Awesome GitHub RepositoriesMessage Queue Metric Consumption

Vector is a high-performance observability data pipeline designed to collect, transform, and route logs, metrics, and traces across distributed infrastructure. It functions as a modular engine that decouples data ingestion from processing and transmission, utilizing a component-based architecture to connect diverse sources to multiple destinations. The project distinguishes itself through a focus on reliability and flow control. It implements backpressure-aware data movement to prevent data loss during traffic spikes and utilizes disk-backed event buffering to ensure durability during network

Telegraf is a modular, cross-platform telemetry pipeline designed to collect, process, and route metrics from diverse infrastructure, applications, and hardware. It functions as a server-side middleware that normalizes heterogeneous data into a unified format, enabling consistent monitoring across complex environments. By utilizing a plugin-driven architecture, the agent manages the entire lifecycle of telemetry data from initial ingestion to final transmission. The project distinguishes itself through a declarative, configuration-driven execution model that allows users to define complex dat

VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term storage and analysis of metric, log, and trace data. It functions as a unified backend for monitoring ecosystems, offering full compatibility with industry-standard protocols and query languages. The system is built to handle massive data volumes through a distributed architecture that supports horizontal scaling and efficient data lifecycle management. The platform distinguishes itself through a storage engine that utilizes consistent hashing for data sharding and log-struct

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

KafkaJS es un cliente de JavaScript puro para Apache Kafka, que proporciona las herramientas necesarias para producir y consumir mensajes de un clúster de Kafka sin requerir dependencias nativas o addons externos. Funciona como una biblioteca de integración integral para aplicaciones Node.js para participar en el procesamiento de mensajes distribuidos y streaming de eventos en tiempo real. El proyecto se distingue por su implementación nativa del protocolo de red de Kafka, evitando dependencias de C++. Cuenta con un cliente de seguridad que soporta autenticación SSL, TLS y SASL, junto con capacidades transaccionales que permiten el envío atómico de mensajes y compromisos de offset vinculados para asegurar un procesamiento exactamente una vez. La biblioteca cubre una amplia gama de áreas operativas, incluyendo administración completa de clústeres para gestionar temas y grupos de consumidores, estrategias avanzadas de enrutamiento y asignación de particiones, y telemetría integral mediante monitoreo basado en eventos. También implementa patrones de fiabilidad de red como reintentos con retroceso exponencial (exponential backoff) y obtención de datos consciente del rack para optimizar la latencia.