Awesome GitHub RepositoriesExactly-Once Processing Guarantees

Storm is a distributed stream processing framework and fault-tolerant compute engine designed for executing real-time continuous computations across a cluster of machines. It functions as a stateful stream processor and cluster topology manager, enabling the deployment and monitoring of distributed data flow configurations. The system ensures exactly-once semantics by utilizing transactional state management to guarantee that every message in a data stream is processed exactly one time. It further operates as a distributed RPC system, allowing for the integration of non-native languages throu

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

This project is a streaming data integration framework that captures real-time database changes and synchronizes them with downstream systems. It operates as a distributed streaming ETL and database synchronizer, reading database logs and snapshots to propagate row-level modifications to target sinks. The system supports declarative data integration, allowing users to define source-to-sink data flows using SQL or YAML configurations. It distinguishes itself by automating schema evolution to maintain synchronization when source structures change and ensuring exactly-once delivery and processin

Ce projet est une traduction en langue chinoise du document de recherche original détaillant le protocole de consensus Raft. Il sert de traduction de recherche technique et de guide de protocole de consensus, rendant les spécifications de l'algorithme Raft accessibles aux sinophones. La documentation couvre les mécanismes fondamentaux des systèmes distribués, notamment l'élection de leader, la réplication de journal et les protocoles de sécurité. Elle fournit une explication détaillée sur la façon de maintenir une source de vérité unique à travers plusieurs serveurs pour atteindre une gestion de cluster tolérante aux pannes. Le matériel aborde la réplication de machine à état distribuée et la gestion de journal. Il couvre des concepts techniques tels que le suivi de vivacité piloté par heartbeat, les élections à timeout aléatoire, le versionnage de cohérence basé sur les termes et le compactage de journal basé sur des snapshots.

KafkaJS is a pure JavaScript client for Apache Kafka, providing the necessary tools to produce and consume messages from a Kafka cluster without requiring native dependencies or external addons. It functions as a comprehensive integration library for Node.js applications to engage in distributed message processing and real-time event streaming. The project is distinguished by its native implementation of the Kafka wire protocol, avoiding C++ dependencies. It features a security client supporting SSL, TLS, and SASL authentication, alongside transactional capabilities that allow for atomic mess