Chapter 2. Monitoring Camel K integrations Red Hat Integration - Camel K monitoring is based on the OpenShift monitoring system . This chapter explains how to use the available options for monitoring Red Hat Integration - Camel K integrations at runtime. You can use the Prometheus Operator that is already deployed as part of OpenShift Monitoring to monitor your own applications. Section 2.1, "Enabling user workload monitoring in OpenShift" Section 2.2, "Configuring Camel K integration metrics" Section 2.3, "Adding custom Camel K integration metrics" 2.1. Enabling user workload monitoring in OpenShift OpenShift 4.3 or higher includes an embedded Prometheus Operator already deployed as part of OpenShift Monitoring. This section explains how to enable monitoring of your own application services in OpenShift Monitoring. This option avoids the additional overhead of installing and managing a separate Prometheus instance. Prerequisites You must have cluster administrator access to an OpenShift cluster on which the Camel K Operator is installed. See Installing Camel K . Procedure Enter the following command to check if the cluster-monitoring-config ConfigMap object exists in the openshift-monitoring project : USD oc -n openshift-monitoring get configmap cluster-monitoring-config Create the cluster-monitoring-config ConfigMap if this does not already exist: USD oc -n openshift-monitoring create configmap cluster-monitoring-config Edit the cluster-monitoring-config ConfigMap: USD oc -n openshift-monitoring edit configmap cluster-monitoring-config Under data:config.yaml: , set enableUserWorkload to true : apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | enableUserWorkload: true Additional resources Enabling monitoring for user-defined projects 2.2. Configuring Camel K integration metrics You can configure monitoring of Camel K integrations automatically using the Camel K Prometheus trait at runtime. This automates the configuration of dependencies and integration Pods to expose a metrics endpoint, which is then discovered and displayed by Prometheus. The Camel Quarkus MicroProfile Metrics extension automatically collects and exposes the default Camel K metrics in the OpenMetrics format. Prerequisites You must have already enabled monitoring of your own services in OpenShift. See Enabling user workload monitoring in OpenShift . Procedure Enter the following command to run your Camel K integration with the Prometheus trait enabled: kamel run myIntegration.java -t prometheus.enabled=true Alternatively, you can enable the Prometheus trait globally once, by updating the integration platform as follows: USD oc patch itp camel-k --type=merge -p '{"spec":{"traits":{"prometheus":{"configuration":{"enabled":true}}}}}' View monitoring of Camel K integration metrics in Prometheus. For example, for embedded Prometheus, select Monitoring > Metrics in the OpenShift administrator or developer web console. Enter the Camel K metric that you want to view. For example, in the Administrator console, under Insert Metric at Cursor , enter application_camel_context_uptime_seconds , and click Run Queries . Click Add Query to view additional metrics. Default Camel Metrics provided by PROMETHEUS TRAIT Some Camel specific metrics are available out of the box. Name Type Description application_camel_message_history_processing timer Sample of performance of each node in the route when message history is enabled application_camel_route_count gauge Number of routes added application_camel_route_running_count gauge Number of routes runnning application_camel_[route or context]_exchanges_inflight_count gauge Route inflight messages for a CamelContext or a route application_camel_[route or context]_exchanges_total counter Total number of processed exchanges for a CamelContext or a route application_camel_[route or context]_exchanges_completed_total counter Number of successfully completed exchange for a CamelContext or a route application_camel_[route or context]_exchanges_failed_total counter Number of failed exchanges for a CamelContext or a route application_camel_[route or context]_failuresHandled_total counter Number of failures handled for a CamelContext or a route application_camel_[route or context]_externalRedeliveries_total counter Number of external initiated redeliveries (such as from JMS broker) for a CamelContext or a route application_camel_context_status gauge The status of the Camel Context application_camel_context_uptime_seconds gauge The amount of time since the Camel Context was started application_camel_[route or exchange] processing [rate_per_second or one_min_rate_per_second or five_min_rate_per_second or fifteen_min_rate_per_second or min_seconds or max_seconds or mean_second or stddev_seconds] gauge Exchange message or route processing with multiple options application_camel_[route or exchange]_processing_seconds summary Exchange message or route processing metric Additional resources Prometheus Trait Camel Quarkus MicroProfile Metrics 2.3. Adding custom Camel K integration metrics You can add custom metrics to your Camel K integrations by using Camel MicroProfile Metrics component and annotations in your Java code. These custom metrics will then be automatically discovered and displayed by Prometheus. This section shows examples of adding Camel MicroProfile Metrics annotations to Camel K integration and service implementation code. Prerequisites You must have already enabled monitoring of your own services in OpenShift. See Enabling user workload monitoring in OpenShift . Procedure Register the custom metrics in your Camel integration code using Camel MicroProfile Metrics component annotations. The following example shows a Metrics.java integration: // camel-k: language=java trait=prometheus.enabled=true dependency=mvn:org.my/app:1.0 1 import org.apache.camel.Exchange; import org.apache.camel.LoggingLevel; import org.apache.camel.builder.RouteBuilder; import org.apache.camel.component.microprofile.metrics.MicroProfileMetricsConstants; import