When working with data at scale, one of the most important concepts to understand is the distinction between SMP (Symmetric Multiprocessing) and MPP (Massively Parallel Processing). These two architectures form the backbone of how modern data systems are designed and perform—and they're especially important when evaluating platforms like Azure Synapse Analytics.
What is SMP?
SMP systems are based on a single system with multiple processors that share memory and I/O. Think of this like having several chefs working in the same kitchen. They can collaborate and access the same ingredients, but eventually, they'll start bumping elbows.
In an SMP environment:
- All processors share access to the same memory and disk.
- A single operating system manages all resources.
- Performance gains are limited as more processors are added due to resource contention.
This architecture is common in traditional database systems like Microsoft SQL Server, Oracle Database, MySQL, or PostgreSQL running on a single server.
What is MPP?
MPP systems, on the other hand, are built for scale. Each processor (or node) has its own memory, storage, and even operating system. They communicate via a high-speed network to solve problems in parallel.
Think of this as a team of chefs, each in their own fully stocked kitchen, working on separate parts of the same recipe. No elbow bumping—just parallel efficiency.
In an MPP environment:
- Data is distributed across nodes.
- Each node processes its portion of the data independently.
- Results are aggregated and returned to the user.
MPP is ideal for analytics, large joins, aggregations, and workloads involving massive datasets. Azure Synapse leverages this architecture in its Dedicated SQL Pool to deliver performance at scale.
Why This Matters
Understanding the difference between SMP and MPP helps you:
- Choose the right platform for your workload
- Design your architecture for scalability
- Avoid bottlenecks caused by resource contention
As we move deeper into the world of cloud data warehousing, this architectural choice becomes even more critical. In the next post, we’ll explore why Azure Synapse is uniquely suited for modern data workloads—and how its architecture builds on the strengths of MPP to offer flexible, scalable analytics in the cloud.
Stay tuned for Part 2: Why Choose Azure Synapse for Cloud Data Warehousing