awesome-kubernetes/docs/monitoring.md

OpenShift Monitoring and Performance. Prometheus, Grafana, APMs and more

• Monitoring
• OpenShift Cluster Monitoring Built-in solutions
  • OpenShift 3.11 Metrics and Logging
    • Prometheus and Grafana
    • Custom Grafana Dashboard for OpenShift 3.11
    • Capacity Management Grafana Dashboard
    • Software Delivery Metrics Grafana Dashboard
    • Prometheus for OpenShift 3.11
  • OpenShift 4
• Prometheus
  • Prometheus Storage
    • Scalability, High Availability (HA) and Long-Term Storage
    • Storage Solutions for Prometheus
  • Collectors. Software exposing Prometheus metrics
    • Prometheus Exporters. Plug-in architecture and extensibility with Prometheus Exporters (collectors)
    • Prometheus Exporters Development. Node Exporter
    • Prometheus Third-party Collectors/Exporters
      • OpenTelemetry Collector
      • Telegraf Collector
      • Micrometer Collector
  • Prometheus Alarms and Event Tracking
  • Prometheus and Cloud Monitoring
  • Prometheus Installers
    • Binaries, source code or Docker
    • Ansible Roles
  • Prometheus SaaS Solutions
• Grafana
  • Grafana Dashboards
• Kibana
• Interactive Learning
• Performance
• Distributed Tracing. OpenTelemetry and Jaeger
• Application Performance Management (APM)
  • Dynatrace APM
• Message Queue Monitoring
  • Red Hat AMQ 7 Broker Monitoring solutions based on Prometheus and Grafana
• Other Awesome Lists

Monitoring

• Wikipedia: Application Performance Index
• thenewstack.io: The Challenges of Monitoring Kubernetes and OpenShift
• dzone.com: Kubernetes Monitoring: Best Practices, Methods, and Existing Solutions Kubernetes handles containers in several computers, removing the complexity of handling distributed processing. But what's the best way to perform Kubernetes monitoring?
• blog.cloud-mercato.com: New HTTP benchmark tool pycurlb

OpenShift Cluster Monitoring Built-in solutions

OpenShift 3.11 Metrics and Logging

OpenShift Container Platform Monitoring ships with a Prometheus instance for cluster monitoring and a central Alertmanager cluster. In addition to Prometheus and Alertmanager, OpenShift Container Platform Monitoring also includes a Grafana instance as well as pre-built dashboards for cluster monitoring troubleshooting. The Grafana instance that is provided with the monitoring stack, along with its dashboards, is read-only.

Monitoring Component	Release	URL
ElasticSearch	5	OpenShift 3.11 Metrics & Logging
Fluentd	0.12	OpenShift 3.11 Metrics & Logging
Kibana	5.6.13	kibana 5.6.13
Prometheus	2.3.2	OpenShift 3.11 Prometheus Cluster Monitoring
Prometheus Operator		Prometheus Operator technical preview
Prometheus Alert Manager	0.15.1	OpenShift 3.11 Configuring Prometheus Alert Manager
Grafana	5.2.3	OpenShift 3.11 Prometheus Cluster Monitoring

Prometheus and Grafana

• redhat.com: How to gather and display metrics in Red Hat OpenShift (Prometheus + Grafana)
• Generally Available today: Red Hat OpenShift Container Platform 3.11 is ready to power enterprise Kubernetes deployments 🌟
• The Challenges of Monitoring Kubernetes and OpenShift 3.11 🌟
• OCP 3.11 Metrics and Logging
• Prometheus Cluster Monitoring 🌟
• Prometheus Alert Manager
• Leveraging Kubernetes and OpenShift for automated performance tests (part 1)
• Building an observability stack for automated performance tests on Kubernetes and OpenShift (part 2) 🌟

Custom Grafana Dashboard for OpenShift 3.11

By default OpenShift 3.11 Grafana is a read-only instance. Many organizations may want to add new custom dashboards. This custom grafana will interact with existing Prometheus and will also add all out-of-the-box dashboards plus few more interesting dashboards which may require from day to day operation. Custom Grafana pod uses OpenShift oAuth to authenticate users and assigns "Admin" role to all users so that users can create their own dashboards for additional monitoring.

