---
title: Update a log-based metric
description: Datadog, the leading service for cloud-scale monitoring.
breadcrumbs: Docs > API Reference > Logs Metrics
---

# Update a log-based metric{% #update-a-log-based-metric %}
Copy pageCopied
{% tab title="v2" %}

| Datadog site      | API endpoint                                                               |
| ----------------- | -------------------------------------------------------------------------- |
| ap1.datadoghq.com | PATCH https://api.ap1.datadoghq.com/api/v2/logs/config/metrics/{metric_id} |
| ap2.datadoghq.com | PATCH https://api.ap2.datadoghq.com/api/v2/logs/config/metrics/{metric_id} |
| app.datadoghq.eu  | PATCH https://api.datadoghq.eu/api/v2/logs/config/metrics/{metric_id}      |
| app.ddog-gov.com  | PATCH https://api.ddog-gov.com/api/v2/logs/config/metrics/{metric_id}      |
| us2.ddog-gov.com  | PATCH https://api.us2.ddog-gov.com/api/v2/logs/config/metrics/{metric_id}  |
| app.datadoghq.com | PATCH https://api.datadoghq.com/api/v2/logs/config/metrics/{metric_id}     |
| us3.datadoghq.com | PATCH https://api.us3.datadoghq.com/api/v2/logs/config/metrics/{metric_id} |
| us5.datadoghq.com | PATCH https://api.us5.datadoghq.com/api/v2/logs/config/metrics/{metric_id} |

### Overview

Update a specific log-based metric from your organization. Returns the log-based metric object from the request body when the request is successful. This endpoint requires the `logs_generate_metrics` permission.

### Arguments

#### Path Parameters

| Name                        | Type   | Description                       |
| --------------------------- | ------ | --------------------------------- |
| metric_id [*required*] | string | The name of the log-based metric. |

### Request

#### Body Data (required)

New definition of the log-based metric.

{% tab title="Model" %}

| Parent field | Field                        | Type     | Description                                                                                                                                |
| ------------ | ---------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|              | data [*required*]       | object   | The new log-based metric properties.                                                                                                       |
| data         | attributes [*required*] | object   | The log-based metric properties that will be updated.                                                                                      |
| attributes   | compute                      | object   | The compute rule to compute the log-based metric.                                                                                          |
| compute      | include_percentiles          | boolean  | Toggle to include or exclude percentile aggregations for distribution metrics. Only present when the `aggregation_type` is `distribution`. |
| attributes   | filter                       | object   | The log-based metric filter. Logs matching this filter will be aggregated in this metric.                                                  |
| filter       | query                        | string   | The search query - following the log search syntax.                                                                                        |
| attributes   | group_by                     | [object] | The rules for the group by.                                                                                                                |
| group_by     | path [*required*]       | string   | The path to the value the log-based metric will be aggregated over.                                                                        |
| group_by     | tag_name                     | string   | Eventual name of the tag that gets created. By default, the path attribute is used as the tag name.                                        |
| data         | type [*required*]       | enum     | The type of the resource. The value should always be logs_metrics. Allowed enum values: `logs_metrics`                                     |

{% /tab %}

{% tab title="Example" %}
##### 

```json
{
  "data": {
    "type": "logs_metrics",
    "attributes": {
      "filter": {
        "query": "service:web* AND @http.status_code:[200 TO 299]-updated"
      }
    }
  }
}
```

##### 

```json
{
  "data": {
    "type": "logs_metrics",
    "attributes": {
      "compute": {
        "include_percentiles": false
      }
    }
  }
}
```

{% /tab %}

### Response

{% tab title="200" %}
OK
{% tab title="Model" %}
The log-based metric object.

