Hadoop HDFS

Hadoop HDFS (Hadoop Distributed File System) is the primary storage system in the Hadoop ecosystem. It is designed to store and manage large volumes of data across a distributed cluster of commodity hardware. HDFS is a key component of Hadoop and plays a crucial role in supporting big data processing and analysis. Here are some key characteristics and features of Hadoop HDFS:

1. Distributed Storage: HDFS stores data across multiple machines (nodes) in a Hadoop cluster. This distributed storage architecture provides fault tolerance and high availability.

2. Data Replication: HDFS replicates data blocks to multiple nodes in the cluster to ensure data durability and fault tolerance. By default, each data block is replicated three times, but the replication factor can be configured.

3. Scalability: HDFS can scale horizontally by adding more machines to the cluster. This scalability allows HDFS to handle extremely large datasets.

4. Block-Based Storage: Data in HDFS is divided into fixed-size blocks (typically 128 MB or 256 MB). Each block is independently stored and replicated across the cluster. This block-based storage simplifies data management and enables parallel processing.

5. Write-Once, Read-Many Model: HDFS follows a write-once, read-many model, meaning that once data is written to HDFS, it is not typically updated. Instead, new versions of data are written as new files.

6. High Throughput: HDFS is optimized for high throughput rather than low-latency access. It is well-suited for batch processing workloads, such as MapReduce jobs.

7. Data Integrity: Data integrity is maintained through checksums for each block. HDFS detects and handles data corruption automatically.

8. NameNode and DataNode: HDFS has two types of nodes:

   • NameNode: It manages the metadata and namespace of the file system. It keeps track of file and directory structures, as well as the locations of data blocks.
   • DataNode: These nodes store the actual data blocks and report their status to the NameNode.

9. Web-Based Interfaces: HDFS provides web-based user interfaces, including the HDFS NameNode UI and HDFS DataNode UI, for monitoring and managing the file system.

10. Integration with Hadoop Ecosystem: HDFS is tightly integrated with other Hadoop ecosystem components, such as MapReduce, Hive, HBase, and Spark, allowing seamless data processing and analytics.

11. Data Compression: HDFS supports data compression, which helps reduce storage space and improve data transfer efficiency.

12. Security: HDFS offers security features such as access control lists (ACLs), encryption at rest, and integration with authentication mechanisms like Kerberos.