MapReduce job execution flow in Hadoop - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the first step in a MapReduce job execution?

In the MapReduce job execution flow, which step happens first?

A Splitting the input data into chunks

B Shuffling and sorting the map output

C Reducing the intermediate data

D Writing the final output to HDFS

💡 Hint

Think about how the data is prepared before processing.

The first step is splitting the input data into chunks so that each map task can process a part of the data in parallel.

🧠 Conceptual

intermediate

What happens during the shuffle phase in MapReduce?

During the MapReduce job execution, what is the main purpose of the shuffle phase?

A Executing the map function on input data

B Splitting the input data into smaller chunks

C Writing the final output to the distributed file system

D Sorting and transferring map outputs to reducers

💡 Hint

Think about how data moves from mappers to reducers.

The shuffle phase sorts and transfers the output of map tasks to the appropriate reduce tasks based on keys.

❓ data_output

advanced

Identify the output of the map phase given this input

Given the input data ["apple", "banana", "apple"] and a map function that outputs (word, 1) for each word, what is the map phase output?

Hadoop

input_data = ["apple", "banana", "apple"]
map_output = [(word, 1) for word in input_data]
print(map_output)

A [('banana', 1), ('apple', 1), ('apple', 1)]

B [('apple', 1), ('banana', 1), ('apple', 1)]

C [('apple', 1), ('banana', 1)]

D [('apple', 2), ('banana', 1)]

💡 Hint

The map function emits one pair per input word.

The map phase outputs a list of key-value pairs, one for each input element, without aggregation.

❓ data_output

advanced

What is the reducer output for this intermediate data?

Given the intermediate data {'apple': [1, 1], 'banana': [1]}, what is the output of the reducer that sums the values?

Hadoop

intermediate_data = {'apple': [1, 1], 'banana': [1]}
reducer_output = {k: sum(v) for k, v in intermediate_data.items()}
print(reducer_output)

A {'apple': 2, 'banana': 1}

B {'apple': 1, 'banana': 1}

C {'apple': [2], 'banana': [1]}

D {'apple': 3, 'banana': 1}

💡 Hint

The reducer sums all values for each key.

The reducer adds the list of values for each key to produce the final count.

🧠 Conceptual

expert

Which component manages the overall MapReduce job execution?

In Hadoop's MapReduce architecture, which component is responsible for managing the entire job execution, including resource allocation and task scheduling?

A NameNode

B TaskTracker

C JobTracker

D DataNode

💡 Hint

Think about the component that coordinates tasks across the cluster.

The JobTracker manages job execution, resource allocation, and task scheduling in Hadoop MapReduce.