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The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. HDFS is Hadoop’s flagship filesystem. HDFS is a filesystem designed for storing very large files with streaming data access patterns, running on clusters of commodity hardware.
Hadoop doesn’t require expensive, highly reliable hardware. It’s designed to run on clusters of commodity hardware (commonly available hardware that can be obtained from multiple vendors) for which the chance of node failure across the cluster is high, at least for large clusters. HDFS is designed to carry on working without a noticeable interruption to the user in the face of such failure.
HDFS has many goals. Here are some of the most notable:
HDFS has a master/slave architecture. HDFS stores filesystem metadata and application data separately. HDFS stores metadata on a dedicated server, called the NameNode. Application data are stored on other servers called DataNodes. NameNode is a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files.
Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode.
All servers are fully connected and communicate with each other using TCP-based protocols. Unlike other filsystem, the DataNodes in HDFS do not rely on data protection mechanisms such as RAID to make the data durable. Instead, the file content is replicated on multiple DataNodes for reliability. While ensuring data durability, this strategy has the added advantage that data transfer bandwidth is multiplied, and there are more opportunities for locating computation near the needed data.