Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has several key characteristics:

1. Fault Tolerance: HDFS is highly fault-tolerant. It is designed to continue operating without any disruption even in the case of a failure. This is achieved by replicating the data across multiple nodes in the cluster.

2. High Throughput: HDFS provides high throughput access to application data. It is designed to support large data sets, with a focus on batch processing rather than interactive use.

3. Scalability: HDFS can easily scale out by adding more nodes to the cluster. It supports thousands of nodes and millions of files in a single system.

4. Data Locality: HDFS works on the principle of 'moving computation to data'. It allows computations to be done where the actual data resides, which reduces network congestion and increases the overall throughput of the system.

5. Large Data Sets: HDFS is designed to handle very large files, and it does this by breaking the data into smaller blocks. These blocks are then distributed across the nodes in the cluster.

6. Simple Coherency Model: HDFS follows a write-once-read-many access model. Once a file is created, written, and closed, it cannot be changed except for appends.

7. Portability: HDFS is portable across various hardware platforms and for different types of data. It has been designed to be easily portable from one platform to another.

Question

Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has several key characteristics:

1. Fault Tolerance: HDFS is highly fault-tolerant. It is designed to continue operating without any disruption even in the case of a failure. This is achieved by replicating the data across multiple nodes in the cluster.

2. High Throughput: HDFS provides high throughput access to application data. It is designed to support large data sets, with a focus on batch processing rather than interactive use.

3. Scalability: HDFS can easily scale out by adding more nodes to the cluster. It supports thousands of nodes and millions of files in a single system.

4. Data Locality: HDFS works on the principle of 'moving computation to data'. It allows computations to be done where the actual data resides, which reduces network congestion and increases the overall throughput of the system.

5. Large Data Sets: HDFS is designed to handle very large files, and it does this by breaking the data into smaller blocks. These blocks are then distributed across the nodes in the cluster.

6. Simple Coherency Model: HDFS follows a write-once-read-many access model. Once a file is created, written, and closed, it cannot be changed except for appends.

7. Portability: HDFS is portable across various hardware platforms and for different types of data. It has been designed to be easily portable from one platform to another.

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