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Techniques for reducing memory footprint by mapping repeated values to numeric identifiers.
Distinguishing note: Focuses on memory-efficient storage representations rather than general data compression.
Explore 3 awesome GitHub repositories matching data & databases · Data Encoding Optimizations. Refine with filters or upvote what's useful.
Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It functions as a structured data library that organizes information into typed columns, utilizing the Apache Arrow memory format to enable zero-copy data sharing and cache-friendly, vectorized operations. The engine is built to handle large-scale tabular datasets, providing both local and distributed analytical runtimes that scale from single-machine environments to multi-node clusters. The project distinguishes itself through a sophisticated lazy query engine that constructs abstract e
Optimizes memory usage by representing repeated string data as numeric placeholders.
node-qrcode is a JavaScript library and command-line tool for generating scannable QR codes from text or binary data. It functions as both a generator library for Node.js and web browser environments and a standalone command-line interface. The project supports producing QR codes in multiple formats, including raster images and scalable vector graphics. It can also render barcodes as text representations directly within a terminal for rapid visual verification. The generator includes capabilities for configuring error correction levels and optimizing data density through various encoding mod
Reduces QR code size by automatically selecting the most efficient encoding modes for different text segments.
Velox is a high-performance C++ query execution engine and columnar data processing library. It serves as a composable framework for implementing analytical query engines, providing a vectorized expression evaluator and a toolkit for data management systems. The project is distinguished by its use of vectorized columnar execution and arena-based memory allocation to process large-scale datasets. It features specialized optimizations such as broadcast join table caching, dynamic filter push-down, and dictionary encoding to reduce memory overhead and accelerate analytical reads. The engine cov
Reduces memory footprint for duplicate values by mapping indices to a base vector without data copying.