Hadoop ecosystem overview - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Core components of Hadoop ecosystem

Which of the following is NOT a core component of the Hadoop ecosystem?

A HDFS (Hadoop Distributed File System)

B YARN (Yet Another Resource Negotiator)

C MapReduce

D Hive

💡 Hint

Think about which components handle storage, processing, and resource management.

HDFS, MapReduce, and YARN are core components of Hadoop. Hive is a data warehouse tool built on top of Hadoop.

❓ Predict Output

intermediate

YARN resource management output

What will be the output of the following YARN command simulation in a Hadoop cluster?

yarn node -list

Assuming the cluster has 3 nodes registered and all are healthy.

A Error: Command not found

B

Total Nodes: 3
Node-1: Unhealthy
Node-2: Healthy
Node-3: Healthy

C

Total Nodes: 3
Node-1: Healthy
Node-2: Healthy
Node-3: Healthy

D

Total Nodes: 0
No nodes registered

💡 Hint

Consider what the command 'yarn node -list' shows when nodes are healthy.

The command lists all nodes registered with YARN and their health status. Since all 3 nodes are healthy, it shows them as such.

❓ data_output

advanced

MapReduce job output count

Given a MapReduce job that processes 1000 input records and outputs key-value pairs, which option correctly shows the number of output records if the reducer combines all values by key and there are 10 unique keys?

A 10 output records

B 1000 output records

C 100 output records

D 0 output records

💡 Hint

Think about how reducers aggregate values by keys.

The reducer combines all values for each unique key, so the output count equals the number of unique keys, which is 10.

❓ visualization

advanced

Hadoop ecosystem tool usage visualization

Which visualization best represents the relationship between Hadoop ecosystem tools: HDFS, YARN, MapReduce, Hive, and Pig?

A HDFS at the base, YARN managing resources, MapReduce processing data, Hive and Pig as query layers on top

B MapReduce at the base, HDFS managing resources, YARN processing data, Hive and Pig as storage layers

C Pig at the base, Hive managing resources, HDFS processing data, YARN and MapReduce as query layers

D Hive at the base, MapReduce managing resources, YARN processing data, HDFS and Pig as query layers

💡 Hint

Consider the roles of storage, resource management, processing, and querying in Hadoop.

HDFS stores data, YARN manages resources, MapReduce processes data, and Hive and Pig provide higher-level query interfaces.

🔧 Debug

expert

Diagnosing a failed MapReduce job

A MapReduce job fails with the error: 'java.lang.OutOfMemoryError: Java heap space'. Which option is the most likely cause?

A YARN is not running on the cluster

B The reducer is trying to load too much data into memory at once

C The input data is too small for the job to run

D HDFS is full and cannot store output

💡 Hint

Think about what causes Java heap space errors in processing jobs.

The error indicates the Java process ran out of memory, often because the reducer tried to load too much data at once.