Architure upgrade_ asynchronous refactoring_English

1. Functional Correctness Testing

Ensure the asynchronous workflow accurately completes all business objectives.

  • Core Process Completeness:
    • Simulate a user placing an order and verify the order information is correctly created and persisted.
    • Verify that after order creation, a message is successfully sent to the designated Kafka Topic (e.g., order-created).
    • Verify that downstream consumers (e.g., inventory service, notification service, points service) can correctly consume the message and complete their respective business logic (deduct inventory, send SMS, award points, etc.).
  • Message Content Accuracy:
    • Check that the message body (Payload) sent to Kafka contains all required fields (e.g., order_id, user_id, item_id, quantity) with correct data types and values.
    • Verify the message Key (e.g., user_id or order_id) is set correctly to ensure messages are routed to the appropriate partition (to guarantee ordering).
  • Idempotency Testing:
    • Critical! Simulate a consumer processing the same message multiple times (via retry mechanisms or manual message re-sending).
    • Verify that critical operations like inventory deduction, points distribution, and coupon redemption are idempotent, preventing overselling or duplicate rewards due to repeated consumption.
  • Failure and Retry Mechanisms:
    • Simulate temporary failures or processing timeouts in downstream services (e.g., inventory service).
    • Verify that the Kafka consumer can handle the exception correctly and trigger retries (verify the number of retries and intervals are reasonable).
    • Verify that the business logic executes correctly after a retry succeeds.
  • Dead Letter Queue (DLQ) Testing:
    • Simulate a message that is destined to fail (e.g., malformed message body, associated data not found).
    • Verify that after a predefined number of retries, the message is correctly routed to the Dead Letter Queue (DLQ).
    • Verify that operations personnel can detect messages in the DLQ via monitoring or alerts and perform manual intervention or fixes.

2. Performance & Stress Testing

Verify the performance of the asynchronous architecture under real high-concurrency scenarios.

  • Baseline Performance Comparison:
    • Use the same load testing tools (e.g., JMeter, wrk) and scripts in both the pre-refactoring (synchronous) and post-refactoring (asynchronous) environments.
    • Compare key metrics: P95/P99 latency of the order submission API, overall system throughput (TPS), database QPS/TPS, server resources (CPU, memory).
  • High-Concurrency Stress Testing:
    • Simulate the "traffic surge" at the moment of a flash sale (e.g., tens or hundreds of thousands of concurrent users competing for items).
    • Verify:
      • The order service can respond quickly and maintain low latency.
      • The Kafka cluster can withstand high-throughput write pressure (Producer TPS).
      • Consumer groups can consume accumulated messages in a timely manner, preventing excessive message backlog (Lag).
      • Database write pressure (especially on the inventory table) remains within manageable limits.
  • Message Backlog Testing:
    • Artificially pause consumer services to allow a large number of messages to accumulate in the Kafka Topic.
    • Resume the consumers and observe their "catch-up" speed, verifying the system can quickly process the backlog and return to a normal state.
  • Kafka Cluster Performance Testing:
    • Test the throughput, latency, and stability of the Kafka cluster separately to ensure it is not a bottleneck in the entire chain.

3. Stability & Fault Tolerance Testing

Simulate various failures to test the resilience of the system.

  • Kafka Broker Failure:
    • Simulate the failure of a Kafka Broker node.
    • Verify that Producers and Consumers can automatically reconnect to other Brokers, and that message production and consumption can resume automatically after a brief interruption.
  • Network Partition:
    • Simulate network issues causing partial disconnection between producers/consumers and the Kafka cluster.
    • Verify the system's degradation strategies (e.g., local caching in the order service, fallback switches).
  • Prolonged Unavailability of Downstream Services:
    • Simulate the inventory service being down for several hours.
    • Verify:
      • Orders can still be created normally, with messages persisted in Kafka.
      • After the service recovers, consumers can continue processing and complete inventory deduction.
      • The processing of accumulated messages is ordered and does not cause subsequent business issues.
  • Slow Consumer Processing:
    • Simulate a consumer taking a very long time to process a single message.
    • Verify the extent of message backlog and its impact on Kafka disk space.

4. Data Consistency & Eventual Consistency Verification

Asynchronous architectures sacrifice strong consistency; eventual consistency must be verified.

  • End-to-End Consistency Check:
    • After load testing or test scenarios, write scripts to verify:
      • Total number of orders vs. Total number of items with inventory successfully deducted.
      • Number of winning users vs. Total number of prizes distributed.
      • Ensure no "overselling" (inventory deducted below zero) or "missed deductions" (order succeeded but inventory not deducted) occur.
  • Latency Monitoring:
    • Monitor the latency from order creation to the final completion of inventory deduction.
    • Although asynchronous, the latency should be within business-acceptable limits (e.g., seconds or minutes).

5. Monitoring, Alerting & Observability Validation

Ensure the system can be detected promptly when issues arise.

  • Kafka Monitoring:
    • Monitor the message production rate (Producer TPS), consumption rate (Consumer TPS), message backlog (Consumer Lag), and Broker resources for the Topic.
  • Consumer Monitoring:
    • Monitor consumption latency, error rate, and number of retries for each consumer group.
  • Alerting Setup:
    • Set up critical alerts for:
      • Consumer Lag exceeding a threshold (e.g., > 1000).
      • High consumer error rates.
      • New messages appearing in the Dead Letter Queue (DLQ).
      • High resource usage (disk, CPU) in the Kafka cluster.
  • Distributed Tracing:
    • Ensure the complete chain—from order creation, message sending, to consumer processing—can be traced in a distributed tracing system (e.g., SkyWalking, Zipkin) for easier troubleshooting.

Summary

Testing for an asynchronous refactoring of a flash sale scenario must cover five key dimensions: Functionality, Performance, Stability, Data Consistency, and Observability. Particular emphasis should be placed on idempotency, message backlog, eventual consistency, and monitoring/alerting. It is recommended to conduct thorough end-to-end load testing before going live and to adopt a canary release strategy initially to minimize risks.

posted @ 2025-09-01 11:56  bestsarah  阅读(4)  评论(0)    收藏  举报