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Creating custom metrics

Custom metrics are metrics defined by users. This page describes how to create metric descriptors for custom metrics and how to write custom metric data.

Custom metrics use the same elements that the built-in Cloud Monitoring metrics use:

  • A set of data points.
  • Metric-type information, which tells you what the data points represent.
  • Monitored-resource information, which tells you where the data points originated.

Custom metrics can be used in the same way as built-in metrics. That is, you can create charts and alerts for your custom metric data.

Before you begin

If you aren't familiar with the structures that underlie all metrics, see Metrics, time series, and resources.

Getting started with custom metrics

To use a custom metric, you must have a metric descriptor for your new metric type. The metric descriptor defines how your data is organized. Before you start writing custom metric data, determine how you want to use the data you collect, and ensure that the data organization supports those objectives.

Creating a metric descriptor

Cloud Monitoring can create the metric descriptor for you automatically, or you can use the metricDescriptors.create method in the Monitoring API to create it yourself. You can't manipulate metric descriptors by using the Google Cloud Console for Monitoring.

To have Cloud Monitoring create the metric descriptor for you, you simply write time series data for your metric, and Cloud Monitoring creates a descriptor based on the data you are writing. For more information, see Auto-creation of metric descriptors. There are limits to auto-creation, so it's helpful to know what information goes into a metric definition. After you have a new custom metric descriptor, whether you or Monitoring created it, you can use the metric descriptor with the metric descriptor API methods and the time series API methods.

Whether you explicitly create the metric descriptor or let Monitoring create it for you based on your data, you still need to ensure the organization of the data matches how you want to query it.

Organizing your data

Suppose you have a program that runs on a single machine, and this program calls auxiliary programs A and B. You want to count how often programs A and B are called. You also want to know when program A is called more than 10 times per minute and when program B is called more than 5 times per minute.

To simplify the discussion, assume that you have a single Google Cloud project and you plan to use the global monitored resource. For more information about this choice, see Monitored resources for custom metrics.

Consider the following approaches:

  • You use two custom metrics: Metric-type-A counts calls to program A and Metric-type-B counts calls to program B. In this case, Metric-type-A contains 1 time series, and Metric-type-B contains 1 time series.

    With this data model, you can create a single alerting policy with two conditions, or you can create two alerting policies each with one condition. An alerting policy can support multiple conditions, but it has a single configuration for the notification channels.

    This model might be appropriate when you aren't interested in similarities in the data between the activities being monitored. In this example, the activities are the rate of calls to programs A and B.

  • You use a single custom metric and use a label to store a program identifier. For example, the label might store the value A or B. Because Monitoring creates a time series for each unique combination of labels, there are two time series for this single metric type.

    As with the previous model, you can create a single alerting policy or two alerting policies. However, the conditions for the alerting policy are more complicated. To create a condition that generates an incident if the rate of calls for program A exceeds a threshold, then the condition needs to include a filter that includes data points whose label value is A and excludes all other data points.

    One advantage of this model is that it is simple to compute ratios. For example, you can determine how much of the total is due to calls to A.

  • You use a single custom metric to count the number of calls, but you don't use a label to record which program was called. In this model, there is a single time series that combines the data for the two programs. However, you can't create an alerting policy that meets your objectives because the data for two programs can't be separated.

Naming custom metrics

When you create a custom metric, you define a string identifier that represents the metric type. This string must be unique among the custom metrics in your Google Cloud project and it must use a prefix that marks the metric as a user-defined metric. For Monitoring, the allowable prefixes are custom.googleapis.com/ and external.googleapis.com/prometheus. The prefix is followed by a name that describes what you are collecting. For details on the recommended way to name a custom metric, see Naming conventions. Here are examples of the two kinds of identifiers for metric types:

custom.googleapis.com/cpu_utilization
custom.googleapis.com/instance/cpu/utilization

All metric types have globally unique identifiers called resource names. The structure of a resource name for a metric type is:

projects/[PROJECT_ID]/metricDescriptors/[METRIC_TYPE]

where METRIC_TYPE is the string identifier of the metric type. If the previous custom metric examples were created in project my-project-id, then their resource names for these metrics would be the following:

projects/my-project-id/metricDescriptors/custom.googleapis.com/cpu_utilization
projects/my-project-id/metricDescriptors/custom.googleapis.com/instance/cpu/utilization

Name or type? In the metric descriptor, the name field stores the metric type's resource name and the type field stores the METRIC_TYPE string.

