Hadoop ecosystem overview - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of the Hadoop ecosystem?

The Hadoop ecosystem is designed to store, process, and analyze large sets of data across many computers in a simple and efficient way.

beginner

Name the core components of the Hadoop ecosystem.

The core components are HDFS (Hadoop Distributed File System) for storage, and MapReduce for processing data in parallel across clusters.

beginner

What is HDFS and why is it important?

HDFS is a file system that splits big data files into smaller blocks and stores them across many computers. It makes data storage reliable and fast.

intermediate

Explain the role of YARN in the Hadoop ecosystem.

YARN manages and schedules resources in the cluster, allowing multiple applications to run and share resources efficiently.

intermediate

What are some popular tools in the Hadoop ecosystem besides HDFS and MapReduce?

Popular tools include Hive (for SQL-like queries), Pig (for scripting data flows), HBase (NoSQL database), and Spark (fast data processing).

Which Hadoop component is responsible for storing data across multiple machines?

A Hive

B MapReduce

C YARN

D HDFS

HDFS stores data by splitting it into blocks and distributing across machines.

What does MapReduce do in the Hadoop ecosystem?

A Manages cluster resources

B Processes data in parallel

C Stores data files

D Runs SQL queries

MapReduce processes large data sets by dividing tasks across many computers.

Which tool in Hadoop allows you to write SQL-like queries on big data?

A Hive

B HBase

C Pig

D Spark

Hive provides a SQL-like interface to query data stored in Hadoop.

What is the role of YARN in Hadoop?

A Resource management

B Data storage

C Data processing

D Data visualization

YARN manages and schedules resources for running applications in the cluster.

Which Hadoop component is a NoSQL database?

A Pig

B Hive

C HBase

D MapReduce

HBase is a NoSQL database built on top of Hadoop for fast random access to big data.

Describe the main components of the Hadoop ecosystem and their roles.

Think about how data is stored, processed, and managed in Hadoop.

Explain how Hadoop handles big data storage and processing in a simple way.

Imagine sharing a big task among many friends to finish faster.

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