| Parent field | Field               | Type     | Description                                                                                                                                |
| ------------ | ------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|              | data                | object   | The log-based metric properties.                                                                                                           |
| data         | attributes          | object   | The object describing a Datadog log-based metric.                                                                                          |
| attributes   | compute             | object   | The compute rule to compute the log-based metric.                                                                                          |
| compute      | aggregation_type    | enum     | The type of aggregation to use. Allowed enum values: `count,distribution`                                                                  |
| compute      | include_percentiles | boolean  | Toggle to include or exclude percentile aggregations for distribution metrics. Only present when the `aggregation_type` is `distribution`. |
| compute      | path                | string   | The path to the value the log-based metric will aggregate on (only used if the aggregation type is a "distribution").                      |
| attributes   | filter              | object   | The log-based metric filter. Logs matching this filter will be aggregated in this metric.                                                  |
| filter       | query               | string   | The search query - following the log search syntax.                                                                                        |
| attributes   | group_by            | [object] | The rules for the group by.                                                                                                                |
| group_by     | path                | string   | The path to the value the log-based metric will be aggregated over.                                                                        |
| group_by     | tag_name            | string   | Eventual name of the tag that gets created. By default, the path attribute is used as the tag name.                                        |
| data         | id                  | string   | The name of the log-based metric.                                                                                                          |
| data         | type                | enum     | The type of the resource. The value should always be logs_metrics. Allowed enum values: `logs_metrics`                                     |

{% /tab %}

{% tab title="Example" %}

```json
{
  "data": {
    "attributes": {
      "compute": {
        "aggregation_type": "distribution",
        "include_percentiles": true,
        "path": "@duration"
      },
      "filter": {
        "query": "service:web* AND @http.status_code:[200 TO 299]"
      },
      "group_by": [
        {
          "path": "@http.status_code",
          "tag_name": "status_code"
        }
      ]
    },
    "id": "logs.page.load.count",
    "type": "logs_metrics"
  }
}
```

{% /tab %}

{% /tab %}

{% tab title="400" %}
Bad Request
{% tab title="Model" %}
API error response.

| Field                    | Type     | Description       |
| ------------------------ | -------- | ----------------- |
| errors [*required*] | [string] | A list of errors. |

{% /tab %}

{% tab title="Example" %}

```json
{
  "errors": [
    "Bad Request"
  ]
}
```

{% /tab %}

{% /tab %}

{% tab title="403" %}
Not Authorized
{% tab title="Model" %}
API error response.

| Field                    | Type     | Description       |
| ------------------------ | -------- | ----------------- |
| errors [*required*] | [string] | A list of errors. |

{% /tab %}

{% tab title="Example" %}

```json
{
  "errors": [
    "Bad Request"
  ]
}
```

{% /tab %}

{% /tab %}

{% tab title="404" %}
Not Found
{% tab title="Model" %}
API error response.

| Field                    | Type     | Description       |
| ------------------------ | -------- | ----------------- |
| errors [*required*] | [string] | A list of errors. |

{% /tab %}

{% tab title="Example" %}

```json
{
  "errors": [
    "Bad Request"
  ]
}
```

{% /tab %}

{% /tab %}

{% tab title="429" %}
Too many requests
{% tab title="Model" %}
API error response.

| Field                    | Type     | Description       |
| ------------------------ | -------- | ----------------- |
| errors [*required*] | [string] | A list of errors. |

{% /tab %}

{% tab title="Example" %}

```json
{
  "errors": [
    "Bad Request"
  ]
}
```

{% /tab %}

{% /tab %}

### Code Example

##### 
                          \## default
# 
 \# Path parameters export metric_id="CHANGE_ME" \# Curl command curl -X PATCH "https://api.datadoghq.com/api/v2/logs/config/metrics/${metric_id}" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "DD-API-KEY: ${DD_API_KEY}" \
-H "DD-APPLICATION-KEY: ${DD_APP_KEY}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "compute": {
        "include_percentiles": true
      },
      "filter": {
        "query": "service:web* AND @http.status_code:[200 TO 299]"
      },
      "group_by": [
        {
          "path": "@http.status_code",
          "tag_name": "status_code"
        }
      ]
    },
    "type": "logs_metrics"
  }
}
EOF 
                        
##### 
                          \## default
# 
 \# Path parameters export metric_id="CHANGE_ME" \# Curl command curl -X PATCH "https://api.datadoghq.com/api/v2/logs/config/metrics/${metric_id}" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "DD-API-KEY: ${DD_API_KEY}" \
-H "DD-APPLICATION-KEY: ${DD_APP_KEY}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "compute": {
        "include_percentiles": true
      },
      "filter": {
        "query": "service:web* AND @http.status_code:[200 TO 299]"
      },
      "group_by": [
        {
          "path": "@http.status_code",
          "tag_name": "status_code"
        }
      ]
    },
    "type": "logs_metrics"
  }
}
EOF 
                        
