Hadoop Distributed File System (HDFS)
HDFS is a distributed file system that provides high-throughput access to data. It breaks down large datasets into smaller blocks, typically 128MB or 256MB in size, and distributes these blocks across multiple nodes in the cluster. This distribution is crucial for parallel processing, as it allows data to be processed simultaneously across different nodes.
Key Features of HDFS:
- Block Storage: Data is divided into blocks and distributed across DataNodes.
- Replication: Blocks are replicated across multiple nodes to ensure fault tolerance.
- Data Locality: Processing tasks are scheduled on nodes where data blocks reside to minimize network transfer.
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.