This article explains how to handle massive concurrent requests in a group‑buy scenario by using Redis distributed locks to avoid database overselling, MQ delayed messages for timeout processing, and a local outbox table to guarantee reliable cross‑service notifications.
The article explains a simplified order‑payment flow, identifies common causes of lost orders and duplicate payments, and provides practical backend strategies such as intermediate payment states, timeout queries, idempotent notifications, and Redis‑based duplicate‑submission protection.
This article explains the inner workings of InnoDB’s lock‑wait timeout thread, detailing how it scans waiting slots, identifies timed‑out transactions, processes timeout logic, and notifies the affected transactions, while also covering related data structures and configuration variables.
This article analyzes why distributed transactions are difficult in large‑scale microservice architectures, explains the importance of unique transaction IDs, idempotency, and timeout handling, and presents pragmatic approaches such as local logging, undo‑log tables, NoSQL ID arrays, and selective locking to achieve reliable consistency.
The article explains that distributed transactions in micro‑service architectures are difficult because they must guarantee atomicity across heterogeneous services, handle time‑outs, and reconcile results using unique transaction IDs, undo‑logs, or NoSQL strategies, while balancing locking, availability, and consistency trade‑offs.
This article explains how to handle asynchronous operations and waiting in Python Playwright using proper wait conditions, explicit waits, and condition variables to improve test stability.
This article analyzes a real production incident caused by improper thread‑pool usage, explains common misconceptions about core parameters, demonstrates faulty code examples, and provides concrete recommendations for configuring thread pools, handling timeouts, and avoiding deadlocks in Java backend services.
The article explains why distributed transactions in micro‑service architectures are hard—due to remote failures, timeouts, and lack of atomic guarantees—and outlines practical approaches such as unique transaction IDs, audit logs, SQL/NoSQL persistence, TCC, Saga or compensation patterns to achieve reliable consistency while balancing availability.
This article explains the typical order flow in payment systems, identifies external and internal order loss scenarios, and provides concrete measures such as adding a "payment in progress" state, setting timeout queries, ensuring idempotent notifications, and using Redis hashes to avoid duplicate submissions.