Getting Started with Custom Dashboarding on OpenShift using Grafana. This repository contains scaffolding and automation for developing a custom dashboarding strategy on OpenShift using the OpenShift Monitoring stac

Capacity Management Grafana Dashboard

This repo adds a capacity management Grafana dashboard. The intent of this dashboard is to answer a single question: Do I need a new node? . We believe this is the most important question when setting up a capacity management process. We are aware that this is not the only question a capacity management process may need to be able to answer. Thus, this should be considered as the starting point for organizations to build their capacity management process.

Software Delivery Metrics Grafana Dashboard

This repo contains tooling to help organizations measure Software Delivery and Value Stream metrics.

Prometheus for OpenShift 3.11

This repo contains example components for running either an operational Prometheus setup for your OpenShift cluster, or deploying a standalone secured Prometheus instance for configurating yourself.

OpenShift 4

OpenShift Container Platform includes a pre-configured, pre-installed, and self-updating monitoring stack that is based on the Prometheus open source project and its wider eco-system. It provides monitoring of cluster components and includes a set of alerts to immediately notify the cluster administrator about any occurring problems and a set of Grafana dashboards. The cluster monitoring stack is only supported for monitoring OpenShift Container Platform clusters.

OpenShift Cluster Monitoring components cannot be extended since they are read only.

Monitor your own services (technology preview): The existing monitoring stack can be extended so you can configure monitoring for your own Services.

Monitoring Component	Deployed By Default	OCP 4.1	OCP 4.2	OCP 4.3	OCP 4.4
ElasticSearch	No	5.6.13.6
Fluentd	No	0.12.43
Kibana	No	5.6.13
Prometheus	Yes	2.7.2		2.14.0	2.15.2
Prometheus Operator	Yes			0.34.0	0.35.1
Prometheus Alert Manager	Yes	0.16.2		0.19.0	0.20.0
kube-state-metrics	Yes			1.8.0	1.9.5
Grafana	Yes	5.4.3	6.2.4	6.4.3	6.5.3

