21. Which object can be used to get the progress of a particular job?

Ans: Context

22. What is next step after Mapper or MapTask?

Ans: The output of the Mapper is sorted and Partitions will be created for the output. Number of partition depends on the number of reducer.

23. How can we control particular key should go in a specific reducer?

Ans: Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner.

24. What is the use of Combiner?

Ans: It is an optional component or class, and can be specify via Job.setCombinerClass (Class Name), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.

25. How many maps are there in a particular Job?

Ans: the number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files.

Generally it is around 10-100 maps per-node. Task setup takes awhile, so it is best if the maps take at least a minute to execute.

Suppose, if you expect 10TB of input data and have a block size of 128MB, you’ll end up with 82,000 maps, to control the number of block you can use the mapreduce.job.maps parameter (which only provides a hint to the framework). Ultimately, the number of tasks is controlled by the number of splits returned by the InputFormat.getSplits () method (which you can override).

26. What is the Reducer used for?

Ans: Reducer reduces a set of intermediate values which share a key to a (usually smaller) set of values. The number of reduces for the job is set by the user via Job.setNumReduceTasks (int).

27. Explain the core methods of the Reducer?

Ans: The API of Reducer is very similar to that of Mapper, there’s a run() method that receives a Context containing the job’s configuration as well as interfacing methods that return data from the reducer itself back to the framework. The run() method calls setup() once, reduce() once for each key associated with the reduce task, and cleanup() once at the end. Each of these methods can access the job’s configuration data by using Context.getConfiguration ().

As in Mapper, any or all of these methods can be overridden with custom implementations. If none of these methods are overridden, the default reducer operation is the identity function; values are passed through without further processing.

The heart of Reducer is it’s reduce () method. This is called once per key; the second argument is an Iterable which returns all the values associated with that key.

28. What are the primary phases of the Reducer?

Ans: Shuffle, Sort and Reduce

29. Explain the shuffle?

Ans: Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches the relevant partition of the output of all the mappers, via HTTP.

30. Explain the Reducer’s Sort phase?

Ans: The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage. The shuffle and sort phases occur simultaneously; while map-outputs are being fetched they are merged (It is similar to merge-sort).