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    Enable application logging and monitoring

    This page shows how to configure a cluster for Distributed Cloud so that custom logs and metrics from user applications are sent to Cloud Logging and Cloud Monitoring and Managed Service for Prometheus .

    For the best user application logging and monitoring experience, we strongly recommend that you use the following configuration:

    • Enable Google Cloud Managed Service for Prometheus by setting enableGMPForApplications to true in the Stackdriver object. This configuration lets you monitor and alert on your workloads globally, using Prometheus . For instructions and additional information, see Enable Managed Service for Prometheus on this page.

    • Enable Cloud Logging for user applications by setting enableCloudLoggingForApplications to true in the Stackdriver object. This configuration provides logging for your workloads. For instructions and additional information, see Enable Cloud Logging for user applications on this page.

    Enable Managed Service for Prometheus

    The configuration for Managed Service for Prometheus is specified in a Stackdriver object named stackdriver . For additional information, including best practices and troubleshooting, see the Managed Service for Prometheus documentation .

    To configure the stackdriver object to enable Google Cloud Managed Service for Prometheus:

    1. Open the stackdriver object for editing:

       kubectl  
--kubeconfig = 
 CLUSTER_KUBECONFIG 
  
 \ 
  
--namespace  
kube-system  
edit  
stackdriver  
stackdriver

      Replace CLUSTER_KUBECONFIG with the path of your cluster kubeconfig file.

    2. Under spec , set enableGMPForApplications to true :

        apiVersion 
 : 
  
 addons.gke.io/v1alpha1 
 kind 
 : 
  
 Stackdriver 
 metadata 
 : 
  
 name 
 : 
  
 stackdriver 
  
 namespace 
 : 
  
 kube-system 
 spec 
 : 
  
 projectID 
 : 
  
 ... 
  
 clusterName 
 : 
  
 ... 
  
 clusterLocation 
 : 
  
 ... 
  
 proxyConfigSecretName 
 : 
  
 ... 
   
 enableGMPForApplications 
 : 
  
 true 
  
 enableVPC 
 : 
  
 ... 
  
 optimizedMetrics 
 : 
  
 true

    3. Save and close the edited file.

      The Google-managed Prometheus components start automatically in the cluster in the gmp-system namespace.

    4. Check the Google-managed Prometheus components:

       kubectl  
--kubeconfig = 
 CLUSTER_KUBECONFIG 
  
--namespace  
gmp-system  
get  
pods

      The output of this command is similar to the following:

       NAME                              READY   STATUS    RESTARTS        AGE
collector-abcde                   2/2     Running   1 (5d18h ago)   5d18h
collector-fghij                   2/2     Running   1 (5d18h ago)   5d18h
collector-klmno                   2/2     Running   1 (5d18h ago)   5d18h
gmp-operator-68d49656fc-abcde     1/1     Running   0               5d18h
rule-evaluator-7c686485fc-fghij   2/2     Running   1 (5d18h ago)   5d18h

    Managed Service for Prometheus supports rule evaluation and alerting. To set up rule evaluation, see Rule evaluation .

    Run an example application

    The managed service provides a manifest for an example application, prom-example , that emits Prometheus metrics on its metrics port. The application uses three replicas.

    To deploy the application:

    1. Create the gmp-test namespace for resources that you create as part of the example application:

       kubectl  
--kubeconfig = 
 CLUSTER_KUBECONFIG 
  
create  
ns  
gmp-test

    2. Apply the application manifest with the following command:

       kubectl  
-n  
gmp-test  
apply  
 \ 
  
-f  
https://raw.githubusercontent.com/GoogleCloudPlatform/prometheus-engine/v0.4.1/examples/example-app.yaml

    Configure a PodMonitoring resource

    In this section, you configure a PodMonitoring custom resource to capture metrics data emitted by the example application and send it to Managed Service for Prometheus. The PodMonitoring custom resource uses target scraping. In this case, the collector agents scrape the /metrics endpoint to which the sample application emits data.

    A PodMonitoring custom resource scrapes targets in the namespace in which it's deployed only. To scrape targets in multiple namespaces, deploy the same PodMonitoring custom resource in each namespace. You can verify the PodMonitoring resource is installed in the intended namespace by running the following command:

     kubectl  
--kubeconfig  
 CLUSTER_KUBECONFIG 
  
get  
podmonitoring  
-A

    For reference documentation about all the Managed Service for Prometheus custom resources, see the prometheus-engine/doc/api reference .

