Custom Legacy OpenMetrics Check

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Overview

This page dives into the OpenMetricsBaseCheck interface for more advanced usage, including an example of a simple check that collects timing metrics and status events from Kube DNS. For details on configuring a basic OpenMetrics check, see Kubernetes Prometheus and OpenMetrics metrics collection.

Advanced usage: OpenMetrics check interface

If you have more advanced needs than the generic check, such as metrics preprocessing, you can write a custom OpenMetricsBaseCheck. It’s the base class of the generic check, and it provides a structure and some helpers to collect metrics, events, and service checks exposed with Prometheus. The minimal configuration for checks based on this class include:

Creating a default instance with namespace and metrics mapping.
Implementing the check() method AND/OR:
Creating a method named after the OpenMetric metric handled (see self.prometheus_metric_name).

Writing a custom Prometheus check

This is a simple example of writing a Kube DNS check to illustrate usage of the OpenMetricsBaseCheck class. The example below replicates the functionality of the following generic Prometheus check:

instances:
  - prometheus_url: http://localhost:10055/metrics
    namespace: "kubedns"
    metrics:
      - kubedns_kubedns_dns_response_size_bytes: response_size.bytes
      - kubedns_kubedns_dns_request_duration_seconds: request_duration.seconds
      - kubedns_kubedns_dns_request_count_total: request_count
      - kubedns_kubedns_dns_error_count_total: error_count
      - kubedns_kubedns_dns_cachemiss_count_total: cachemiss_count

Configuration

The names of the configuration and check files must match. If your check is called mycheck.py your configuration file must be named mycheck.yaml.

Configuration for a Prometheus check is almost the same as a regular Agent check. The main difference is to include the variable prometheus_url in your check.yaml file. This goes into conf.d/kube_dns.yaml:

init_config:

instances:
    # URL of the metrics endpoint of Prometheus
  - prometheus_url: http://localhost:10055/metrics

Writing the check

All OpenMetrics checks inherit from the OpenMetricsBaseCheck class:

from datadog_checks.base import OpenMetricsBaseCheck

class KubeDNSCheck(OpenMetricsBaseCheck):

Define a metrics mapping

from datadog_checks.base import OpenMetricsBaseCheck

class KubeDNSCheck(OpenMetricsBaseCheck):
    def __init__(self, name, init_config, instances=None):
        METRICS_MAP = {
            #metrics have been renamed to kubedns in kubernetes 1.6.0
            'kubedns_kubedns_dns_response_size_bytes': 'response_size.bytes',
            'kubedns_kubedns_dns_request_duration_seconds': 'request_duration.seconds',
            'kubedns_kubedns_dns_request_count_total': 'request_count',
            'kubedns_kubedns_dns_error_count_total': 'error_count',
            'kubedns_kubedns_dns_cachemiss_count_total': 'cachemiss_count'
        }

Define a default instance

A default instance is the basic configuration used for the check. The default instance should override namespace, metrics, and prometheus_url.

Note: The default values for some config options in the OpenMetricsBaseCheck are overwritten, so there is increased metric behavior correlation between Prometheus and Datadog metric types.

from datadog_checks.base import OpenMetricsBaseCheck

class KubeDNSCheck(OpenMetricsBaseCheck):
    def __init__(self, name, init_config, instances=None):
        METRICS_MAP = {
            #metrics have been renamed to kubedns in kubernetes 1.6.0
            'kubedns_kubedns_dns_response_size_bytes': 'response_size.bytes',
            'kubedns_kubedns_dns_request_duration_seconds': 'request_duration.seconds',
            'kubedns_kubedns_dns_request_count_total': 'request_count',
            'kubedns_kubedns_dns_error_count_total': 'error_count',
            'kubedns_kubedns_dns_cachemiss_count_total': 'cachemiss_count'
        }
        super(KubeDNSCheck, self).__init__(
            name,
            init_config,
            instances,
            default_instances={
                'kubedns': {
                    'prometheus_url': 'http://localhost:8404/metrics',
                    'namespace': 'kubedns',
                    'metrics': [METRIC_MAP],
                    'send_histograms_buckets': True,
                    'send_distribution_counts_as_monotonic': True,
                    'send_distribution_sums_as_monotonic': True,
                }
            },
            default_namespace='kubedns',
        )

Implementing the check method

If you want to implement additional features, override the check() function.

