Database indexing in HLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a database index?

A database index is a data structure that helps speed up data retrieval by allowing the database to find rows faster without scanning the entire table.

beginner

How does an index improve query performance?

An index works like a book's index: it points to the location of data, so the database can jump directly to the needed rows instead of checking every row.

intermediate

What are the common types of database indexes?

Common types include B-tree indexes for range queries, hash indexes for exact matches, and bitmap indexes for columns with few unique values.

intermediate

What is a trade-off when using indexes?

Indexes speed up reads but slow down writes because the index must be updated whenever data changes, and they also use extra storage space.

intermediate

What is a composite index?

A composite index is an index on multiple columns, which helps queries that filter or sort by more than one column efficiently.

Which data structure is commonly used for database indexes to support range queries?

A B-tree

B Hash table

C Linked list

D Stack

B-tree indexes allow efficient range queries by keeping data sorted and balanced.

What is a downside of adding many indexes to a database table?

A Loss of data integrity

B Slower data retrieval

C Reduced query flexibility

D Increased storage and slower writes

More indexes require extra storage and slow down insert, update, and delete operations.

Which type of index is best for exact match queries?

A Hash index

B B-tree index

C Bitmap index

D Full-text index

Hash indexes provide fast lookups for exact matches but are not suitable for range queries.

What does a composite index index?

A A single column

B Multiple columns

C Only primary keys

D Only foreign keys

Composite indexes cover multiple columns to optimize queries filtering on those columns.

Why might a database choose not to use an index for a query?

A The query is very complex

B The index is corrupted

C The table is very small

D The database is offline

For small tables, scanning all rows can be faster than using an index.

Explain how database indexes improve query speed and what trade-offs they introduce.

Think about how a book index helps find pages faster but takes space.

Describe different types of database indexes and when to use each.

Consider the kind of queries and data uniqueness.

Practice

1. What is the primary purpose of a database index?

easy

A. To increase the size of the database

B. To speed up data retrieval by allowing quick lookups

C. To store backup copies of data

D. To encrypt the database contents

Solution

1. Step 1: Understand what an index does

   An index helps the database find data faster by creating a quick lookup structure.

2. Step 2: Identify the main benefit

   This quick lookup reduces the time needed to search large tables.

3. Final Answer:

   To speed up data retrieval by allowing quick lookups -> Option B

4. Quick Check:

   Index = Faster data search [OK]

Hint: Indexes speed up searches by creating shortcuts [OK]

Common Mistakes:

• Thinking indexes store extra data copies
• Confusing indexes with backups
• Assuming indexes encrypt data

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

easy

A. CREATE INDEX idx_user ON users (user_id);

B. INDEX CREATE user_id ON users;

C. CREATE users INDEX ON user_id;

D. MAKE INDEX user_id IN users;

Solution

1. Step 1: Recall SQL syntax for creating indexes

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

2. Step 2: Match the syntax with options

   CREATE INDEX idx_user ON users (user_id); matches the correct syntax exactly.

3. Final Answer:

   CREATE INDEX idx_user ON users (user_id); -> Option A

4. Quick Check:

   CREATE INDEX ... ON ... (column) [OK]

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

Common Mistakes:

• Swapping table and column names
• Using wrong keyword order
• Missing parentheses around column

3. Given a table orders with an index on order_date, what will be the output of the query plan for SELECT * FROM orders WHERE order_date = '2023-01-01';?

medium

A. Full table scan without using the index

B. Ignoring the order_date column completely

C. Sorting all rows by order_date before filtering

D. Index seek on order_date to quickly find matching rows

Solution

1. Step 1: Understand how indexes affect query plans

   An index on order_date allows the database to quickly locate rows matching the date.

2. Step 2: Identify the expected query plan

   The database will perform an index seek to find matching rows efficiently, not scan the whole table.

3. Final Answer:

   Index seek on order_date to quickly find matching rows -> Option D

4. Quick Check:

   Indexed column = index seek [OK]

Hint: Indexed WHERE columns use index seek, not full scan [OK]

Common Mistakes:

• Assuming full table scan despite index
• Confusing sorting with filtering
• Ignoring index usage in query plan

4. A developer created an index on a column but notices no improvement in query speed. What could be a likely reason?

medium

A. The database automatically disables all indexes

B. The index was created with the wrong SQL syntax

C. The queries do not filter or sort using the indexed column

D. Indexes always slow down queries

Solution

1. Step 1: Analyze index usage conditions

   Indexes only help if queries use the indexed column in WHERE, JOIN, or ORDER BY clauses.

2. Step 2: Identify why no speed improvement occurs

   If queries don't use the indexed column, the index is ignored, so no speed gain.

3. Final Answer:

   The queries do not filter or sort using the indexed column -> Option C

4. Quick Check:

   Index helps only if column used in query [OK]

Hint: Index unused if queries ignore indexed columns [OK]

Common Mistakes:

• Assuming syntax error disables index
• Believing all indexes slow queries
• Thinking database disables indexes by default

5. You have a large table with frequent inserts and queries filtering by status and created_at. Which indexing strategy balances read speed and write overhead best?

hard

A. Create a composite index on (status, created_at)

B. Create separate indexes on status and created_at individually

C. Create no indexes to avoid write overhead

D. Create a full-text index on status and created_at

Solution

1. Step 1: Understand composite vs separate indexes

   A composite index on (status, created_at) helps queries filtering on both columns efficiently.

2. Step 2: Consider write overhead and query patterns

   Separate indexes increase write overhead more than one composite index and may be less efficient for combined filters.

3. Step 3: Exclude unsuitable options

   No indexes hurt read speed; full-text index is for text search, not filtering by status/date.

4. Final Answer:

   Create a composite index on (status, created_at) -> Option A

5. Quick Check:

   Composite index = balanced read/write [OK]

Hint: Composite indexes optimize multi-column filters with less overhead [OK]

Common Mistakes:

• Creating many separate indexes causing slow writes
• Skipping indexes hurting read performance
• Using full-text index for non-text filtering