The Symmetrical Multi-Processing (SMP) Architecture was once the champion of Data Warehousing. However, it faced several disadvantages:
Component failures did not result in a graceful decline in performance but led to system failure and data loss
Upon recovery, failed components were unable to rejoin the system easily
Failures should not result in data loss
The system must be scalable and support increased load capacity and performance agility
2) MPP Architecture = Share Nothing = Divide and Conquer
MPP systems consist of large numbers of processors, each with its own memory, backplane, and storage. The no-shared resources approach of pure MPP systems allows nearly linear scalability.
High availability is another advantage β when one node fails, another can take over.
In Teradataβs MPP Architecture, processor-RAM-storage disk pairs ("nodes") operate in parallel to divide the workload and execute queries over large sets of data. Each processor communicates with its associated disk drive to get raw data and perform calculations. One SMP Host collects intermediate results and assembles the query response for delivery back to the requesting application.
Paragraph: This article provides a comparative analysis of two popular database architecture models: SMP (Symmetric Multi-Processing) and MPP (Massively Parallel Processing). We will discuss their key features, advantages, and use cases to help you understand which one might be suitable for your specific needs.
SMP Architecture
Definition and Key Features
Paragraph: SMP (Symmetric Multi-Processing) is a computer system architecture that allows multiple processors to share the same physical memory, executing different tasks concurrently. The main advantages of an SMP system are its scalability, simplicity, and cost-effectiveness.
Advantages
Paragraph: SMP systems offer several benefits, including:
- Efficient resource utilization, as the shared memory allows for easy communication between processors
- Improved performance due to concurrent execution of tasks
- Simplified software development, thanks to a uniform addressing space for all processors
Use Cases
Paragraph: SMP systems are commonly used in general-purpose computing, such as servers running web applications or databases. They excel in scenarios where high throughput is required and the workload can be evenly distributed among multiple processors.
MPP Architecture
Definition and Key Features
Paragraph: MPP (Massively Parallel Processing) is a computer architecture that distributes data across many computers or nodes, each with its own memory. These nodes communicate and collaborate to process large datasets in parallel. The main advantage of an MPP system is its ability to handle massive amounts of data quickly.
Advantages
Paragraph: MPP systems offer several benefits, including:
- Scalability for handling extremely large datasets
- High performance for parallel processing tasks
- Fault tolerance due to the distributed nature of the system
Use Cases
Paragraph: MPP systems are commonly used in data warehousing, scientific computing, and big data analytics. They excel in scenarios where large datasets must be processed quickly or where complex computations require extensive parallelization.
Comparing SMP and MPP
Paragraph: While both architectures aim to improve performance, they target different types of workloads and are more suitable for specific use cases. Here's a comparison table to help you understand the differences between the two:
SMP
MPP
Shared Memory
Yes
No (Distributed Memory)
Scalability
Limited (Additional processors require more memory)
High (More nodes can be added to handle larger datasets)
Cost-effectiveness
Generally cost-effective for small to medium workloads
Expensive due to the need for specialized hardware and software
Use Cases
General-purpose computing, web applications, databases
Data warehousing, big data analytics, scientific computing
Conclusion
Paragraph: Understanding the differences between SMP and MPP architectures is crucial for making informed decisions when choosing a database system. While both offer significant benefits, they are optimized for different workloads and use cases. Make sure to consider your specific needs and requirements before making a decision.