Pig vs Hive comparison in Hadoop - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Primary Language Used in Pig and Hive

Which language is primarily used to write scripts in Apache Pig and Apache Hive respectively?

A Pig Latin for Pig and HiveQL for Hive

B SQL for Pig and Java for Hive

C Python for Pig and Pig Latin for Hive

D HiveQL for Pig and Pig Latin for Hive

💡 Hint

Think about the scripting languages designed specifically for each tool.

Apache Pig uses Pig Latin, a procedural language, while Apache Hive uses HiveQL, a SQL-like declarative language.

🧠 Conceptual

intermediate

Data Processing Model Difference

What is the main difference in data processing models between Pig and Hive?

A Both use declarative models but Hive supports more data types

B Pig uses a procedural data flow model; Hive uses a declarative SQL-like model

C Both use procedural models but with different syntax

D Pig uses a declarative model; Hive uses a procedural model

💡 Hint

Consider how users specify data transformations in each tool.

Pig scripts describe step-by-step data transformations (procedural), while Hive queries specify what data to retrieve (declarative).

❓ Predict Output

advanced

Output of Pig Latin Script

What is the output of this Pig Latin script given the input data below?

Input data (file):
1,apple,10
2,banana,20
3,apple,15

Script:
fruit_data = LOAD 'input' USING PigStorage(',') AS (id:int, name:chararray, quantity:int);
apple_data = FILTER fruit_data BY name == 'apple';
total = FOREACH (GROUP apple_data ALL) GENERATE SUM(apple_data.quantity) as total_quantity;
DUMP total;

Hadoop

fruit_data = LOAD 'input' USING PigStorage(',') AS (id:int, name:chararray, quantity:int);
apple_data = FILTER fruit_data BY name == 'apple';
total = FOREACH (GROUP apple_data ALL) GENERATE SUM(apple_data.quantity) as total_quantity;
DUMP total;

A ({45})

B ({30})

C ({15})

D ({25})

💡 Hint

Add the quantities for all rows where name is 'apple'.

The script filters rows with name 'apple' (quantities 10 and 15) and sums them: 10 + 15 = 25. The output is ({25}).

❓ Predict Output

advanced

Hive Query Output for Grouping

Given a Hive table 'fruits' with columns (id INT, name STRING, quantity INT) and data:
1,apple,10
2,banana,20
3,apple,15

What is the output of this HiveQL query?
SELECT name, SUM(quantity) as total_quantity FROM fruits GROUP BY name ORDER BY total_quantity DESC;

Hadoop

SELECT name, SUM(quantity) as total_quantity FROM fruits GROUP BY name ORDER BY total_quantity DESC;

A [('banana', 30), ('apple', 25)]

B [('banana', 20), ('apple', 25)]

C [('apple', 25), ('banana', 20)]

D [('apple', 15), ('banana', 20)]

💡 Hint

Sum quantities per fruit and order descending by total quantity.

Apple total: 10 + 15 = 25, Banana total: 20.

Ordering by total_quantity DESC means apple first, then banana.

🚀 Application

expert

Choosing Between Pig and Hive for a Task

You have a large dataset with complex data transformations involving multiple steps and custom functions. You want to write scripts that allow step-by-step data manipulation and debugging. Which tool is more suitable?

A Pig, because it supports procedural scripts with stepwise transformations and custom functions

B Hive, because it supports SQL-like queries and is easier for analysts

C Hive, because it is faster for all types of data processing

D Pig, because it only supports simple queries and no custom functions

💡 Hint

Think about which tool is designed for complex data flows and custom code.

Pig is designed for procedural data flows and supports custom functions, making it better for complex transformations.

Hive is more declarative and suited for SQL-like queries.

Practice

1. Which statement best describes the main difference between Pig and Hive in Hadoop?

easy

A. Pig stores data, Hive processes data.

B. Pig is declarative, and Hive is procedural.

C. Pig is used only for batch processing, Hive only for real-time.

D. Pig uses a scripting language, while Hive uses SQL-like queries.

Solution

1. Step 1: Understand Pig and Hive languages

   Pig uses a scripting language called Pig Latin, which is procedural. Hive uses a SQL-like declarative language called HiveQL.