Choosing a monitored-resource type

When you write your data to a time series, you must indicate where the data came from. To do this, you choose a monitored-resource type that represents where your data comes from, and then use that to describe the specific origin. The monitored resource isn't part of the metric type; instead, the time series to which you write data includes a reference to the metric type, which describes the data, and a reference to the monitored resource, which indicates where the data originated.

Consider the monitored resource before creating your metric descriptor. The monitored-resource type you use affects which labels you need to include in the metric descriptor. For example, if you use a VM instance monitored resource with your data, then you won't need a metric label to specify the instance. For more information, see Monitored resources for custom metrics.

Each of your metric's data points must be associated with a monitored resource object. Points from different monitored resource objects are held in different time series.

Monitored resources for custom metrics

You can use only the following monitored-resource types in your custom metrics.

A common practice is to use the monitored resource objects that represent the physical resources where your application code is running. This approach has several advantages:

  • You get better performance compared with using a single resource type.
  • You avoid out-of-order data caused by multiple processes writing to the same time series.
  • Your custom metric data can be grouped with other metric data from the same resources.

global versus generic resources

The generic_task and generic_node resource types are useful in situations where none of the more specific resource types are appropriate. The generic_task type is useful for defining task-like resources such as applications. The generic_node type is useful for defining node-like resources such as virtual machines. Both generic_* types have several common labels you can use to define unique resource objects, making it easy to use them in metric filters for aggregations and reductions.

In contrast, the global resource type has only project_id and location labels. If you have many sources of metrics within a project, using the same global resource object can cause collisions and over-writes of your metric data.

Creating metric descriptors

The data you collect for a custom metric must be associated with a MetricDescriptor object that specifies various information about the metric.

You can create the metric descriptor yourself, or Monitoring can create it for you.

You can change an existing descriptor in limited ways; see Modifying metric descriptors for more information.

Auto-creation of metric descriptors

If you write metric data when a metric descriptor for that custom metric doesn't yet exist, then a metric descriptor is created automatically. However, this new metric descriptor might not be exactly what you want; auto-creation of metric descriptors involves some assumptions and defaults.

Specifically, during a call to timeSeries.create, if a TimeSeries object includes a Metric object that specifies a nonexistent metric-type name, then Cloud Monitoring creates a new MetricDescriptor with the following fields:

  • type: The type is copied from the Metric object's type field.
  • name: The name is created from the project ID in the method call and the value of the type in the Metric object.
  • labels: The labels are those that appear in the Metric object. Each label descriptor in the new metric descriptor has the following fields:
    • key: the label key in the Metric object.
    • valueType: STRING
    • description: not set
  • metricKind: The default metric kind is GAUGE, but if you specify the metricKind parameter of the TimeSeries object, then the new metric has that kind. You can specify only GAUGE and CUMULATIVE kinds.
  • valueType: The value type is taken from the typed value of the Point being written. which must be BOOL, INT64, DOUBLE, or DISTRIBUTION. If you specify a value type in the valueType field of the TimeSeries, then that type must match the type of the Point.
  • unit: not set
  • description: "Auto created custom metric.".
  • displayName: not set

In a single timeSeries.create call, you can include multiple TimeSeries objects that refer to the same nonexistent metric type. In that case, the new type's fields are set in the same way, except that the labels in the new metric descriptor consist of the union of all the labels in the Metric objects in all the time series in this call to create.

Next step: See Writing metric data.

Creating metric descriptors manually

This section describes how you can create the metric descriptor for your custom metric.

Before you begin

Complete the following steps:

  • Choose a metric name or identifier for your custom metric.

  • Choose a display name and description for your metric. The display name is used in the Google Cloud Console.

  • Choose a project or projects in which to define your custom metric and write its time series data. If you need the same metric in several projects, then make identical definitions of the metric in each project.

    To write custom metrics from resources managed by an AWS account, create the metric descriptor in the AWS connector project for that account.

  • Determine the metric's kind, value type, and (optionally) units. Not all value types and metric kinds are supported for custom metrics. For more information on these fields, see Value types and metric kinds.

  • Choose the metric's labels—their names, value types, and descriptions.

For more help in making these choices, you can browse the built-in metrics and look at their time series data.

Creating a metric descriptor

To create the descriptor for your custom metric, call the metricDescriptors.create method, passing in a MetricDescriptor object.

