5 مستودعات
Mechanisms for performing database write operations on non-blocking background threads.
Distinct from Write Skew Mitigations: Focuses on non-blocking write execution for UI responsiveness, whereas write-skew mitigations focus on data integrity.
Explore 5 awesome GitHub repositories matching data & databases · Asynchronous Write Operations. Refine with filters or upvote what's useful.
This is a mobile object database and NoSQL local data store that replaces relational tables with a schema-based model. It functions as a reactive data store, using live object observations and change notifications to trigger automatic user interface refreshes. The system provides built-in mobile cloud data synchronization to keep local datasets consistent with a remote server across multiple devices. It also includes security features for encrypted local storage, protecting sensitive on-disk data using at-rest encryption keys and fine-grained access control. Broad capabilities include object
Supports modifying data on background threads to maintain a responsive main user interface.
Falcor is a JavaScript library that models remote data as a single virtual JSON graph, providing a path-based query engine for efficient client-side data retrieval and updates. It represents multiple remote data sources as a unified document where entities are accessed via globally unique identity paths. The system distinguishes itself by treating the remote data model as a virtual JSON resource, allowing the client to query specific paths without managing individual endpoints. It uses a reference-aware graph model to handle many-to-many relationships and prevents data duplication. Network ef
Updates values in a graph object at specified paths and returns the modified subset asynchronously.
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Performs non-blocking batch writes using an async Redis client for use in async serving frameworks.
Booster هو أداة لتحسين بناء تطبيقات Android ومعالج لـ bytecode مصمم لتقليل حجم الملف الثنائي، وإصلاح الأعطال على مستوى النظام، وتحسين أداء التطبيق. يعمل كإضافة لعملية البناء قابلة للتوسيع تقوم بتعديل ملفات الفئات المترجمة ومخرجات البناء لحقن إصلاحات الاستقرار والتحسينات دون تغيير الكود المصدري الأصلي. يتميز المشروع بمعالجة منخفضة المستوى لـ bytecode لتصحيح أخطاء نظام التشغيل وإدارة مجموعات الخيوط (Thread Pools) أثناء مرحلة التجميع. كما يوفر مجموعة أدوات لتوصيف الأداء لتحديد العمليات التي تحظر الخيط الرئيسي وتحليل مشكلات الاستقرار المتعلقة بالموارد. تغطي الأداة نطاقاً واسعاً من قدرات تحسين الحزم، بما في ذلك ضغط الصور لـ PNG وWebP، وإزالة الموارد الزائدة، وتحسين فهرس الموارد. تشمل الوظائف الإضافية ضبط الأداء لإدارة الخيوط، وكتابة التفضيلات غير المتزامنة، والتحليل الساكن لمسح مخرجات البناء لاكتشاف المخاطر الأمنية والاختناقات. يستخدم النظام واجهة مزود الخدمة (SPI) لتكامل الإضافات المخصصة ويمكن تشغيله كتطبيق مستقل منفصل عن بيئة البناء الأساسية.
Executes preference commits on asynchronous worker threads to prevent main thread blocking and UI freezes.
This project is a framework for implementing event sourcing and command query responsibility segregation within containerized microservices. It provides a structured approach to managing business state as a sequence of immutable events, ensuring a reliable audit trail and the ability to reconstruct system state at any point in time. The framework distinguishes itself by enforcing a clear separation between data modification and data retrieval paths. By utilizing event-driven data synchronization, it allows for the asynchronous updating of materialized views and read models, ensuring that quer
Performs non-blocking background updates to refresh query-optimized database views.