Crear una métrica basada en logs

// Create a log-based metric returns "OK" response

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"os"

	"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
	"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)

func main() {
	body := datadogV2.LogsMetricCreateRequest{
		Data: datadogV2.LogsMetricCreateData{
			Id:   "ExampleLogsMetric",
			Type: datadogV2.LOGSMETRICTYPE_LOGS_METRICS,
			Attributes: datadogV2.LogsMetricCreateAttributes{
				Compute: datadogV2.LogsMetricCompute{
					AggregationType:    datadogV2.LOGSMETRICCOMPUTEAGGREGATIONTYPE_DISTRIBUTION,
					IncludePercentiles: datadog.PtrBool(true),
					Path:               datadog.PtrString("@duration"),
				},
			},
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewLogsMetricsApi(apiClient)
	resp, r, err := api.CreateLogsMetric(ctx, body)

	if err != nil {
		fmt.Fprintf(os.Stderr, "Error when calling `LogsMetricsApi.CreateLogsMetric`: %v\n", err)
		fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
	}

	responseContent, _ := json.MarshalIndent(resp, "", "  ")
	fmt.Fprintf(os.Stdout, "Response from `LogsMetricsApi.CreateLogsMetric`:\n%s\n", responseContent)
}

// Create a log-based metric returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.LogsMetricsApi;
import com.datadog.api.client.v2.model.LogsMetricCompute;
import com.datadog.api.client.v2.model.LogsMetricComputeAggregationType;
import com.datadog.api.client.v2.model.LogsMetricCreateAttributes;
import com.datadog.api.client.v2.model.LogsMetricCreateData;
import com.datadog.api.client.v2.model.LogsMetricCreateRequest;
import com.datadog.api.client.v2.model.LogsMetricResponse;
import com.datadog.api.client.v2.model.LogsMetricType;

public class Example {
  public static void main(String[] args) {
    ApiClient defaultClient = ApiClient.getDefaultApiClient();
    defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
    LogsMetricsApi apiInstance = new LogsMetricsApi(defaultClient);

    LogsMetricCreateRequest body =
        new LogsMetricCreateRequest()
            .data(
                new LogsMetricCreateData()
                    .id("ExampleLogsMetric")
                    .type(LogsMetricType.LOGS_METRICS)
                    .attributes(
                        new LogsMetricCreateAttributes()
                            .compute(
                                new LogsMetricCompute()
                                    .aggregationType(LogsMetricComputeAggregationType.DISTRIBUTION)
                                    .includePercentiles(true)
                                    .path("@duration"))));

    try {
      LogsMetricResponse result = apiInstance.createLogsMetric(body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsMetricsApi#createLogsMetric");
      System.err.println("Status code: " + e.getCode());
      System.err.println("Reason: " + e.getResponseBody());
      System.err.println("Response headers: " + e.getResponseHeaders());
      e.printStackTrace();
    }
  }
}

"""
Create a log-based metric returns "OK" response
"""

from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.logs_metrics_api import LogsMetricsApi
from datadog_api_client.v2.model.logs_metric_compute import LogsMetricCompute
from datadog_api_client.v2.model.logs_metric_compute_aggregation_type import LogsMetricComputeAggregationType
from datadog_api_client.v2.model.logs_metric_create_attributes import LogsMetricCreateAttributes
from datadog_api_client.v2.model.logs_metric_create_data import LogsMetricCreateData
from datadog_api_client.v2.model.logs_metric_create_request import LogsMetricCreateRequest
from datadog_api_client.v2.model.logs_metric_type import LogsMetricType

body = LogsMetricCreateRequest(
    data=LogsMetricCreateData(
        id="ExampleLogsMetric",
        type=LogsMetricType.LOGS_METRICS,
        attributes=LogsMetricCreateAttributes(
            compute=LogsMetricCompute(
                aggregation_type=LogsMetricComputeAggregationType.DISTRIBUTION,
                include_percentiles=True,
                path="@duration",
            ),
        ),
    ),
)

configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
with ApiClient(configuration) as api_client:
    api_instance = LogsMetricsApi(api_client)
    response = api_instance.create_logs_metric(body=body)

    print(response)

# Create a log-based metric returns "OK" response

require "datadog_api_client"
DatadogAPIClient.configure do |config|
  config.access_token = ENV["DD_BEARER_TOKEN"]
end
api_instance = DatadogAPIClient::V2::LogsMetricsAPI.new

body = DatadogAPIClient::V2::LogsMetricCreateRequest.new({
  data: DatadogAPIClient::V2::LogsMetricCreateData.new({
    id: "ExampleLogsMetric",
    type: DatadogAPIClient::V2::LogsMetricType::LOGS_METRICS,
    attributes: DatadogAPIClient::V2::LogsMetricCreateAttributes.new({
      compute: DatadogAPIClient::V2::LogsMetricCompute.new({
        aggregation_type: DatadogAPIClient::V2::LogsMetricComputeAggregationType::DISTRIBUTION,
        include_percentiles: true,
        path: "@duration",
      }),
    }),
  }),
})
p api_instance.create_logs_metric(body)

// Create a log-based metric returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_logs_metrics::LogsMetricsAPI;
use datadog_api_client::datadogV2::model::LogsMetricCompute;
use datadog_api_client::datadogV2::model::LogsMetricComputeAggregationType;
use datadog_api_client::datadogV2::model::LogsMetricCreateAttributes;
use datadog_api_client::datadogV2::model::LogsMetricCreateData;
use datadog_api_client::datadogV2::model::LogsMetricCreateRequest;
use datadog_api_client::datadogV2::model::LogsMetricType;

#[tokio::main]
async fn main() {
    let body = LogsMetricCreateRequest::new(LogsMetricCreateData::new(
        LogsMetricCreateAttributes::new(
            LogsMetricCompute::new(LogsMetricComputeAggregationType::DISTRIBUTION)
                .include_percentiles(true)
                .path("@duration".to_string()),
        ),
        "ExampleLogsMetric".to_string(),
        LogsMetricType::LOGS_METRICS,
    ));
    let configuration = datadog::Configuration::new();
    let api = LogsMetricsAPI::with_config(configuration);
    let resp = api.create_logs_metric(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

/**
 * Create a log-based metric returns "OK" response
 */

import { client, v2 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration({
  authMethods: {
    AuthZ: {
      accessToken: process.env.DD_BEARER_TOKEN as string,
    },
  },
});
const apiInstance = new v2.LogsMetricsApi(configuration);

const params: v2.LogsMetricsApiCreateLogsMetricRequest = {
  body: {
    data: {
      id: "ExampleLogsMetric",
      type: "logs_metrics",
      attributes: {
        compute: {
          aggregationType: "distribution",
          includePercentiles: true,
          path: "@duration",
        },
      },
    },
  },
};

apiInstance
  .createLogsMetric(params)
  .then((data: v2.LogsMetricResponse) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));