Awesome GitHub RepositoriesData Shuffling Algorithms

Ray is a distributed computing framework designed to scale Python and Java applications across clusters by abstracting task scheduling and resource management. It functions as a resource-aware execution engine that manages task dependencies, placement, and fault tolerance across networked compute nodes. At its core, the system provides a stateful actor model, allowing developers to define classes that run in dedicated processes to maintain and mutate internal state across remote method calls. The framework distinguishes itself through a robust cross-language interoperability layer, enabling f

This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

Apache Druid is a real-time analytics database and distributed columnar time-series store designed for sub-second analytical queries. It functions as a data platform featuring a distributed SQL query engine and a real-time data ingestion system for moving historical and streaming data from external sources. The system is distinguished by its ability to provide low-latency analytics under high concurrency to power operational dashboards. It implements a Kerberos-secured environment for user authentication and employs a shared-nothing cluster architecture to enable horizontal scaling. The plat

SparkInternals ist ein technisches Referenz- und Architekturhandbuch, das das interne Design und die Implementierung der verteilten Computing-Engine Apache Spark detailliert beschreibt. Es dient als Analyse von Big-Data-Engines und konzentriert sich darauf, wie das System die Cluster-Ausführung sowie das Zusammenspiel zwischen Driver-Nodes, Executors und Workern verwaltet. Das Projekt bietet eine detaillierte Aufschlüsselung, wie logische Pläne in physische Ausführungsstufen konvertiert werden. Es analysiert spezifisch die Mechanik von Data-Shuffle-Operationen, Speicherverwaltung und die Koordination der verteilten Job-Planung. Die Dokumentation deckt ein breites Spektrum an verteilten Computing-Funktionen ab, einschließlich Query-Execution-Planung, Datenabhängigkeitsmanagement und In-Memory-Caching-Strategien. Zudem werden Aufgabenverteilung, parallele Ausführung sowie Prozesse zur Fehlerwiederherstellung und Datenpersistenz untersucht.