Hadoop MapReduce

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. Venkata Kowsik Temididapathi iy~ NE CS 6350.002 - Big Data Management and Analytics - S22 Course Homepage Review Test Submission: Hadoop MapReduce Quiz Review Test Submission: Hadoop MapReduce Quiz User Venkata Kowsik Temididapathi Course CS 6350.002 - Big Data Management and Analytics - S22 Test Hadoop MapReduce Quiz Started 2/12/22 12:40 PM Submitted 2/12/22 1:16 PM Due Date 2/13/22 11:59 PM Status Completed Attempt Score 100 out of 100 points Time Elapsed 35 minutes Results Displayed All Answers, Submitted Answers, Correct Answers Question 1 10 out of 10 points In Hadoop MapReduce output of Mapper is stored on: Selected Answer: & Local Disk Answers: Memory HDFS & Local Disk Remote Location Question 2 10 out of 10 points Which of the following is the correct order of operations for MapReduce in Hadoop Answers Selected Answer 1. Reading input data from HDFS @ 1. Reading input data from HDFS

@ 2. Map operation performed is performed so that useful (key, value) pairs can be identified from input data Intermediate outputs are stored on local disk @ 3. Data is sorted and shuffled to group by key values &4 @ 5. Reduce operation is performed @ 6. Results are written back to HDFS Question 3 What is meant by locality of computation in Hadoop? @ 2. Map operation performed is performed so that useful (key, value) pairs can be identified from input data Intermediate outputs are stored on local disk @ 3. Data is sorted and shuffled to group by key values &4 @ 5. Reduce operation is performed @ 6. Results are written back to HDFS 10 out of 10 points Selected Answer: & Data storage and processing can be co-located on the same node to optimize overall performance. Answers: Computation should be performed at a remote location Data storage should be as distributed as possible All data processing should happen at only a single node Question 4 What is the input to the reduce function? Selected Answer: @ One key and list of all associated values Answers: @ One key and list of all associated values One key and one value One value and a list of all associated keys Question 5 The number of map tasks is dependent on: @ Data storage and processing can be co-located on the same node to optimize overall performance. 10 out of 10 points One key and a list of some (partial) values associated with the key 10 out of 10 points

Selected Answer: & size of input data Answers: none of the above @ size of input data number of useful values in the data number of useful keys in the data Question 6 10 out of 10 points The number of reduce tasks is dependent on: Selected Answer: @ number of useful keys in the data Answers: @ number of useful keys in the data number of useful values in the data none of the above size of input data Question 7 10 out of 10 points You have a Hadoop cluster with 20 machines each having 250 GB of HDFS disk space. The system settings are: block size = 128 MB, replication factor = 3. Assuming the cluster is totally free i.e. no stored data and no jobs. You want to upload 10 text files, each of size 250 GB and then perform a Wordcount job i.e. count the frequency of words. What is going to happen? Selected Answer: @ The data upload fails for an intermediate file Answers: The data upload fails for the first file @ The data upload fails for an intermediate file WordCount will run successfully Map step fails as there are too many inputs Question 8 10 out of 10 points If a node running map tasks fails, how will the map tasks be recovered?

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Selected Answer: & The application master will re-run the completed map tasks on another node, since their results will be lost when node crashes. Answers: @ The application master will re-run the completed map tasks on another node, since their results will be lost when node crashes. The completed map tasks' results can be recovered because their results will be stored on HDFS Entire Mapreduce job will have to be re-run A node can never crash Question 9 10 out of 10 points In Hadoop MapReduce, reduce tasks takes input from: Selected Answer: @ multiple map tasks Answers: @ multiple map tasks single map task all map tasks that ran on the same machine as the reducer reduce tasks doesn't need any input. Question 10 10 out of 10 points Suppose | create the following function in Scala: def myFunction(x: Int): Int = { if(x%2==0) 2*x else 3*x } | also define a list as: val list = List(1, 2, 3, 4, 5) Now, | want to run a MapReduce job that will apply the function myFunction to every element of the list and then reduce it using the max operator. Which of the following accomplishes this? Selected Answer: import scala.math._ @ list.map(myFunction).reduce(max) Answers: None of the above import scala.math._ list.reduce(max.map(myFunction)) import scala.math._ list.reduce(max).map(myFunction)

import scala.math._ @ list. map(myFunction).reduce(max) Saturday, March 12, 2022 9:19:20 AM CST «— 0K

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