MPP (Massively Parallel Processing) architecture achieves cross-node data merging through a distributed computing approach. In an MPP system, data is partitioned and distributed across multiple nodes, and each node operates independently on its subset of the data. To merge data across nodes, MPP systems typically employ a technique known as "shuffle" or "redistribute" phase.
During the shuffle phase, each node processes its local data and produces intermediate results. These intermediate results are then redistributed across the nodes based on a predefined key or criteria. This ensures that all relevant data for a particular key is brought together on the same node.
Once the data has been redistributed, each node can perform further computations on the merged data, if necessary, and produce the final output.
For example, consider a scenario where you have an MPP database with two nodes, Node A and Node B. Node A has data about customers in the United States, while Node B has data about customers in Europe. If you want to find all customers who have made a purchase in the last month, the MPP system would first process the local data on each node to identify the relevant customers. Then, during the shuffle phase, the system would redistribute the data so that all customers who made a purchase in the last month are brought together on one node (either Node A or Node B). Finally, the merged data can be processed to produce the final result.
In the context of cloud computing, MPP architectures are commonly used in distributed databases and data warehousing solutions. Tencent Cloud offers a distributed database service called Tencent Cloud TDSQL-C, which leverages MPP architecture to provide high-performance and scalable data processing capabilities.