Distributed Cache is a facility provided by the Map/Reduce framework to cache files (text, archives, jars and so on) needed by applications during execution of the job. The framework will copy the necessary files to the slave node before any tasks for the job are executed on that node.
Q23. What is the benifit of Distributed cache, why can we just have the file in HDFS and have the application read it
This is because distributed cache is much faster. It copies the file to all trackers at the start of the job. Now if the task tracker runs 10 or 100 mappers or reducer, it will use the same copy of distributed cache. On the other hand, if you put code in file to read it from HDFS in the MR job then every mapper will try to access it from HDFS hence if a task tracker run 100 map jobs then it will try to read this file 100 times from HDFS. Also HDFS is not very efficient when used like this.
Q.24 What mechanism does Hadoop framework provides to synchronize changes made in Distribution Cache during runtime of the application
This is a trick questions. There is no such mechanism. Distributed Cache by design is read only during the time of Job execution
Q25. Have you ever used Counters in Hadoop. Give us an example scenario
Anybody who claims to have worked on a Hadoop project is expected to use counters
Q26. Is it possible to provide multiple input to Hadoop? If yes then how can you give multiple directories as input to the Hadoop job
Yes, The input format class provides methods to add multiple directories as input to a Hadoop job
Q27. Is it possible to have Hadoop job output in multiple directories. If yes then how
Yes, by using Multiple Outputs class
Q28. What will a hadoop job do if you try to run it with an output directory that is already present? Will it
- overwrite it
- warn you and continue
- throw an exception and exit
The hadoop job will throw an exception and exit.
Q29. How can you set an arbitary number of mappers to be created for a job in Hadoop
This is a trick question. You cannot set it
Q30. How can you set an arbitary number of reducers to be created for a job in Hadoop
You can either do it progamatically by using method setNumReduceTasksin the JobConfclass or set it up as a configuration setting
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