Reduce Prometheus metrics usage with relabeling
This guide describes several techniques you can use to reduce your Prometheus metrics usage on Grafana Cloud.
Before applying these techniques, ensure that you’re deduplicating any samples sent from high-availability Prometheus clusters. This will cut your active series count in half. To learn how to do this, please see Sending data from multiple high-availability Prometheus instances.
You can reduce the number of active series sent to Grafana Cloud in two ways:
Allowlisting: This involves keeping a set of “important” metrics and labels that you explicitly define, and dropping everything else. To allowlist metrics and labels, you should identify a set of core important metrics and labels that you’d like to keep. To enable allowlisting in Prometheus, use the
keep
andlabelkeep
actions with any relabeling configuration.Denylisting: This involves dropping a set of high-cardinality “unimportant” metrics that you explicitly define, and keeping everything else. Denylisting becomes possible once you’ve identified a list of high-cardinality metrics and labels that you’d like to drop. To learn how to discover high-cardinality metrics, please see Analyzing Prometheus metric usage. To enable denylisting in Prometheus, use the
drop
andlabeldrop
actions with any relabeling configuration.
Both of these methods are implemented through Prometheus’s metric filtering and relabeling feature, relabel_config
. This feature allows you to filter through series labels using regular expressions and keep or drop those that match. You can also manipulate, transform, and rename series labels using relabel_config
.
Prom Labs’s Relabeler tool may be helpful when debugging relabel configs. Relabeler allows you to visually confirm the rules implemented by a relabel config.
This guide expects some familiarity with regular expressions. To learn more, please see Regular expression on Wikipedia. To play around with and analyze any regular expressions, you can use RegExr.
Relabel_config syntax
You can filter series using Prometheus’s relabel_config
configuration object. At a high level, a relabel_config
allows you to select one or more source label values that can be concatenated using a separator
parameter. The result can then be matched against using a regex
, and an action
operation can be performed if a match occurs.
You can perform the following common action
operations:
keep
: Keep a matched target or series, drop all othersdrop
: Drop a matched target or series, keep all othersreplace
: Replace or rename a matched label with a new one defined by thetarget_label
andreplacement
parameterslabelkeep
: Match theregex
against all label names, drop all labels that don’t match (ignoressource_labels
and applies to all label names)labeldrop
: Match theregex
against all label names, drop all labels that match (ignoressource_labels
and applies to all label names)
For a full list of available actions, please see relabel_config
from the Prometheus documentation.
Any relabel_config
must have the same general structure:
- source_labels = [source_label_1, source_label_2, ...]
separator: ;
action: replace
regex: (.*)
replacement: $1
These default values should be modified to suit your relabeling use case.
source_labels
: Select one or more labels from the available setseparator
: Concatenate selected label values using this characterregex
: Match this regular expression on concatenated dataaction
: Execute the specified relabel actionreplacement
: If using one ofreplace
orlabelmap
, define the replacement value. You can use regex match groups to access data captured by theregex
. To learn more about regex match groups, please see this StackOverflow answer.target_label
: Assign the extracted and modified label value defined byreplacement
to this label name.
Parameters that aren’t explicitly set will be filled in using default values. For readability it’s usually best to explicitly define a relabel_config
. To learn more about the general format for a relabel_config
block, please see relabel_config
from the Prometheus docs.
Here’s an example:
- source_labels: [ instance_ip ]
separator: ;
action: replace
regex: (.*)
replacement: $1
target_label: host_ip
This minimal relabeling snippet searches across the set of scraped labels for the instance_ip
label. If it finds the instance_ip
label, it renames this label to host_ip
. Since the (.*)
regex captures the entire label value, replacement references this capture group, $1
, when setting the new target_label
. Since we’ve used default regex
, replacement
, action
, and separator
values here, they can be omitted for brevity. However, it’s usually best to explicitly define these for readability.
To drop a specific label, select it using source_labels
and use a replacement value of ""
. To bulk drop or keep labels, use the labelkeep
and labeldrop
actions.
You can use a relabel_config
to filter through and relabel:
- Scrape targets
- Samples and labels to ingest into Prometheus storage
- Samples and labels to ship to remote storage
You’ll learn how to do this in the next section.
