Map-reduce is a framework to process large data sets, splitting them into subsets, processing each subset on a different server and then blending results obtained on each.
MapReduce is a programming model and framework for processing and generating large datasets in parallel across a distributed cluster of computers. It was popularized by Google and later implemented in open-source frameworks like Apache Hadoop.
The basic idea behind MapReduce is to divide a large computational task into smaller sub-tasks, known as mappers, and then perform a reduction operation on the results, known as reducers. Here’s how it works:
- Map Phase: In this phase, the input data is divided into smaller chunks and processed independently in parallel across multiple nodes in the cluster. Each node runs a mapper function that processes its assigned portion of the data and produces intermediate key-value pairs.
- Shuffle and Sort: After the map phase, the intermediate key-value pairs are shuffled and sorted based on their keys. This ensures that all values associated with the same key are grouped together and ready for the reduce phase.
- Reduce Phase: In this phase, the reducer function is applied to each unique key and its associated list of values. The reducer function aggregates, summarizes, or transforms the values to produce the final output.
MapReduce offers several advantages:
- Scalability: MapReduce can scale horizontally across a large number of machines, allowing it to handle massive datasets efficiently.
- Fault-tolerance: It automatically handles failures by re-executing failed tasks on other nodes in the cluster.
- Ease of programming: Developers can focus on writing simple mapper and reducer functions without needing to worry about the complexities of parallelism and distributed computing.
Overall, MapReduce is a powerful paradigm for processing big data in a distributed computing environment, enabling efficient and scalable data analytics and processing.