MapReduce
MapReduce is the computational model that Hadoop uses to process large datasets in parallel. It divides a job into smaller tasks that can be executed concurrently across multiple nodes. The model consists of two main phases:
- Map Phase
- Reduce Phase
Map Phase
In the Map phase, input data is split into chunks and processed independently by Mapper tasks. Each Mapper reads a block of data, processes it, and produces intermediate key-value pairs.
Key Characteristics:
- Data Splitting: Input data is split into smaller chunks.
- Independent Processing: Each chunk is processed independently by a Mapper task.
- Key-Value Pairs: Mappers output intermediate key-value pairs.
Reduce Phase
In the Reduce phase, the intermediate key-value pairs produced by the Mappers are shuffled and sorted, then processed by Reducer tasks to produce the final output. Each Reducer processes all values associated with a particular key.
Key Characteristics:
- Shuffling and Sorting: Intermediate data is shuffled and sorted based on keys.
- Aggregating Results: Reducers aggregate and process values for each key.
- Final Output: Reducers produce the final output, which is written to HDFS.
How Does Hadoop Handle Parallel Processing of Large Datasets Across a Distributed Cluster?
Apache Hadoop is a powerful framework that enables the distributed processing of large datasets across clusters of computers. At its core, Hadoop’s ability to handle parallel processing efficiently is what makes it indispensable for big data applications. This article explores how Hadoop achieves parallel processing of large datasets across a distributed cluster, focusing on its architecture, key components, and mechanisms.
Hadoop processes large datasets across distributed clusters using HDFS to distribute data and MapReduce for parallel processing. It optimizes tasks with data locality, manages resources via YARN, and ensures scalability and fault tolerance through automatic task redistribution among nodes, maximizing efficiency and reliability in data handling.
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