Relabel_config in a Prometheus configuration file
You can apply a relabel_config
to filter and manipulate labels at the following stages of metric collection:
- Target selection in the
relabel_configs
section of ascrape_configs
job. This allows you to use arelabel_config
object to select targets to scrape and relabel metadata created by any service discovery mechanism. - Metric selection in the
metric_relabel_configs
section of ascrape_configs
job. This allows you to use arelabel_config
object to select labels and series that should be ingested into Prometheus storage. - Remote Write in the
write_relabel_configs
section of aremote_write
configuration. This allows you to use arelabel_config
to control which labels and series Prometheus ships to remote storage.
This sample configuration file skeleton demonstrates where each of these sections lives in a Prometheus config:
global:
. . .
rule_files:
. . .
scrape_configs:
- job_name: sample_job_1
kubernetes_sd_configs:
- . . .
relabel_configs:
- source_labels: [. . .]
. . .
- source_labels: [. . .]
. . .
metric_relabel_configs:
- source_labels: [. . .]
. . .
- source_labels: [. . .]
. . .
- job_name: sample_job_2
static_configs:
- targets: [. . .]
metric_relabel_configs:
- source_labels: [. . .]
. . .
. . .
remote_write:
- url: . . .
write_relabel_configs:
- source_labels: [. . .]
. . .
- source_labels: [. . .]
. . .
Use relabel_configs
in a given scrape job to select which targets to scrape. This is often useful when fetching sets of targets using a service discovery mechanism like kubernetes_sd_configs
, or Kubernetes service discovery. To learn more about Prometheus service discovery features, please see Configuration from the Prometheus docs.
Use metric_relabel_configs
in a given scrape job to select which series and labels to keep, and to perform any label replacement operations. This occurs after target selection using relabel_configs
.
Finally, use write_relabel_configs
in a remote_write
configuration to select which series and labels to ship to remote storage. This configuration does not impact any configuration set in metric_relabel_configs
or relabel_configs
. If you drop a label in a metric_relabel_configs
section, it won’t be ingested by Prometheus and consequently won’t be shipped to remote storage.
Scrape target selection using relabel_configs
A relabel_configs
configuration allows you to keep
or drop
targets returned by a service discovery mechanism like Kubernetes service discovery or AWS EC2 instance service discovery. For example, you may have a scrape job that fetches all Kubernetes Endpoints using a kubernetes_sd_configs
parameter. By using the following relabel_configs
snippet, you can limit scrape targets for this job to those whose Service label corresponds to app=nginx
and port name to web
:
scrape_configs:
- job_name: kubernetes_nginx
honor_timestamps: true
scrape_interval: 30s
scrape_timeout: 10s
metrics_path: /metrics
scheme: http
kubernetes_sd_configs:
- role: endpoints
namespaces:
names:
- default
relabel_configs:
- source_labels: [__meta_kubernetes_service_label_app]
regex: nginx
action: keep
- source_labels: [__meta_kubernetes_endpoint_port_name]
regex: web
action: keep
The initial set of endpoints fetched by kuberentes_sd_configs
in the default
namespace can be very large depending on the apps you’re running in your cluster. Using the __meta_kubernetes_service_label_app
label filter, endpoints whose corresponding services do not have the app=nginx
label will be dropped by this scrape job.
Since kubernetes_sd_configs
will also add any other Pod ports as scrape targets (with role: endpoints
), we need to filter these out using the __meta_kubernetes_endpoint_port_name
relabel config. For example, if a Pod backing the Nginx service has two ports, we only scrape the port named web
and drop the other.
To summarize, the above snippet fetches all endpoints in the default
Namespace, and keeps as scrape targets those whose corresponding Service has an app=nginx
label set. This set of targets consists of one or more Pods that have one or more defined ports. We drop all ports that aren’t named web
.
Using relabeling at the target selection stage, you can selectively choose which targets and endpoints you want to scrape (or drop) to tune your metric usage.
Metric and label selection using metric_relabel_configs
Relabeling and filtering at this stage modifies or drops samples before Prometheus ingests them locally and ships them to remote storage. This relabeling occurs after target selection. Once Prometheus scrapes a target, metric_relabel_configs
allows you to define keep
, drop
and replace
actions to perform on scraped samples:
- job_name: monitoring/kubelet/1
honor_labels: true
honor_timestamps: false
scrape_interval: 30s
scrape_timeout: 10s
metrics_path: /metrics/cadvisor
scheme: https
kubernetes_sd_configs:
- role: endpoints
namespaces:
names:
- kube-system
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
tls_config:
insecure_skip_verify: true
relabel_configs:
- source_labels: [__meta_kubernetes_service_label_k8s_app]
regex: kubelet
action: keep
- source_labels: [__meta_kubernetes_endpoint_port_name]
regex: https-metrics
action: keep
. . .
