Why Hive enables SQL on Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Hive table named 'employees'.

Hadoop

CREATE TABLE employees (id INT, name STRING) [1];

Drag options to blanks, or click blank then click option'

ASTORED AS TEXTFILE

BSELECT * FROM employees

CINSERT INTO employees

DDROP TABLE employees

Attempts:

3 left

💡 Hint

Common Mistakes

Using SELECT or INSERT statements instead of storage format.

Omitting the storage format clause.

The STORED AS TEXTFILE clause defines the file format for the Hive table, which is necessary when creating tables.

2fill in blank

medium

Complete the code to query all records from the 'employees' table.

Hadoop

SELECT [1] FROM employees;

Drag options to blanks, or click blank then click option'

Aname

Bid

CCOUNT(*)

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting only one column when all are needed.

Using aggregate functions without GROUP BY.

The * symbol selects all columns from the table.

3fill in blank

hard

Fix the error in the Hive query to count employees.

Hadoop

SELECT COUNT([1]) FROM employees;

Drag options to blanks, or click blank then click option'

Aid

B*

Cname

Demployees

Attempts:

3 left

💡 Hint

Common Mistakes

Using table name inside COUNT instead of * or column.

Using a column that may have NULL values.

Using COUNT(*) counts all rows in the table.

4fill in blank

hard

Fill both blanks to create a Hive query that filters employees with id greater than 100.

Hadoop

SELECT * FROM employees WHERE id [1] [2];

Drag options to blanks, or click blank then click option'

A>

B100

C<

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than operator instead of greater than.

Using wrong number for filtering.

The query filters rows where id > 100.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps employee names to their ids if id is less than 200.

Hadoop

{ [3].[1]: [3].[2] for [3] in employees if [3].id < 200 }

Drag options to blanks, or click blank then click option'

Aname

Bid

Cemployee

Demp

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names in comprehension.

Mixing keys and values in the dictionary.

This comprehension creates a dictionary with employee names as keys and ids as values for employees with id less than 200.

Practice

1. Why does Hive enable SQL queries on Hadoop data?

easy

A. Because it translates SQL queries into MapReduce jobs

B. Because it stores data in a relational database

C. Because it replaces Hadoop's file system

D. Because it uses Python scripts to process data

Solution

1. Step 1: Understand Hive's role in Hadoop

   Hive acts as a bridge by converting SQL-like queries into Hadoop's processing framework.

2. Step 2: Recognize how Hive executes queries

   Hive converts SQL queries into MapReduce or other execution engines to process big data on Hadoop.

3. Final Answer:

   Because it translates SQL queries into MapReduce jobs -> Option A

4. Quick Check:

   Hive translates SQL to MapReduce [OK]

Hint: Hive converts SQL to MapReduce for Hadoop processing [OK]

Common Mistakes:

• Thinking Hive stores data like a traditional database
• Believing Hive replaces Hadoop's file system
• Assuming Hive uses Python for data processing

2. Which of the following is the correct way to write a Hive query to select all columns from a table named sales?

easy

A. SELECT * FROM sales;

B. GET ALL FROM sales;

C. FETCH * sales;

D. SHOW ALL FROM sales;

Solution

1. Step 1: Recall HiveQL syntax

   HiveQL uses SQL-like syntax, and SELECT * FROM table_name; is standard to select all columns.

2. Step 2: Identify correct syntax among options

   Only SELECT * FROM sales; matches the correct SQL/HiveQL syntax for selecting all columns.

3. Final Answer:

   SELECT * FROM sales; -> Option A

4. Quick Check:

   Standard SQL SELECT syntax = SELECT * FROM sales; [OK]

Hint: Use standard SQL SELECT syntax in Hive [OK]

Common Mistakes:

• Using non-SQL keywords like GET or FETCH
• Omitting FROM keyword
• Incorrect order of clauses

3. Given the Hive query:

SELECT region, COUNT(*) FROM sales GROUP BY region;

What does this query output?

medium

A. The total sales amount for each region

B. The average sales per region

C. The list of regions without counts

D. The number of sales records for each region

Solution

1. Step 1: Analyze the SELECT and GROUP BY clauses

   The query selects region and counts rows grouped by region.

2. Step 2: Understand COUNT(*) function

   COUNT(*) counts the number of rows in each group, so it counts sales records per region.

3. Final Answer:

   The number of sales records for each region -> Option D

4. Quick Check:

   COUNT(*) with GROUP BY = count of records per group [OK]

Hint: COUNT(*) counts rows per group in GROUP BY [OK]

Common Mistakes:

• Confusing COUNT(*) with SUM of sales
• Ignoring GROUP BY effect
• Assuming it returns averages

4. Identify the error in this Hive query:

SELECT name, age FROM users WHERE age = 'twenty';

medium

A. Using SELECT instead of FETCH

B. Using quotes around a numeric value in WHERE clause

C. Incorrect table name syntax

D. Missing GROUP BY clause

Solution

1. Step 1: Check data type usage in WHERE clause

   The query compares age to the string 'twenty', but age is numeric, so quotes cause a type mismatch.

2. Step 2: Confirm other parts of query

   Table name and SELECT syntax are correct; GROUP BY is not needed without aggregation.

3. Final Answer:

   Using quotes around a numeric value in WHERE clause -> Option B

4. Quick Check:

   Numeric values should not be quoted in WHERE [OK]

Hint: Match data types in WHERE clause (no quotes for numbers) [OK]

Common Mistakes:

• Adding unnecessary GROUP BY
• Misnaming tables
• Using wrong keywords like FETCH

5. You want to analyze sales data stored in Hadoop using Hive. Which approach best explains why Hive is suitable for this task?

hard

A. Hive replaces Hadoop's file system with a relational database

B. Hive stores data in a traditional SQL database for faster queries

C. Hive allows writing SQL queries that Hadoop converts to MapReduce jobs, enabling easy big data analysis

D. Hive requires users to write Java MapReduce code for data processing

Solution

1. Step 1: Understand Hive's integration with Hadoop

   Hive provides a SQL-like interface that translates queries into Hadoop jobs, making big data analysis easier.

2. Step 2: Compare other options

   Hive does not store data in traditional databases, nor does it replace Hadoop's file system or require Java coding from users.

3. Final Answer:

   Hive allows writing SQL queries that Hadoop converts to MapReduce jobs, enabling easy big data analysis -> Option C

4. Quick Check:

   Hive simplifies big data SQL querying on Hadoop [OK]

Hint: Hive translates SQL to Hadoop jobs for easy analysis [OK]

Common Mistakes:

• Thinking Hive stores data in SQL databases
• Believing Hive replaces Hadoop file system
• Assuming Hive needs Java coding