What are the main functions of MapReduce?

The primary functions of MapReduce include:

1. Distributed computing: MapReduce breaks down computing tasks into multiple subtasks and assigns them to different computing nodes for parallel processing, thus achieving efficient distributed computing.
2. Data splitting and distribution: MapReduce cuts data into multiple segments based on their characteristics, and distributes these segments to different computing nodes for processing.
3. Data sorting and merging: MapReduce will sort and merge the intermediate results generated during the Map phase to reduce data transmission and disk usage, thus improving computational efficiency.
4. Parallel computing: MapReduce efficiently achieves parallel computing by breaking tasks into multiple subtasks and fully utilizing the parallel processing capabilities of computing nodes.
5. Fault tolerance and recovery: MapReduce has fault tolerance, so when a computing node fails, tasks can be automatically reassigned to other available nodes to ensure smooth continuation of the entire computing process.
6. Task scheduling and management: MapReduce uses a task scheduler to monitor and manage all computing tasks, ensuring that tasks are executed in the correct order and priority, and that computing resources are allocated appropriately.
7. Data aggregation and result output: MapReduce will aggregate the computation results from each computing node, and finally output the final calculation results, typically storing them in a file system or database.