Prometheus

• prometheus.io
• dzone.com: Monitoring with Prometheus Learn how to set up a basic instance of Prometheus along with Grafana and the Node Exporter to monitor a simple Linux server.
• github.com/prometheus/prometheus
• Monitoring With Prometheus
• Dzone Refcard: Scaling and Augmenting Prometheus Prometheus is an open-source infrastructure and services monitoring system popular for Kubernetes and cloud-native services and apps. It can help make metric collection easier, correlate events and alerts, provide security, and do troubleshooting and tracing at scale. This Refcard will teach you how to pave the path for Prometheus adoption, what observability looks like beyond Prometheus, and how Prometheus helps provide scalability, high availability, and long-term storage.
• Monitoring Self-Destructing Apps Using Prometheus. Learn how to configure Prometheus collectors and their use cases.
• Monitoring kubernetes with Prometheus
• Focus on Detection: Prometheus and the Case for Time Series Analysis
• Ensure High Availability and Uptime With Kubernetes Horizontal Pod Autoscaler (HPA) and Prometheus
• Prometheus 2 Times Series Storage Performance Analyses
• Set Up and Integrate Prometheus With Grafana for Monitoring. How to set up and configure Prometheus and Grafana to enable application performance monitoring for REST applications.
• Discover Applications Running on Kubernetes With Prometheus
• Prometheus vs. Graphite: Which Should You Choose for Time Series or Monitoring?
• PromQL Tutorial
• How to use Ansible to set up system monitoring with Prometheus
• Initial experiences with the Prometheus monitoring system
• prometheus.io/docs/instrumenting/writing_exporters/
• devconnected.com/complete-node-exporter-mastery-with-prometheus/
• www.scalyr.com/blog/prometheus-metrics-by-example/
• Prometheus es un "time series DBMS" y sistema de monitorización completo, que incluye recogida de datos, almacenamiento, visualización y exportación.
• La arquitectura de Prometheus se basa en "pull metrics" (extracción de métricas). En lugar de empujar las métricas ("pushing metrics") hacia la herramienta de monitorización, extrae ("pull") las métricas de los servicios (por defecto un "/metrics" HTTP endpoint) en texto plano (parseable por humanos y de fácil diagnóstico). Prometheus también tiene un "push gateway", de modo que también soporta "push" para métricas específicas cuando el modelo de "pull" no funciona (si bien este método no es recomendable).
• Prometheus se puede conectar a series de tiempo (time series) con un nombre de métrica y pares clave-valor, simplificando la monitorización en complejos entornos cloud multi-nodo.
• La herramienta también proporciona PromQL, para el procesado de datos "time-series". Permite realizar consultas (queries) para la manipulación de datos y generar nueva información relevante. Con PromQL se pueden generar gráficos, visualizar conjuntos de datos, crear tablas, y generar alertas basadas en parámetros específicos.
• La consola web de Prometheus permite gestionar todas las características y herramientas disponibles en Prometheus. Se pueden utilizar expresiones regulares y consultas avanzadas de PromQL para la creación de conjuntos de datos (datasets) y alertas.
• Prometheus activamente "scrapea" datos, los almacena, y soporta "queries", "gráficos" y "alertas", así como proporciona "endpoints" a otros consumidores API como Grafana. Todo esto lo realiza con los siguientes componentes:
  • Librerías cliente: instrumentación del código de aplicación (para generar eventos).
  • Servidor Prometheus: "scrapeando" y almacenando estos eventos, cuando se generan, como "time series data". Este es el modelo "pull" más común para la recogida general de métricas en Prometheus.
  • Pushgateway: Modelo "Push", soportando trabajos efímeros de importación de datos. Sólo recomendable en aplicaciones "serverless", donde las aplicaciones son lanzadas y destruidas bajo demanda, así como las aplicaciones que manejan "batch jobs".
  • Exportadores de Datos: exportando servicios como HAProxy, StatsD, Graphite, etc.
• Prometheus se diferencia de otros sistemas de monitorización con las siguientes funcionalidades:
  • Modelo de datos multi-dimensional, donde los "time-series data" se definen por el nombre de la métrica y dimensiones clave/valor.
  • Nodos únicos de servidor y autónomos, sin dependencia de almacenamiento distribuido.
  • Recogida de datos via un modelo "pull" sobre HTTP.
  • "Time Series Data" empujado ("pushed") a otros destinos de datos vía un gateway intermediario.
  • "Targets" descubiertos via "service discovery" ó configuración estática.
  • Soporte de federación horizontal y vertical.

Prometheus Storage

• Proporciona etiquetado clave-valor y "time-series". La propia documentación de Prometheus explica cómo se gestiona el almacenamiento en disco (Prometheus Time-Series DB). La ingestión de datos se agrupa en bloques de dos horas, donde cada bloque es un directorio conteniendo uno o más "chunk files" (los datos), además de un fichero de metadatos y un fichero index:
• Almacenamiento de datos en disco (Prometheus Time-Series DB):

./data/01BKGV7JBM69T2G1BGBGM6KB12
./data/01BKGV7JBM69T2G1BGBGM6KB12/meta.json
./data/01BKGV7JBM69T2G1BGBGM6KB12/wal
./data/01BKGV7JBM69T2G1BGBGM6KB12/wal/000002
./data/01BKGV7JBM69T2G1BGBGM6KB12/wal/000001

• Un proceso en segundo plano compacta los bloques de dos horas en otros más grandes.
• Es posible almacenar los datos en otras soluciones de "Time-Series Database" como InfluxDB.

