Overview

This page takes you through the process of building a basic “Hello world!” custom Agent check. It also shows you how to change the minimum collection interval for the check.

Setup

Installation

Before you create a custom Agent check, install the Datadog Agent.

Configuration

1. Change to the conf.d directory on your system. For more information about where to find the conf.d directory, see Agent configuration files.

2. In the conf.d directory, create a new config file for your new Agent check. Name the file custom_checkvalue.yaml.

3. Edit the file to include the following:

   conf.d/custom_checkvalue.yaml

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   init_config:
   instances:
     [{}]

4. Create a check file in the checks.d directory. Name the file custom_checkvalue.py.

   Naming your checks:

   • It's a good idea to prefix your check with custom_ to avoid conflicts with the name of a pre-existing Datadog Agent integration. For example, if you have a custom Postfix check, name your check files custom_postfix.py and custom_postfix.yaml instead of postfix.py and postfix.yaml.
   • The names of the configuration and check files must match. If your check is called custom_checkvalue.py, your configuration file must be named custom_checkvalue.yaml.

5. Edit the file to include the following:

   checks.d/custom_checkvalue.py

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   from checks import AgentCheck
   class HelloCheck(AgentCheck):
     def check(self, instance):
       self.gauge('hello.world', 1)

6. Restart the Agent and wait for a new metric named hello.world to show up in the Metric Summary.

If you’re having issues getting your custom check working, check the file permissions. The check file must be readable and executable by the Agent user. For more troubleshooting steps, see Troubleshoot an Agent Check.

Updating the collection interval

To change the collection interval of your check, use the min_collection_interval setting in your custom_checkvalue.yaml file and specify a setting in seconds. The default value is 15 seconds. You must add the min_collection_interval at an instance level. If your custom check is set up to monitor multiple instances, you must configure the interval individually per instance.

Setting the min_collection_interval to 30 does not guarantee that the metric is collected every 30 seconds. The Agent collector tries to run the check every 30 seconds, but the check might end up queued behind other integrations and checks, depending on how many integrations and checks are enabled on the same Agent. If a check method takes more than 30 seconds to complete, the Agent notices that the check is still running and skips its execution until the next interval.

Set a collection interval

For a single instance, use this configuration to set the collection interval to 30 seconds:

init_config:

instances:
  - min_collection_interval: 30

The example below demonstrates changing the interval for a hypothetical custom check that monitors a service named my_service on two separate servers:

init_config:

instances:
  - host: "http://localhost/"
    service: my_service
    min_collection_interval: 30

  - host: "http://my_server/"
    service: my_service
    min_collection_interval: 30

Verifying your check

To verify your check is running, use the following command:

sudo -u dd-agent -- datadog-agent check <CHECK_NAME>

After you verify that your check is running, restart the Agent to include the check and start reporting data.

Writing checks that run command-line programs

It’s possible to create a custom check that runs a command-line program and captures its output as a custom metric. For example, a check can run the vgs command to report information about volume groups.

Because the Python interpreter that runs the checks is embedded in the multi-threaded Go runtime, using the subprocess or multithreading modules from the Python standard library is not supported. To run a subprocess within a check, use the get_subprocess_output() function from the module datadog_checks.base.utils.subprocess_output. The command and its arguments are passed to get_subprocess_output() in the form of a list, with the command and its arguments as a string within the list.

For example, a command that is entered at the command prompt like this:

vgs -o vg_free

must be passed to get_subprocess_output() like this:

out, err, retcode = get_subprocess_output(["vgs", "-o", "vg_free"], self.log, raise_on_empty_output=True)

When you run the command-line program, the check captures the same output as running on the command line in the terminal. Do string processing on the output and call int() or float() on the result to return a numerical type.

If you do not do string processing on the output of the subprocess, or if it does not return an integer or a float, the check appears to run without errors but doesn’t report any metrics or events. The check also fails to return metrics or events if the Agent user does not have the correct permissions on any files or directories referenced in the command, or the correct permissions to run the command passed as an argument to get_subprocess_output().

Here is an example of a check that returns the results of a command-line program:

# ...
from datadog_checks.base.utils.subprocess_output import get_subprocess_output

class LSCheck(AgentCheck):
    def check(self, instance):
        files, err, retcode = get_subprocess_output(["ls", "."], self.log, raise_on_empty_output=True)
        file_count = len(files.split('\n')) - 1  #len() returns an int by default
        self.gauge("file.count", file_count,tags=['TAG_KEY:TAG_VALUE'] + self.instance.get('tags', []))

Sending data from a load balancer

A common use case for writing a custom Agent check is to send Datadog metrics from a load balancer. Before you get started, follow the steps in Configuration.

To expand the files to send data from your load balancer:

1. Replace the code in custom_checkvalue.py with the following (replacing the value of lburl with the address of your load balancer):

   checks.d/custom_checkvalue.py

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   import urllib2
   import simplejson
   from checks import AgentCheck
   
   class CheckValue(AgentCheck):
     def check(self, instance):
       lburl = instance['ipaddress']
       response = urllib2.urlopen("http://" + lburl + "/rest")
       data = simplejson.load(response)
   
       self.gauge('coreapp.update.value', data["value"])

2. Update the custom_checkvalue.yaml file (replacing ipaddress with your load balancer’s IP address):

   conf.d/custom_checkvalue.yaml

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   init_config:
   
   instances:
     - ipaddress: 1.2.3.4

3. Restart your Agent. Within a minute, you should see a new metric show up in the Metric Summary called coreapp.update.value that sends the metrics from your load balancer.

4. Create a dashboard for this metric.

Agent versioning

Use the following try/except block to make the custom check compatible with any Agent version:

try:
    # first, try to import the base class from new versions of the Agent
    from datadog_checks.base import AgentCheck
except ImportError:
    # if the above failed, the check is running in Agent version < 6.6.0
    from checks import AgentCheck

# content of the special variable __version__ will be shown in the Agent status page
__version__ = "1.0.0"

class HelloCheck(AgentCheck):
    def check(self, instance):
        self.gauge('hello.world', 1, tags=['TAG_KEY:TAG_VALUE'] + self.instance.get('tags', []))

Further Reading

Más enlaces, artículos y documentación útiles:

Developing on DatadogDOCUMENTATION