##### 

```go
// Update 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() {
	// there is a valid "logs_metric" in the system
	LogsMetricDataID := os.Getenv("LOGS_METRIC_DATA_ID")

	body := datadogV2.LogsMetricUpdateRequest{
		Data: datadogV2.LogsMetricUpdateData{
			Type: datadogV2.LOGSMETRICTYPE_LOGS_METRICS,
			Attributes: datadogV2.LogsMetricUpdateAttributes{
				Filter: &datadogV2.LogsMetricFilter{
					Query: datadog.PtrString("service:web* AND @http.status_code:[200 TO 299]-updated"),
				},
			},
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewLogsMetricsApi(apiClient)
	resp, r, err := api.UpdateLogsMetric(ctx, LogsMetricDataID, body)

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

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

##### 

```go
// Update a log-based metric with include_percentiles field 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() {
	// there is a valid "logs_metric_percentile" in the system
	LogsMetricPercentileDataID := os.Getenv("LOGS_METRIC_PERCENTILE_DATA_ID")

	body := datadogV2.LogsMetricUpdateRequest{
		Data: datadogV2.LogsMetricUpdateData{
			Type: datadogV2.LOGSMETRICTYPE_LOGS_METRICS,
			Attributes: datadogV2.LogsMetricUpdateAttributes{
				Compute: &datadogV2.LogsMetricUpdateCompute{
					IncludePercentiles: datadog.PtrBool(false),
				},
			},
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewLogsMetricsApi(apiClient)
	resp, r, err := api.UpdateLogsMetric(ctx, LogsMetricPercentileDataID, body)

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

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

#### Instructions

First [install the library and its dependencies](https://docs.datadoghq.com/api/latest.md?code-lang=go) and then save the example to `main.go` and run following commands:
    DD_SITE="datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
##### 

```java
// Update 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.LogsMetricFilter;
import com.datadog.api.client.v2.model.LogsMetricResponse;
import com.datadog.api.client.v2.model.LogsMetricType;
import com.datadog.api.client.v2.model.LogsMetricUpdateAttributes;
import com.datadog.api.client.v2.model.LogsMetricUpdateData;
import com.datadog.api.client.v2.model.LogsMetricUpdateRequest;

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

    // there is a valid "logs_metric" in the system
    String LOGS_METRIC_DATA_ATTRIBUTES_FILTER_QUERY =
        System.getenv("LOGS_METRIC_DATA_ATTRIBUTES_FILTER_QUERY");
    String LOGS_METRIC_DATA_ID = System.getenv("LOGS_METRIC_DATA_ID");

    LogsMetricUpdateRequest body =
        new LogsMetricUpdateRequest()
            .data(
                new LogsMetricUpdateData()
                    .type(LogsMetricType.LOGS_METRICS)
                    .attributes(
                        new LogsMetricUpdateAttributes()
                            .filter(
                                new LogsMetricFilter()
                                    .query(
                                        "service:web* AND @http.status_code:[200 TO"
                                            + " 299]-updated"))));

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

##### 

```java
// Update a log-based metric with include_percentiles field 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.LogsMetricResponse;
import com.datadog.api.client.v2.model.LogsMetricType;
import com.datadog.api.client.v2.model.LogsMetricUpdateAttributes;
import com.datadog.api.client.v2.model.LogsMetricUpdateCompute;
import com.datadog.api.client.v2.model.LogsMetricUpdateData;
import com.datadog.api.client.v2.model.LogsMetricUpdateRequest;

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

    // there is a valid "logs_metric_percentile" in the system
    String LOGS_METRIC_PERCENTILE_DATA_ID = System.getenv("LOGS_METRIC_PERCENTILE_DATA_ID");

    LogsMetricUpdateRequest body =
        new LogsMetricUpdateRequest()
            .data(
                new LogsMetricUpdateData()
                    .type(LogsMetricType.LOGS_METRICS)
                    .attributes(
                        new LogsMetricUpdateAttributes()
                            .compute(new LogsMetricUpdateCompute().includePercentiles(false))));

