In Hadoop MapReduce, a Reducer is an essential component responsible for processing intermediate key-value pairs generated by the Mapper and generating the final output. The Reducer goes through several states during its execution. These states help manage the flow of the Reducer and keep track of its progress.
The different states of a Reducer are as follows:
1. **Initialization** - In this state, instances of the Reducer are initialized during the setup phase. Environment configuration, Input/Output formats, and user-defined properties are read and initialized at this stage.
2. **Shuffle** - This state kicks in after the initialization, and it is responsible for fetching the intermediate output data (key-value pairs) produced by the Mappers. During this phase, the Reducer fetches the Mapper output data from the distributed file system (e.g., HDFS) or local filesystem and stores it. The process involves sorting, merging, and transferring data over the network.
3. **Sort** - After shuffling the data, the Reducer starts to sort the intermediate key-value pairs by keys. The goal is to group similar keys together for further processing, as is required in the reduce phase. This step is important, as the Reduce function needs all values corresponding to a unique key to generate the final output. Some sorting might have happened during the Shuffle phase, but this stage ensures complete sorting.
4. **Reduce** - In this state, the Reducer applies a user-defined Reduce function to process the collected and sorted key-value pairs. For each unique key, the Reduce function is called with the key and its corresponding list of values. The function processes this data and generates the output, which is written to the final output file on the distributed filesystem.
5. **Clean-up** - The clean-up state is the final stage of the Reducer’s execution, where it performs clean-up tasks, such as closing opened resources like file descriptors, network connections, and cleaning up temporary data. After this stage, the entire MapReduce job is considered complete.
To summarize, here is the flow of the Reducer’s states:
1. Initialization
2. Shuffle
3. Sort
4. Reduce
5. Clean-up