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5 dépôts

Awesome GitHub RepositoriesMessage Queue Metric Consumption

Reads metrics or message payloads from message brokers using standard protocols.

Distinct from Message Queue Integrations: Distinct from Message Queue Integrations: focuses on the consumption of metrics from queues.

Explore 5 awesome GitHub repositories matching data & databases · Message Queue Metric Consumption. Refine with filters or upvote what's useful.

Awesome Message Queue Metric Consumption GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • vectordotdev/vectorAvatar de vectordotdev

    vectordotdev/vector

    22,071Voir sur GitHub↗

    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

    Streams processed metric data into database instances using gRPC with support for authentication and compression.

    Rusteventsforwarderhacktoberfest
    Voir sur GitHub↗22,071
  • influxdata/telegrafAvatar de influxdata

    influxdata/telegraf

    17,619Voir sur GitHub↗

    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

    Reads metrics or message payloads from message brokers and distributed queue services using standard protocols.

    Gogolanghacktoberfestinfluxdb
    Voir sur GitHub↗17,619
  • victoriametrics/victoriametricsAvatar de VictoriaMetrics

    VictoriaMetrics/VictoriaMetrics

    16,343Voir sur GitHub↗

    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

    Streams metric data to message queues using remote write protocols with support for compression and authentication.

    Godatabasegrafanagraphite
    Voir sur GitHub↗16,343
  • airtai/faststreamAvatar de airtai

    airtai/faststream

    5,234Voir sur GitHub↗

    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

    Specifies the starting point for message processing upon connection, such as retrieving only new messages or replaying historical data.

    Python
    Voir sur GitHub↗5,234
  • tulios/kafkajsAvatar de tulios

    tulios/kafkajs

    3,997Voir sur GitHub↗

    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

    Determines whether to start reading from the earliest or latest message when no committed offset exists.

    JavaScriptkafkakafka-clientnodejs
    Voir sur GitHub↗3,997
  1. Home
  2. Data & Databases
  3. Message Queue Integrations
  4. Message Queue Metric Consumption

Explorer les sous-tags

  • Consumption Starting PointsConfiguration settings that determine the initial offset or timestamp for message processing upon connection. **Distinct from Message Queue Metric Consumption:** Distinct from Message Queue Metric Consumption: focuses on the starting position of message streams, not metric retrieval.
  • Metric Stream ExportersTools for streaming metric data to message queues using remote write protocols. **Distinct from Message Queue Metric Consumption:** Distinct from message queue metric consumption: focuses on the export/push of metrics to queues rather than consumption.