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

#### Instructions

First [install the library and its dependencies](https://docs.datadoghq.com/api/latest.md?code-lang=java) and then save the example to `Example.java` and run following commands:
    DD_SITE="datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
##### 

```python
"""
Update 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_filter import LogsMetricFilter
from datadog_api_client.v2.model.logs_metric_type import LogsMetricType
from datadog_api_client.v2.model.logs_metric_update_attributes import LogsMetricUpdateAttributes
from datadog_api_client.v2.model.logs_metric_update_data import LogsMetricUpdateData
from datadog_api_client.v2.model.logs_metric_update_request import LogsMetricUpdateRequest

# there is a valid "logs_metric" in the system
LOGS_METRIC_DATA_ATTRIBUTES_FILTER_QUERY = environ["LOGS_METRIC_DATA_ATTRIBUTES_FILTER_QUERY"]
LOGS_METRIC_DATA_ID = environ["LOGS_METRIC_DATA_ID"]

body = LogsMetricUpdateRequest(
    data=LogsMetricUpdateData(
        type=LogsMetricType.LOGS_METRICS,
        attributes=LogsMetricUpdateAttributes(
            filter=LogsMetricFilter(
                query="service:web* AND @http.status_code:[200 TO 299]-updated",
            ),
        ),
    ),
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsMetricsApi(api_client)
    response = api_instance.update_logs_metric(metric_id=LOGS_METRIC_DATA_ID, body=body)

    print(response)
```

##### 

```python
"""
Update a log-based metric with include_percentiles field 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_type import LogsMetricType
from datadog_api_client.v2.model.logs_metric_update_attributes import LogsMetricUpdateAttributes
from datadog_api_client.v2.model.logs_metric_update_compute import LogsMetricUpdateCompute
from datadog_api_client.v2.model.logs_metric_update_data import LogsMetricUpdateData
from datadog_api_client.v2.model.logs_metric_update_request import LogsMetricUpdateRequest

# there is a valid "logs_metric_percentile" in the system
LOGS_METRIC_PERCENTILE_DATA_ID = environ["LOGS_METRIC_PERCENTILE_DATA_ID"]

body = LogsMetricUpdateRequest(
    data=LogsMetricUpdateData(
        type=LogsMetricType.LOGS_METRICS,
        attributes=LogsMetricUpdateAttributes(
            compute=LogsMetricUpdateCompute(
                include_percentiles=False,
            ),
        ),
    ),
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsMetricsApi(api_client)
    response = api_instance.update_logs_metric(metric_id=LOGS_METRIC_PERCENTILE_DATA_ID, body=body)

    print(response)
```

#### Instructions

First [install the library and its dependencies](https://docs.datadoghq.com/api/latest.md?code-lang=python) and then save the example to `example.py` and run following commands:
    DD_SITE="datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
##### 

```ruby
# Update a log-based metric returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V2::LogsMetricsAPI.new

# there is a valid "logs_metric" in the system
LOGS_METRIC_DATA_ATTRIBUTES_FILTER_QUERY = ENV["LOGS_METRIC_DATA_ATTRIBUTES_FILTER_QUERY"]
LOGS_METRIC_DATA_ID = ENV["LOGS_METRIC_DATA_ID"]

body = DatadogAPIClient::V2::LogsMetricUpdateRequest.new({
  data: DatadogAPIClient::V2::LogsMetricUpdateData.new({
    type: DatadogAPIClient::V2::LogsMetricType::LOGS_METRICS,
    attributes: DatadogAPIClient::V2::LogsMetricUpdateAttributes.new({
      filter: DatadogAPIClient::V2::LogsMetricFilter.new({
        query: "service:web* AND @http.status_code:[200 TO 299]-updated",
      }),
    }),
  }),
})
p api_instance.update_logs_metric(LOGS_METRIC_DATA_ID, body)
```

##### 

```ruby
# Update a log-based metric with include_percentiles field returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V2::LogsMetricsAPI.new

# there is a valid "logs_metric_percentile" in the system
LOGS_METRIC_PERCENTILE_DATA_ID = ENV["LOGS_METRIC_PERCENTILE_DATA_ID"]

body = DatadogAPIClient::V2::LogsMetricUpdateRequest.new({
  data: DatadogAPIClient::V2::LogsMetricUpdateData.new({
    type: DatadogAPIClient::V2::LogsMetricType::LOGS_METRICS,
    attributes: DatadogAPIClient::V2::LogsMetricUpdateAttributes.new({
      compute: DatadogAPIClient::V2::LogsMetricUpdateCompute.new({
        include_percentiles: false,
      }),
    }),
  }),
})
p api_instance.update_logs_metric(LOGS_METRIC_PERCENTILE_DATA_ID, body)
```

