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.

Practice

1. Which component of the Hadoop ecosystem is primarily responsible for storing large data files across multiple machines?

easy

A. MapReduce

B. YARN

C. HDFS

D. Hive

Solution

1. Step 1: Understand Hadoop storage components

   Hadoop uses a distributed file system to store data across many machines.

2. Step 2: Identify the storage system in Hadoop

   HDFS (Hadoop Distributed File System) is designed for this purpose.

3. Final Answer:

   HDFS -> Option C

4. Quick Check:

   Storage = HDFS [OK]

Hint: Storage in Hadoop is always HDFS [OK]

Common Mistakes:

• Confusing YARN with storage
• Thinking MapReduce stores data
• Mixing Hive with storage

2. Which of the following is the correct syntax to submit a MapReduce job using the Hadoop command line?

easy

A. hadoop jar myjob.jar input output

B. hadoop run myjob.jar input output

C. hadoop execute myjob.jar input output

D. hadoop start myjob.jar input output

Solution

1. Step 1: Recall Hadoop job submission syntax

   The standard command to run a MapReduce job is 'hadoop jar'.

2. Step 2: Match the correct command

   hadoop jar myjob.jar input output uses 'hadoop jar' followed by the jar file and input/output paths.

3. Final Answer:

   hadoop jar myjob.jar input output -> Option A

4. Quick Check:

   Submit job = hadoop jar [OK]

Hint: Use 'hadoop jar' to run MapReduce jobs [OK]

Common Mistakes:

• Using 'hadoop run' instead of 'jar'
• Confusing 'execute' with 'jar'
• Using 'start' which is invalid

3. Given the following code snippet in a Hadoop MapReduce job, what will be the output key-value pairs after the map phase?

public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    String line = value.toString();
    for (String word : line.split(" ")) {
        context.write(new Text(word), new IntWritable(1));
    }
}

medium

A. Each character from the line as key with value 1

B. The entire line as key with value 1

C. The length of each word as key with value 1

D. Each word from the input line paired with the number 1

Solution

1. Step 1: Analyze the map function logic

   The code splits the input line into words and writes each word with value 1.

2. Step 2: Determine output key-value pairs

   Output keys are words; values are always 1.

3. Final Answer:

   Each word from the input line paired with the number 1 -> Option D

4. Quick Check:

   Map output = (word, 1) pairs [OK]

Hint: Map outputs (word, 1) pairs for word count [OK]

Common Mistakes:

• Thinking output is whole line
• Using word length as key
• Using characters instead of words

4. You wrote a Pig script to load data but get an error. Which of the following is the most likely cause?

data = LOAD 'input.txt' USING PigStorage(',') AS (name:chararray, age:int);
filtered = FILTER data BY age > 25;
DUMP filtered;

medium

A. The input file path is incorrect or missing

B. PigStorage does not support comma as delimiter

C. The FILTER syntax is invalid

D. DUMP command cannot be used in Pig scripts

Solution

1. Step 1: Check PigStorage usage

   PigStorage supports comma delimiter, so PigStorage does not support comma as delimiter is incorrect.

2. Step 2: Validate FILTER and DUMP syntax

   FILTER and DUMP syntax are correct as per Pig Latin.

3. Step 3: Consider common errors

   Most errors come from wrong file paths or missing files.

4. Final Answer:

   The input file path is incorrect or missing -> Option A

5. Quick Check:

   File path error = The input file path is incorrect or missing [OK]

Hint: Check file path first when Pig script errors occur [OK]

Common Mistakes:

• Assuming delimiter unsupported
• Miswriting FILTER syntax
• Thinking DUMP is invalid

5. You want to analyze large datasets using SQL-like queries without writing MapReduce code. Which Hadoop ecosystem tool should you use and why?

hard

A. HDFS, because it stores data efficiently

B. Hive, because it provides SQL-like querying on Hadoop data

C. YARN, because it manages cluster resources

D. MapReduce, because it simplifies SQL queries

Solution

1. Step 1: Identify tools for SQL-like queries

   Hive is designed to let users write SQL-like queries on Hadoop data.

2. Step 2: Understand other tools' roles

   HDFS stores data, YARN manages resources, MapReduce processes data but is not SQL-like.

3. Final Answer:

   Hive, because it provides SQL-like querying on Hadoop data -> Option B

4. Quick Check:

   SQL queries on Hadoop = Hive [OK]

Hint: Use Hive for SQL queries on Hadoop data [OK]

Common Mistakes:

• Confusing storage with querying
• Thinking YARN runs queries
• Assuming MapReduce is SQL-based