awesome-repositories.com
Blog
MCP
awesome-repositories.com

Descubre los mejores repositorios open-source con nuestra búsqueda potenciada por IA.

ExplorarBúsquedas curadasAlternativas open-sourceSoftware autohospedableBlogMapa del sitio
ProyectoServidor MCPAcerca deCómo clasificamosPrensa
Aviso legalPrivacidadTérminos
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 repositorios

Awesome GitHub RepositoriesCausal Consistency Protocols

Mechanisms for maintaining ordered data visibility across distributed nodes.

Distinct from Data Synchronization and Consistency: Focuses on causal consistency specifically for database clusters, distinct from general data synchronization.

Explore 2 awesome GitHub repositories matching networking & communication · Causal Consistency Protocols. Refine with filters or upvote what's useful.

Awesome Causal Consistency Protocols GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • prefecthq/prefectAvatar de PrefectHQ

    PrefectHQ/prefect

    21,640Ver en GitHub↗

    Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep

    Maintains event buffers to ensure correct causal processing sequence in distributed systems.

    Pythonautomationdatadata-engineering
    Ver en GitHub↗21,640
  • neo4j/neo4jAvatar de neo4j

    neo4j/neo4j

    15,928Ver en GitHub↗

    Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries

    Maintains ordered data visibility across distributed nodes using causal consistency routing.

    Javacypherdatabasegraph
    Ver en GitHub↗15,928
  1. Home
  2. Networking & Communication
  3. Distributed Systems and Peer-to-Peer
  4. Distributed Computing
  5. Data Synchronization and Consistency
  6. Causal Consistency Protocols