2. Step 2: Compare their query styles

   Pig scripts describe how to process data step-by-step (procedural), while Hive queries describe what data to retrieve (declarative).

3. Final Answer:

   Pig uses a scripting language, while Hive uses SQL-like queries. -> Option D

4. Quick Check:

   Language style difference = Pig scripting, Hive SQL [OK]

Hint: Pig = scripting; Hive = SQL-like queries [OK]

Common Mistakes:

• Confusing procedural and declarative terms
• Thinking Pig uses SQL
• Assuming Hive is procedural

2. Which of the following is the correct way to write a simple query in Hive to select all records from a table named users?

easy

A. SELECT * FROM users;

B. LOAD users;

C. FETCH ALL FROM users;

D. GET * FROM users;

Solution

1. Step 1: Recall Hive query syntax

   Hive uses SQL-like syntax. The standard way to select all records is SELECT * FROM table_name;.

2. Step 2: Check each option

   SELECT * FROM users; matches the correct SQL syntax. Options A, C, and D are not valid Hive commands.

3. Final Answer:

   SELECT * FROM users; -> Option A

4. Quick Check:

   Hive uses SQL syntax = SELECT * FROM [table] [OK]

Hint: Hive queries look like standard SQL SELECT statements [OK]

Common Mistakes:

• Using non-SQL keywords like LOAD or FETCH for queries
• Forgetting the FROM keyword
• Using GET instead of SELECT

3. Given the following Pig script:

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

What will be the output if the input data contains:

('Alice', 30)
('Bob', 22)
('Carol', 27)

medium

A. All three records

B. ('Bob', 22) only

C. ('Alice', 30) and ('Carol', 27)

D. No output

Solution

1. Step 1: Understand the FILTER condition

   The script filters records where age is greater than 25.

2. Step 2: Apply filter to input data

   'Alice' (30) and 'Carol' (27) satisfy age > 25; 'Bob' (22) does not.

3. Final Answer:

   ('Alice', 30) and ('Carol', 27) -> Option C

4. Quick Check:

   Filter age > 25 = Alice and Carol [OK]

Hint: Filter keeps only records matching condition [OK]

Common Mistakes:

• Including records with age equal to 25
• Confusing FILTER with LOAD
• Assuming all records are output

4. Identify the error in this Hive query:

SELECT name age FROM users WHERE age > 20;

medium

A. Missing comma between selected columns.

B. Incorrect WHERE clause syntax.

C. Table name should be in quotes.

D. SELECT keyword is misspelled.

Solution

1. Step 1: Analyze the SELECT clause

   The query selects two columns: name and age. They must be separated by a comma.

2. Step 2: Check the query syntax

   The query misses a comma between 'name' and 'age'. The WHERE clause and table name are correct.

3. Final Answer:

   Missing comma between selected columns. -> Option A

4. Quick Check:

   Columns in SELECT need commas [OK]

Hint: Separate columns with commas in SELECT [OK]

Common Mistakes:

• Omitting commas between columns
• Adding quotes around table names unnecessarily
• Miswriting WHERE clause

5. You want to process a complex data flow involving multiple steps of filtering, grouping, and joining large datasets in Hadoop. Which tool is better suited and why?

hard

A. Hive, because it uses SQL and is easier for complex flows.

B. Pig, because it supports procedural scripting for complex data flows.

C. Hive, because it is procedural and faster for joins.

D. Pig, because it only supports simple queries.

Solution

1. Step 1: Understand tool strengths

   Pig is procedural and designed for complex data flows with multiple steps. Hive is declarative and better for simple querying.

2. Step 2: Match tool to task

   For complex filtering, grouping, and joining in multiple steps, Pig's scripting is more flexible and suitable.

3. Final Answer:

   Pig, because it supports procedural scripting for complex data flows. -> Option B

4. Quick Check:

   Complex flows = Pig scripting [OK]

Hint: Use Pig for complex multi-step data processing [OK]

Common Mistakes:

• Choosing Hive for complex procedural flows
• Thinking Pig only supports simple queries
• Confusing procedural and declarative