Understanding HDFS
Hadoop Distributed File System (HDFS) is a distributed file system designed to store and process large datasets across multiple machines. It is a core component of the Apache Hadoop ecosystem and is known for its reliability, scalability, and fault-tolerance.
HDFS follows a master-slave architecture, where the master node is called the NameNode, and the slave nodes are called DataNodes. The NameNode manages the file system metadata, while the DataNodes store the actual data blocks.
The key features of HDFS include:
Data Replication
HDFS replicates data blocks across multiple DataNodes, typically three by default, to ensure data reliability and availability. This redundancy also enables efficient data processing, as tasks can be scheduled closer to the data.
Scalability
HDFS can scale to handle petabytes of data and thousands of client machines by adding more DataNodes to the cluster. The NameNode manages the file system metadata, allowing it to handle a large number of files and directories.
Fault Tolerance
HDFS is designed to be fault-tolerant, with the NameNode and DataNodes continuously monitoring each other. If a DataNode fails, the NameNode automatically redirects clients to the replicated data blocks on other DataNodes.
Command-line Interface
HDFS provides a command-line interface (CLI) that allows users to interact with the file system, perform operations such as creating, deleting, and copying files and directories, and monitor the cluster's status.
graph TD
NameNode -- Manages Metadata --> DataNodes[DataNodes]
DataNodes -- Store Data Blocks --> Clients
By understanding the core concepts and features of HDFS, you can effectively leverage it for your big data processing and storage needs.