If you mostly live in .NET, the Java platform can look like a parallel universe: JVM, JDK, JARs, app servers, bytecode. The useful shortcut is to map each concept back to something you already know from C# and the CLR. This guide is a translation layer for .NET developers: what the JVM is, how the JDK compares to the .NET SDK, and what your real options are when a C# system needs to work with Java
Every distributed system you build is already taking a side in the CAP trade-off. The question is whether you made that choice deliberately or discover it during an incident. CAP states that a distributed system can guarantee at most two of three properties: Consistency, Availability, and Partition Tolerance. The critical insight most teams miss — P is not optional. Networks fail. Pods crash. AZs
💡 Problem: How do we ensure that a class has only ONE instance throughout the application? 💡 Common Use Cases: Logger Configuration Manager Database Connection 💡 Approach: We restrict object creation and provide a global access point. 💡 Key Idea: Private constructor Static instance Public method to access it 💻 Java Example: private static Singleton instance; privat
Em sistemas distribuídos modernos, garantir que todos os nós tenham exatamente os mesmos dados ao mesmo tempo pode ser caro, lento ou simplesmente inviável. É aí que entra o conceito de consistência eventual, um dos pilares fundamentais de arquiteturas escaláveis. O que é Consistência Eventual? Consistência eventual é um modelo de consistência onde, dado tempo suficiente e ausência de novas atuali
The drift problem Every project that ships a translated README has the same lifecycle: Someone writes README.md in English. A contributor opens a PR with README.zh.md. Great. Three months later, English has six new sections. Chinese has the original. A second translator opens README.es.md. Spanish gets translated from… which version? The current README.md? Or README.zh.md, by accident, because t
When people start working with high performance computing or parallel systems, “memory” often sounds like a background detail. It’s not. The way memory is structured can completely change how your applications behave, scale, and even fail. Let’s break it down in a practical way. ⸻ What is Shared Memory? In a shared memory system, all processors access the same memory space. Think of it
Introduction Picture two doctors updating the same patient record at the same time - one in São Paulo, the other in London. Both are offline. When connectivity returns, whose changes prevail? This is not a hypothetical. It is the everyday reality of distributed systems: multiple nodes, no shared clock, no guaranteed network. The conventional answer has long been locking - one node waits while an
Apache Stateful Functions is one of the quietly powerful frameworks in the Flink ecosystem - durable per-key state, exactly-once messaging, polyglot remote functions, all on top of Apache Flink. It's also been functionally dormant since October 2024, and it doesn't run on Flink 2.x. We needed it on Flink 2.x. So we maintained the continuation: kzmlabs/flink-statefun. This post is the why and the h