Why indexing speeds up data retrieval in DBMS Theory - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show how an index helps find data faster.

DBMS Theory

SELECT * FROM employees WHERE employee_id = [1];

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

A'12345'

B12345

Cemployee_id

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Adding quotes around numeric values, causing potential type mismatch.

Using column names instead of values.

The employee_id is typically numeric, so use the numeric literal without quotes to match the column type properly and utilize the index.

2fill in blank

medium

Complete the code to create an index on the 'name' column.

DBMS Theory

CREATE INDEX idx_name ON employees([1]);

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

Aname

Bemployee_id

Csalary

Ddepartment

Attempts:

3 left

💡 Hint

Common Mistakes

Creating index on wrong column.

Using table name instead of column name.

Creating an index on the 'name' column speeds up searches by that column.

3fill in blank

hard

Fix the error in the query that tries to use an index.

DBMS Theory

SELECT * FROM employees WHERE [1] = 'John';

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

Asalary

BName

Cemployee_id

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong case for column names.

Using a column without an index.

The column name is case-sensitive in some databases; 'name' is correct, 'Name' may cause error.

4fill in blank

hard

Fill both blanks to create a query that uses an index to find employees with salary greater than 50000.

DBMS Theory

SELECT * FROM employees WHERE [1] [2] 50000;

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

Asalary

B>

C<

Demployee_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column for filtering.

Using wrong comparison operator.

The query filters employees where salary is greater than 50000, using the 'salary' column and the '>' operator. An index on salary enables faster range scans.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps employee names to their salaries, but only for salaries above 60000.

DBMS Theory

{ [3]['[1]']: [3]['[2]'] for [3] in employees if [3]['[2]'] > 60000 }

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

Aname

Bsalary

Cemployee

Demployee_id

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up keys and values.

Using wrong variable names.

Not filtering correctly.

This comprehension creates a dictionary with employee names as keys and salaries as values, filtering for salaries above 60000.

Practice

1. Why does indexing speed up data retrieval in a database?

easy

A. Because it creates a quick lookup structure like a book's index

B. Because it stores data in random order

C. Because it deletes unnecessary data automatically

D. Because it compresses all data to save space

Solution

1. Step 1: Understand what indexing does

   Indexing creates a special data structure that helps find data quickly without scanning the whole table.

2. Step 2: Compare to a book's index

   Just like a book's index lets you find a topic page fast, database indexes let the system find rows quickly.

3. Final Answer:

   Because it creates a quick lookup structure like a book's index -> Option A

4. Quick Check:

   Index = Quick lookup [OK]

Hint: Think of index as a book's index for fast search [OK]

Common Mistakes:

• Confusing indexing with data compression
• Thinking indexing deletes data
• Assuming indexing randomizes data order

2. Which of the following is the correct way to create an index on the column employee_id in SQL?

easy

A. CREATE employees INDEX idx_emp(employee_id);

B. MAKE INDEX idx_emp FROM employees(employee_id);

C. CREATE INDEX idx_emp ON employees(employee_id);

D. INDEX CREATE idx_emp ON employees(employee_id);

Solution

1. Step 1: Recall SQL syntax for creating an index

   The correct syntax starts with CREATE INDEX, followed by the index name, then ON and the table and column.

2. Step 2: Match syntax with options

   CREATE INDEX idx_emp ON employees(employee_id); matches the correct SQL syntax exactly.

3. Final Answer:

   CREATE INDEX idx_emp ON employees(employee_id); -> Option C

4. Quick Check:

   CREATE INDEX ... ON ... [OK]

Hint: Remember SQL starts with CREATE INDEX for indexes [OK]

Common Mistakes:

• Using wrong keyword order
• Confusing CREATE INDEX with other commands
• Missing ON keyword

3. Consider a table with 1 million rows and an index on the username column. What will likely happen when you run SELECT * FROM users WHERE username = 'alice';?

medium

A. The database uses the index to quickly find 'alice' without scanning all rows

B. The database scans all 1 million rows to find 'alice'

C. The query will fail because indexes cannot be used in SELECT

D. The database deletes all rows except 'alice'

Solution

1. Step 1: Understand the role of index in query

   The index on username helps the database find the row with 'alice' quickly without scanning the entire table.

2. Step 2: Analyze the query execution

   The database uses the index to jump directly to the matching row, improving speed.

3. Final Answer:

   The database uses the index to quickly find 'alice' without scanning all rows -> Option A

4. Quick Check:

   Index speeds up SELECT search [OK]

Hint: Index avoids full table scan for WHERE queries [OK]

Common Mistakes:

• Thinking index slows down SELECT
• Believing index is ignored in queries
• Assuming query deletes data

4. A developer notices that after adding an index, insert operations became slower. What is the most likely reason?

medium

A. The database deletes old data when indexing

B. Indexes require extra work to update during inserts

C. Indexes prevent any data from being inserted

D. The index compresses data causing delays

Solution

1. Step 1: Understand index maintenance during inserts

   When new rows are inserted, the index must also be updated to include the new data, adding extra work.

2. Step 2: Explain why this slows inserts

   This extra step means inserts take longer compared to no index.

3. Final Answer:

   Indexes require extra work to update during inserts -> Option B

4. Quick Check:

   Index update slows inserts [OK]

Hint: Index updates add overhead on inserts [OK]

Common Mistakes:

• Thinking indexes block inserts
• Believing indexes delete data
• Assuming indexes compress data during insert

5. You have a large table with millions of rows and frequent queries filtering by email. You create an index on email. However, queries are still slow. What could be a reason?

hard

A. The table is too big for any index to help

B. Indexes always make queries slow

C. The database ignores indexes on text columns

D. The index is not used because the query filters with a function like LOWER(email)

Solution

1. Step 1: Understand how functions affect index usage

   If a query applies a function like LOWER() on the indexed column, the index may not be used because the function changes the data.

2. Step 2: Explain why this causes slow queries

   Without using the index, the database must scan many rows, causing slow performance.

3. Final Answer:

   The index is not used because the query filters with a function like LOWER(email) -> Option D

4. Quick Check:

   Functions on indexed columns block index use [OK]

Hint: Functions on indexed columns disable index use [OK]

Common Mistakes:

• Assuming indexes always speed queries
• Believing table size alone blocks indexes
• Thinking text columns cannot be indexed