Concurrency considerations in LLD - Architecture Diagram

System Overview - Concurrency considerations

This system manages multiple tasks running at the same time without interfering with each other. It ensures data stays correct and the system stays fast even when many users or processes work together.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
+-------------------+       +----------------+
|   Worker Service   |<----->|  Message Queue  |
+-------------------+       +----------------+
        |  ^                        |
        v  |                        v
+-------------------+       +----------------+
|  Shared Database   |<----->| Distributed Lock|
+-------------------+       +----------------+
        ^
        |
      Cache

Components

User

client

Initiates requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Routes requests to appropriate worker services and handles authentication

Worker Service

service

Processes tasks concurrently, manages business logic

Message Queue

queue

Queues tasks for asynchronous processing to avoid blocking

Shared Database

database

Stores persistent data accessed by multiple workers

Distributed Lock

lock_service

Coordinates access to shared resources to prevent conflicts

Cache

cache

Stores frequently accessed data to reduce database load

Request Flow - 13 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Worker Service

Worker Service → Distributed Lock

Distributed Lock → Worker Service

Worker Service → Cache

Cache → Worker Service

Worker Service → Shared Database

Worker Service → Message Queue

Message Queue → Worker Service

Worker Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Distributed Lock

Impact:Multiple workers may access shared resources simultaneously causing data corruption or race conditions.

Mitigation:Use a highly available distributed lock service with failover and retries. Implement optimistic concurrency controls in the database as backup.

Architecture Quiz - 3 Questions

Which component ensures that multiple workers do not update the same data at the same time?

A Message Queue

B Cache

C Distributed Lock

D Load Balancer

Design Principle

This architecture uses distributed locks and message queues to safely manage concurrent access and asynchronous processing, ensuring data consistency and system responsiveness under multiple simultaneous tasks.

Practice

1. What is the main purpose of using locks in concurrent systems?

easy

A. To allow unlimited access to shared resources

B. To prevent multiple threads from accessing shared data simultaneously

C. To speed up the execution of a single thread

D. To reduce memory usage in the system

Solution

1. Step 1: Understand concurrency risks

   When multiple threads access shared data at the same time, it can cause errors or inconsistent results.

2. Step 2: Role of locks

   Locks ensure only one thread accesses the shared data at a time, preventing conflicts and data corruption.

3. Final Answer:

   To prevent multiple threads from accessing shared data simultaneously -> Option B

4. Quick Check:

   Locks protect shared data = C [OK]

Hint: Locks protect shared data from simultaneous access [OK]

Common Mistakes:

• Thinking locks speed up single-thread execution
• Believing locks allow unlimited resource access
• Confusing locks with memory optimization

2. Which of the following is the correct way to acquire a lock in a typical low-level design?

easy

A. lock.notify() before accessing shared data

B. lock.release() before accessing shared data

C. lock.wait() after accessing shared data

D. lock.acquire() before accessing shared data

Solution

1. Step 1: Understand lock usage order

   To safely access shared data, a thread must first acquire the lock to block others.

2. Step 2: Correct method to acquire lock

   The method lock.acquire() is used to obtain the lock before accessing shared data.

3. Final Answer:

   lock.acquire() before accessing shared data -> Option D

4. Quick Check:

   Acquire lock first = A [OK]

Hint: Acquire lock before shared data access [OK]

Common Mistakes:

• Releasing lock before access
• Using wait or notify incorrectly
• Confusing acquire with release

3. Consider this pseudocode for two threads incrementing a shared counter without locks:

Thread 1: temp = counter
          temp = temp + 1
          counter = temp

Thread 2: temp = counter
          temp = temp + 1
          counter = temp

What is the possible final value of counter if it starts at 0?

medium

A. 2

B. Any negative number

C. 1

D. 0

Solution

1. Step 1: Analyze concurrent increments without locks

   Both threads read the same initial value 0, increment it to 1, and write back 1, causing one increment to be lost.

2. Step 2: Determine final counter value

   Because of race condition, the counter may only increase once, resulting in final value 1 instead of 2.

3. Final Answer:

   1 -> Option C

4. Quick Check:

   Race condition causes lost update = 1 [OK]

Hint: Without locks, increments can overwrite each other [OK]

Common Mistakes:

• Assuming both increments always succeed
• Ignoring race conditions
• Thinking counter can be negative here

4. In the following code snippet, what is the main concurrency issue?

lock.acquire()
shared_data.append(1)
# Missing lock.release()

medium

A. Deadlock due to missing lock release

B. Data race on shared_data

C. Syntax error in lock usage

D. No issue, code is safe

Solution

1. Step 1: Identify missing lock release

   The code acquires a lock but never releases it, so other threads waiting for the lock will block forever.

2. Step 2: Understand deadlock impact

   This causes a deadlock where threads cannot proceed, halting system progress.

3. Final Answer:

   Deadlock due to missing lock release -> Option A

4. Quick Check:

   Missing release causes deadlock = A [OK]

Hint: Always release locks after acquiring [OK]

Common Mistakes:

• Thinking it's a syntax error
• Assuming no issue without release
• Confusing deadlock with data race

5. You design a system where multiple threads read and write a shared cache. To improve performance, you want to allow multiple readers but only one writer at a time. Which concurrency control mechanism fits best?

hard

A. Use a read-write lock allowing concurrent reads but exclusive writes

B. Use a simple mutex lock for all access

C. Use no locks and rely on thread scheduling

D. Use a semaphore with count 1 for all operations

Solution

1. Step 1: Understand concurrency needs for readers and writers

   Multiple readers can safely access shared data simultaneously, but writers need exclusive access to avoid conflicts.

2. Step 2: Choose appropriate lock type

   A read-write lock allows many readers at once but only one writer, balancing concurrency and safety efficiently.

3. Final Answer:

   Use a read-write lock allowing concurrent reads but exclusive writes -> Option A

4. Quick Check:

   Read-write lock fits multiple readers, single writer = B [OK]

Hint: Read-write locks allow many readers, one writer [OK]

Common Mistakes:

• Using simple mutex reduces concurrency
• Ignoring need for exclusive write access
• Relying on no locks causes data races