What are the characteristics of a Hadoop HDFS cluster?

The Hadoop Distributed File System (HDFS) cluster of Hadoop has the following characteristics:

1. Distributed storage: HDFS divides files into data blocks and scatters these data blocks across multiple nodes in the cluster, achieving distributed storage of data.
2. Redundant Backup: HDFS automatically creates multiple redundant backups for each data block and stores these backups on different nodes to enhance data reliability and fault tolerance.
3. High capacity: HDFS can store data at a large scale, supporting storage of data at the petabyte level.
4. High Throughput: The design goal of HDFS is to achieve high throughput data access, suitable for large-scale data processing scenarios.
5. Scalability: HDFS can easily scale the size of the cluster by adding more nodes to increase storage capacity and processing power.
6. Data Locality: HDFS will try to assign computing tasks to nodes that store the data, in order to reduce data network transfer and improve computing efficiency.
7. Automatic recovery from failure: HDFS has automatic fault detection and recovery mechanisms, which enable it to automatically restore backup data to other healthy nodes when a node in the cluster fails.
8. Adaptation to large files: HDFS is suitable for storing and processing large files. Files larger than the HDFS block size (default 128MB) will automatically be divided into multiple data blocks for storage.

In general, HDFS cluster is characterized by high capacity, high throughput, high reliability, scalability, and automatic fault recovery, making it suitable for storing and processing large amounts of data.