Awesome GitHub RepositoriesEvent Stream Implementation

Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

Sui is a blockchain platform featuring an object-centric state model and resource-oriented smart contracts. It utilizes parallel transaction execution to increase network throughput and supports programmable transaction blocks that bundle multiple operations into single atomic units. The platform distinguishes itself with a capability-based access control system and zero-knowledge login mechanisms, enabling users to authenticate via identity providers without seed phrases. It also implements deterministic object addressing to allow predictable state lookups and supports the creation of soulbo

This project is a sample library and implementation guide for using RxJava to manage asynchronous data streams and concurrent tasks in Android applications. It provides a collection of reference implementations for reactive programming, focusing on functional operators to transform and combine asynchronous data flows. The library demonstrates specific Android architectural patterns, such as implementing decoupled event buses for component communication and coordinating parallel network requests. It includes concrete examples of mobile-specific patterns including search input debouncing, list

Hatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it

BLAKE3 是 BLAKE3 加密哈希算法的高性能实现，用于计算安全数据摘要和指纹。它作为一个并行加密哈希工具，将工作负载分布在多个处理器线程上，以快速处理大数据集。 该项目提供用于键控哈希和消息认证码生成的专门工具。它还包括用于加密密钥派生的功能，允许从主密钥和上下文字符串创建唯一的秘密子密钥。 该实现通过并行哈希计算和验证数据流支持数据完整性验证。这些功能作为 Rust 和 C 环境的跨语言库提供，并包含一个用于计算文件或标准输入摘要的命令行界面。

KurrentDB is an event-native database designed for event sourcing and event-driven architectures. It stores application state as an immutable, ordered sequence of events rather than updating rows in place, preserving full history for audit, replay, and distributed consistency. The database combines event storage with real-time streaming and a built-in JavaScript projection engine that transforms and aggregates event streams into materialized views. The system provides official gRPC client libraries for Python, Node.js, Java, .NET, Go, and Rust, enabling multi-language application development

KurrentDB is an event-native database designed for event sourcing and event-driven architectures. It stores events as immutable, ordered records in streams, preserving a complete audit trail and enabling temporal queries. The database uses gRPC for all client-server and inter-node communication, providing efficient binary serialization and bidirectional streaming, and supports atomic multi-stream writes that ensure consistency across multiple streams in a single transaction. The database distinguishes itself with a built-in JavaScript projection engine that transforms, filters, and aggregates

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

FastStream is an asynchronous Python framework designed for building event-driven microservices. It provides a unified abstraction layer for interacting with various message brokers, enabling developers to manage event production and consumption through a consistent interface while maintaining access to native provider-specific features. The framework centers on a decorator-based routing model that binds application logic directly to broker topics, supported by a built-in dependency injection container that resolves resources at runtime. The framework distinguishes itself through its deep int