Scalability, High Availability (HA) and Long-Term Storage

• Prometheus fue diseñado para ser fácil de desplegar. Es extremadamente fácil ponerlo en marcha, recoger algunas métricas, y empezar a construir nuestra propia herramienta de monitorización. Las cosas se complican cuando se intenta operar a un nivel de escalado considerable.
• Para entender si esto va a ser un problema, conviene plantearse las siguiente preguntas:
  • ¿Cuántas métricas puede ingerir el sistema de monitorización y cuántas son necesarias?
  • ¿Cuál es la cardinalidad de las métricas? La cardinalidad es el número de etiquetas que cada métrica puede tener. Es una cuestión muy frecuente en las métricas pertenecientes a entornos dinámicos donde a los contenedores se les asignan un ID ó nombre diferente cada vez que son lanzados, reiniciados o movidos entre nodos (caso de kubernetes).
  • ¿Es necesaria la Alta Disponibilidad (HA)?
  • ¿Durante cuánto tiempo es necesario mantener las métricas y con qué resolución?
• La implementación de HA es laboriosa porque la funcionalidad de cluster requiere añadir plugins de terceros al servidor Prometheus. Es necesario tratar con "backups" y "restores", y el almacenamiento de métricas por un periodo de tiempo extendido hará que la base de datos crezca exponencialmente. Los servidores Prometheus proporcionan almacenamiento persistente, pero Prometheus no fue creado para el almacenamiento distribuido de métricas a lo largo de múltiples nodos de un cluster con replicación y capacidad curativa (como es el caso de Kubernetes). Esto es conocido como "almacenamiento a largo-plazo" (Long-Term) y actualmente es un requisito en unos pocos casos de uso, por ejemplo en la planificación de la capacidad para monitorizar cómo la infraestructura necesita evolucionar, contracargos para facturar diferentes equipos ó departamentos para un caso específico que han hecho de la infraestructura, análisis de tendencias de uso, o adherirse a regulaciones para verticales específicos como banca, seguros, etc.

Storage Solutions for Prometheus

• Prometheus TSDB
• Cortex: Provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex allows for storing time series data in a key-value store like Cassandra, AWS DynamoDB, or Google BigTable. It offers a Prometheus compatible query API, and you can push metrics into a write endpoint. This makes it best suited for cloud environments and multi-tenant scenarios like service providers building hosted and managed platforms.
  • Weave Cortex SaaS (Hosted Prometheus - Public Cloud)
• Thanos: Open source, highly available Prometheus setup with long term storage capabilities.
  • Thanos stores time series data in an object store like AWS S3, Google Cloud Storage, etc. Thanos pushes metrics through a side-car container from each Prometheus server through the gRPC store API to the query service in order to provide a global query view.
  • github.com/ruanbekker: Thanos Cluster Setup How to deploy a HA Prometheus setup with Unlimited Data Retention Capabilities on aws cloud S3 with Thanos Metrics.
• InfluxDB: An open-source time series database (TSDB) developed by InfluxData. It is written in Go and optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, Internet of Things sensor data, and real-time analytics. It also has support for processing data from Graphite.
  • en.wikipedia.org/wiki/InfluxDB
  • en.wikipedia.org/wiki/MIT_License
  • dzone: Flux queries New language being developed at InfluxData.
• M3: An open source, large-scale metrics platform developed by Uber. It has its own time series database, M3DB. Like Thanos, M3 also uses a side-car container to push the metrics to the DB. In addition, it supports metric deduplication and merging, and provides distributed query support. Although it's exciting to see attempts to address the challenges of running Prometheus at scale, these are very young projects that are not widely used yet.

