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
💡 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
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
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
How We Improved Payment System Throughput by 25% Using Apache Kafka at a Fortune 500 FinTech By Disha Sune — Java Backend Engineer | Spring Boot | Kafka | AWS | Fiserv At Fiserv, our payment processing platform handled millions of financial transactions daily for 600+ enterprise clients including McDonald's, Google, and Domino's. As transaction volumes grew, our legacy synchronous REST API archi
Hello Developers! 👋 Most developers today pick a side: Let’s talk about combining C++ and JavaScript—the ultimate hybrid stack for high-performance applications. 👇 1. The Core Engine (C++) ⚙️ 2. The Browser Bridge (WebAssembly) 🌉 3. The Cinematic Experience (Vanilla JS + UI/UX) ✨ The Takeaway 🎯 Keep optimizing, keep building! 💻✨ ~ Ujjwal Sharma | @stackbyujjwal About the Author 👨💻 Ujjwal
I built a Vamana-based vector search engine in C++ called sembed-engine. Recently I made a pull request that sped up queries by 16x and builds by 9x. The algorithm stayed exactly the same. The recall stayed at 1.0. The number of visited nodes did not change. The speedup came from data layout. The original code stored vectors as separate objects pointed to by shared_ptr: struct Record { int64_t
The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode