Skip to content
intermediate Phase · Concurrency

Parallelism

Execute multiple computations simultaneously for faster processing.

30m
0 problems
Topic Progress 0%

Parallelism

Concurrency vs Parallelism

Concurrency (one core, switching):
[A][B][A][B][A]

Parallelism (multiple cores, simultaneously):
Core 1: [A][A][A]
Core 2: [B][B][B]

Parallel Processing

Type Example
Data parallelism Same operation on different data
Task parallelism Different operations on different data

Key Points

  • Understanding Parallelism is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Best Practices

Key Principles

  1. Follow SOLID principles
  2. Write clean, readable code
  3. Test thoroughly
  4. Document decisions
  5. Monitor in production

Implementation

  • Start simple, refactor as needed
  • Use established patterns
  • Consider trade-offs
  • Review with peers

Continuous Improvement

  • Learn from incidents
  • Update documentation
  • Share knowledge
  • Mentor others

Key Points

  • Understanding Parallelism is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Practice Problems

0 / 3 solved
Implement Parallelism

Design and implement a solution for Parallelism in a backend system. Consider scalability, error handling, and production readiness.

Solution 
// Parallelism implementation
// Key aspects: validation, error handling, logging, testing

public class Parallelism {
    // Production-ready implementation
}
Parallelism Edge Cases

Identify and handle edge cases for Parallelism. What happens under high load, with invalid input, or during failures?

Solution 
// Edge case handling:
// 1. Null/empty input -> validation
// 2. High load -> rate limiting, queuing
// 3. Failures -> retries, circuit breaker
// 4. Concurrent access -> locks, idempotency
Parallelism Testing Strategy

Write a testing strategy for Parallelism. Include unit tests, integration tests, and performance tests.

Solution 
// Test plan:
// - Unit: 80% coverage target
// - Integration: API contracts
// - Performance: latency, throughput
// - Chaos: failure injection

Quiz

1. Parallelism needs?

Question 1 options

2. Data parallelism is?

Question 2 options

3. What is a common mistake when implementing Parallelism?

Question 3 options

Flashcards

Question

Concurrency vs parallelism?

Click to reveal answer

Answer

Concurrency: structure. Parallelism: execution.

Question

Parallelism needs?

Click to reveal answer

Answer

Multiple CPU cores

Question

Parallelism best practices

Click to reveal answer

Answer

Follow SOLID principles, write clean code, test thoroughly, document decisions, and monitor in production.

Revision Notes

Key Takeaways

  • 1. Concurrency: structure (dealing with many)
  • 2. Parallelism: execution (doing many at once)
  • 3. Parallelism needs multiple cores
  • 4. Both improve throughput

Interview Tips

  • • Explain concurrency vs parallelism
  • • Know when each applies

Cheat Sheet

Parallelism

  • Concurrency: structure
  • Parallelism: execution (needs multiple cores)
  • Data parallelism: same op, different data
  • Task parallelism: different ops, different data