Difference between HDFS and NFS

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I am newbie to Hadoop. I would like to know about the basic differences between hadoop distributed file system and network file system and are there any benefits of hdfs over nfs?

Thanks in advance!
May 8, 2018 in Big Data Hadoop by Shubham
• 13,490 points
14,981 views

2 answers to this question.

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First, let me tell you about both NFS and HDFS:

NFS (Network File system): A protocol developed that allows clients to access files over the network. NFS clients allow files to be accessed as if the files reside on the local machine, even though they reside on the disk of a networked machine.

HDFS (Hadoop Distributed File System): A file system that is distributed amongst many networked computers or nodes. HDFS is fault tolerant because it stores multiple replicas of files on the file system, the default replication level is 3.

The major difference between the two is Replication/Fault Tolerance. HDFS was designed to survive failures. NFS does not have any fault tolerance built in.

Benefits of HDFS over NFS:

Other than fault tolerance, HDFS does support multiple replicas of files. This eliminates (or eases) the common bottleneck of many clients accessing a single file. Since files have multiple replicas, on different physical disks, reading performance scales better than NFS.

Learn more about Big Data and its applications from the Azure Data Engineer Certification.

Hope this will help!

answered May 8, 2018 by nitinrawat895
• 11,380 points
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In NFS, the data is stored only on one main system. All the other systems in that network can access the data stored in that as if it was stored in their local system. But the problem with this is that, if the main system goes down, then the data is lost and also, the storage depends on the space available on that system. 

In HDFS, data is distributed among different systems called datanodes. Here, the storage capacity is comparatively high. HDFS is mainly used to store Big Data and enable fast data transaction. 

answered Feb 4, 2019 by Kumar

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