Why indexes matter in SQL - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an index on the column 'email' in the 'users' table.

SQL

CREATE INDEX [1] ON users(email);

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

Aidx_email

Bindex_email

Cemail_index

Dusers_email_idx

Attempts:

3 left

💡 Hint

Common Mistakes

Using a name that is too generic or conflicts with existing indexes.

The index name is usually descriptive. Here, users_email_idx is a common naming style for indexes.

2fill in blank

medium

Complete the query to find users with the last name 'Smith' using the index.

SQL

SELECT * FROM users WHERE last_name [1] 'Smith';

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

A=

BLIKE

C!=

DIN

Attempts:

3 left

💡 Hint

Common Mistakes

Using LIKE without wildcards, which is less efficient.

Using != which finds non-matching rows.

The = operator is used to find exact matches, which can use an index efficiently.

3fill in blank

hard

Fix the error in the query to use the index on 'email' correctly.

SQL

SELECT * FROM users WHERE email [1] '[e-mail]';

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

AIN

BLIKE

CBETWEEN

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' which looks for exact matches only.

Using IN or BETWEEN which are not suitable here.

The LIKE operator with a wildcard is used to find emails ending with '@example.com'. Note: Leading wildcards may prevent full index usage in some databases.

4fill in blank

hard

Fill both blanks to create a unique index on the 'username' column in the 'accounts' table.

SQL

CREATE [1] INDEX [2] ON accounts(username);

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

AUNIQUE

BPRIMARY

CTEMPORARY

Daccounts_username_idx

Attempts:

3 left

💡 Hint

Common Mistakes

Using PRIMARY which is for primary keys only.

Using TEMPORARY which is for temporary indexes.

The UNIQUE keyword creates a unique index, preventing duplicate usernames. accounts_username_idx is a descriptive name following common conventions.

5fill in blank

hard

Fill all three blanks to write a query that uses the index on 'created_at' to find records after '2023-01-01'.

SQL

SELECT [1] FROM orders WHERE created_at [2] '[3]';

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

A*

B>

C2023-01-01

DCOUNT(*)

Attempts:

3 left

💡 Hint

Common Mistakes

Using COUNT(*) when all columns are needed.

Using '=' instead of '>' for records after the date.

Using an incorrect date format.

The query selects all columns (*) where the date is greater than (>) '2023-01-01'. This range query can efficiently use an index on 'created_at'.

Practice

1. Why do indexes matter in a database?

easy

A. They increase the size of the database without any benefit.

B. They automatically fix data errors in tables.

C. They slow down all queries regardless of type.

D. They make searching data faster by creating a quick lookup structure.

Solution

1. Step 1: Understand what indexes do

   Indexes create a special structure that helps the database find data quickly, like a book's index.

2. Step 2: Identify the main benefit

   This structure speeds up searching and filtering operations, making queries faster.

3. Final Answer:

   They make searching data faster by creating a quick lookup structure. -> Option D

4. Quick Check:

   Indexes improve search speed = B [OK]

Hint: Indexes speed up searches by acting like a book's index [OK]

Common Mistakes:

• Thinking indexes slow down all queries
• Believing indexes fix data errors
• Assuming indexes only increase database size

2. Which SQL statement correctly creates an index on the column email in the users table?

easy

A. CREATE INDEX users ON idx_email(email);

B. MAKE INDEX idx_email ON users(email);

C. CREATE INDEX idx_email ON users(email);

D. INDEX CREATE idx_email ON users(email);

Solution

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

   The correct syntax is CREATE INDEX index_name ON table_name(column_name);.

2. Step 2: Match the syntax with options

   CREATE INDEX idx_email ON users(email); matches the correct syntax exactly, others have wrong keywords or order.

3. Final Answer:

   CREATE INDEX idx_email ON users(email); -> Option C

4. Quick Check:

   Correct CREATE INDEX syntax = C [OK]

Hint: Remember: CREATE INDEX index_name ON table(column) [OK]

Common Mistakes:

• Using wrong keywords like MAKE or INDEX CREATE
• Swapping table and index names
• Missing ON keyword

3. Given a table orders with 1 million rows and an index on customer_id, what is the expected result of this query?

SELECT * FROM orders WHERE customer_id = 123;

medium

A. The query will use the index to quickly find matching rows.

B. The query will return no rows because indexes hide data.

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

D. The query will scan all 1 million rows, taking a long time.

Solution

1. Step 1: Understand index usage in WHERE clause

   An index on customer_id helps the database quickly locate rows matching that value.

2. Step 2: Predict query behavior

   The database uses the index to avoid scanning all rows, speeding up the query.

3. Final Answer:

   The query will use the index to quickly find matching rows. -> Option A

4. Quick Check:

   Index speeds WHERE search = D [OK]

Hint: Indexes speed up WHERE filters on indexed columns [OK]

Common Mistakes:

• Thinking indexes cause full table scans
• Believing indexes prevent queries from running
• Assuming indexes hide data from results

4. You created an index on the username column, but your query is still slow:

SELECT * FROM users WHERE username LIKE '%admin%';

What is the most likely reason?

medium

A. The index was created on the wrong table.

B. Indexes cannot speed up LIKE queries with a leading wildcard '%'.

C. The query syntax is incorrect and causes errors.

D. Indexes always slow down SELECT queries.

Solution

1. Step 1: Analyze the LIKE pattern

   A LIKE pattern starting with '%' means the search can match anywhere in the string, preventing index use.

2. Step 2: Understand index limitations

   Indexes work best when the search starts from the beginning of the string, so leading wildcards disable index usage.

3. Final Answer:

   Indexes cannot speed up LIKE queries with a leading wildcard '%'. -> Option B

4. Quick Check:

   Leading '%' disables index use = A [OK]

Hint: Leading '%' in LIKE disables index use [OK]

Common Mistakes:

• Assuming all LIKE queries use indexes
• Thinking index on wrong table causes this
• Believing indexes always slow SELECT

5. You have a table sales with columns sale_id, product_id, and sale_date. You often run this query:

SELECT * FROM sales WHERE product_id = 101 AND sale_date >= '2024-01-01';

Which indexing strategy will likely improve query speed the most?

hard

A. Create a composite index on (product_id, sale_date).

B. Create separate indexes on product_id and sale_date.

C. Create a single index on sale_date only.

D. Do not create any index to keep inserts fast.

Solution

1. Step 1: Understand query filters

   The query filters by product_id and a range on sale_date.

2. Step 2: Choose best index type

   A composite index on both columns helps the database quickly find rows matching both conditions together.

3. Step 3: Compare with other options

   Separate indexes may help but are less efficient than a composite index for combined filters.

4. Final Answer:

   Create a composite index on (product_id, sale_date). -> Option A

5. Quick Check:

   Composite index matches multi-column filter = A [OK]

Hint: Use composite index for multi-column WHERE filters [OK]

Common Mistakes:

• Creating index on only one column
• Using separate indexes instead of composite
• Avoiding indexes to speed inserts without reason