Hadoop Map Reduce

Hadoop MapReduce is a programming model and processing framework for distributed data processing in the Apache Hadoop ecosystem. It is a core component of Hadoop and is designed to process and analyze large datasets in a parallel and distributed manner. MapReduce takes its inspiration from functional programming concepts and is particularly well-suited for batch processing tasks. Here’s how Hadoop MapReduce works and some key concepts:

1. Mapper Function (Map):

• The input data is divided into smaller chunks called input splits.
• The Mapper function is applied to each input split individually.
• The Mapper processes each record in the input split and generates intermediate key-value pairs.

2. Shuffle and Sort:

• After the Mapper phase, all intermediate key-value pairs are sorted by their keys. This sorting process is crucial for grouping related data together for the Reduce phase.
• Data with the same key is grouped together in preparation for the Reducer phase.

3. Reducer Function (Reduce):

• The Reducer function is responsible for processing the grouped and sorted key-value pairs.
• Each Reducer receives a subset of the data for a specific key and processes it to produce the final output.
• Reducers run in parallel, and the final output consists of key-value pairs.

4. Input and Output Formats:

• Hadoop MapReduce supports various input and output formats, including text, sequence files, and custom formats.
• Users can specify input and output formats depending on the nature of the data being processed.

5. Distributed Execution:

• Hadoop automatically divides the input data into input splits and assigns them to available nodes in the cluster.
• Each node processes its assigned input split independently, which allows for massive parallelism.

6. Fault Tolerance:

• Hadoop MapReduce provides fault tolerance by re-executing tasks that fail during processing.
• If a Mapper or Reducer task fails, the framework reschedules it on another node.

7. Combiner Function (Optional):

• A Combiner function can be used to perform a local reduction on the output of the Mapper before data is shuffled to the Reducers. This helps reduce the volume of data transferred during the Shuffle and Sort phase.

8. Partitioner:

• The Partitioner determines how the intermediate key-value pairs are distributed among the Reducers. It ensures that all key-value pairs with the same key end up at the same Reducer.

9. Counters:

• Hadoop MapReduce allows the use of counters to keep track of various statistics during job execution, such as the number of records processed or custom metrics.

10. Job Configuration:

• Users can configure MapReduce jobs using job configuration files, setting parameters like input paths, output paths, and various job-specific settings.

11. Task Scheduler:

• The framework’s task scheduler ensures that tasks are executed efficiently across available resources, taking into account factors like data locality.