HDFS: Hadoop Distributed File System

Hadoop Distributed File System (HDFS) is a distributed file system designed for storing large amounts of data on commodity hardware. It is the primary storage system used in the Hadoop ecosystem, enabling the processing and analysis of big data. HDFS is highly fault-tolerant and optimized for reading and writing large files.
HDFS Architecture
The HDFS architecture consists of two main components: NameNode and DataNodes. The NameNode is the master node that manages the file system namespace and controls client access. It maintains the metadata of the file system, including file permissions, file-to-block mappings, and location information of the blocks. The DataNodes are the slave nodes that store the actual data blocks and serve read/write requests from clients.
In HDFS, files are divided into blocks that are typically 128 MB in size. These blocks are then stored on multiple DataNodes in the cluster. The NameNode dynamically assigns blocks to DataNodes based on their storage capacity and network proximity to ensure data locality and efficient data retrieval.
The NameNode also handles file system namespace operations such as creating directories, uploading files, and deleting files. It ensures that these operations are atomic and consistent across the cluster.
HDFS Operations
HDFS operations are designed to handle failures gracefully. When a block replica fails on a DataNode, the NameNode automatically detects this failure and triggers the replication of the block to another DataNode. This process is transparent to the client and does not affect ongoing read/write operations.
When a client wants to read a file, it first contacts the NameNode to get the location of the block(s) containing the desired data. The NameNode provides a list of DataNode addresses that store the required blocks. The client then directly fetches the blocks from the closest DataNodes based on network proximity. This process is called block localization, which improves data retrieval performance.
Similarly, when a client wants to write a file, it contacts the NameNode to obtain a handle to a new file in the file system. The NameNode assigns a new block to a suitable DataNode and returns the address of the DataNode to the client. The client then sends the file data to the assigned DataNode, which stores the block and replicates it to other DataNodes for fault tolerance.
HDFS Performance Optimization
To optimize performance, HDFS provides several features such as data locality, load balancing, and compression. Data locality ensures that the client reads and writes blocks from/to DataNodes close to its physical location, reducing network latency. Load balancing distributes data evenly across DataNodes to prevent hotspots and maximize resource utilization. Compression can be applied to reduce storage space and network bandwidth usage when transferring data between nodes.
In addition, HDFS supports snapshots, which create point-in-time copies of directories or files. Snapshots provide an efficient way to backup data without impacting ongoing read/write operations. They can be used for recovery purposes or as a mechanism for version control in collaborative workflows.
Conclusion
HDFS is a distributed file system designed for storing and processing large datasets on commodity hardware. It provides fault tolerance, scalability, and performance optimization through its architecture and features like data locality, load balancing, compression, snapshots, and block replication. HDFS is widely used in big data analytics frameworks like Hadoop for storing and processing large amounts of data efficiently.