In Previous Years Questions
HDFS is a distributed file system designed to store and manage large data sets across a cluster of machines.
It adopts a simple but effective approach to data storage:
1. Data Splitting
- Large files are broken down into fixed-size blocks, typically 64MB or 128MB.
- This partitioning enables parallel processing, where each block can be processed independently across different nodes in the cluster.
2. Block Replication
- Each data block is replicated across multiple nodes in the cluster, ensuring data availability even if one node fails.
- Replication factor is configurable, allowing for a balance between data redundancy and storage efficiency.
3. Metadata Management
- The NameNode acts as the central authority, storing metadata about all files and blocks in the system.
- This metadata includes block locations, replication factors, and file permissions.
- The DataNodes store the actual data blocks and report their health status to the NameNode.
4. Data Read and Write Operations
- Clients interact with the NameNode to locate the desired data blocks.
- The NameNode directs the client to the DataNodes where the blocks are located.
- Clients can then read or write data directly to the DataNodes.
Imagine you want to store a 1GB file containing weather data in HDFS.
The process would be as follows:
- File Splitting: The file is split into 16 blocks of 64MB each.
- Block Replication: Each block is replicated 3 times across different DataNodes in the cluster.
- Metadata Management: The NameNode stores the information about the file, including the block locations and replication factors.
- Data Storage: Each DataNode stores three copies of each block, resulting in a total of 48 blocks stored across the cluster.