HDFS

Hadoop Distributed File System (HDFS) is the primary storage system used by Hadoop, an open-source framework for distributed storage and processing of large datasets. HDFS is designed to store and manage massive volumes of data across a distributed cluster of commodity hardware, making it a fundamental component of the Hadoop ecosystem. Here are key characteristics and features of HDFS:

1. Distributed Storage:

   • HDFS distributes data across multiple nodes in a Hadoop cluster. Each file is divided into blocks (typically 128 MB or 256 MB in size), and these blocks are replicated to provide fault tolerance.

2. Scalability:

   • HDFS is highly scalable, allowing organizations to scale their storage infrastructure by adding more nodes to the cluster as data volume grows. This horizontal scalability makes it suitable for handling massive datasets.

3. Fault Tolerance:

   • HDFS is designed with fault tolerance in mind. Data blocks are replicated across multiple nodes (usually three copies by default) to ensure data durability. If a node or data block becomes unavailable, HDFS can still retrieve the data from one of the replicas.

4. Write-Once, Read-Many Model:

   • HDFS follows a write-once, read-many model, which means that data is written once and is not modified in place. This model is well-suited for batch processing and data warehousing.

5. High Throughput:

   • HDFS is optimized for high-throughput data access. It is particularly efficient for applications that require sequential data access, such as batch processing.

6. Data Integrity:

   • HDFS ensures data integrity through checksums. Each data block is associated with a checksum, and the system verifies checksums during data reads to detect and correct data corruption.

7. Data Replication:

   • Data replication is a key feature of HDFS. Replicating data blocks across multiple nodes provides both fault tolerance and data locality, which can improve query performance.

8. Block Placement and Rack Awareness:

   • HDFS is rack-aware, which means it takes into account the physical location of nodes in the cluster. Data blocks are placed on different racks to enhance fault tolerance and reduce data transfer over the network.

9. Metadata Management:

   • HDFS separates data and metadata storage. Metadata, such as file names and directory structures, is stored in a separate server called the NameNode. This architecture allows for efficient scaling and easy management of metadata.

10. Access Control and Security:

    • HDFS provides access control mechanisms and supports authentication and authorization to control who can access and modify data in the file system.

11. Ecosystem Integration:

    • HDFS is tightly integrated with various components of the Hadoop ecosystem, including MapReduce, Hive, Pig, and more, enabling data processing and analytics on the stored data.

12. Data Compression and Storage Formats:

    • HDFS supports various data compression and storage formats, such as Avro, Parquet, and ORC, which can be used to optimize storage and query performance.