7 مستودعات
High-performance engines for processing data organized in columns.
Distinguishing note: Focuses on the core columnar processing architecture.
Explore 7 awesome GitHub repositories matching data & databases · Columnar Data Processors. 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
Organizes information into typed columns to enable fast analytical queries and efficient memory utilization.
Arrow is a cross-language development platform for in-memory data. It provides a standardized, language-independent columnar memory format designed to accelerate analytical operations and improve memory efficiency on modern computing hardware. By utilizing a schema-driven approach, the framework enables the efficient organization of both flat and nested data structures. The project functions as an analytical data processing engine that facilitates high-performance computation directly on memory-resident datasets. It distinguishes itself through a zero-copy architecture, which allows multiple
Organizes large datasets into memory-efficient columnar structures to accelerate analytical queries.
Perspective is a columnar data analytics engine and high-performance visualization component powered by WebAssembly. It provides a system for analyzing and visualizing large or streaming datasets through interactive data grids and charts, utilizing a compiled binary to achieve near-native performance within the browser. The project distinguishes itself through a WebSocket-based data streaming interface and deep Apache Arrow integration, which minimize memory overhead when synchronizing tables between servers and clients. It acts as a remote query proxy capable of translating visualization con
Implements a query engine that performs aggregations, filtering, and pivoting on strongly typed columnar data.
Perspective is a columnar data analytics library and streaming data visualization engine. It provides an interactive data grid component and notebook analytics widgets designed for processing high-volume data and rendering interactive charts and grids. The system utilizes a high-performance query engine to enable real-time data analysis and streaming dataset visualization. It supports the creation of customizable dashboards and reports that update automatically as new data arrives without requiring full dataset reloads. The project covers large-scale dataset analytics through a schema-driven
A high-performance engine for processing high-volume data organized in columns using a memory-efficient expression language.
Fory is a cross-language serialization framework and binary data serializer designed to convert complex object graphs into a compact binary format for high-performance data exchange. It includes an IDL-based schema compiler to transform interface definition language files into type-safe native data models and a schema evolution manager to maintain forward and backward compatibility. The project features a zero-copy data access layer that allows reading specific fields from binary rows without deserializing the entire object. It supports dual-mode serialization, enabling a toggle between a por
Implements a high-performance engine for processing and converting row-based serialized data into columnar layouts.
Velox هو محرك تنفيذ استعلامات عالي الأداء ومكتبة لمعالجة البيانات العمودية بلغة C++. يعمل كإطار عمل قابل للتركيب لتنفيذ محركات الاستعلام التحليلية، ويوفر مقيماً للتعبيرات المتجهة (vectorized) ومجموعة أدوات لأنظمة إدارة البيانات. يتميز المشروع باستخدامه للتنفيذ العمودي المتجه وتخصيص الذاكرة القائم على الساحة (arena-based) لمعالجة مجموعات البيانات واسعة النطاق. يتميز بتحسينات متخصصة مثل التخزين المؤقت لجدول الربط الإذاعي (broadcast join)، ودفع الفلتر الديناميكي للأسفل، وترميز القاموس لتقليل حمل الذاكرة وتسريع القراءات التحليلية. يغطي المحرك مجموعة واسعة من القدرات التحليلية، بما في ذلك تنفيذ عمليات الربط (hash, merge, semi joins)، بالإضافة إلى التجميع المتوازي متعدد المراحل وحساب دوال النافذة. يوفر بدائيات للتخزين العمودي في الذاكرة، وفك تشفير بيانات Parquet، والتكامل مع التخزين السحابي. يتم توفير القابلية للتوسع من خلال نظام تسجيل الدوال للدوال العددية والتجميعية المخصصة، مع توفر روابط عالية المستوى لربط منطق C++ بلغة Python.
Provides a high-performance engine for managing and transforming large datasets organized in columns.
Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di
Loads data from multiple NumPy files into a database table by mapping individual files to specific table columns in a defined order.