Database Design
SQL vs NoSQL
| Aspect | SQL | NoSQL |
|---|---|---|
| Schema | Fixed | Flexible |
| ACID | Yes | Varies |
| Scaling | Vertical | Horizontal |
Normalization
1NF: Atomic values
2NF: No partial dependencies
3NF: No transitive dependencies
When to Denormalize
- Read-heavy workloads
- Complex JOINs hurt performance
Key Points
- Understanding Database Questions 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
Indexing & Optimization
Index Types
B-tree: Range queries (default)
Hash: Equality only
GIN: Full-text, JSONB
GiST: Geospatial
EXPLAIN ANALYZE
Shows: sequential scan, rows removed, execution time
Identifies: missing indexes, slow queries
Optimization Steps
- EXPLAIN ANALYZE
- Add indexes
- Rewrite queries
- Connection pooling
- Caching
Key Points
- Understanding Database Questions 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
Transactions
ACID
Atomicity, Consistency, Isolation, Durability
Isolation Levels
| Level | Dirty Read | Phantom |
|---|---|---|
| READ_COMMITTED | No | Yes |
| REPEATABLE_READ | No | No |
| SERIALIZABLE | No | No |
Locking
- Pessimistic: SELECT FOR UPDATE
- Optimistic: version check
Key Points
- Understanding Database Questions 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 Database Questions in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Database Questions implementation
// Key aspects: validation, error handling, logging, testing
public class DatabaseQuestions {
// Production-ready implementation
}Identify and handle edge cases for Database Questions. 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 Database Questions. 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. When to denormalize?
2. Isolation level preventing phantom reads?
3. What is the primary purpose of Database Questions?
4. What is a common mistake when implementing Database Questions?
Flashcards
Question
ACID properties?
Click to reveal answer
Answer
Atomicity, Consistency, Isolation, Durability
Question
Optimistic vs pessimistic locking?
Click to reveal answer
Answer
Optimistic: version check. Pessimistic: row lock
Question
What is Database Questions?
Click to reveal answer
Answer
Database Questions is a key concept in backend development.
Question
When to use Database Questions?
Click to reveal answer
Answer
Use Database Questions when building production systems that require reliability, scalability, and maintainability.
Question
Database Questions 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.Normalize to 3NF, denormalize for reads
- 2.Index columns in WHERE/JOIN/ORDER BY
- 3.EXPLAIN ANALYZE to diagnose slow queries
- 4.SERIALIZABLE prevents all concurrency issues
Interview Tips
- •Design schema for real scenario
- •Walk through EXPLAIN output
- •Discuss ACID and isolation levels
Cheat Sheet
Database Interview
- Normalize: 3NF
- Index: B-tree, Hash, GIN, GiST
- ACID: Atomicity, Consistency, Isolation, Durability
- Locking: Pessimistic (SELECT FOR UPDATE) vs Optimistic (version)