Hadoop Distributed File System (HDF) is a distributed file system designed to run on commodity hardware. It provides high-throughput access to application data and is suitable for applications that have large data sets. HDF is highly fault-tolerant and is designed to be deployed on low-cost hardware.
One of the key features of HDF is its ability to store very large files across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence does not require RAID storage on hosts. The default replication factor is 3, meaning each block of data is stored on three different nodes.
HDF follows a master/slave architecture with a single NameNode (master) managing the file system namespace and regulating access to files by clients. The DataNodes (slaves) manage storage attached to the nodes that they run on. HDF exposes a file system namespace and allows user data to be stored in files.
With its scalable architecture, HDF can support clusters with thousands of nodes and petabytes of data. It's particularly well-suited for batch processing systems like MapReduce, which require high throughput rather than low latency. The system is designed to handle hardware failures gracefully, providing a robust platform for big data applications.
