Availability checking in LLD - Architecture Diagram

System Overview - Availability checking

This system checks if a resource (like a product, room, or service) is available for booking or use. It must respond quickly and handle many users checking availability at the same time without errors.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Availability Service
  |
  +-----> Cache
  |
  v
Database

Components

User

client

Sends availability check requests

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Receives requests, handles authentication and routing to services

Availability Service

service

Processes availability check logic, queries cache and database

Cache

cache

Stores recent availability results for fast response

Database

database

Stores authoritative availability data

Request Flow - 11 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Availability Service

Availability Service → Cache

Cache → Availability Service

Availability Service → Database

Database → Availability Service

Availability Service → Cache

Availability Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Availability Service cannot get fast cached data, causing slower responses as it queries the database directly.

Mitigation:System continues working by querying the database; cache can be rebuilt asynchronously. Adding cache replication or fallback caches can improve resilience.

Architecture Quiz - 3 Questions

Which component first receives the user's availability check request?

A Availability Service

B API Gateway

C Load Balancer

D Cache

Design Principle

This design uses a cache to speed up availability checks and reduce database load. The load balancer and API gateway ensure requests are distributed and managed securely. The system gracefully handles cache failures by falling back to the database, ensuring availability and reliability.

Practice

1. What is the main purpose of availability checking in system design?

easy

A. To create backups of system data

B. To increase the speed of data processing

C. To encrypt user data for security

D. To determine if a resource is free or ready to use

Solution

1. Step 1: Understand the concept of availability checking

   Availability checking is about verifying if a resource like a room, item, or slot is free to be used or booked.

2. Step 2: Identify the main goal

   The main goal is to know if the resource is ready or free, not about speed, security, or backups.

3. Final Answer:

   To determine if a resource is free or ready to use -> Option D

4. Quick Check:

   Availability checking = resource readiness [OK]

Hint: Availability checking means resource is free or not [OK]

Common Mistakes:

• Confusing availability with performance optimization
• Mixing availability with security features
• Thinking availability means data backup

2. Which of the following code snippets correctly checks if a room is available given a list of booked rooms booked_rooms = [101, 102, 103] and a requested room requested_room = 104?

easy

A. if requested_room in booked_rooms:
print('Available')

B. if requested_room == booked_rooms:
print('Available')

C. if requested_room not in booked_rooms:
print('Available')

D. if requested_room > booked_rooms:
print('Available')

Solution

1. Step 1: Understand the list and requested room

   booked_rooms contains rooms already taken: 101, 102, 103. requested_room is 104.

2. Step 2: Check correct condition for availability

   Room is available if requested_room is NOT in booked_rooms. So, 'if requested_room not in booked_rooms' is correct.

3. Final Answer:

   if requested_room not in booked_rooms: print('Available') -> Option C

4. Quick Check:

   Not in booked_rooms means available [OK]

Hint: Check 'not in' to confirm availability [OK]

Common Mistakes:

• Using 'in' instead of 'not in' to check availability
• Comparing equality of a number to a list
• Using greater than operator on list

3. Given the following code, what will be the output?

booked_slots = {"9AM": True, "10AM": False}
requested_slot = "10AM"
if not booked_slots.get(requested_slot, False):
    print("Slot Available")
else:
    print("Slot Booked")

medium

A. Slot Available

B. Slot Booked

C. KeyError

D. No output

Solution

1. Step 1: Understand the dictionary and requested slot

   booked_slots maps times to True (booked) or False (free). "10AM" is False, meaning free.

2. Step 2: Evaluate the condition

   booked_slots.get("10AM", False) returns False. 'not False' is True, so it prints "Slot Available".

3. Final Answer:

   Slot Available -> Option A

4. Quick Check:

   False means free, so output is Slot Available [OK]

Hint: False means free slot, so print available [OK]

Common Mistakes:

• Assuming True means available instead of booked
• Expecting KeyError when key exists
• Ignoring default value in get()

4. Identify the bug in the following availability check code:

def is_available(stock, requested):
    if requested > stock:
        return True
    else:
        return False

print(is_available(5, 10))

medium

A. The function should return False when requested is greater than stock

B. The function is correct and returns True

C. The condition should be 'requested <= stock' to return True

D. The function should compare 'stock > requested' instead

Solution

1. Step 1: Analyze the condition logic

   Current code returns True if requested > stock, meaning more requested than available stock.

2. Step 2: Correct logic for availability

   Availability means stock should be enough or more than requested. So, if requested > stock, return False.

3. Final Answer:

   The function should return False when requested is greater than stock -> Option A

4. Quick Check:

   Requested > stock means not available [OK]

Hint: Availability means stock >= requested, else False [OK]

Common Mistakes:

• Returning True when requested exceeds stock
• Confusing greater than with less than
• Not testing with example values

5. You are designing an availability checking system for a hotel booking platform. Which approach best ensures high availability and scalability when checking room availability in real-time?

hard

A. Use a centralized database with locking to check and update availability synchronously

B. Cache availability data in memory with periodic sync to the database and use optimistic concurrency

C. Check availability by scanning all booking records on every request without caching

D. Allow double booking and resolve conflicts manually later

Solution

1. Step 1: Understand requirements for high availability and scalability

   System must respond quickly and handle many requests without blocking.

2. Step 2: Evaluate options for real-time availability checking

   Cache availability data in memory with periodic sync to the database and use optimistic concurrency uses caching and optimistic concurrency, reducing database load and avoiding locks, improving scalability and availability.

3. Final Answer:

   Cache availability data in memory with periodic sync to the database and use optimistic concurrency -> Option B

4. Quick Check:

   Caching + optimistic concurrency = scalable availability [OK]

Hint: Cache data and use optimistic concurrency for scalable availability [OK]

Common Mistakes:

• Using locking causing bottlenecks
• Scanning all records causing slow response
• Allowing double booking causing user issues