Hadoop Map

Hadoop and MapReduce, “Map” refers to the first phase of a two-step data processing paradigm known as the MapReduce framework. MapReduce is a programming model and processing framework designed for distributed data processing on a Hadoop cluster. Here’s an overview of the Map phase in MapReduce:

Map Phase:

1. Input: The Map phase takes a set of input data, typically in the form of key-value pairs. This input data can be stored in the Hadoop Distributed File System (HDFS) or another storage system.

2. Map Function: During the Map phase, a user-defined Map function is applied to each input record. The Map function processes the input data and produces intermediate key-value pairs as output. The Map function is written by the developer to perform a specific data transformation or filtering task.

3. Key-Value Pairs: The Map function emits one or more key-value pairs for each input record. These intermediate key-value pairs are generated based on the logic defined in the Map function. The keys are used for grouping and sorting in the subsequent Shuffle and Reduce phases.

4. Parallel Execution: Multiple Map tasks run in parallel across the nodes of the Hadoop cluster, processing different portions of the input data. This parallelization enables Hadoop to efficiently process large datasets in a distributed fashion.

5. Output: The output of the Map phase consists of a large set of intermediate key-value pairs. These pairs are not the final result but are essential for the subsequent Reduce phase.