Explain HDFS

Hadoop Distributed File System (HDFS) is a distributed and scalable file system designed for storing and managing very large files and datasets across a cluster of commodity hardware. It is a core component of the Apache Hadoop ecosystem, which is widely used for big data processing and analytics. Here’s an overview of HDFS and its key features:

Key Characteristics of HDFS:

1. Distributed Storage: HDFS distributes data across multiple servers (nodes) in a cluster. Each file is split into smaller blocks (typically 128 MB or 256 MB in size), and these blocks are stored on different nodes. This distribution allows for parallel processing and high fault tolerance.

2. Replication: HDFS ensures data durability and fault tolerance by replicating each block multiple times across different nodes. The default replication factor is usually set to three, meaning that each block exists in three copies. If a node or block becomes unavailable due to hardware failures, HDFS can still retrieve the data from its replicas.

3. Write Once, Read Many: HDFS is optimized for write-once, read-many workloads. Once data is written to HDFS, it is typically not updated. Instead, new data is appended, and older versions are retained. This immutability simplifies data consistency and ensures data integrity.

4. Data Locality: HDFS aims to maximize data locality. When processing data, Hadoop’s MapReduce and other processing frameworks attempt to execute computations on the same nodes where the data is stored. This minimizes data transfer over the network, improving performance.

5. High Throughput: HDFS is designed for high throughput data access. It is well-suited for batch processing, data warehousing, and analytics workloads that require reading and writing large volumes of data.

Components of HDFS:

1. NameNode: The NameNode is the master server in an HDFS cluster. It manages the file system namespace and stores metadata about files and directories, such as their names, permissions, and block locations. The NameNode does not store the actual data; instead, it keeps track of where data blocks are located on DataNodes.

2. DataNode: DataNodes are worker nodes in the HDFS cluster. They store the actual data blocks and report regular heartbeat signals to the NameNode to confirm their health and data availability. DataNodes replicate and distribute data blocks as instructed by the NameNode.

HDFS Workflow:

1. Write Operations:

   • When a file is written to HDFS, it is broken into blocks, and each block is replicated across multiple DataNodes.
   • The client interacts with the NameNode to determine the DataNodes where the blocks should be stored.
   • Data is written to the chosen DataNodes, and the write is considered successful when a minimum number of replicas have been written.

2. Read Operations:

   • When a client requests a file, it contacts the NameNode to obtain the block locations.
   • The client then reads data directly from the nearest DataNodes, optimizing data locality.