Currently, Codacy Self-hosted supports two monitoring solutions:
- Crow: A simple, lightweight, and built-in monitoring solution, enabled by default when you install Codacy.
- Prometheus + Grafana + Loki: A comprehensive third-party monitoring solution, recommended for more advanced usage.
The sections below provide instructions on how to set up each monitoring solution.
Setting up monitoring using Crow#
Crow displays information about the projects that are pending analysis and the jobs currently running on Codacy.
Crow is installed alongside Codacy when the Helm chart is deployed to the cluster. By default, you can access Crow as follows:
http://<codacy hostname>/monitoring, where
<codacy hostname>is the hostname of your Codacy instance
We highly recommend that you define a custom password for Crow, if you haven't already done it when installing Codacy:
Edit the value of
values-production.yamlfile that you used to install Codacy:
global: crow: config: passwordAuth: password: C0dacy123
Apply the new configuration by performing a Helm upgrade. To do so execute the command used to install Codacy:
If you're using MicroK8s you must use the file
values-microk8s.yamltogether with the file
To do this, uncomment the last line before running the
helm upgradecommand below.
helm upgrade (...options used to install Codacy...) \ --version 9.0.0 \ --values values-production.yaml \ # --values values-microk8s.yaml
Setting up monitoring using Grafana, Prometheus, and Loki#
Prometheus is an open-source systems monitoring and alerting toolkit. Logs can be collected using Loki, which is a horizontally-scalable, highly-available, multi-tenant log aggregation system. Its data can be visualized with Grafana, a widely used open source analytics and monitoring solution.
This solution is considerably more resource demanding than Crow, and is recommended only for more advanced usage. Furthermore, its installation, configuration, and management require a deeper knowledge of Kubernetes as each component must be carefully tweaked to match your specific use case, using as starting point the
.yaml values files provided by us.
The instructions below cover the basic installation of these monitoring services using the Kube Prometheus Stack.
If you're using MicroK8s run
1. Install custom resources#
Add the custom resources required for installing the monitoring bundle in your cluster:
kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_alertmanagerconfigs.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_alertmanagers.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_podmonitors.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_probes.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_prometheuses.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_prometheusrules.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_servicemonitors.yaml kubectl apply --server-side -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/v0.58.0/example/prometheus-operator-crd/monitoring.coreos.com_thanosrulers.yaml
2. Installing Loki#
Obtain the configuration file for Loki,
values-loki.yaml, and install it by running the command below. While the default storage class setting for Loki persistence should suit most use cases, you may need to adjust it to your specific Kubernetes installation. For instance, for MicroK8s use
helm repo add grafana https://grafana.github.io/helm-charts kubectl create namespace monitoring helm upgrade --install --atomic --timeout 600s loki grafana/loki \ --version 2.14.1 --namespace monitoring --values values-loki.yaml
3. Installing Promtail#
Promtail is an agent that ships the contents of local logs to a Loki instance.
Obtain the configuration file for Promtail,
values-promtail.yaml, and install it by running the command below.
helm upgrade --install --atomic --timeout 600s promtail grafana/promtail \ --version 2.2.0 --namespace monitoring --values values-promtail.yaml
4. Installing Prometheus and Grafana#
Edit the Grafana password for the
adminuser and the hostname for Grafana in the
Install the bundle on your cluster by running the command below.
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm upgrade --install --atomic --timeout 600s monitoring prometheus-community/kube-prometheus-stack \ --version 39.9.0 --namespace monitoring --values values-prometheus-operator.yaml
Grafana will be available on the domain you configured in your
values-prometheus-operator.yaml file, with Prometheus and Loki configured as datasources. Follow the Kubernetes documentation if you need to access other monitoring services that are now running on your cluster, using the method that best suits your use case.
5. Enable service dashboards#
Now that you have Prometheus and Grafana installed you can enable metrics reporting for Codacy components.
Create a file named
values-monitoring.yamlwith the following content:
global: metrics: kamon: enabled: true prometheusReporter: enabled: true serviceMonitor: enabled: true grafana: enabled: true
Apply this configuration by performing a Helm upgrade. To do so append
--values values-monitoring.yamlto the command used to install Codacy:
helm upgrade (...options used to install Codacy...) \ --version 9.0.0 \ --values values-monitoring.yaml
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