When you use window functions in SQL, you can't filter their results directly in a WHERE or HAVING clause — that's a well‑known limitation across many databases. GBase 8a, the China‑domestically developed MPP database from GBASE, solves this elegantly with the QUALIFY clause. Let's break down how it works, what it can do, and where you need to be careful. DROP TABLE IF EXISTS emp; CREATE TABLE emp
As data grows, you'll likely need to add nodes to your existing GBase 8a MPP cluster without downtime. This hands‑on guide walks through the full process of adding a composite GNode to a running GBASE cluster. Existing cluster: A healthy GBase 8a cluster New node: A server with a static IP address configured Network: All nodes must be able to communicate with each other Stop services on all existi
By default, identifiers in GBase 8s are case‑insensitive: uppercase letters are silently treated as lowercase. Setting the environment variable DELIMIDENT=Y changes how double‑quoted identifiers behave, enabling case‑sensitive table and column names. Here's a demonstration and a deep dive into the option, as used in a gbase database. With DELIMIDENT=y exported, execute the following statements: ex
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
A few years ago I solved 200 LeetCode problems and still froze on Mediums I hadn't seen. The breakthrough wasn't another hundred problems. It was a different loop. A problem asks for the longest substring with at most K distinct characters. You've solved sliding window before. Maximum sum subarray of size K, done. Longest substring without repeating characters, done. This third one stalls you. Twe
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
Hash tables feel like the default choice for membership tests. std::unordered_set promises average O(1) lookup, so we reach for it automatically. In performance-sensitive C++ code, that habit can cost you an order of magnitude. I ran into this while building a Vamana graph index for approximate nearest neighbor search. The algorithm needs to track visited nodes. Node ids are dense integers, and th