Collectors. Software exposing Prometheus metrics

Prometheus Exporters. Plug-in architecture and extensibility with Prometheus Exporters (collectors)

• Prometheus proporciona un ecosistema de "exporters", los cuales permiten que herramientas de terceros puedan exportar sus datos en Prometheus. Muchos componentes de software de código abierto son compatibles por defecto.
• Un "exporter" expone las métricas de uno ó varios "collectors".
• Prometheus Exporters
  • prometheus.io/download/
  • github.com/prometheus
• Prometheus JMX Exporter: A process for exposing JMX Beans via HTTP for Prometheus consumption.
• Example: How to Use Prometheus Monitoring With Java to Gather Data. Gathering Java Metrics with Prometheus Monitoring (ActiveMQ)
• Maven Prometheus instrumentation library for JVM applications (client library)
  • github.com/prometheus/client_java
• Example: JMX Exporter with ActiveMQ

Prometheus Exporters Development. Node Exporter

• Node exporter puede ser utilizado para exportar las métricas de nuestra aplicación ya que permite exportar un "text-file". Nuestra aplicación puede escribir datos en un fichero de texto con el formato de datos de Prometheus. Este fichero de texto con datos agregados sería exportado a Prometheus con Node Exporter.
• dzone.com: Monitoring Self-Destructing Apps Using Prometheus Learn how to configure Prometheus collectors and their use cases.
• prometheus.io: Writing Exporters
• devconnected.com: Complete Node Exporter Mastery with Prometheus
• scalyr.com: Prometheus metrics by example: 5 things you can learn

Prometheus Third-party Collectors/Exporters

• Some third-party software exposes metrics in the Prometheus format, so no separate exporters are needed.
• Prometheus Third Party Exporters

OpenTelemetry Collector

• OpenTelemetry Collector

Telegraf Collector

• Telegraf Collector
• Telegraf Prometheus Output Plugin
• Telegraf Ansible Role
• Grafana Dashboards with Telegraf Collectors
• dzone: Synthetic Monitoring With Telegraf (white-box monitoring) Monitoring based on metrics exposed by the internals of the system

Micrometer Collector

• Micrometer Collector
• Micrometer Prometheus

Prometheus Alarms and Event Tracking

• Prometheus no soporta rastreo de eventos (event tracking), pero ofrece un soporte completo de alarmas y gestión de alarmas. El lenguaje de consultas (queries) de Prometheus permite en cambio implementar rastreo de eventos por cuenta propia.

Prometheus and Cloud Monitoring

• AWS CloudWatch is supported by Prometheus.

Prometheus Installers

Binaries, source code or Docker

• prometheus.io: Installarion
• prometheus.io: Getting Started
• github.com/prometheus/prometheus

Ansible Roles

• Cloud Alchemy: Deploy prometheus node exporter using ansible.
  • galaxy.ansible.com/cloudalchemy/node-exporter
  • github.com/cloudalchemy/ansible-prometheus
• Idealista: This ansible role installs a Prometheus Node Exporter in a debian environment
• Alexdzyoba: This ansible role installs a Prometheus JMX exporter java agent in a debian nvironment. Inspired by Idealista prometheus_jmx_exporter-role.
  • galaxy.ansible.com/alexdzyoba/jmx-exporter
  • github.com/alexdzyoba/ansible-jmx-exporter
• Mesaguy: Installs and manages Prometheus and Prometheus exporters.
  • Installs and manages Prometheus server, Alertmanager, PushGateway, and numerous Prometheus exporters
  • This role was designed to allow adding new exporters with ease. Regular releases ensure it always provides the latest Prometheus software.
  • This role can register client exporters with the Prometheus server/s automatically (see tgroup management below).
  • This Ansible role will be migrated to an Ansible Collection.
  • galaxy.ansible.com/mesaguy/prometheus
  • github.com/mesaguy/ansible-prometheus
• William Yeh: Prometheus for Ansible Galaxy. This role only installs 3 components: Prometheus server, Node exporter, and Alertmanager.
  • galaxy.ansible.com/William-Yeh/prometheus
  • github.com/William-Yeh/ansible-prometheus
  • awesomeopensource.com/project/William-Yeh/ansible-prometheus
• Undergreen: An Ansible role that installs Prometheus Node Exporter on Ubuntu|Debian|redhat-based machines with systemd|Upstart|sysvinit.
  • galaxy.ansible.com/UnderGreen/prometheus-node-exporter
  • github.com/UnderGreen/ansible-prometheus-node-exporter
• Mitesh Sharma: Prometheus With Grafana Using Ansible
  • itnext.io/prometheus-with-grafana-using-ansible-549e575c9dfa
  • github.com/MiteshSharma/PrometheusWithGrafana

