse appliances, used for big data analysis and deep insight, typically combine MPP architecture into the database to provide high performance and easier platform scalability.
What Can I Use MPP Databases For?
An organization collects tremendous amounts of information. Storing data on a single server with enough computing power to handle processing on a single operating system is cost-prohibitive and often unwieldy.
While there are different approaches to solve this problem, companies integrate MPP as part of their storage structure. These parallel systems use independent nodes with their own operating systems to create a more efficient model.
For example, more people in an organization can run queries in a data warehouse at the same time without lengthy response times.
MMP databases are also especially helpful for centralizing massive amounts of data in a single location, such as a data warehouse. Central storage allows users at different locations to access the same set of data. Everybody works off a single source of truth rather than data silos, ensuring everyone has the most recent data available. There is no worry about whether you have the updated version or access to different data than others.
This helps create better alignment between departments. For example, when sales and marketing both use the same set of data, marketing can create better synergy to support sales efforts. The finance department will be better able to forecast and plan when they see the same pending sales data that sales teams are seeing. HR, logistics, operations, and web departments all benefit from a central repository and fast processing.
MPP databases are more prevalent than you might think. You can thank MPP for all sorts of things we now take for granted. For example, when you click the Weather Channel app on your smartphone to check the weather report, parallel processing allows computer models to analyze vast data points and weather patterns and compare them to historical norms to predict future forecasts.
MPP Database vs. SMP Database
Symmetric Multiprocessor (SMP) systems share software, I/O resources, and memory. SMPs typically use one CPU to handle database queries but can have hundreds of CPUs. Most commonly, however, they are configured with two, four, eight, or 16 machines.
SMP databases can run on more than one server, but they will share resources in a cluster configuration. The database assigns a task to an individual CPU, no matter how many CPUs are connected to the system.
In comparison to MPP databases, SMP databases usually have lower administrative costs. The tradeoff is often speed.
An MPP database sends the search request to each of the individual processors in the MPP. When two MPP databases use an interconnect, search times can be nearly half the time of an SMP database search. It is the most efficient way to handle large amounts of data.
You will typically see an SMP database used for email servers, small websites, or applications that don’t require significant computing power, such as recording timecards or running payroll. MPP databases are most commonly used for data warehousing of large datasets, big data processing, and data mining applications.
MPP is best suited for structured data found in structured datasets rather than unstructured data commonly found in data lakes.
Types of MPP Database Architecture
There are two common ways IT teams set up database architecture.
- Grid computing
- Computer clustering
1. Grid Computing
With grid computing, multiple computers are used across a distributed network. Resources are used as they are available. While this reduces hardware costs, such as server space, it can also limit capacity when bandwidth is consumed for other tasks or too many simultaneous requests are being processed.
2. Computer Clustering
Computer clustering links the nodes, which can communicate with each other to handle multiple tasks simultaneously. The more nodes that are attached to the MPP database, the faster queries will be handled.
Within the MPP architecture, there are several hardware components.
Processing Nodes
Processing nodes can be considered the building blocks for MPP. Nodes are simple homogenous processing cores with one or more processing units. A node might be a server, a desktop PC, or a virtual server.
High-Speed Interconnect
MPP breaks down queries into chunks, which are distributed to nodes. Each node works independently on its part of the parallel processing system tasks. It takes centralized communication and a high bandwidth connection between nodes. This high-speed interconnect is typically handled by ethernet or a distributed fiber data interface.
Distributed Lock Manager
A distributed lock manager (DLM) coordinates resource sharing when external memory or disk space is shared among nodes. The DLM handles resource requests from nodes and connects them when resources are available. DLM also helps with data consistency and recovery of node failures.
The Benefits of MPP Architecture
Besides the speed of processing queries, there are other advantages of deploying an MPP architecture.
- Scalability: You can scale out in a nearly unlimited way. MPP databases can add additional nodes to the architecture to store and process larger data volumes.
- Cost-efficiency: You don’t necessarily have to buy the fastest or most expensive hardware to accommodate tasks. When you add more nodes, you distribute the workload, which can then be handled with less expensive hardware.
- Eliminating the single point of failure: If a node fails for some reason, other nodes are still active and can pick up the slack until the failed node can be returned to the mix.
- Elasticity: Nodes can be added without having to render the entire cluster unavailable.
Unlock the Power of Your Data
Massively parallel processing can unlock the power of your data and create deeper analysis and insight into big data.
If you would like to learn more about multi-cloud data services solutions that may work in conjunction with your MPP database, talk to the experts at Faction. Faction’s platform enables you to connect your data warehouse or data lake to all hyperscale cloud providers at the same time, allowing you to consolidate cloud and on-premises data into the platform. Once consolidated, you can use apps hosted in the cloud or on-premises to analyze your data from a single data source.
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