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
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- Documentation: Keep docs updated with code changes
Best Practices
Key Principles
- Follow SOLID principles
- Write clean, readable code
- Test thoroughly
- Document decisions
- 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
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- Documentation: Keep docs updated with code changes
Practice Problems
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
}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, idempotencyWrite 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 injectionQuiz
1. Parallelism needs?
2. Data parallelism is?
3. What is the primary purpose of Parallelism?
4. What is a common mistake when implementing Parallelism?
Flashcards
Question
Concurrency vs parallelism?
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Answer
Concurrency: structure. Parallelism: execution.
Question
Parallelism needs?
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Answer
Multiple CPU cores
Question
What is Parallelism?
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Answer
Parallelism is a key concept in backend development.
Question
When to use Parallelism?
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Answer
Use Parallelism when building production systems that require reliability, scalability, and maintainability.
Question
Parallelism best practices
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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