DistributedComputing

distributed.md

## Map Reduce
1. Map/Reduce Library partitions the input data into M 
   pieces of typically 16-64 MB.

   True.

2. If there are M partitions of the input, there are M map workers 
   running simultaneously.

   False. There are generally less worker nodes than partitions.

3. Reduce function accepts intermediate key and a set of values for that 
   key  and merges together these values to form possibly a smaller set 
   of values.

   True.

4. Map and Reduce are user defined functions 

   True.

5. MapReduce is a restricted programming model?

   True.

Following are multiple Choice Questions.

Question 1: Which of the following functions of cloud computing is 
            optimized by MapReduce?

           e) All of the Above
      
Question 2: The value of R,  the number of Reduce workers, is determined by 

     b)  master program - number of partitions are defined by user though 

Question 3: In Map Reduce function, what can be said about the input key, 
            output key and the intermediate key values?

   C. The input keys are drawn from a different domain as output keys and 
      the intermediate keys are drawn from the same domain as output keys.

Question 4: What is the relationship between intermediate keys and 
            intermediate value?
   A  1:N

Question 5: Which of the following Key/Value pair is used by Map function 
            in Map Reduce?
  
  b)   (k1, v1)-->  List(k2 , v2)

Question 6: How is the intermediate Key/Value pair  arranged and processed 
            during partition of tasks?

   a)  Key/value pairs are processed in increasing key order

Question 7: Which is the reduce function in the example of Distributed Grep:

   c) Copy the intermediate data to output

Question 8: Which is the reduce function in the example of Text indexing?

   d) none of the above - count document IDs for each word

Question 9: Which of the following properties of Worker Machine is stored 
            by MapReduce?

   a)  State
   b)  Identity

Question 10: Map is phased in M pieces and Reduce is phased into R pieces. 
            What is the maximum number of scheduling operations required 
            to be performed?

    b)  O(M+R)

Question 11: How is the network bandwidth  maintained by Map Reduce in 
             cloud computing?

   a)  Data is stored locally on disks on different machines with 
       64 MB blocks

Question 12: How MapReduce environment handle failures?

   b)  Master worker pings the client worker regularly to check the status

Question 13: MapReduce provides the following benefits:

   a)  Reducing the amount of data sent across the network
   b)  Optimization the data by storing it locally
   c)  Redundant execution to handle machine failures and data loss

Question 14: What is the difference between the Combiner function and 
             Reduce function?

   b)  Output of combiner function is written to intermediate file and 
       Reduce function to output file