What storage architecture does the MPP architecture use? What are its core design principles?

The MPP (Massively Parallel Processing) architecture typically employs a distributed storage architecture. In this setup, data is divided into smaller chunks and stored across multiple nodes in a cluster. Each node operates independently and processes its own portion of the data, enabling parallel processing and high scalability.

Core design principles of MPP architecture include:

1. Data Distribution: Data is distributed across multiple nodes to ensure that each node has a subset of the total data. This allows for parallel processing and reduces the load on individual nodes.

   • Example: In a database system using MPP architecture, a large table might be split into smaller segments, with each segment stored on a different node.

2. Parallel Processing: Each node in the cluster can process its portion of the data simultaneously, significantly improving query performance.

   • Example: When executing a complex query, different nodes can work on different parts of the query simultaneously, with the results combined at the end.

3. Decentralization: There is no single point of control or failure. Each node operates independently, and the system can continue functioning even if some nodes fail.

   • Example: If one node in an MPP cluster goes down, the remaining nodes can still process queries using their local data copies.

4. Load Balancing: The system ensures that the workload is evenly distributed across all nodes to maximize efficiency and minimize response times.

   • Example: When new data is added or queries are executed, the system dynamically adjusts the distribution of work to maintain balanced loads on all nodes.

In the context of cloud computing, services like Tencent Cloud's Cloud Database for MySQL (CDB for MySQL) offer MPP-like capabilities through features such as sharding and parallel query processing, enabling high-performance and scalable database operations.