Why indexes matter in SQL - Quick Recap

Recall & Review

beginner

What is an index in a database?

An index is a special data structure that helps the database find rows faster, like a book's index helps you find pages quickly.

beginner

How do indexes improve query performance?

Indexes let the database skip scanning every row by quickly locating the data, making searches much faster.

intermediate

Can indexes slow down data changes? Why?

Yes, because when you add, update, or delete data, the index must also be updated, which takes extra time.

beginner

What types of queries benefit most from indexes?

Queries that search for specific values, join tables, or sort data benefit most because indexes speed up these operations.

intermediate

Why shouldn't you index every column?

Because indexes use extra space and slow down data changes, so only important columns used in searches or joins should be indexed.

What does a database index do?

A Speeds up data retrieval

B Deletes duplicate rows

C Compresses data

D Backs up the database

Indexes help the database find data faster, improving retrieval speed.

Which operation can become slower because of indexes?

A Writing or updating data

B Reading data

C Backing up data

D Creating tables

Indexes need updating when data changes, which can slow down write operations.

Which query type benefits most from indexes?

A Creating new tables

B Full table scans

C Searching for specific values

D Deleting tables

Searching for specific values is faster with indexes.

Why is it not good to index every column?

A Indexes delete data

B Indexes slow down data changes and use extra space

C Indexes make queries slower

D Indexes prevent backups

Too many indexes slow down writes and use more storage.

What is a real-life example of how an index works?

A A dictionary definition

B A bookmark in a book

C A page number written on every page

D A table of contents in a book

An index is like a table of contents that helps you find information quickly.

Explain why indexes matter in databases and how they affect query speed and data updates.

Think about how a book index helps you find pages fast but takes time to update if pages change.

Describe when you should create an index on a database column and when you should avoid it.

Not every column needs an index; focus on important ones for queries.

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