It is usually an error to call metricDescriptors.create using the same type name as an existing custom metric descriptor. However, if all the fields of the new MetricDescriptor object match the fields of the existing descriptor exactly, then it isn't an error but it has no effect.

In the following example, you create a gauge custom metric.

Protocol

To create a metric descriptor, use the metricDescriptors.create method. You can execute this method by using the API Explorer widget on the method's reference page. See API Explorer for more information.

The following are the sample parameters to metricDescriptors.create:

  • name (URL): projects/[PROJECT_ID]

  • Request body: supply a MetricDescriptor object such as the following:

    {
  "name": "",
  "description": "Daily sales records from all branch stores.",
  "displayName": "Sales",
  "type": "custom.googleapis.com/stores/sales",
  "metricKind": "CUMULATIVE",
  "valueType": "DOUBLE",
  "unit": "{USD}",
  "labels": [
    {
      "key": "store_id",
      "valueType": "STRING",
      "description": "The ID of the store."
    },
  ],
}

Supply these values to the fields in the widget, using your project's ID in place of [PROJECT_ID]:

Try this API dialog populated with the request body to create a metric descriptor.

Click the Execute button to run the method.

Try It!

When creating a new custom metric, the name field in the MetricDescriptor is ignored and can be omitted. The create method returns the new metric descriptor with the name field filled in, which in this example would be the following:

"name": "projects/[PROJECT_ID]/metricDescriptors/custom.googleapis.com/stores/daily_sales"

If, for example, you want to get a metric's descriptor, you use this name.

C#

        public static object CreateMetric(string projectId,
            string metricType = "custom.googleapis.com/stores/daily_sales")
        {
            // Create client.
            MetricServiceClient metricServiceClient = MetricServiceClient.Create();

            // Prepare custom metric descriptor.      
            MetricDescriptor metricDescriptor = new MetricDescriptor();
            metricDescriptor.DisplayName = "Daily Sales";
            metricDescriptor.Description = "Daily sales records from all branch stores.";
            metricDescriptor.MetricKind = MetricKind.Gauge;
            metricDescriptor.ValueType = MetricDescriptor.Types.ValueType.Double;
            metricDescriptor.Type = metricType;
            metricDescriptor.Unit = "{USD}";
            LabelDescriptor labels = new LabelDescriptor();
            labels.Key = "store_id";
            labels.ValueType = LabelDescriptor.Types.ValueType.String;
            labels.Description = "The ID of the store.";
            metricDescriptor.Labels.Add(labels);
            CreateMetricDescriptorRequest request = new CreateMetricDescriptorRequest
            {
                ProjectName = new ProjectName(projectId),
            };
            request.MetricDescriptor = metricDescriptor;
            // Make the request.
            MetricDescriptor response = metricServiceClient.CreateMetricDescriptor(request);
            Console.WriteLine("Done creating metric descriptor:");
            Console.WriteLine(JObject.Parse($"{response}").ToString());
            return 0;
        }

Go


import (
	"context"
	"fmt"
	"io"

	monitoring "cloud.google.com/go/monitoring/apiv3"
	"google.golang.org/genproto/googleapis/api/label"
	"google.golang.org/genproto/googleapis/api/metric"
	metricpb "google.golang.org/genproto/googleapis/api/metric"
	monitoringpb "google.golang.org/genproto/googleapis/monitoring/v3"
)

// createCustomMetric creates a custom metric specified by the metric type.
func createCustomMetric(w io.Writer, projectID, metricType string) (*metricpb.MetricDescriptor, error) {
	ctx := context.Background()
	c, err := monitoring.NewMetricClient(ctx)
	if err != nil {
		return nil, err
	}
	defer c.Close()
	md := &metric.MetricDescriptor{
		Name: "Custom Metric",
		Type: metricType,
		Labels: []*label.LabelDescriptor{{
			Key:         "environment",
			ValueType:   label.LabelDescriptor_STRING,
			Description: "An arbitrary measurement",
		}},
		MetricKind:  metric.MetricDescriptor_GAUGE,
		ValueType:   metric.MetricDescriptor_INT64,
		Unit:        "s",
		Description: "An arbitrary measurement",
		DisplayName: "Custom Metric",
	}
	req := &monitoringpb.CreateMetricDescriptorRequest{
		Name:             "projects/" + projectID,
		MetricDescriptor: md,
	}
	m, err := c.CreateMetricDescriptor(ctx, req)
	if err != nil {
		return nil, fmt.Errorf("could not create custom metric: %v", err)
	}

	fmt.Fprintf(w, "Created %s\n", m.GetName())
	return m, nil
}

Java

// Your Google Cloud Platform project ID
String projectId = System.getProperty("projectId");
String metricType = CUSTOM_METRIC_DOMAIN + "/" + type;

final MetricServiceClient client = MetricServiceClient.create();
ProjectName name = ProjectName.of(projectId);