    The following manifest defines a PodMonitoring resource, prom-example , in the gmp-test namespace. The resource finds all Pods in the namespace that have the label app with the value prom-example . The matching Pods are scraped on a port named metrics , every 30 seconds, on the /metrics HTTP path.

      apiVersion 
 : 
  
 monitoring.googleapis.com/v1 
 kind 
 : 
  
 PodMonitoring 
 metadata 
 : 
  
 name 
 : 
  
 prom-example 
 spec 
 : 
  
 selector 
 : 
  
 matchLabels 
 : 
  
 app 
 : 
  
 prom-example 
  
 endpoints 
 : 
  
 - 
  
 port 
 : 
  
 metrics 
  
 interval 
 : 
  
 30s

    To apply this resource, run the following command:

     kubectl  
--kubeconfig  
 CLUSTER_KUBECONFIG 
  
-n  
gmp-test  
apply  
 \ 
  
-f  
https://raw.githubusercontent.com/GoogleCloudPlatform/prometheus-engine/v0.4.1/examples/pod-monitoring.yaml

    Managed Service for Prometheus is now scraping the matching Pods.

    Query metrics data

    The simplest way to verify that your Prometheus data is being exported is to use PromQL queries in the Metrics Explorerin the Google Cloud console.

    To run a PromQL query, do the following:

    1. In the Google Cloud console, go to the Monitoringpage or click the following button:

      Go to Monitoring

    2. In the navigation pane, select Metrics Explorer.

    3. Use Prometheus Query Language (PromQL) to specify the data to display on the chart:

      1. In the toolbar of the Select a metricpane, select Code Editor.

      2. Select PromQLin the Languagetoggle. The language toggle is at the bottom of the Code Editorpane.

      3. Enter your query into the query editor. For example, to chart the average number of seconds CPUs spent in each mode over the past hour, use the following query:

          avg 
 ( 
 rate 
 ( 
 kubernetes_io 
 : 
 anthos_container_cpu_usage_seconds_total 
 { 
 monitored_resource 
 = 
 " 
 k8s_node 
 "}[ 
 1h 
 ] 
 ))

      For more information about using PromQL, see PromQL in Cloud Monitoring .

    The following screenshot shows a chart that displays the anthos_container_cpu_usage_seconds_total metric:

    Managed Service for Prometheus chart for the Prometheus `anthos_container_cpu_usage_seconds_total` metric.

    If you collect large amounts of data, you might want to filter exported metrics to keep down costs.

    Enable Cloud Logging for user applications

    The configuration for Cloud Logging and Cloud Monitoring is held in a Stackdriver object named stackdriver .

    1. Open the stackdriver object for editing:

       kubectl  
--kubeconfig = 
 CLUSTER_KUBECONFIG 
  
 \ 
  
--namespace  
kube-system  
edit  
stackdriver  
stackdriver

      Replace CLUSTER_KUBECONFIG with the path of your user cluster kubeconfig file.

    2. In the spec section, set enableCloudLoggingForApplications to true :

        apiVersion 
 : 
  
 addons.gke.io/v1alpha1 
  
 kind 
 : 
  
 Stackdriver 
  
 metadata 
 : 
  
 name 
 : 
  
 stackdriver 
  
 namespace 
 : 
  
 kube-system 
  
 spec 
 : 
  
 projectID 
 : 
  
 ... 
  
 clusterName 
 : 
  
 ... 
  
 clusterLocation 
 : 
  
 ... 
  
 proxyConfigSecretName 
 : 
  
 ... 
   
 enableCloudLoggingForApplications 
 : 
  
 true 
  
 enableVPC 
 : 
  
 ... 
  
 optimizedMetrics 
 : 
  
 true

    3. Save and close the edited file.

    Run an example application

    In this section, you create an application that writes custom logs.

    1. Save the following Deployment manifests to a file named my-app.yaml .

        apiVersion 
 : 
  
 apps/v1 
 kind 
 : 
  
 Deployment 
 metadata 
 : 
  
 name 
 : 
  
 "monitoring-example" 
  
 namespace 
 : 
  
 "default" 
  
 labels 
 : 
  
 app 
 : 
  
 "monitoring-example" 
 spec 
 : 
  
 replicas 
 : 
  
 1 
  
 selector 
 : 
  
 matchLabels 
 : 
  
 app 
 : 
  
 "monitoring-example" 
  
 template 
 : 
  
 metadata 
 : 
  
 labels 
 : 
  
 app 
 : 
  
 "monitoring-example" 
  
 spec 
 : 
  
 containers 
 : 
  
 - 
  
 image 
 : 
  
 gcr.io/google-samples/prometheus-dummy-exporter:latest 
  
 name 
 : 
  
 prometheus-example-exporter 
  
 imagePullPolicy 
 : 
  
