Google Managed Service for Prometheus Exporter
Status |
|
Stability |
beta: metrics |
Distributions |
contrib, [observiq] |
Issues |
|
[observiq]:
This exporter can be used to send metrics and traces to Google Cloud Managed Service for Prometheus. The difference between this exporter and the googlecloud
exporter is that metrics sent with this exporter are queried using promql, rather than standard the standard MQL.
This exporter is not the standard method of ingesting metrics into Google Cloud Managed Service for Prometheus, which is built on a drop-in replacement for the Prometheus server: https://github.com/GoogleCloudPlatform/prometheus. This exporter does not support the full range of Prometheus functionality, including the UI, recording and alerting rules, and can't be used with the GMP Operator, but does support sending metrics.
Configuration Reference
The following configuration options are supported:
project
(optional): GCP project identifier.
user_agent
(optional): Override the user agent string sent on requests to Cloud Monitoring (currently only applies to metrics). Specify {{version}}
to include the application version number. Defaults to opentelemetry-collector-contrib {{version}}
.
metric
(optional): Configuration for sending metrics to Cloud Monitoring.
endpoint
(optional): Endpoint where metric data is going to be sent to. Replaces endpoint
.
use_insecure
(optional): If true, use gRPC as their communication transport. Only has effect if Endpoint is not "".
retry_on_failure
(optional): Configuration for how to handle retries when sending data to Google Cloud fails.
enabled
(default = false)
initial_interval
(default = 5s): Time to wait after the first failure before retrying; ignored if enabled
is false
max_interval
(default = 30s): Is the upper bound on backoff; ignored if enabled
is false
max_elapsed_time
(default = 120s): Is the maximum amount of time spent trying to send a batch; ignored if enabled
is false
sending_queue
(optional): Configuration for how to buffer traces before sending.
enabled
(default = true)
num_consumers
(default = 10): Number of consumers that dequeue batches; ignored if enabled
is false
queue_size
(default = 1000): Maximum number of batches kept in memory before data; ignored if enabled
is false
;
User should calculate this as num_seconds * requests_per_second
where:
num_seconds
is the number of seconds to buffer in case of a backend outage
requests_per_second
is the average number of requests per seconds.
Note: These retry_on_failure
and sending_queue
are provided (and documented) by the Exporter Helper
Example Configuration
receivers:
prometheus:
config:
scrape_configs:
# Add your prometheus scrape configuration here.
# Using kubernetes_sd_configs with namespaced resources (e.g. pod)
# ensures the namespace is set on your metrics.
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: (.+):(?:\d+);(\d+)
replacement: $$1:$$2
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
processors:
batch:
# batch metrics before sending to reduce API usage
send_batch_max_size: 200
send_batch_size: 200
timeout: 5s
memory_limiter:
# drop metrics if memory usage gets too high
check_interval: 1s
limit_percentage: 65
spike_limit_percentage: 20
resourcedetection:
# detect cluster name and location
detectors: [gcp]
timeout: 10s
transform:
# "location", "cluster", "namespace", "job", "instance", and "project_id" are reserved, and
# metrics containing these labels will be rejected. Prefix them with exported_ to prevent this.
metric_statements:
- context: datapoint
statements:
- set(attributes["exported_location"], attributes["location"])
- delete_key(attributes, "location")
- set(attributes["exported_cluster"], attributes["cluster"])
- delete_key(attributes, "cluster")
- set(attributes["exported_namespace"], attributes["namespace"])
- delete_key(attributes, "namespace")
- set(attributes["exported_job"], attributes["job"])
- delete_key(attributes, "job")
- set(attributes["exported_instance"], attributes["instance"])
- delete_key(attributes, "instance")
- set(attributes["exported_project_id"], attributes["project_id"])
- delete_key(attributes, "project_id")
exporters:
googlemanagedprometheus:
service:
pipelines:
metrics:
receivers: [prometheus]
processors: [batch, memory_limiter, transform, resourcedetection]
exporters: [googlemanagedprometheus]
Resource Attribute Handling
The Google Managed Prometheus exporter maps metrics to the
prometheus_target
monitored resource. The logic for mapping to monitored resources is designed to
be used with the prometheus receiver, but can be used with other receivers as
well. To avoid collisions (i.e. "duplicate timeseries enountered" errors), you
need to ensure the prometheus_target resource uniquely identifies the source of
metrics. The exporter uses the following resource attributes to determine
monitored resource:
- location: [
location
, cloud.availability_zone
, cloud.region
]
- cluster: [
cluster
, k8s.cluster.name
]
- namespace: [
namespace
, k8s.namespace.name
]
- job: [
service.name
+ service.namespace
]
- instance: [
service.instance.id
]
In the configuration above, cloud.availability_zone
, cloud.region
, and
k8s.cluster.name
are detected using the resourcedetection
processor with
the gcp
detector. The prometheus receiver sets service.name
to the
configured job_name
, and service.instance.id
is set to the scrape target's
instance
. The prometheus receiver sets k8s.namespace.name
when using
role: pod
.
Manually Setting location, cluster, or namespace
In GMP, the above attributes are used to identify the prometheus_target
monitored resource. As such, it is recommended to avoid writing metric or resource labels
that match these keys. Doing so can cause errors when exporting metrics to
GMP or when trying to query from GMP. So, the recommended way to set them
is with the resourcedetection processor.
If you still need to set location
, cluster
, or namespace
labels
(such as when running in non-GCP environments), you can do so with the
resource processor like so:
processors:
resource:
attributes:
- key: "location"
value: "us-east-1"
action: upsert
Setting cluster, location or namespace using metric labels
This example copies the location
metric attribute to a new exported_location
attribute, then deletes the original location
. It is recommended to use the exported_*
prefix, which is consistent with GMP's behavior.
You can also use the groupbyattrs processor
to move metric labels to resource labels. This is useful in situations
where, for example, an exporter monitors multiple namespaces (with
each namespace exported as a metric label). One such example is kube-state-metrics.
Using groupbyattrs
will promote that label to a resource label and
associate those metrics with the new resource. For example:
processors:
groupbyattrs:
keys:
- namespace
- cluster
- location