MetricDescriptor descriptor =
    MetricDescriptor.newBuilder()
        .setType(metricType)
        .addLabels(
            LabelDescriptor.newBuilder()
                .setKey("store_id")
                .setValueType(LabelDescriptor.ValueType.STRING))
        .setDescription("This is a simple example of a custom metric.")
        .setMetricKind(MetricDescriptor.MetricKind.GAUGE)
        .setValueType(MetricDescriptor.ValueType.DOUBLE)
        .build();

CreateMetricDescriptorRequest request =
    CreateMetricDescriptorRequest.newBuilder()
        .setName(name.toString())
        .setMetricDescriptor(descriptor)
        .build();

client.createMetricDescriptor(request);

Node.js

// Imports the Google Cloud client library
const monitoring = require('@google-cloud/monitoring');

// Creates a client
const client = new monitoring.MetricServiceClient();

/**
 * TODO(developer): Uncomment and edit the following lines of code.
 */
// const projectId = 'YOUR_PROJECT_ID';

const request = {
  name: client.projectPath(projectId),
  metricDescriptor: {
    description: 'Daily sales records from all branch stores.',
    displayName: 'Daily Sales',
    type: 'custom.googleapis.com/stores/daily_sales',
    metricKind: 'GAUGE',
    valueType: 'DOUBLE',
    unit: '{USD}',
    labels: [
      {
        key: 'store_id',
        valueType: 'STRING',
        description: 'The ID of the store.',
      },
    ],
  },
};

// Creates a custom metric descriptor
const [descriptor] = await client.createMetricDescriptor(request);
console.log('Created custom Metric:\n');
console.log(`Name: ${descriptor.displayName}`);
console.log(`Description: ${descriptor.description}`);
console.log(`Type: ${descriptor.type}`);
console.log(`Kind: ${descriptor.metricKind}`);
console.log(`Value Type: ${descriptor.valueType}`);
console.log(`Unit: ${descriptor.unit}`);
console.log('Labels:');
descriptor.labels.forEach(label => {
  console.log(`  ${label.key} (${label.valueType}) - ${label.description}`);
});

PHP

use Google\Cloud\Monitoring\V3\MetricServiceClient;
use Google\Api\LabelDescriptor;
use Google\Api\LabelDescriptor_ValueType;
use Google\Api\MetricDescriptor;
use Google\Api\MetricDescriptor_MetricKind;
use Google\Api\MetricDescriptor_ValueType;

/**
 * Create a new metric in Stackdriver Monitoring.
 * Example:
 * ```
 * create_metric($projectId);
 * ```
 *
 * @param string $projectId Your project ID
 */
function create_metric($projectId)
{
    $metrics = new MetricServiceClient([
        'projectId' => $projectId,
    ]);

    $projectName = $metrics->projectName($projectId);

    $descriptor = new MetricDescriptor();
    $descriptor->setDescription('Daily sales records from all branch stores.');
    $descriptor->setDisplayName('Daily Sales');
    $descriptor->setType('custom.googleapis.com/stores/daily_sales');
    $descriptor->setMetricKind(MetricDescriptor_MetricKind::GAUGE);
    $descriptor->setValueType(MetricDescriptor_ValueType::DOUBLE);
    $descriptor->setUnit('{USD}');
    $label = new LabelDescriptor();
    $label->setKey('store_id');
    $label->setValueType(LabelDescriptor_ValueType::STRING);
    $label->setDescription('The ID of the store.');
    $labels = [$label];
    $descriptor->setLabels($labels);

    $descriptor = $metrics->createMetricDescriptor($projectName, $descriptor);
    printf('Created a metric: ' . $descriptor->getName() . PHP_EOL);
}

Python

client = monitoring_v3.MetricServiceClient()
project_name = f"projects/{project_id}"
descriptor = ga_metric.MetricDescriptor()
descriptor.type = "custom.googleapis.com/my_metric" + str(uuid.uuid4())
descriptor.metric_kind = ga_metric.MetricDescriptor.MetricKind.GAUGE
descriptor.value_type = ga_metric.MetricDescriptor.ValueType.DOUBLE
descriptor.description = "This is a simple example of a custom metric."

