Apache Hadoop HDFS

HDFS, which stands for Hadoop Distributed File System, is a core component of the Apache Hadoop ecosystem. It is a distributed file system designed to store and manage large volumes of data across a cluster of commodity hardware. HDFS is designed for scalability, fault tolerance, and high throughput, making it well-suited for storing and processing big data. Here are some key characteristics and features of HDFS:

1. Distributed Storage:

   • HDFS divides large files into smaller blocks (typically 128 MB or 256 MB in size) and distributes these blocks across multiple data nodes in a cluster.
   • Data replication is used for fault tolerance. Each data block is replicated to multiple nodes (usually three by default) to ensure data durability and availability in case of node failures.

2. Write Once, Read Many (WORM):

   • HDFS follows a WORM model, which means that once data is written to a block, it is not modified. Instead, if updates are required, new data is written to a new block, and the older versions are retained. This simplifies data consistency and allows for efficient data processing.

3. High Throughput:

   • HDFS is optimized for high-throughput data access, making it suitable for batch processing workloads like those used in Hadoop MapReduce jobs.

4. Data Locality:

   • HDFS aims to reduce data transfer times by placing computation close to data. It tries to schedule tasks on the same node where the data is stored (data locality), minimizing network traffic.

5. Master-Slave Architecture:

   • HDFS has a master-slave architecture consisting of two main components: the NameNode and DataNodes.
   • The NameNode manages the metadata and namespace of the file system, while DataNodes store the actual data blocks.

6. Scalability:

   • HDFS is designed to scale horizontally by adding more data nodes to the cluster as data volume grows. It can handle petabytes of data across thousands of nodes.

7. Fault Tolerance:

   • HDFS provides fault tolerance through data replication. If a DataNode or block becomes unavailable, HDFS can still retrieve the data from replicas stored on other nodes.

8. Consistency Model:

   • HDFS provides a consistent view of the data, ensuring that all clients see the same data, even when multiple clients are writing to the same file.

9. Access Control:

   • HDFS supports access control mechanisms to manage file and directory permissions, ensuring that only authorized users can read or write data.

10. Integration with Hadoop Ecosystem:

    • HDFS is the primary storage system used by various components of the Hadoop ecosystem, including Hadoop MapReduce, Apache Hive, Apache Pig, and Apache Spark.