CCD-410 Online Practice Questions and Answers

Correct Answer: C

In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

Note: The reduce phase has 3 steps: shuffle, sort, reduce. Shuffle is where the data is collected by the reducer from each mapper. This can happen while mappers are generating data since it is only a data transfer. On the other hand, sort and reduce can only start once all the mappers are done.

Why is starting the reducers early a good thing? Because it spreads out the data transfer from the mappers to the reducers over time, which is a good thing if your network is the bottleneck.

Why is starting the reducers early a bad thing? Because they "hog up" reduce slots while only copying data. Another job that starts later that will actually use the reduce slots now can't use them.

You can customize when the reducers startup by changing the default value of mapred.reduce.slowstart.completed.maps in mapred-site.xml. A value of 1.00 will wait for all the mappers to finish before starting the reducers. A value of 0.0 will start the reducers right away. A value of 0.5 will start the reducers when half of the mappers are complete. You can also change mapred.reduce.slowstart.completed.maps on a job-by-job basis. Typically, keep mapred.reduce.slowstart.completed.maps above 0.9 if the system ever has multiple jobs running at once. This way the job doesn't hog up reducers when they aren't doing anything but copying data. If you only ever have one job running at a time, doing 0.1 would probably be appropriate.

Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, When is the reducers are started in a MapReduce job?

Correct Answer: D

Correct Answer: D

Hadoop streaming is a utility that comes with the Hadoop distribution. The utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.

Reference: http://hadoop.apache.org/common/docs/r0.20.1/streaming.html (Hadoop Streaming, second sentence)

Correct Answer: C

The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, Where is the Mapper Output (intermediate kay-value data) stored ?

Correct Answer: E

As the Map operation is parallelized the input file set is first split to several pieces called FileSplits. If an individual file is so large that it will affect seek time it will be split to several Splits. The splitting does not know anything about the input file's internal logical structure, for example line-oriented text files are split on arbitrary byte boundaries. Then a new map task is created per FileSplit.

When an individual map task starts it will open a new output writer per configured reduce task. It will then proceed to read its FileSplit using the RecordReader it gets from the specified InputFormat. InputFormat parses the input and generates key-value pairs. InputFormat must also handle records that may be split on the FileSplit boundary. For example TextInputFormat will read the last line of the FileSplit past the split boundary and, when reading other than the first FileSplit, TextInputFormat ignores the content up to the first newline.

Reference: How Map and Reduce operations are actually carried out

Correct Answer: E

Reducer reduces a set of intermediate values which share a key to a smaller set of values.

Reducing lets you aggregate values together. A reducer function receives an iterator of input values from an input list. It then combines these values together, returning a single output value. Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce

Correct Answer: B

reduce() gets called once for each [key, (list of values)] pair. To explain, let's say you called:

out.collect(new Text("Car"),new Text("Subaru");

out.collect(new Text("Car"),new Text("Honda");

out.collect(new Text("Car"),new Text("Ford");

out.collect(new Text("Truck"),new Text("Dodge");

out.collect(new Text("Truck"),new Text("Chevy");

Then reduce() would be called twice with the pairs

reduce(Car, )

reduce(Truck, )

Reference: Mapper output.collect()?

Correct Answer: D

JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)

Client applications submit jobs to the Job tracker.

The JobTracker talks to the NameNode to determine the location of the data

The JobTracker locates TaskTracker nodes with available slots at or near the data

The JobTracker submits the work to the chosen TaskTracker nodes.

The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.

A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable.

When the work is completed, the JobTracker updates its status.

Client applications can poll the JobTracker for information.

Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?

Correct Answer: D

Correct Answer: A

A MapReduce job with m mappers and r reducers involves up to m * r distinct copy operations, since each mapper may have intermediate output going to every reducer.