3 repositorios
Execution models that defer data calculation until explicitly requested by the user.
Distinct from Lazy Evaluation Patterns: Distinct from general lazy patterns: specifically focuses on demand-driven intelligence for editor responsiveness.
Explore 3 awesome GitHub repositories matching software engineering & architecture · Demand-Driven Evaluators. Refine with filters or upvote what's useful.
Sharp is a high-performance image processing library for Node.js. It serves as a native extension and wrapper for the libvips framework, providing tools for image resizing, format conversion, and programmatic data manipulation. The project enables the transformation of images into web-friendly formats such as WebP and AVIF while preserving color profiles and alpha channels. It also provides capabilities for generating blank image buffers with specified dimensions and background colors. The library covers a broad range of image manipulation utilities, including rotation, extraction, compositi
Utilizes image streaming pipelines to process pixels on demand, minimizing memory usage.
Rust-analyzer is a language server implementation that provides real-time code intelligence, static analysis, and development productivity tools for the Rust programming language. It functions as a backend engine that communicates with text editors to deliver deep structural understanding of source code, enabling features like semantic analysis, symbol navigation, and automated refactoring. The project distinguishes itself through a core engine designed for high-performance responsiveness, utilizing incremental query-based compilation and lazy demand-driven evaluation to minimize resource con
Defers code intelligence calculations until requested to minimize resource consumption and maintain editor responsiveness.
Libvips is a C-based image processing library designed to manipulate large visual assets through a low-memory, parallel processing pipeline. It functions as a streaming image processor that avoids loading entire files into system memory, enabling the handling of massive images in resource-constrained environments. The library distinguishes itself through a demand-driven architecture that constructs a deferred execution plan, computing only the necessary pixels for a final output. By utilizing a cache-friendly tiled processing model and memory-mapped file access, it minimizes latency and redun
Processes images by pulling small rectangular regions through a processing graph to avoid loading entire files into system memory.