Benefits and Challenges

Benefits and Challenges :

Benefits of HDFS:

• HDFS can store a large amount of information.
• HDFS is a simple & robust coherency model.
• HDFS is scalable and has fast access to required information.
• HDFS also serve a substantial number of clients by adding more machines to the cluster.
• HDFS provides streaming read access.
• HDFS can be used to read data stored multiple times but the data will be written to the HDFS once.
• The recovery techniques will be applied very quickly.
• Hardware and operating systems portability across is heterogeneous commodities.
• High Economy by distributing data and processing across clusters of commodity personal computers.
• High Efficiency by distributing data, logic on parallel nodes to process it from where data is located.
• High Reliability by automatically maintaining multiple copies of data and automatically redeploying processing logic in the event of failures.

Challenges for HDFS :

• HDFS does not give any reliability if that machine goes down.
• An enormous number of clients must be handled if all the clients need the data stored on a single machine.
• Clients need to copy the data to their local machines before they can operate it.
• Applications that require low-latency access to data, in the tens of milliseconds range, will not work well with HDFS.
• Since the namenode holds filesystem metadata in memory, the limit to the number of files in a filesystem is governed by the amount of memory on the namenode.
• Files in HDFS may be written by a single writer. Writers are always made at the end of the file.
• There is no support for multiple writers, or for modifications at arbitrary offsets in the file.