Prometheus SaaS Solutions

• Weave Cortex SaaS (Hosted Prometheus - Public Cloud)

Grafana

• Grafana
• Prometheus utiliza plantillas de consola para los dashboards, si bien su curva de aprendizaje de sus múltiples funcionalidades es alta, con una interfaz de usuario insuficiente. Por este motivo es muy habitual utilizar Grafana como interfaz de usuario.
• grafana.com: Provisioning Grafana 🌟 Las últimas versiones de Grafana permiten la creación de "datasources" y "dashboards" con Ansible, mediante las opciones de provisión de Grafana. Funciona con cualquier "datasource" (Prometheus, InfluxDB, etc), incluyendo la configuración de Grafana correspondiente y dejando poco margen para el error humano.
  • Grafana provisioning Ansible Role

Grafana Dashboards

• Grafana Dashboards
• github.com/mlabouardy: Grafana Dashboards
• openlogic.com: How to develop Grafana Dashboards 🌟
• Percona Grafana dashboards for MySQL and MongoDB monitoring using Prometheus 🌟
• Prometheus Monitoring With Grafana. Prometheus Stats Dashboard and Prometheus Benchmark Dashboard. How you construct your Prometheus monitoring dashboard involves trial and error. Grafana makes this exploration very easy and Prometheus has good built-in functionality.

Monitored Component	Collector	Dashboard Number	URL
ActiveMQ 5.x "classic"	Telegraf	10702	Ref1, Ref2, Ref3, Ref4
ActiveMQ Artemis/Red Hat AMQ Broker	JMX Exporter	9087	Ref1, Ref2, Ref3
Message Streams like Kafka/Red Hat AMQ Streams	Other	9777

Kibana

• Kibana
  • Kibana Docs
• dzone: Kibana Tutorial: Part 1 - Getting Started
  • dzone: Kibana Tutorial: Part 2 - Creating Visualizations
• dzone: Getting Started With Kibana Advanced Searches
• dzone: Kibana Hacks: 5 Tips and Tricks

Interactive Learning

• katacoda.com: Getting Started with Prometheus
• katacoda.com: Creating Dashboards with Grafana

Performance

• dzone.com: The Keys to Performance Tuning and Testing
• dzone.com: How Performance Tuning and Testing are Changing
• Java:
  • developers.redhat.com: Troubleshooting java applications on openshift
  • dzone.com: how to take thread dumps
• Performance Patterns in Microservices-Based Integrations 🌟 Almost all applications that perform anything useful for a given business need to be integrated with one or more applications. With microservices-based architecture, where a number of services are broken down based on the services or functionality offered, the number of integration points or touch points increases massively.

Distributed Tracing. OpenTelemetry and Jaeger

• opentelemetry.io 🌟 (OpenTracing.io + OpenCensus.io = OpenTelemetry.io)
• Jaeger 🌟
  • Jaeger Demo1
  • Jaeger Demo 2
  • medium.com: Jaeger embraces OpenTelemetry collector 🌟
• zipkin.io
• OpenTracing.io
  • lightstep.com: Understand Distributed Tracing

Application Performance Management (APM)