labels = ga_label.LabelDescriptor()
labels.key = "TestLabel"
labels.value_type = ga_label.LabelDescriptor.ValueType.STRING
labels.description = "This is a test label"
descriptor.labels.append(labels)

descriptor = client.create_metric_descriptor(
    name=project_name, metric_descriptor=descriptor
)
print("Created {}.".format(descriptor.name))

Ruby

# Your Google Cloud Platform project ID
# project_id = "YOUR_PROJECT_ID"

# Example metric type
# metric_type = "custom.googleapis.com/my_metric"

client = Google::Cloud::Monitoring.metric_service
project_name = client.project_path project: project_id

descriptor = Google::Api::MetricDescriptor.new(
  type:        metric_type,
  metric_kind: Google::Api::MetricDescriptor::MetricKind::GAUGE,
  value_type:  Google::Api::MetricDescriptor::ValueType::DOUBLE,
  description: "This is a simple example of a custom metric."
)

result = client.create_metric_descriptor name:              project_name,
                                         metric_descriptor: descriptor
p "Created #{result.name}"
p result

See Troubleshooting API calls if you have difficulty.

Next step: See Writing metric data.

Deleting metrics

To delete a custom metric, delete its metric descriptor. You can't delete the time-series data stored in your Google Cloud project; however, deleting the metric descriptor renders the data inaccessible. The data expires and is deleted according to the data retention policy.

You can't delete the metric descriptor for a built-in metric.

To delete your custom metric descriptor, call the metricDescriptors.delete method.

Protocol

To delete a metric descriptor, use the metricDescriptors.delete method. You can execute this method by using the API Explorer widget on the method's reference page. See API Explorer for more information.

To delete the stores/daily_sales custom metric created in Creating metric descriptors manually:

  1. Go to the reference page for metricDescriptors.delete:

  2. Supply the name of the metric descriptor to the API Explorer widget:

    name: projects/[PROJECT_ID]/metricDescriptors/custom.googleapis.com/stores/daily_sales

  3. Click the Execute button.

Try It!

C#

public static object DeleteMetric(string projectId, string metricType)
{
    // Create client.
    MetricServiceClient metricServiceClient = MetricServiceClient.Create();
    // Initialize request argument(s).
    MetricDescriptorName name = new MetricDescriptorName(projectId, metricType);
    // Make the request.
    metricServiceClient.DeleteMetricDescriptor(name);
    Console.WriteLine($"Done deleting metric descriptor: {name}");
    return 0;
}

Go


import (
	"context"
	"fmt"
	"io"

	monitoring "cloud.google.com/go/monitoring/apiv3"
	monitoringpb "google.golang.org/genproto/googleapis/monitoring/v3"
)

// deleteMetric deletes the given metric. name should be of the form
// "projects/PROJECT_ID/metricDescriptors/METRIC_TYPE".
func deleteMetric(w io.Writer, name string) error {
	ctx := context.Background()
	c, err := monitoring.NewMetricClient(ctx)
	if err != nil {
		return err
	}
	defer c.Close()
	req := &monitoringpb.DeleteMetricDescriptorRequest{
		Name: name,
	}

	if err := c.DeleteMetricDescriptor(ctx, req); err != nil {
		return fmt.Errorf("could not delete metric: %v", err)
	}
	fmt.Fprintf(w, "Deleted metric: %q\n", name)
	return nil
}

Java

String projectId = System.getProperty("projectId");
final MetricServiceClient client = MetricServiceClient.create();
MetricDescriptorName metricName = MetricDescriptorName.of(projectId, name);
client.deleteMetricDescriptor(metricName);
System.out.println("Deleted descriptor " + name);

Node.js

// Imports the Google Cloud client library
const monitoring = require('@google-cloud/monitoring');

// Creates a client
const client = new monitoring.MetricServiceClient();

/**
 * TODO(developer): Uncomment and edit the following lines of code.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const metricId = 'custom.googleapis.com/stores/daily_sales';

const request = {
  name: client.projectMetricDescriptorPath(projectId, metricId),
};

// Deletes a metric descriptor
const [result] = await client.deleteMetricDescriptor(request);
console.log(`Deleted ${metricId}`, result);

PHP

use Google\Cloud\Monitoring\V3\MetricServiceClient;

