Implementation of Prometheus Cortex
Using Kubernetes’ Helm package manager, Cortex is simple to install. We must first install consul as the data store inside the cluster before we can use the standard helm chart that the Cortex team created.
$ helm repo add hashicorp https://helm.releases.hashicorp.com
$ helm search repo hashicorp/consul
$ helm install consul hashicorp/consul --set global.name=consul --namespace cortex
Verify the consul nodes by using kubectl.
After setting up the datastore, we must set up the storage gateway to communicate with a distant storage backend.
Default values file of Cortex according to our use-case:
$ helm repo add cortex-helm https://cortexproject.github.io/cortex-helm-chart
$ helm install cortex --namespace cortex -f my-cortex-values.yaml cortex-helm/cortex
Here we are pretty much done with the cortex setup, and now it’s time to configure the prometheus to connect with the cortex. Now that the Cortex setup, and the initial part of the setup are complete, it’s time to configure Prometheus. We only need to supply a remote write URL in Prometheus; other than that, not many configuration changes are required. Prometheus includes a remote write and read API for sending and receiving metrics samples to a third-party API, in this case, Cortex.
All we need to do is include these block lines in our prometheus.yaml file.
remote_write:
url: http://cortex.cortex/api/prom/push
What is Prometheus Cortex ?
In the current digital era, where milliseconds count, software applications must be sustained, which calls for a robust monitoring and warning system. Introducing Prometheus Cortex, a powerful addition to the Prometheus ecosystem that provides long-term, scalable storage along with sophisticated querying capabilities. Let’s take a closer look at Prometheus Cortex, going over all of its features, from its practical applications to its foundations.