Rack awareness: to take a node’s physical location into account while scheduling tasks and allocating storage. The following is a subset of useful features in HDFS: New features and improvements are regularly implemented in HDFS. The NameNode and Datanodes have built in web servers that makes it easy to check current status of the cluster. Hadoop supports shell-like commands to interact with HDFS directly. Hadoop is written in Java and is supported on all major platforms. Most of the time, configuration needs to be tuned only for very large clusters. HDFS is highly configurable with a default configuration well suited for many installations. MapReduce, well known for its simplicity and applicability for large set of distributed applications, is an integral part of Hadoop. It is fault tolerant, scalable, and extremely simple to expand. Hadoop, including HDFS, is well suited for distributed storage and distributed processing using commodity hardware. The following are some of the salient features that could be of interest to many users. Clients contact NameNode for file metadata or file modifications and perform actual file I/O directly with the DataNodes. The HDFS architecture diagram depicts basic interactions among NameNode, the DataNodes, and the clients. This user guide primarily deals with the interaction of users and administrators with HDFS clusters. The HDFS Architecture Guide describes HDFS in detail.
A HDFS cluster primarily consists of a NameNode that manages the file system metadata and DataNodes that store the actual data.
HDFS is the primary distributed storage used by Hadoop applications.
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