 Always 
  
 command 
 : 
  
 - 
  
 /bin/sh 
  
 - 
  
 -c 
  
 - 
  
 ./prometheus-dummy-exporter --metric-name=example_monitoring_up --metric-value=1 --port=9090 
  
 resources 
 : 
  
 requests 
 : 
  
 cpu 
 : 
  
 100m

    2. Create the Deployment

       kubectl  
--kubeconfig  
 CLUSTER_KUBECONFIG 
  
apply  
-f  
my-app.yaml

    View application logs

    Console

    1. Go to the Logs Explorer in the Google Cloud console.

      Go to the Logs Explorer

    2. Click Resource. In the ALL RESOURCE TYPESmenu, select Kubernetes Container.

    3. Under CLUSTER_NAME, select the name of your user cluster.

    4. Under NAMESPACE_NAME, select default.

    5. Click Addand then click Run Query.

    6. Under Query results, you can see log entries from the monitoring-example Deployment. For example:

        { 
  
 "textPayload" 
 : 
  
 "2020/11/14 01:24:24 Starting to listen on :9090\n" 
 , 
  
 "insertId" 
 : 
  
 "1oa4vhg3qfxidt" 
 , 
  
 "resource" 
 : 
  
 { 
  
 "type" 
 : 
  
 "k8s_container" 
 , 
  
 "labels" 
 : 
  
 { 
  
 "pod_name" 
 : 
  
 "monitoring-example-7685d96496-xqfsf" 
 , 
  
 "cluster_name" 
 : 
  
 ... 
 , 
  
 "namespace_name" 
 : 
  
 "default" 
 , 
  
 "project_id" 
 : 
  
 ... 
 , 
  
 "location" 
 : 
  
 "us-west1" 
 , 
  
 "container_name" 
 : 
  
 "prometheus-example-exporter" 
  
 } 
  
 }, 
  
 "timestamp" 
 : 
  
 "2020-11-14T01:24:24.358600252Z" 
 , 
  
 "labels" 
 : 
  
 { 
  
 "k8s-pod/pod-template-hash" 
 : 
  
 "7685d96496" 
 , 
  
 "k8s-pod/app" 
 : 
  
 "monitoring-example" 
  
 }, 
  
 "logName" 
 : 
  
 "projects/.../logs/stdout" 
 , 
  
 "receiveTimestamp" 
 : 
  
 "2020-11-14T01:24:39.562864735Z" 
 }

    gcloud CLI

    1. Run this command:

       gcloud  
logging  
 read 
  
 'resource.labels.project_id=" PROJECT_ID 
" AND \ 
 resource.type="k8s_container" AND resource.labels.namespace_name="default"'

      Replace PROJECT_ID with the ID of your project.

    2. In the output, you can see log entries from the monitoring-example Deployment. For example:

        insertId 
 : 
  
 1oa4vhg3qfxidt 
 labels 
 : 
  
 k8s-pod/app 
 : 
  
 monitoring-example 
  
 k8s- pod/pod-template-hash 
 : 
  
 7685d96496 
 logName 
 : 
  
 projects/.../logs/stdout 
 receiveTimestamp 
 : 
  
 '2020-11-14T01:24:39.562864735Z' 
 resource 
 : 
  
 labels 
 : 
  
 cluster_name 
 : 
  
 ... 
  
 container_name 
 : 
  
 prometheus-example-exporter 
  
 location 
 : 
  
 us-west1 
  
 namespace_name 
 : 
  
 default 
  
 pod_name 
 : 
  
 monitoring-example-7685d96496-xqfsf 
  
 project_id 
 : 
  
 ... 
  
 type 
 : 
  
 k8s_container 
 textPayload 
 : 
  
 | 
  
 2020/11/14 01:24:24 Starting to listen on :9090 
 timestamp 
 : 
  
 '2020-11-14T01:24:24.358600252Z'

    Filter application logs

    Application log filtering can reduce application logging billing and network traffic from the cluster to Cloud Logging. Starting with Google Distributed Cloud release 1.15.0, when enableCloudLoggingForApplications is set to true , you can filter application logs by the following criteria:

    • Pod labels ( podLabelSelectors )
    • Namespaces ( namespaces )
    • Regular expressions for log content ( contentRegexes )

    Google Distributed Cloud sends only the filter results to Cloud Logging.

    Define application log filters

    The configuration for Logging is specified in a Stackdriver object named stackdriver .

