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Mechanisms for passing references to remote stateful services across distributed task boundaries.
Distinguishing note: Focuses on the serialization and transmission of actor references rather than general data object passing.
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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
Ray allows passing actor handles as arguments to remote tasks to enable distributed components to interact with the same stateful service.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Enables efficient remote interaction with shared data structures by transmitting object identifiers across the cluster.
FluidFramework ist ein Framework für Echtzeit-Zusammenarbeit und eine Engine zur Synchronisierung verteilter Zustände. Es bietet eine Bibliothek für kollaborative Datenmodelle und ein Cloud-synchronisiertes Dokumentensystem, das darauf ausgelegt ist, Datenstrukturen über verbundene Clients hinweg zu replizieren, wobei geordnete Operationen verwendet werden, um eine letztendliche Konsistenz sicherzustellen. Das Framework nutzt eine Client-Server-Relay-Architektur, um Operationen weiterzuleiten und zu persistieren, ohne dass benutzerdefinierte serverseitige Geschäftslogik erforderlich ist. Es verwaltet den Lebenszyklus kollaborativer Sitzungen durch gemeinsam genutzte Datencontainer und implementiert Strategien zur Konfliktlösung, wie z. B. Last-Writer-Wins, neben optimistischen Update-Mechanismen, um eine reaktionsschnelle Benutzererfahrung aufrechtzuerhalten. Die Funktionsbereiche umfassen die Synchronisierung hierarchischer Daten, Schlüssel-Wert-Paare und Echtzeit-Textbearbeitung. Das System enthält Mechanismen für die exklusive Aktionskoordination, Echtzeit-Zustandsabonnements für UI-Updates und ein integriertes Cloud-Dokumentenmanagement zur Speicherung von Anwendungsdaten auf Unternehmensplattformen. Das Projekt enthält eine lokale Dienstsimulation für das Prototyping kollaborativer Funktionen, ohne dass ein Cloud-Deployment erforderlich ist.
Provides an abstraction layer over distributed data structures so developers interact with them like local JavaScript objects.
Capnweb is a distributed object communication library and Cap'n Proto RPC framework. It enables type-safe remote procedure calls between clients and servers using shared schemas and generated stubs to invoke methods on remote objects as if they were local. The project utilizes an object-capability security model to govern access to remote resources through unforgeable tokens. It provides a bidirectional network layer that multiplexes asynchronous calls and data streams over persistent WebSocket connections and includes a remote resource lifecycle manager that uses reference counting to automa
Implements shared object abstractions that allow developers to interact with distributed data structures as if they were local.