Hadoop reducer works by taking the output of the mapper stage and combining or reducing it based on a key. The reducer receives data in the form of key-value pairs, where the key represents a unique identifier and the value represents the data associated with that key. The reducer then groups together all the values associated with the same key and performs any necessary aggregation or computation on them. Finally, the reducer outputs the results in the form of key-value pairs, which can be further processed by other reducers or stored as the final output of the MapReduce job. Overall, the reducer stage plays a crucial role in processing and summarizing the data generated by the mapper stage in a Hadoop job.
What is the difference between Mapper and Reducer in Hadoop?
Mapper and Reducer are two key components of MapReduce programming paradigm in Hadoop.
Mapper: Mapper is the initial phase of the MapReduce job. It processes input data and converts it into key-value pairs. Each Mapper processes a small portion of the input data and produces intermediate key-value pairs as output. These intermediate key-value pairs are then shuffled and sorted before being passed on to the Reducer.
Reducer: Reducer is the final phase of the MapReduce job. It receives the intermediate key-value pairs produced by the Mapper, sorts them, and performs aggregation operations on them. The output of the Reducer is the final result of the MapReduce job.
In summary, Mapper is responsible for processing input data and producing intermediate key-value pairs, while Reducer is responsible for aggregating and processing the intermediate key-value pairs to produce the final output.
What is the function of combiner in Hadoop Reducer?
In Hadoop Reducer, the combiner function is an optional optimization step that is used to reduce the amount of data transferred between the map and reduce tasks. The combiner function takes the output from the map tasks as input and performs a local aggregation operation before sending the intermediate results to the reducer. This helps to reduce the amount of data that needs to be shuffled and sorted over the network, resulting in improved performance and reduced workload on the reducer task. The combiner function is used to efficiently combine and compress the intermediate key-value pairs emitted by the mapper tasks before they are sent to the reducer.
What is the final output produced by Reducer in Hadoop?
The final output produced by the Reducer in Hadoop is a set of key-value pairs that have been aggregated and processed based on the input data that was passed to it by the Mapper. This output can be further used for analysis, visualization, or any other downstream processing tasks.
