HDFS, or Hadoop Distributed File System, is a distributed, scalable, and reliable file system designed for big data storage and processing. Its file storage principle is based on the following key concepts:
1. Distributed Storage:
HDFS stores files across multiple machines in a cluster, dividing them into blocks and replicating these blocks for fault tolerance.
Example: If a file is 128 MB and the block size is 64 MB, HDFS will split this file into two blocks and store each block on different nodes in the cluster.
2. Replication:
Each block of a file is replicated multiple times (default is three) across different nodes to ensure data reliability and availability in case of node failures.
Example: If one node storing a block fails, HDFS can quickly retrieve the data from another node where the same block is replicated.
3. High Throughput:
HDFS is optimized for high throughput access to large files, making it suitable for data processing tasks like batch processing and analytics.
4. Write Once, Read Many (WORM):
Files in HDFS are typically written once and read many times, which aligns with the use cases of big data processing where data is generated once but analyzed repeatedly.
5. Rack Awareness:
HDFS replicates data across different racks to protect against rack-level failures, enhancing data reliability and availability.
Example: If a rack fails, HDFS can still provide access to the data from replicas stored in other racks.
For cloud-based solutions that leverage similar principles, you might consider services like Tencent Cloud's Object Storage (COS), which offers scalable and reliable storage for large volumes of data, with features like data redundancy and high availability.
Tencent Cloud COS provides a highly available and durable object storage service, supporting large files storage and high-throughput access, which aligns with the core principles of HDFS but in a cloud-native and more flexible environment.