• en.wikipedia.org/wiki/Application_performance_management
• Awesome APM 🌟
• dzone.com: APM Tools Comparison
• dzone.com: Java Performance Monitoring: 5 Open Source Tools You Should Know
• dzone.com: 14 best performance testing tools and APM solutions
• elastic.co: Using the Elastic APM Java Agent on Kubernetes
• Exception Tracking:
  • sentry.io
  • APMs like Dynatrace, etc.
• APM Tools:
  • datadoghq.com
  • honeycomb.io
  • lightstep.com
  • skywalking.apache.org
  • Elastic APM
  • AppDynamics 🌟
  • New Relic 🌟
  • Dynatrace 🌟
• List of Performance Analysis Tools:
  • Threadumps + heapdumps + GC analysis tools
  • en.wikipedia.org/wiki/List_of_performance_analysis_tools
  • InspectIT
  • VisualVM 🌟
  • OverOps
  • FusionReactor
  • tier1app.com
  • fastthread.io 🌟
  • gceasy.io 🌟
  • heaphero.io

Dynatrace APM

• adictosaltrabajo.com: Monitorización y análisis de rendimiento de aplicaciones con Dynatrace APM
• dynatrace.com: openshift monitoring
• dynatrace.com: Dynatrace monitoring for Kubernetes and OpenShift
• dynatrace.com: Deploy OneAgent on OpenShift Container Platform
• Successful Kubernetes Monitoring – Three Pitfalls to Avoid
• My Dynatrace proof of concept 🌟

Message Queue Monitoring

Messaging Solution	Monitoring Solution	URL
ActiveMQ 5.8.0+	Dynatrace	ref
ActiveMQ Artemis	Micrometer Collector + Prometheus	ref1, ref2
IBM MQ	IBM MQ Exporter for Prometheus	ref
Kakfa	Dynatrace	ref1, ref2, ref3
Kafka	Prometheus JMX Exporter	ref1, ref2, ref3, ref4, ref5, ref6, ref7
Kafka	Kafka Exporter	ref, Use JMX Exporter to export other metrics
Kafka	Kafdrop – Kafka Web UI	ref
Kafka	ZooNavigator: Web-based ZooKeeper UI	ref
Kafka	CMAK (Cluster Manager for Apache Kafka, previously known as Kafka Manager)	ref
Kafka	Xinfra Monitor (renamed from Kafka Monitor, created by Linkedin)	ref
Kafka	Telegraf + InfluxDB	ref
Red Hat AMQ Broker (ActiveMQ Artemis)	Prometheus plugin for AMQ Broker	ref1, ref2, ref3
Red Hat AMQ Streams (Kafka)	JMX, OpenTracing+Jaeger	ref1,ref2
Red Hat AMQ Streams Operator	AMQ Streams Operator (Prometheus & Jaeger), strimzi, jmxtrans	ref1, ref2, ref3 strimzi, ref4: jmxtrans, ref5: banzai operator
Red Hat AMQ Broker Operator	Prometheus (recommended) or Jolokia REST to JMX	ref1, ref2, ref3, ref4, ref5

Red Hat AMQ 7 Broker Monitoring solutions based on Prometheus and Grafana

This is a selection of monitoring solutions suitable for RH AMQ 7 Broker based on Prometheus and Grafana:

Environment	Collector/Exporter	Details/URL
RHEL	Prometheus Plugin for AMQ Broker	ref
RHEL	Prometheus JMX Exporter	Same solution applied to ActiveMQ Artemis
OpenShift 3	Prometheus Plugin for AMQ Broker	Grafana Dashboard not available, ref1, ref2
OpenShift 4	Prometheus Plugin for AMQ Broker	Check if Grafana Dashboard is automatically setup by Red Hat AMQ Operator
OpenShift 3	Prometheus JMX Exporter	Grafana Dashboard not available, ref1, ref2

Other Awesome Lists

• Awesome APM

Challenges in a Microservices Age: Monitoring, Logging and Tracing on Red Hat OpenShift from Martin Etmajer

Monitoring Microservices at Scale on OpenShift (OpenShift Commons Briefing #52) from Martin Etmajer

Dynatrace from Purnima Kurella