/**
 * Example:
 * ```
 * delete_metric($projectId, $databaseId);
 * ```
 *
 * @param string $projectId Your project ID
 * @param string $metricId  The ID of the Metric Descriptor to delete
 */
function delete_metric($projectId, $metricId)
{
    $metrics = new MetricServiceClient([
        'projectId' => $projectId,
    ]);

    $metricPath = $metrics->metricDescriptorName($projectId, $metricId);
    $ret = $metrics->deleteMetricDescriptor($metricPath);

    printf('Deleted a metric: ' . $metricPath . PHP_EOL);
}

Python

client = monitoring_v3.MetricServiceClient()
client.delete_metric_descriptor(name=descriptor_name)
print("Deleted metric descriptor {}.".format(descriptor_name))

Ruby

# Your Google Cloud Platform project ID
# project_id = "YOUR_PROJECT_ID"

# Example metric type
# metric_type = "custom.googleapis.com/my_metric"

client = Google::Cloud::Monitoring.metric_service
metric_name = client.metric_descriptor_path project:           project_id,
                                            metric_descriptor: metric_type

client.delete_metric_descriptor name: metric_name
p "Deleted metric descriptor #{metric_name}."

See Troubleshooting API calls if you have difficulty.

Writing metric data

You can write data only to metric types for custom metrics. To write your data, use the timeSeries.create method. If the time series already exists, this method appends a new data point to the existing time series. If the time series doesn't exist, this method creates it and appends the data.

You write data points by passing a list of TimeSeries objects to timeSeries.create. The maximum list size is 200 and each object in the list must specify a different time series:

  • Each time series is identified by the metric and resource fields of the TimeSeries object. These fields represent the metric type of the data and the monitored resource from which the data was collected.
  • Omit the fields metricKind and valueType; they are ignored when writing data points.

  • Each TimeSeries object must contain only a single Point object:

    • The point's value and time interval must be consistent with the metric type's definition. For information on time intervals for different metric kinds, see TimeInterval.
    • The point's time interval must be later than any point already in the time series.
    • The end time of the interval must not be more than 25 hours in the past or more than five minutes in the future.

  • If you want to write more than one point to the same time series, then use a separate call to the timeSeries.create method for each point. Don't write data to a single time series faster than one point each 10 seconds. If you are adding data points to different time series, then there is no rate limitation.

Protocol

To write metric data, use the timeSeries.create method. You can execute this method by using the API Explorer widget on the method's reference page. See API Explorer for more information.

To write a point to the stores/daily_sales custom metric created in the Creating metric descriptors manually:

  1. Go to the reference page for timeSeries.create.
  2. Supply the parameters below to the API Explorer widget.
  3. Click the Execute button.

Use the following sample parameters:

  • name: projects/[PROJECT_ID]

  • request body: include a list of TimeSeries objects. The following sample has only one time series in the list.

    {
 "timeSeries": [
  {
   "metric": {
    "type": "custom.googleapis.com/my_metric",
    "labels": {
     "my_label": "my_value"
    }
   },
   "resource": {
    "type": "gce_instance",
    "labels": {
     "project_id": "[PROJECT_ID]",
     "instance_id": "1234567890123456789",
     "zone": "us-central1-f"
    }
   },
   "points": [
    {
     "interval": {
      "endTime": "2018-06-01T10:00:00-04:00"
     },
     "value": {
      "doubleValue": 123.45
     }
    }
   ]
  }
 ]
}

Try It!

C#

        public static object WriteTimeSeriesData(string projectId)
        {
            // Create client.
            MetricServiceClient metricServiceClient = MetricServiceClient.Create();
            // Initialize request argument(s).
            ProjectName name = new ProjectName(projectId);
            // Prepare a data point. 
            Point dataPoint = new Point();
            TypedValue salesTotal = new TypedValue();
            salesTotal.DoubleValue = 123.45;
            dataPoint.Value = salesTotal;
            Timestamp timeStamp = new Timestamp();
            timeStamp.Seconds = (long)(DateTime.UtcNow - s_unixEpoch).TotalSeconds;
            TimeInterval interval = new TimeInterval();
            interval.EndTime = timeStamp;
            dataPoint.Interval = interval;

            // Prepare custom metric.
            Metric metric = new Metric();
            metric.Type = "custom.googleapis.com/stores/daily_sales";
            metric.Labels.Add("store_id", "Pittsburgh");

            // Prepare monitored resource.
            MonitoredResource resource = new MonitoredResource();
            resource.Type = "global";
            resource.Labels.Add("project_id", projectId);