From instance, use endpoint, which is the Prometheus or OpenMetrics metrics endpoint to poll metrics from:

def check(self, instance):
    endpoint = instance.get('prometheus_url')

Exceptions

If a check cannot run because of improper configuration, a programming error, or because it could not collect any metrics, it should raise a meaningful exception. This exception is logged and is shown in the Agent status command for debugging. For example:

$ sudo /etc/init.d/datadog-agent info

  Checks
  ======

    my_custom_check
    ---------------
      - instance #0 [ERROR]: Unable to find prometheus_url in config file.
      - Collected 0 metrics & 0 events

Improve your check() method with ConfigurationError:

from datadog_checks.base import ConfigurationError

def check(self, instance):
    endpoint = instance.get('prometheus_url')
    if endpoint is None:
        raise ConfigurationError("Unable to find prometheus_url in config file.")

Then as soon as you have data available, flush:

from datadog_checks.base import ConfigurationError

def check(self, instance):
    endpoint = instance.get('prometheus_url')
    if endpoint is None:
        raise ConfigurationError("Unable to find prometheus_url in config file.")

    self.process(instance)

Putting it all together

from datadog_checks.base import ConfigurationError, OpenMetricsBaseCheck

class KubeDNSCheck(OpenMetricsBaseCheck):
    """
    Collect kube-dns metrics from Prometheus endpoint
    """
    def __init__(self, name, init_config, instances=None):
        METRICS_MAP = {
            #metrics have been renamed to kubedns in kubernetes 1.6.0
            'kubedns_kubedns_dns_response_size_bytes': 'response_size.bytes',
            'kubedns_kubedns_dns_request_duration_seconds': 'request_duration.seconds',
            'kubedns_kubedns_dns_request_count_total': 'request_count',
            'kubedns_kubedns_dns_error_count_total': 'error_count',
            'kubedns_kubedns_dns_cachemiss_count_total': 'cachemiss_count'
        }
        super(KubeDNSCheck, self).__init__(
            name,
            init_config,
            instances,
            default_instances={
                'kubedns': {
                    'prometheus_url': 'http://localhost:8404/metrics',
                    'namespace': 'kubedns',
                    'metrics': [METRIC_MAP],
                    'send_histograms_buckets': True,
                    'send_distribution_counts_as_monotonic': True,
                    'send_distribution_sums_as_monotonic': True,
                }
            },
            default_namespace='kubedns',
        )

    def check(self, instance):
        endpoint = instance.get('prometheus_url')
        if endpoint is None:
            raise ConfigurationError("Unable to find prometheus_url in config file.")

        self.process(instance)

Going further

To read more about Prometheus and OpenMetrics base integrations, see the integrations developer docs.

You can improve your OpenMetrics check by including default values for additional configuration options:

ignore_metrics: Some metrics are ignored because they are duplicates or introduce a high cardinality. Metrics included in this list are silently skipped without an Unable to handle metric debug line in the logs.
labels_mapper: If the labels_mapper dictionary is provided, the metrics labels in labels_mapper use the corresponding value as tag name when sending the gauges.
exclude_labels: exclude_labels is an array of labels to exclude. Those labels are not added as tags when submitting the metric.
type_overrides: type_overrides is a dictionary where the keys are Prometheus or OpenMetrics metric names, and the values are a metric type (name as string) to use instead of the one listed in the payload. This can be used to force a type on untyped metrics. Available types are: counter, gauge, summary, untyped, and histogram.; Note: This value is empty in the base class, but needs to be overloaded/hardcoded in the final check to not be counted as a custom metric.