11 posts
How I separated review mutations from statistics calculation, then had a Review Consumer recompute statistics from the data available when each RabbitMQ event was processed. This article also covers functional idempotency and the message-loss windows that remained.
An event is being held to celebrate a celebrity's visit, with tickets available only to the first 1,000 people. Applications will be accepted through a web application. Suppose you protect the system with a distributed lock. Are you confident that concurrency is now completely under control?
This article shows how to use Spring AOP to separate distributed-lock handling completely from business logic. When you use the Lock interface for concurrency control, try-finally blocks and lock-management code can overwhelm the business logic. Just as Spring's @Transactional simplifies transaction management, we will solve this problem with a DistributedLock aspect and extend @DistributedLock with a SpEL evaluator modeled after Spring Cache.
This article continues from the previous post, "Understanding Pessimistic Locks Through a Like Feature." It corrects the misconception that "Lettuce supports only spin locks" and explains how Spring RedisLockRegistry's Pub/Sub lock mode can implement an efficient distributed lock without Redisson.
This article follows the process of solving the concurrency problems in a like feature with database locks. It explains how pessimistic locking works and examines the performance degradation caused by next-key locks.
This article explores how the JVM monitor behind synchronized actually works, examines the concurrency-control features provided by ReentrantLock, and solves the producer-consumer problem that can arise in concurrent programs. It then improves performance with a lock-striping strategy based on per-user keys.
This article traces how I diagnosed thread blocking caused by network I/O and found a fundamental solution with JDK 21 virtual threads. Beyond the initial fix, it examines the unexpected challenges of thread pinning and overwhelming that emerged during adoption, along with their solutions.
Suppose we are designing a first-come, first-served event system. The requirements are listed below. If 1,000 people request 100 available items at the same time, how can we ensure that exactly 100 people receive one? The solutions commonly proposed in this situation are listed below.
Our team began a project integrating with an external system. The requirement seemed simple: "We need to use an external API to determine whether an order can be placed on a given date." In practice, however, giving customers accurate information required fetching 40–60 date-specific delivery plans at once on a single screen. Even with parallel processing…
In Spring, @Transactional(readOnly = true) is commonly said to optimize performance by changing dirty checking to Manual mode. In practice, however, unnecessary JDBC calls can actually make it slower. Based on real call traces from Elastic APM, this post analyzes how read-only transactions affect performance and suggests a more practical alternative.
Have you ever seen a system that appeared perfectly healthy in production suddenly slow down, take longer to respond, and eventually fail? Did you know that a single line of code we commonly overlook can degrade application performance and even cause a server outage? Problems like these usually begin with a small memory-management mistake…