hotelapp-dev-1-pessimistic-lock #214
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Summary of Changes
Hello @yennanliu, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a foundational design document for managing concurrency within the hotel booking application. Its primary purpose is to outline robust strategies to prevent common issues like double booking and data inconsistency in a multi-instance environment. The document provides a comprehensive guide to various locking mechanisms, their trade-offs, and practical implementation considerations, ensuring the system's reliability and data integrity as it scales.
Highlights
- New Concurrency Design Document: A new Markdown document CONCURRENCY_LOCKING_DESIGN.md has been added, detailing strategies for handling concurrency in the hotel booking system.
- Comprehensive Locking Strategies: The document covers optimistic locking, pessimistic locking, a hybrid approach combining both, and the use of database constraints to prevent double bookings.
- Implementation Guidance: It provides Java code snippets for JPA-based optimistic and pessimistic locking, as well as considerations for retry mechanisms, lock timeouts, exception handling, and monitoring.
- Testing and Recommendations: The document includes strategies for concurrency and load testing, along with clear recommendations for production implementation and a migration strategy.
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Code Review
This is an excellent and comprehensive design document for handling concurrency in the hotel booking system. The different strategies are well-explained with clear code examples. My review includes a few suggestions to refine some of the logic in the examples, particularly around the hybrid strategy, metrics collection to prevent a memory leak, and improving debuggability in the concurrency test example. Overall, this is a solid foundation for implementation.
| public class BookingMetrics { | ||
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| private final MeterRegistry meterRegistry; | ||
| private final Map<Long, AtomicInteger> roomContentionMap = new ConcurrentHashMap<>(); |
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The roomContentionMap is declared as a ConcurrentHashMap which will grow indefinitely as new rooms are booked. This will cause a memory leak over time. Since the goal is to track "recent" booking attempts, this map should have a strategy for evicting old entries. Consider using a cache with a time-to-live (TTL) policy (e.g., from Google Guava or Caffeine) or implementing a periodic cleanup mechanism.
| if (getCurrentSystemLoad() > 0.8) { | ||
| return LockType.OPTIMISTIC; // Prefer throughput | ||
| } |
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The logic to switch to optimistic locking when system load is high seems counter-intuitive. High system load often correlates with high contention. In such scenarios, optimistic locking can lead to frequent transaction rollbacks and retries, which can further increase system load and decrease overall throughput. It might be more effective to use pessimistic locking during high load to guarantee transaction success and avoid retry storms.
| } catch (Exception e) { | ||
| return null; | ||
| } |
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The concurrency test example swallows exceptions by catching a generic Exception and returning null. This practice is also repeated later when retrieving future results. This makes debugging test failures very difficult because the root cause of the failure is hidden. It's better practice to at least log the exception before returning null to provide more insight when a test fails unexpectedly.
| } catch (Exception e) { | |
| return null; | |
| } | |
| } catch (Exception e) { | |
| // It's good practice to log exceptions in tests to make debugging easier. | |
| System.err.println("Booking failed during concurrency test: " + e.getMessage()); | |
| return null; | |
| } |
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