            // Create a new time series using inputs.
            TimeSeries timeSeriesData = new TimeSeries();
            timeSeriesData.Metric = metric;
            timeSeriesData.Resource = resource;
            timeSeriesData.Points.Add(dataPoint);

            // Add newly created time series to list of time series to be written.
            IEnumerable<TimeSeries> timeSeries = new List<TimeSeries> { timeSeriesData };
            // Write time series data.
            metricServiceClient.CreateTimeSeries(name, timeSeries);
            Console.WriteLine("Done writing time series data:");
            Console.WriteLine(JObject.Parse($"{timeSeriesData}").ToString());
            return 0;
        }

Go


// writeTimeSeriesValue writes a value for the custom metric created
func writeTimeSeriesValue(projectID, metricType string) error {
	ctx := context.Background()
	c, err := monitoring.NewMetricClient(ctx)
	if err != nil {
		return err
	}
	defer c.Close()
	now := &timestamp.Timestamp{
		Seconds: time.Now().Unix(),
	}
	req := &monitoringpb.CreateTimeSeriesRequest{
		Name: "projects/" + projectID,
		TimeSeries: []*monitoringpb.TimeSeries{{
			Metric: &metricpb.Metric{
				Type: metricType,
				Labels: map[string]string{
					"environment": "STAGING",
				},
			},
			Resource: &monitoredres.MonitoredResource{
				Type: "gce_instance",
				Labels: map[string]string{
					"instance_id": "test-instance",
					"zone":        "us-central1-f",
				},
			},
			Points: []*monitoringpb.Point{{
				Interval: &monitoringpb.TimeInterval{
					StartTime: now,
					EndTime:   now,
				},
				Value: &monitoringpb.TypedValue{
					Value: &monitoringpb.TypedValue_Int64Value{
						Int64Value: rand.Int63n(10),
					},
				},
			}},
		}},
	}
	log.Printf("writeTimeseriesRequest: %+v\n", req)

	err = c.CreateTimeSeries(ctx, req)
	if err != nil {
		return fmt.Errorf("could not write time series value, %v ", err)
	}
	return nil
}

Java

String projectId = System.getProperty("projectId");
// Instantiates a client
MetricServiceClient metricServiceClient = MetricServiceClient.create();

// Prepares an individual data point
TimeInterval interval =
    TimeInterval.newBuilder()
        .setEndTime(Timestamps.fromMillis(System.currentTimeMillis()))
        .build();
TypedValue value = TypedValue.newBuilder().setDoubleValue(123.45).build();
Point point = Point.newBuilder().setInterval(interval).setValue(value).build();

List<Point> pointList = new ArrayList<>();
pointList.add(point);

ProjectName name = ProjectName.of(projectId);

// Prepares the metric descriptor
Map<String, String> metricLabels = new HashMap<>();
Metric metric =
    Metric.newBuilder()
        .setType("custom.googleapis.com/my_metric")
        .putAllLabels(metricLabels)
        .build();

// Prepares the monitored resource descriptor
Map<String, String> resourceLabels = new HashMap<>();
resourceLabels.put("instance_id", "1234567890123456789");
resourceLabels.put("zone", "us-central1-f");

MonitoredResource resource =
    MonitoredResource.newBuilder().setType("gce_instance").putAllLabels(resourceLabels).build();

// Prepares the time series request
TimeSeries timeSeries =
    TimeSeries.newBuilder()
        .setMetric(metric)
        .setResource(resource)
        .addAllPoints(pointList)
        .build();

List<TimeSeries> timeSeriesList = new ArrayList<>();
timeSeriesList.add(timeSeries);

CreateTimeSeriesRequest request =
    CreateTimeSeriesRequest.newBuilder()
        .setName(name.toString())
        .addAllTimeSeries(timeSeriesList)
        .build();

// Writes time series data
metricServiceClient.createTimeSeries(request);
System.out.println("Done writing time series value.");

Node.js

// Imports the Google Cloud client library
const monitoring = require('@google-cloud/monitoring');

// Creates a client
const client = new monitoring.MetricServiceClient();

/**
 * TODO(developer): Uncomment and edit the following lines of code.
 */
// const projectId = 'YOUR_PROJECT_ID';

const dataPoint = {
  interval: {
    endTime: {
      seconds: Date.now() / 1000,
    },
  },
  value: {
    doubleValue: 123.45,
  },
};

const timeSeriesData = {
  metric: {
    type: 'custom.googleapis.com/stores/daily_sales',
    labels: {
      store_id: 'Pittsburgh',
    },
  },
  resource: {
    type: 'global',
    labels: {
      project_id: projectId,
    },
  },
  points: [dataPoint],
};

const request = {
  name: client.projectPath(projectId),
  timeSeries: [timeSeriesData],
};

