Availability checking in LLD - Scalability & System Analysis

Scalability Analysis - Availability checking

Growth Table: Availability Checking System

Users	Requests per Second	Data Stored	Latency Requirements	System Changes
100	~10-50	Small (KBs)	Low (seconds)	Single server, simple DB
10,000	~1,000	MBs	Low (seconds)	Load balancer, caching, DB indexing
1,000,000	~100,000	GBs	Very low (milliseconds)	Horizontal scaling, DB sharding, CDN, async processing
100,000,000	~10,000,000	TBs	Very low (milliseconds)	Multi-region deployment, advanced caching, microservices

First Bottleneck

At small scale, the database is the first bottleneck because it handles all availability check requests and stores status data. As traffic grows, the DB CPU and I/O limits are reached first, causing slow responses.

Scaling Solutions

• Database Read Replicas: Offload read queries to replicas to reduce load on primary DB.
• Caching: Use in-memory caches (like Redis) to store recent availability results and reduce DB hits.
• Horizontal Scaling: Add more application servers behind a load balancer to handle more requests.
• Sharding: Partition database by user or region to distribute load.
• Asynchronous Processing: Use queues to handle availability checks in background, improving responsiveness.
• CDN: Cache static availability data closer to users to reduce latency.

Back-of-Envelope Cost Analysis

For 1 million users with 100k requests/sec:

• Database: Needs to handle ~100k QPS, requiring sharding and replicas.
• Cache: Should handle 100k+ ops/sec, requiring Redis cluster.
• Bandwidth: Assuming 1 KB per request, ~100 MB/s bandwidth needed.
• Storage: GBs to TBs depending on data retention and history.

Interview Tip

Start by defining the scale and requirements. Identify the first bottleneck clearly. Discuss scaling solutions step-by-step, focusing on database and caching. Mention trade-offs and latency impact. Use real numbers to support your reasoning.

Self Check Question

Your database handles 1000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first and why?

Key Result

The database is the first bottleneck in availability checking systems as traffic grows; scaling requires caching, read replicas, and horizontal scaling to maintain low latency and high availability.

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