How does the MPP architecture achieve horizontal expansion? What are its scalability limitations?

MPP (Massively Parallel Processing) architecture achieves horizontal expansion by distributing data and processing tasks across multiple nodes in a cluster. Each node in the MPP system operates independently and can perform both storage and computational functions. This parallel processing allows for faster query response times and the ability to handle large data volumes.

How MPP Achieves Horizontal Expansion:

1. Data Distribution: Data is partitioned and distributed across multiple nodes. Each node contains a portion of the overall dataset.
2. Parallel Processing: Queries are broken down into smaller tasks that are executed simultaneously across the nodes.
3. Communication: Nodes communicate with each other to coordinate tasks and aggregate results.

Example: In an MPP database, a query to find all customers in a specific region might be split into sub-queries that run on different nodes, each handling a subset of the data. The results are then combined to form the final output.

Scalability Limitations:

1. Network Latency: As the number of nodes increases, the communication overhead between nodes can become a bottleneck, leading to increased latency.
2. Data Skew: Uneven distribution of data can lead to some nodes being overloaded while others are underutilized, reducing overall performance.
3. Complexity in Management: Managing a large number of nodes can be complex, requiring sophisticated tools and expertise.
4. Cost: Adding more nodes to an MPP system can be expensive, both in terms of hardware and maintenance costs.

For organizations looking to leverage MPP architecture for scalable database solutions, Tencent Cloud offers services like Tencent Cloud Database (TDSQL) which provides a distributed, high-performance, and highly available database service suitable for large-scale applications.