How did you debug your Hadoop code?

There can be several ways of doing this but most common ways are:-
 - By using counters.
 - The web interface provided by Hadoop framework.

How will you write a custom partitioner for a Hadoop job?

To have Hadoop use a custom partitioner you will have to do minimum the following three:
 - Create a new class that extends Partitioner Class
 - Override method getPartition
 - In the wrapper that runs the Mapreduce, either
 - Add the custom partitioner to the job programmatically using method set Partitioner Class or – add the custom partitioner to the job as a config file (if your wrapper reads from config file or oozie)

How can you set an arbitrary number of Reducers to be created for a job in Hadoop?

You can either do it programmatically by using method setNumReduceTasks in the Jobconf Class or set it up as a configuration setting.

How can you set an arbitrary number of mappers to be created for a job in Hadoop?

You cannot set it.

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.

Is it possible to have Hadoop job output in multiple directories? If yes, how?

Yes, by using Multiple Outputs class.

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.

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.

What mechanism does Hadoop framework provide to synchronise changes made in Distribution Cache during runtime of the application?

This is a tricky question. There is no such mechanism. Distributed Cache by design is read only during the time of Job execution.

What is the benefit 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 TaskTracker 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.

What is Distributed Cache in Hadoop?

Distributed Cache is a facility provided by the MapReduce 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.

What is the characteristic of streaming API that makes it flexible run MapReduce jobs in languages like Perl, Ruby, Awk etc.?

Hadoop Streaming allows to use arbitrary programs for the Mapper and Reducer phases of a MapReduce job by having both Mappers and Reducers receive their input on stdin and emit output (key, value) pairs on stdout.

What is Hadoop Streaming?

Streaming is a generic API that allows programs written in virtually any language to be used as Hadoop Mapper and Reducer implementations.

Using command line in Linux, how will you - See all jobs running in the Hadoop cluster - Kill a job?

Hadoop job – list
 Hadoop job – kill jobID

How does speculative execution work in Hadoop?

JobTracker makes different TaskTrackers pr2ocess same input. When tasks complete, they announce this fact to the JobTracker. Whichever copy of a task finishes first becomes the definitive copy. If other copies were executing speculatively, Hadoop tells the TaskTrackers to abandon the tasks and discard their outputs. The Reducers then receive their inputs from whichever Mapper completed successfully, first.

Hadoop achieves parallelism by dividing the tasks across many nodes, it is possible for a few slow nodes to rate-limit the rest of the program and slow down the program. What mechanism Hadoop provides to combat this?

Speculative Execution.

Suppose Hadoop spawned 100 tasks for a job and one of the task failed. What will Hadoop do?

It will restart the task again on some other TaskTracker and only if the task fails more than four (default setting and can be changed) times will it kill the job.

What is the relationship between Jobs and Tasks in Hadoop?

One job is broken down into one or many tasks in Hadoop.

What is TaskTracker?

TaskTracker is a node in the cluster that accepts tasks like MapReduce and Shuffle operations – from a JobTracker.

What are some typical functions of Job Tracker?

The following are some typical tasks of JobTracker:-
 - Accepts jobs from clients
 - It talks to the NameNode to determine the location of the data.
 - It locates TaskTracker nodes with available slots at or near the data.
 - It submits the work to the chosen TaskTracker nodes and monitors progress of each task by receiving heartbeat signals from Task tracker.