HDFS (Hadoop Distributed File System) is a fundamental component of the Apache Hadoop ecosystem, and it serves as the primary storage system for Hadoop. HDFS is designed to store and manage large volumes of data across a distributed cluster of commodity hardware. It provides a reliable and scalable framework for storing and processing big data. Here are the key components and features of the HDFS framework:

1. NameNode: The NameNode is the master server in the HDFS architecture. It manages the metadata and namespace of the file system. This includes information about the structure of directories and files, as well as the mapping of data blocks to their respective DataNodes. The NameNode keeps this metadata in memory for fast access.

2. DataNode: DataNodes are worker nodes in the HDFS cluster. They are responsible for storing the actual data blocks. DataNodes periodically send heartbeats and block reports to the NameNode to inform it about their status and the data blocks they are responsible for.

3. Block Size: HDFS divides large files into fixed-size blocks (typically 128MB or 256MB in size). This block size is much larger than traditional file systems, which use smaller block sizes. Larger blocks in HDFS reduce the overhead of metadata management.

4. Replication: HDFS follows a data replication strategy to ensure data durability and fault tolerance. Each data block is replicated multiple times across different DataNodes. The default replication factor is three, which means that each block has three copies stored on separate nodes.

5. Rack Awareness: HDFS is aware of the network topology and organizes data block replicas to ensure they are distributed across racks in the data center. This design improves fault tolerance in case of rack or network failures.

6. Write-once, Read-many Model: HDFS is optimized for large-scale batch processing and analytics. It is designed for a write-once, read-many model, which means that data is typically written once and then read multiple times.

7. Data Integrity: HDFS uses checksums to verify data integrity. When data is read from a DataNode, the client checks the checksum to ensure that the data has not been corrupted during transmission.

8. Scalability: HDFS is highly scalable and can handle petabytes of data. You can easily add new DataNodes to the cluster to increase storage capacity and performance.

9. Parallel Data Access: HDFS is designed for parallel data access, which makes it suitable for distributed processing frameworks like MapReduce and Apache Spark.

10. High Availability (HA): HDFS supports high availability configurations with the use of multiple NameNodes in an active-standby setup. This ensures that if one NameNode fails, another can take over without disrupting data access.