metric_relabel_configs:
- source_labels: [__name__]
regex: container_(network_tcp_usage_total|network_udp_usage_total|tasks_state|cpu_load_average_10s)
action: drop
This sample piece of configuration instructs Prometheus to first fetch a list of endpoints to scrape using Kubernetes service discovery (kubernetes_sd_configs
). Endpoints are limited to the kube-system
namespace. Next, using relabel_configs
, only Endpoints with the Service Label k8s_app=kubelet
are kept. Furthermore, only Endpoints that have https-metrics
as a defined port name are kept. This reduced set of targets corresponds to Kubelet https-metrics
scrape endpoints.
After scraping these endpoints, Prometheus applies the metric_relabel_configs
section, which drop
s all metrics whose metric name matches the specified regex
. You can extract a sample’s metric name using the __name__
meta-label. In this case Prometheus would drop a metric like container_network_tcp_usage_total(. . .)
. Prometheus keeps all other metrics. You can add additional metric_relabel_configs
sections that replace
and modify labels here.
metric_relabel_configs
are commonly used to relabel and filter samples before ingestion, and limit the amount of data that gets persisted to storage. Using metric_relabel_configs
, you can drastically reduce your Prometheus metrics usage by throwing out unneeded samples.
If shipping samples to Grafana Cloud, you also have the option of persisting samples locally, but preventing shipping to remote storage. To do this, use a relabel_config
object in the write_relabel_configs
subsection of the remote_write
section of your Prometheus config. This can be useful when local Prometheus storage is cheap and plentiful, but the set of metrics shipped to remote storage requires judicious curation to avoid excess costs.
Controlling remote write behavior using write_relabel_configs
Relabeling and filtering at this stage modifies or drops samples before Prometheus ships them to remote storage. Using this feature, you can store metrics locally but prevent them from shipping to Grafana Cloud. To learn more about remote_write
, please see remote_write
from the official Prometheus docs.
Prometheus applies this relabeling and dropping step after performing target selection using relabel_configs
and metric selection and relabeling using metric_relabel_configs
.
The following snippet of configuration demonstrates an “allowlisting” approach, where the specified metrics are shipped to remote storage, and all others dropped. Recall that these metrics will still get persisted to local storage unless this relabeling configuration takes place in the metric_relabel_configs
section of a scrape job.
remote_write:
- url: <Your Metrics instance remote_write endpoint>
remote_timeout: 30s
write_relabel_configs:
- source_labels: [__name__]
regex: "apiserver_request_total|kubelet_node_config_error|kubelet_runtime_operations_errors_total"
action: keep
basic_auth:
username: <your_remote_endpoint_username_here>
password: <your_remote_endpoint_password_here>
queue_config:
capacity: 500
max_shards: 1000
min_shards: 1
max_samples_per_send: 100
batch_send_deadline: 5s
min_backoff: 30ms
max_backoff: 100ms
This piece of remote_write
configuration sets the remote endpoint to which Prometheus will push samples. The write_relabel_configs
section defines a keep
action for all metrics matching the apiserver_request_total|kubelet_node_config_error|kubelet_runtime_operations_errors_total
regex, dropping all others. You can additionally define remote_write
-specific relabeling rules here.
Finally, this configures authentication credentials and the remote_write
queue. To learn more about remote_write
configuration parameters, please see remote_write
from the Prometheus docs.
Conclusion
In this guide, we’ve presented an overview of Prometheus’s powerful and flexible relabel_config
feature and how you can leverage it to control and reduce your local and Grafana Cloud Prometheus usage.
Choosing which metrics and samples to scrape, store, and ship to Grafana Cloud can seem quite daunting at first. Curated sets of important metrics can be found in Mixins. Mixins are a set of preconfigured dashboards and alerts. The PromQL queries that power these dashboards and alerts reference a core set of “important” observability metrics. There are Mixins for Kubernetes, Consul, Jaeger, and much more. To learn more about them, please see Prometheus Monitoring Mixins. Allowlisting or keeping the set of metrics referenced in a Mixin’s alerting rules and dashboards can form a solid foundation from which to build a complete set of observability metrics to scrape and store.
References
- Life of Label
- relabel_configs vs metric_relabel_configs
- Advanced Service Discovery in Prometheus 0.14.0
- How relabeling in Prometheus works
- Some gists: this one and this one
- Configuration from the Prometheus docs
Related resources from Grafana Labs