    1. Open the stackdriver object for editing:

       kubectl  
--kubeconfig  
 USER_CLUSTER_KUBECONFIG 
  
--namespace  
kube-system  
 \ 
  
edit  
stackdriver  
stackdriver

      Replace USER_CLUSTER_KUBECONFIG with the path to your user cluster kubeconfig file.

    2. Add an appLogFilter section to the spec :

         
 apiVersion 
 : 
  
 addons.gke.io/v1alpha1 
  
 kind 
 : 
  
 Stackdriver 
  
 metadata 
 : 
  
 name 
 : 
  
 stackdriver 
  
 namespace 
 : 
  
 kube-system 
  
 spec 
 : 
   
 enableCloudLoggingForApplications 
 : 
  
 true 
  
 projectID 
 : 
  
 ... 
  
 clusterName 
 : 
  
 ... 
  
 clusterLocation 
 : 
  
 ... 
   
 appLogFilter 
 : 
  
 keepLogRules 
 : 
  
 - 
  
 namespaces 
 : 
  
 - 
  
 prod 
  
 ruleName 
 : 
  
 include-prod-logs 
  
 dropLogRules 
 : 
  
 - 
  
 podLabelSelectors 
 : 
  
 - 
  
 disableGCPLogging=yes 
  
 ruleName 
 : 
  
 drop-logs

    3. Save and close the edited file.

    4. (Optional) If you're using podLabelSelectors , restart the stackdriver-log-forwarder DaemonSet to effect your changes as soon as possible:

       kubectl  
--kubeconfig  
 USER_CLUSTER_KUBECONFIG 
  
--namespace  
kube-system  
 \ 
  
rollout  
restart  
daemonset  
stackdriver-log-forwarder

      Normally, podLabelSelectors are effective after 10 minutes. Restarting the DaemonSet stackdriver-log-forwarder makes the changes take effect more quickly.

    Example: Include ERROR or WARN logs in prod namespace only

    The following example illustrates an application log filter works. You define a filter that uses a namespace ( prod ), a regular expression ( .*(ERROR|WARN).* ), and a Pod label ( disableGCPLogging=yes ). Then, to verify that the filter works, you run a Pod in the prod namespace to test these filter conditions.

    To define and test an application log filter:

    1. Specify an application log filter in the Stackdriver object:

      In the following appLogFilter example, only ERROR or WARN logs in the prod namespace are kept. Any logs for Pods with the label disableGCPLogging=yes are dropped:

        apiVersion 
 : 
  
 addons.gke.io/v1alpha1 
 kind 
 : 
  
 Stackdriver 
 metadata 
 : 
  
 name 
 : 
  
 stackdriver 
  
 namespace 
 : 
  
 kube-system 
 spec 
 : 
  
 ... 
  
 appLogFilter 
 : 
  
 keepLogRules 
 : 
  
 - 
  
 namespaces 
 : 
  
 - 
  
 prod 
  
 contentRegexes 
 : 
  
 - 
  
 ".*(ERROR|WARN).*" 
  
 ruleName 
 : 
  
 include-prod-logs 
  
 dropLogRules 
 : 
  
 - 
  
 podLabelSelectors 
 : 
  
 - 
  
 disableGCPLogging=yes 
  
 # kubectl label pods pod disableGCPLogging=yes 
  
 ruleName 
 : 
  
 drop-logs 
 ...

    2. Deploy a Pod in the prod namespace and run a script that generates ERROR and INFO log entries:

       kubectl  
--kubeconfig  
 USER_CLUSTER_KUBECONFIG 
  
run  
pod1  
 \ 
  
--image  
gcr.io/cloud-marketplace-containers/google/debian10:latest  
 \ 
  
--namespace  
prod  
--restart  
Never  
--command  
--  
 \ 
  
/bin/sh  
-c  
 "while true; do echo 'ERROR is 404\\nINFO is not 404' && sleep 1; done"

      The filtered logs should contain the ERROR entries only, not the INFO entries.

    3. Add the label disableGCPLogging=yes to the Pod:

       kubectl  
--kubeconfig  
 USER_CLUSTER_KUBECONFIG 
  
label  
pods  
pod1  
 \ 
  
--namespace  
prod  
 disableGCPLogging 
 = 
yes

      The filtered log should no longer contain any entries for the pod1 Pod.

    Application log filter API definition

    The definition for the application log filter is declared within the stackdriver custom resource definition.

    To get the stackdriver custom resource definition, run the following command:

     kubectl  
--kubeconfig  
 USER_CLUSTER_KUBECONFIG 
  
get  
crd  
stackdrivers.addons.gke.io  
 \ 
  
--namespace  
kube-system  
-o  
yaml
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