Database indexing in HLD - Architecture Diagram

System Overview - Database indexing

This system explains how database indexing improves data retrieval speed. It shows how queries use indexes to find data faster instead of scanning the whole database. The key requirement is to reduce query response time while keeping data consistent.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Server
  |
  v
Database Engine
  |       
  |---> Index Structure (B-Tree)
  |
  v
Data Storage

Components

User

client

Sends data queries to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API servers

API Server

service

Receives queries and interacts with the database engine

Database Engine

service

Processes queries using indexes and accesses data storage

Index Structure (B-Tree)

index

Organizes keys to quickly locate data without scanning entire storage

Data Storage

database_storage

Stores the actual data records on disk

Request Flow - 10 Hops

User → Load Balancer

Load Balancer → API Server

API Server → Database Engine

Database Engine → Index Structure (B-Tree)

Index Structure (B-Tree) → Database Engine

Database Engine → Data Storage

Data Storage → Database Engine

Database Engine → API Server

API Server → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Index Structure (B-Tree)

Impact:Queries slow down significantly because the database must scan all data records without index help.

Mitigation:System falls back to full table scan; rebuild or repair index asynchronously to restore performance.

Architecture Quiz - 3 Questions

Which component helps the database find data quickly without scanning all records?

A Load Balancer

B API Server

C Index Structure (B-Tree)

D Data Storage

Design Principle

Using an index structure like a B-Tree allows the database engine to quickly locate data without scanning the entire dataset. This improves query performance significantly. The architecture separates concerns by using a load balancer to distribute requests and an API server to handle queries before reaching the database engine.

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