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Batch Processing

Process large datasets efficiently with batch operations.

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Batch Processing

Batch Insert

// Bad: individual inserts
for (Product p : products) {
    productRepository.save(p);  // 1000 DB calls!
}

// Good: batch insert
productRepository.saveAll(products);  // 1 DB call!

Batch Size

spring.jpa.properties.hibernate.jdbc.batch_size=50
spring.jpa.properties.hibernate.order_inserts=true

Bulk Operations

@Modifying
@Query("UPDATE Product p SET p.status = :status WHERE p.id IN :ids")
int bulkUpdateStatus(@Param("ids") List<Long> ids, @Param("status") String status);

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 Batch Processing 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

0/3solved
Implement Batch Processing

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

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

public class BatchProcessing {
    // Production-ready implementation
}
Batch Processing Edge Cases

Identify and handle edge cases for Batch Processing. 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
Batch Processing Testing Strategy

Write a testing strategy for Batch Processing. 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. saveAll() vs save() in loop?

Question 1 options

2. Hibernate batch size?

Question 2 options

3. What is the primary purpose of Batch Processing?

Question 3 options

4. What is a common mistake when implementing Batch Processing?

Question 4 options

Flashcards

Question

saveAll() benefit?

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Answer

One DB call instead of N individual calls

Question

Batch size recommendation?

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Answer

50 (adjust based on testing)

Question

What is Batch Processing?

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Answer

Batch Processing is a key concept in backend development.

Question

When to use Batch Processing?

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Answer

Use Batch Processing when building production systems that require reliability, scalability, and maintainability.

Question

Batch Processing 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.Batch operations: one call vs N calls
  • 2.saveAll() for bulk inserts
  • 3.Set Hibernate batch_size=50
  • 4.Bulk update with @Modifying @Query

Interview Tips

  • •Implement batch operations
  • •Optimize batch size

Cheat Sheet

Batch Processing

  • saveAll() vs save() in loop
  • batch_size=50
  • Bulk: @Modifying @Query
  • One call vs N calls