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micrometer-metrics/micrometer

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4,850 estrellas·1,104 forks·Java·Apache-2.0·13 vistasmicrometer.io↗

Micrometer

Micrometer es una librería de métricas dimensionales y una fachada de métricas de aplicación que proporciona una interfaz neutral respecto al proveedor para registrar datos de rendimiento. Desacopla la instrumentación de la aplicación de backends de observabilidad específicos, permitiendo el registro de contadores, medidores (gauges) y temporizadores utilizando etiquetas clave-valor para un análisis granular.

El proyecto cuenta con un sistema de adaptadores de backend que transforman y enrutan los datos instrumentados a varias herramientas de monitoreo externas. Esto incluye la normalización de nombres para asegurar la portabilidad a través de diferentes sistemas de monitoreo y la capacidad de mapear datos dimensionales a formatos jerárquicos para backends que no soportan etiquetas.

La librería incluye capacidades integrales para la gestión de registros, definiciones de tipos de métricas y control de cardinalidad para proteger la memoria de la aplicación de combinaciones excesivas de etiquetas únicas. También proporciona instrumentación preconfigurada para los internos del sistema JVM, incluyendo recolección de basura, utilización del procesador y pools de hilos.

Features

  • Observability - Decouples application instrumentation from vendor-specific monitoring libraries through a standardized set of metric interfaces.
  • Dimensional Application Instrumentation - Collects dimensional measurements using counters, gauges, and timers to track system behavior across backends.
  • Dimensional Data Slicing - Assigns tags to measurements to enable drilling down and filtering data across different categories.
  • Metric Rate Tracking - Converts discrete data samples into rates or cumulative values as required by the monitoring backend.
  • Metrics Facades - Provides a vendor-neutral interface for recording dimensional metrics that decouples application code from specific observability backends.
  • Application Performance Monitoring - Records counters, gauges, and timers to track system behavior and performance across different monitoring backends.
  • Observability Backend Adapters - Transforms and routes instrumented data to various external monitoring tools using a consistent abstraction layer.
  • Observability Backend Routing - Routes instrumented application data to external monitoring systems through a vendor-neutral interface.
  • Dimensional Meter Management - Tracks and stores dimensional meters using a registry that maps them to specific monitoring implementations.
  • Dimensional Metrics Libraries - Provides a library for tracking application performance using counters, gauges, and timers with key-value tags.
  • Global Metric Registries - Provides a shared entry point to create and look up meters without passing registry instances throughout the application.
  • Meter Registries - Centralizes the creation and storage of meters to ensure consistent lifecycle management and global accessibility.
  • Metric Dimensional Attributes - Associates metrics with key-value tags to enable granular filtering and aggregation across different backends.
  • Event Frequency Monitoring - Increments monotonically increasing values to measure the rate at which specific events occur.
  • Observation Entry Tagging - Attaches common tags to every reported metric to enable environment-wide analysis by host, region, or instance.
  • Telemetry Collectors - Integrates with external telemetry collectors to gather and report usage metrics from software environments.
  • Time Duration Monitoring - Tracks time-based values and scales them to the base unit required by the monitoring backend.
  • Vendor-Neutral Observability Interfaces - Provides a vendor-neutral interface for recording metrics to decouple application code from specific observability backends.
  • Cardinality Limiters - Protects application memory by limiting the number of unique tag combinations registered for any single metric.
  • Cardinality Detection - Identifies metrics that produce an excessive number of unique time series to prevent resource exhaustion.
  • Cardinality Metadata Filtering - Allows marking specific metadata as high cardinality to exclude it from metrics while keeping it for tracing.
  • Local Distribution Sampling - Computes histograms and percentiles locally before shipping aggregated data to a monitoring backend.
  • Metric Delta Tracking - Tracks functions that only increase over time to report either the cumulative value or the rate of change.
  • Metric Push Clients - Supports pushing summarized telemetry data to remote monitoring services on a fixed schedule.
  • Metric Name Normalizations - Transforms dot-notation names into the specific format required by a monitoring backend to ensure portability.
  • Backend Name Normalizations - Transforms generic metric names and tags into the specific formatting and naming conventions required by different monitoring systems.
  • Event Distribution Histograms - Records non-temporal values to analyze the distribution and spread of events, such as payload sizes.
  • Execution Duration Tracking - Measures the execution time of ongoing processes in real-time before the tasks complete.
  • Instantaneous State Sampling - Samples the current state of a numeric value or object property at the moment of reporting.
  • JVM Metrics Collectors - Provides pre-configured bindings for gathering telemetry from JVM garbage collection, processor utilization, and thread pools.
  • Metric Name Normalization - Provides name normalization to ensure metrics are portable across different monitoring systems with varying naming requirements.
  • Internal Telemetry Monitors - Gathers pre-configured instrumentation data for system internals such as garbage collection and processor utilization.
  • Percentile Distribution Computation - Computes value distributions via server-side histogram buckets or client-side approximations for performance analysis.
  • Pre-configured System Instrumentation - Instruments standard components like caches, garbage collection, and thread pools for immediate operational insight.
  • Registration Filtering - Controls which meters are allowed to be registered in the registry based on specific criteria.
  • System Resource Monitors - Collects pre-configured telemetry from internal components like garbage collection, processor utilization, and thread pools.
  • Runtime Component Bindings - Binds pre-configured collectors to internal runtime components like garbage collection and thread pools for immediate telemetry.
  • Statistical Distribution Analysis - Sets distribution parameters for timers and summaries, including pre-computed percentiles and histograms.
  • Monitoring - Vendor-neutral metrics facade for Java.
  • JVM Profiling Tools - Application metrics facade for monitoring tools.

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Preguntas frecuentes

¿Qué hace micrometer-metrics/micrometer?

Micrometer es una librería de métricas dimensionales y una fachada de métricas de aplicación que proporciona una interfaz neutral respecto al proveedor para registrar datos de rendimiento. Desacopla la instrumentación de la aplicación de backends de observabilidad específicos, permitiendo el registro de contadores, medidores (gauges) y temporizadores utilizando etiquetas clave-valor para un análisis granular.

¿Cuáles son las características principales de micrometer-metrics/micrometer?

Las características principales de micrometer-metrics/micrometer son: Observability, Dimensional Application Instrumentation, Dimensional Data Slicing, Metric Rate Tracking, Metrics Facades, Application Performance Monitoring, Observability Backend Adapters, Observability Backend Routing.

¿Qué alternativas de código abierto existen para micrometer-metrics/micrometer?

Las alternativas de código abierto para micrometer-metrics/micrometer incluyen: uptrace/uptrace — Uptrace is an OpenTelemetry-based observability platform designed to collect, store, and analyze distributed traces,… dropwizard/metrics — This project is an application performance monitoring tool and JVM metrics library designed to measure workload… metrics-rs/metrics — This project is a unified observability framework for Rust applications, designed to capture, aggregate, and report… open-telemetry/opentelemetry-specification — This repository contains the specifications for a standardized observability data standard and telemetry… tokio-rs/tracing — This project is a structured tracing framework for Rust that serves as an async-aware instrumentation library and… naver/pinpoint — Pinpoint is a distributed application performance monitoring and tracing system. It functions as an application…