MapReduce Framework Steps Explained

The MapReduce framework processes large datasets in parallel. Understanding the sequence of its core steps is crucial.

Core MapReduce Workflow

The MapReduce process involves several distinct stages executed in a specific order:

• Input Splitting (A): The initial step where the input dataset is divided into smaller, manageable chunks called splits. This allows for parallel processing.
• Mapping (C): Each input split is processed independently by a mapper function. Mappers transform input data into intermediate key-value pairs.
• Shuffling & Sorting (D): Intermediate key-value pairs from all mappers are gathered. They are then sorted by key and grouped, ensuring all pairs with the same key are sent to the same reducer.
• Reducing (B): Reducer functions process the grouped intermediate data. They aggregate or summarize the values for each key to produce the final output.

Therefore, the correct sequence of the MapReduce framework steps is Input Splitting, Mapping, Shuffling & Sorting, and Reducing.

Order of MapReduce Operations

The correct order is represented by the sequence:

A (Input Splitting) → C (Mapping) → D (Shuffling & Sorting) → B (Reducing)

This corresponds to the order A, C, D, B.