javax.enterprise.context.ApplicationScoped; @ApplicationScoped public class Metrics extends RouteBuilder { @Override public void configure() { onException() .handled(true) .maximumRedeliveries(2) .logStackTrace(false) .logExhausted(false) .log(LoggingLevel.ERROR, "Failed processing USD{body}") // Register the 'redelivery' meter .to("microprofile-metrics:meter:redelivery?mark=2") // Register the 'error' meter .to("microprofile-metrics:meter:error"); 2 from("timer:stream?period=1000") .routeId("unreliable-service") .setBody(header(Exchange.TIMER_COUNTER).prepend("event #")) .log("Processing USD{body}...") // Register the 'generated' meter .to("microprofile-metrics:meter:generated") 3 // Register the 'attempt' meter via @Metered in Service.java .bean("service") 4 .filter(header(Exchange.REDELIVERED)) .log(LoggingLevel.WARN, "Processed USD{body} after USD{header.CamelRedeliveryCounter} retries") .setHeader(MicroProfileMetricsConstants.HEADER_METER_MARK, header(Exchange.REDELIVERY_COUNTER)) // Register the 'redelivery' meter .to("microprofile-metrics:meter:redelivery") 5 .end() .log("Successfully processed USD{body}") // Register the 'success' meter .to("microprofile-metrics:meter:success"); 6 } } 1 Uses the Camel K modeline to automatically configure the Prometheus trait and Maven dependencies 2 error : Metric for the number of errors corresponding to the number of events that have not been processed 3 generated : Metric for the number of events to be processed 4 attempt : Metric for the number of calls made to the service bean to process incoming events 5 redelivery : Metric for the number of retries made to process the event 6 success : Metric for the number of events successfully processed Add Camel MicroProfile Metrics annotations to any implementation files as needed. The following example shows the service bean called by the Camel K integration, which generates random failures: package com.redhat.integration; import java.util.Random; import org.apache.camel.Exchange; import org.apache.camel.RuntimeExchangeException; import org.eclipse.microprofile.metrics.Meter; import org.eclipse.microprofile.metrics.annotation.Metered; import org.eclipse.microprofile.metrics.annotation.Metric; import javax.inject.Named; import javax.enterprise.context.ApplicationScoped; @Named("service") @ApplicationScoped @io.quarkus.arc.Unremovable public class Service { //Register the attempt meter @Metered(absolute = true) public void attempt(Exchange exchange) { 1 Random rand = new Random(); if (rand.nextDouble() < 0.5) { throw new RuntimeExchangeException("Random failure", exchange); 2 } } } 1 The @Metered MicroProfile Metrics annotation declares the meter and the name is automatically generated based on the metrics method name, in this case, attempt . 2 This example fails randomly to help generate errors for metrics. Follow the steps in Configuring Camel K integration metrics to run the integration and view the custom Camel K metrics in Prometheus. In this case, the example already uses the Camel K modeline in Metrics.java to automatically configure Prometheus and the required Maven dependencies for Service.java . Additional resources Camel MicroProfile Metrics component Camel Quarkus MicroProfile Metrics Extension

Commands:
oc -n openshift-monitoring get configmap cluster-monitoring-config
oc -n openshift-monitoring create configmap cluster-monitoring-config
oc -n openshift-monitoring edit configmap cluster-monitoring-config
apiVersion: v1 kind: ConfigMap metadata: name: cluster-monitoring-config namespace: openshift-monitoring data: config.yaml: | enableUserWorkload: true
kamel run myIntegration.java -t prometheus.enabled=true
oc patch itp camel-k --type=merge -p '{"spec":{"traits":{"prometheus":{"configuration":{"enabled":true}}}}}'
// camel-k: language=java trait=prometheus.enabled=true dependency=mvn:org.my/app:1.0 1 import org.apache.camel.Exchange; import org.apache.camel.LoggingLevel; import org.apache.camel.builder.RouteBuilder; import org.apache.camel.component.microprofile.metrics.MicroProfileMetricsConstants; import javax.enterprise.context.ApplicationScoped; @ApplicationScoped public class Metrics extends RouteBuilder { @Override public void configure() { onException() .handled(true) .maximumRedeliveries(2) .logStackTrace(false) .logExhausted(false) .log(LoggingLevel.ERROR, "Failed processing USD{body}") // Register the 'redelivery' meter .to("microprofile-metrics:meter:redelivery?mark=2") // Register the 'error' meter .to("microprofile-metrics:meter:error"); 2 from("timer:stream?period=1000") .routeId("unreliable-service") .setBody(header(Exchange.TIMER_COUNTER).prepend("event #")) .log("Processing USD{body}...") // Register the 'generated' meter .to("microprofile-metrics:meter:generated") 3 // Register the 'attempt' meter via @Metered in Service.java .bean("service") 4 .filter(header(Exchange.REDELIVERED)) .log(LoggingLevel.WARN, "Processed USD{body} after USD{header.CamelRedeliveryCounter} retries") .setHeader(MicroProfileMetricsConstants.HEADER_METER_MARK, header(Exchange.REDELIVERY_COUNTER)) // Register the 'redelivery' meter .to("microprofile-metrics:meter:redelivery") 5 .end() .log("Successfully processed USD{body}") // Register the 'success' meter .to("microprofile-metrics:meter:success"); 6 } }
package com.redhat.integration; import java.util.Random; import org.apache.camel.Exchange; import org.apache.camel.RuntimeExchangeException; import org.eclipse.microprofile.metrics.Meter; import org.eclipse.microprofile.metrics.annotation.Metered; import org.eclipse.microprofile.metrics.annotation.Metric; import javax.inject.Named; import javax.enterprise.context.ApplicationScoped; @Named("service") @ApplicationScoped @io.quarkus.arc.Unremovable public class Service { //Register the attempt meter @Metered(absolute = true) public void attempt(Exchange exchange) { 1 Random rand = new Random(); if (rand.nextDouble() < 0.5) { throw new RuntimeExchangeException("Random failure", exchange); 2 } } }