// Writes time series data
const result = await client.createTimeSeries(request);
console.log('Done writing time series data.', result);

PHP

use Google\Api\Metric;
use Google\Api\MonitoredResource;
use Google\Cloud\Monitoring\V3\MetricServiceClient;
use Google\Cloud\Monitoring\V3\Point;
use Google\Cloud\Monitoring\V3\TimeInterval;
use Google\Cloud\Monitoring\V3\TimeSeries;
use Google\Cloud\Monitoring\V3\TypedValue;
use Google\Protobuf\Timestamp;

/**
 * Example:
 * ```
 * write_timeseries($projectId);
 * ```
 *
 * @param string $projectId Your project ID
 */
function write_timeseries($projectId)
{
    $metrics = new MetricServiceClient([
        'projectId' => $projectId,
    ]);

    $projectName = $metrics->projectName($projectId);

    $endTime = new Timestamp();
    $endTime->setSeconds(time());
    $interval = new TimeInterval();
    $interval->setEndTime($endTime);

    $value = new TypedValue();
    $value->setDoubleValue(123.45);

    $point = new Point();
    $point->setValue($value);
    $point->setInterval($interval);
    $points = [$point];

    $metric = new Metric();
    $metric->setType('custom.googleapis.com/stores/daily_sales');
    $labels = ['store_id' => 'Pittsburg'];
    $metric->setLabels($labels);

    $resource = new MonitoredResource();
    $resource->setType('global');
    $labels = ['project_id' => $projectId];
    $resource->setLabels($labels);

    $timeSeries = new TimeSeries();
    $timeSeries->setMetric($metric);
    $timeSeries->setResource($resource);
    $timeSeries->setPoints($points);

    $result = $metrics->createTimeSeries(
        $projectName,
        [$timeSeries]);

    printf('Done writing time series data.' . PHP_EOL);
}

Python

client = monitoring_v3.MetricServiceClient()
project_name = f"projects/{project_id}"

series = monitoring_v3.TimeSeries()
series.metric.type = "custom.googleapis.com/my_metric" + str(uuid.uuid4())
series.resource.type = "gce_instance"
series.resource.labels["instance_id"] = "1234567890123456789"
series.resource.labels["zone"] = "us-central1-f"
now = time.time()
seconds = int(now)
nanos = int((now - seconds) * 10 ** 9)
interval = monitoring_v3.TimeInterval(
    {"end_time": {"seconds": seconds, "nanos": nanos}}
)
point = monitoring_v3.Point({"interval": interval, "value": {"double_value": 3.14}})
series.points = [point]
client.create_time_series(name=project_name, time_series=[series])

Ruby

# Your Google Cloud Platform project ID
# project_id = "YOUR_PROJECT_ID"

# Example metric type
# metric_type = "custom.googleapis.com/my_metric"

client = Google::Cloud::Monitoring.metric_service
project_name = client.project_path project: project_id

series = Google::Cloud::Monitoring::V3::TimeSeries.new
series.metric = Google::Api::Metric.new type: metric_type

resource = Google::Api::MonitoredResource.new type: "global"
resource.labels["project_id"] = project_id
series.resource = resource

point = Google::Cloud::Monitoring::V3::Point.new
point.value = Google::Cloud::Monitoring::V3::TypedValue.new double_value: 3.14
now = Time.now
end_time = Google::Protobuf::Timestamp.new seconds: now.to_i, nanos: now.nsec
point.interval = Google::Cloud::Monitoring::V3::TimeInterval.new end_time: end_time
series.points << point

client.create_time_series name: project_name, time_series: [series]
p "Time series created."

See Troubleshooting API calls if you have difficulty.

Modifying metric descriptors

To add labels to an existing custom metric, use the timeSeries.create method and include the new labels with the time-series data. If the labels you attempt to write are valid and don't cause the number of labels in the metric descriptor to exceed the label limit of 10, then the labels are added. The time series data is then written as though the label had been there from the beginning.

If you want to do more than add new labels, you must delete and recreate the metric descriptor. In this case, you lose all the time series data previously collected for the old metric descriptor. See Deleting metrics for more information.

You can't rename a metric.

What's next