MapReduce is a programming model and processing framework used for distributed data processing in large-scale data processing tasks. It was originally developed by Google and later popularized by Apache Hadoop. The MapReduce framework is designed to process and analyze vast amounts of data in a parallel and distributed manner across a cluster of commodity hardware. Here are the key components and concepts of the MapReduce framework:

1. Mapper Function: In the MapReduce model, data processing begins with a Mapper function. The Mapper takes input data and processes it into key-value pairs. Each Mapper runs in parallel across different portions of the input data.

2. Shuffle and Sort: After the Mappers have processed their respective input data, a Shuffle and Sort phase occurs. During this phase, the framework groups and sorts the key-value pairs generated by the Mappers based on their keys. This process ensures that all values associated with the same key are brought together for the subsequent Reduce phase.

3. Reducer Function: The Reduce phase is where the actual data aggregation and computation take place. The Reducer function receives key-value pairs from the Shuffle and Sort phase, processes them, and produces a set of output key-value pairs. Reducers run in parallel as well.

4. Input and Output Formats: MapReduce supports various input and output formats for reading and writing data, such as text, sequence files, and custom formats.

5. Data Distribution: Data is distributed across the cluster, and computation is performed locally on each node where the data resides. This minimizes data movement across the network, which can be a significant bottleneck in distributed computing.

6. Fault Tolerance: MapReduce provides fault tolerance by re-executing tasks that fail during processing. If a Mapper or Reducer fails, the framework reschedules the task on another node.

7. Distributed Execution: The MapReduce framework automatically divides the input data into splits, assigns tasks to available nodes, and manages the overall execution of the job across the cluster.

8. Combiner Function: A Combiner function (optional) can be used to perform a local reduction on the output of the Mapper before data is shuffled to the Reducers. It helps reduce the volume of data transferred during the Shuffle and Sort phase.

9. Partitioner: The Partitioner determines how the key-value pairs are distributed among the Reducers. It ensures that all key-value pairs with the same key end up at the same Reducer.

10. Counters: MapReduce allows the use of counters to keep track of various statistics during job execution, such as the number of records processed or custom metrics.

11. Job Configuration: Users can configure MapReduce jobs using job configuration files, setting parameters like input paths, output paths, and various job-specific settings.

12. Task Scheduler: The framework’s task scheduler ensures that tasks are executed efficiently across available resources, taking into account factors like data locality.