This is archived documentation for v1.5.0. Go to the latest version.
Label-based access control can be used to create access policies that will only allow for data to be queried that meets specific label requirements. The feature allows multiple sets of Prometheus label selectors to be associated with a policy and queries will only return data from series that match at least one of the provided selectors. This correlates to disjunctive normal form which allows any required policy to be expressed.
Setting up a label policy
Label policies are set when creating an access policy on a per instance basis. This means each instance associated with an access policy can have a unique label policy.
Exclude a label
One common use case for creating an LBAC policy is to exclude metrics with a specific label. For instance, a label policy that excludes all series with the label
secret=true would be created by just adding a select with
secret!="true" when creating an access policy. This can be seen in the image below:
Exclude a metric
Expanding upon the previous example, lets say we wanted to create an access policy that only excludes metrics with the label
secret=true on the metric named
sensitive_requests_total. Since the name of a metric is actually just a label with the key
__name__, we can leverage the existing LBAC label selector syntax to enforce this:
You may notice above that two different selectors where added to enforce the policy. Specifically:
The first selector will match when returning a series from the metrics
sensitive_requests_total and will ensure all of the returned series do not have the
secret: true label. However, when requesting a metric besides
sensitive_requests_total, the second label selector will match and return any data even if it has the
secret: true label.
Related Metrics Enterprise resources
Running Prometheus-as-a-service with Grafana Enterprise Metrics
Introducing Grafana Enterprise Metrics (GME), a simple and scalable Prometheus service that is seamless to use, simple to maintain, and supported by Grafana Labs.
How Robinhood scaled from 100M to 700M time series with Grafana Enterprise Metrics
In this GrafanaCONline session, the Robinhood team tells how GME (GameStop) led to GEM (Grafana Enterprise Metrics).
Benchmarking Grafana Enterprise Metrics for horizontally scaling Prometheus up to 500 million active series
We stress-tested GEM to show how it horizontally scaled. One takeaway: Hardware usage scales linearly up to 500 million active series.