Whether our application is made up of a boundless multitude of microservices or a single monolith, one of the main needs is our ability to determine its actual load and its good working order. In other words, as soon as we put our application into production, we need to be able to continuously answer questions such as:
- is our application working?
- how many requests per second is it responding to?
- what are the response times?
- how much network traffic is it producing?
- how stressed are the servers on which our application is located?
- what is the http request that always responds in a very long time?
- the database is unable to respond quickly enough, but maybe there is a bottleneck somewhere?
The Prometheus opensource monitoring solution can answer these and many other questions and addresses and solves these problems thanks also to the excellent travel companion Grafana. Grafana is a web application that creates graphs divided into panels, with data coming from a variety of different sources, such as OpenTSDB, InfluxDB, ElasticSearc and Prometheus itself.
Installation with Helm
Probably the fastest and most efficient way to get Prometheus is via Helm chart. Add the repo and install the chart (here we work in namespace monitoring)
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
# Add the prometheus-community repo and perform a general update of the repositories
$ helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
$ helm repo update
# Use the --create-namespace flag, if the namespace does not exist
$ helm install <chart_name> prometheus-community/kube-prometheus-stack -n monitoring [--create-namespace] |
In this way we will have already deployed all the components in our cluster. To perform a quick test, you can connect, via browser, to the user interfaces of Prometheus and Grafana, modifying the two services in a similar way to what was seen for the Kubernetes dashboard: edit the type of service, from ClusterIP to NodePort, and select a port in the range 30000-32767. At the first access to Grafana you will be asked for your credentials, which you can later change. Credentials are present in github site.
| Section | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
Upgrade or uninstall chart
To upgrade all the Kubernetes components associated with the chart use or to remove them and delete the release
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
$ helm upgrade <chart_name> prometheus-community/kube-prometheus-stack -n monitoring
$ helm uninstall <chart_name> -n monitoring |
Custom Resource Definitions (CRDs) created by this chart are not removed by default and should be manually cleaned up
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
kubectl delete crd alertmanagerconfigs.monitoring.coreos.com
kubectl delete crd alertmanagers.monitoring.coreos.com
kubectl delete crd podmonitors.monitoring.coreos.com
kubectl delete crd probes.monitoring.coreos.com
kubectl delete crd prometheuses.monitoring.coreos.com
kubectl delete crd prometheusrules.monitoring.coreos.com
kubectl delete crd servicemonitors.monitoring.coreos.com
kubectl delete crd thanosrulers.monitoring.coreos.com |
| Info | ||
|---|---|---|
| ||
The CRD API resource allows you to define custom resources. Defining a CRD object creates a new custom resource with a name and schema that you specify. The Kubernetes API serves and handles the storage of your custom resource. The name of a CRD object must be a valid DNS subdomain name. |
Prometheus is a monitoring platform that collects the metrics of the machines on which it is installed, "scraping" the parameters from the HTTP endpoints of the latter.
Installation
We present a procedure that establishes the service using Docker-compose. Obviously, Docker-compose must be present on the system (if not present in your system install docker-compose).
We create a folder (eg "mkdir prometheus") in which we insert the docker-compose.yml file
...
| title | docker-compose.yml |
|---|
...
Always inside the prometheus folder we create 2 other folders, called promconf and promdata, where we will insert, respectively, our configurations, present in the prometheus.yml file, and storage. The latter allows you to configure Prometheus to monitor itself. The just mentioned configuration file is
...
| title | prometheus.yml |
|---|
...
Prometheus collects metrics of monitored targets by scraping the HTTP endpoints of these targets. Since Prometheus himself exposes his internal metrics through the same mechanism, it is possible to scrape and monitor his health through the same mechanism.
Now let's launch the background service with the command
| Code Block | ||||
|---|---|---|---|---|
| ||||
| $ docker-compose up -d # To check the logs $ docker-compose logs |