The primary purpose of the map phase in the MapReduce framework is to process the input data by applying a user-defined map function to each piece of data, typically line by line. This function transforms the input data into intermediate key-value pairs. The map phase essentially filters, sorts, and organizes the data into manageable chunks that can be processed in parallel across the nodes of a cluster. These intermediate key-value pairs are then passed on to the next phases (shuffle and reduce) for further aggregation and summarization
. In summary:
- The input data is split into smaller chunks.
- Each chunk is processed independently by the map function.
- The map function outputs key-value pairs that represent the processed data.
- These outputs are temporarily stored and later shuffled and sorted for the reduce phase
This parallel processing in the map phase enables efficient handling of large datasets by distributing the workload across many nodes, reducing processing time and improving scalability