#### Instructions

First [install the library and its dependencies](https://docs.datadoghq.com/api/latest.md?code-lang=ruby) and then save the example to `example.rb` and run following commands:
    DD_SITE="datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
##### 

```rust
// Update 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::LogsMetricFilter;
use datadog_api_client::datadogV2::model::LogsMetricType;
use datadog_api_client::datadogV2::model::LogsMetricUpdateAttributes;
use datadog_api_client::datadogV2::model::LogsMetricUpdateData;
use datadog_api_client::datadogV2::model::LogsMetricUpdateRequest;

#[tokio::main]
async fn main() {
    // there is a valid "logs_metric" in the system
    let logs_metric_data_id = std::env::var("LOGS_METRIC_DATA_ID").unwrap();
    let body = LogsMetricUpdateRequest::new(LogsMetricUpdateData::new(
        LogsMetricUpdateAttributes::new().filter(
            LogsMetricFilter::new()
                .query("service:web* AND @http.status_code:[200 TO 299]-updated".to_string()),
        ),
        LogsMetricType::LOGS_METRICS,
    ));
    let configuration = datadog::Configuration::new();
    let api = LogsMetricsAPI::with_config(configuration);
    let resp = api
        .update_logs_metric(logs_metric_data_id.clone(), body)
        .await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}
```

##### 

```rust
// Update a log-based metric with include_percentiles field returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_logs_metrics::LogsMetricsAPI;
use datadog_api_client::datadogV2::model::LogsMetricType;
use datadog_api_client::datadogV2::model::LogsMetricUpdateAttributes;
use datadog_api_client::datadogV2::model::LogsMetricUpdateCompute;
use datadog_api_client::datadogV2::model::LogsMetricUpdateData;
use datadog_api_client::datadogV2::model::LogsMetricUpdateRequest;

#[tokio::main]
async fn main() {
    // there is a valid "logs_metric_percentile" in the system
    let logs_metric_percentile_data_id = std::env::var("LOGS_METRIC_PERCENTILE_DATA_ID").unwrap();
    let body = LogsMetricUpdateRequest::new(LogsMetricUpdateData::new(
        LogsMetricUpdateAttributes::new()
            .compute(LogsMetricUpdateCompute::new().include_percentiles(false)),
        LogsMetricType::LOGS_METRICS,
    ));
    let configuration = datadog::Configuration::new();
    let api = LogsMetricsAPI::with_config(configuration);
    let resp = api
        .update_logs_metric(logs_metric_percentile_data_id.clone(), body)
        .await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}
```

#### Instructions

First [install the library and its dependencies](https://docs.datadoghq.com/api/latest.md?code-lang=rust) and then save the example to `src/main.rs` and run following commands:
    DD_SITE="datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
##### 

```typescript
/**
 * Update a log-based metric returns "OK" response
 */

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

const configuration = client.createConfiguration();
const apiInstance = new v2.LogsMetricsApi(configuration);

// there is a valid "logs_metric" in the system
const LOGS_METRIC_DATA_ID = process.env.LOGS_METRIC_DATA_ID as string;

const params: v2.LogsMetricsApiUpdateLogsMetricRequest = {
  body: {
    data: {
      type: "logs_metrics",
      attributes: {
        filter: {
          query: "service:web* AND @http.status_code:[200 TO 299]-updated",
        },
      },
    },
  },
  metricId: LOGS_METRIC_DATA_ID,
};

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

##### 

```typescript
/**
 * Update a log-based metric with include_percentiles field returns "OK" response
 */

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

const configuration = client.createConfiguration();
const apiInstance = new v2.LogsMetricsApi(configuration);

// there is a valid "logs_metric_percentile" in the system
const LOGS_METRIC_PERCENTILE_DATA_ID = process.env
  .LOGS_METRIC_PERCENTILE_DATA_ID as string;

const params: v2.LogsMetricsApiUpdateLogsMetricRequest = {
  body: {
    data: {
      type: "logs_metrics",
      attributes: {
        compute: {
          includePercentiles: false,
        },
      },
    },
  },
  metricId: LOGS_METRIC_PERCENTILE_DATA_ID,
};

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

#### Instructions

First [install the library and its dependencies](https://docs.datadoghq.com/api/latest.md?code-lang=typescript) and then save the example to `example.ts` and run following commands:
    DD_SITE="datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"
{% /tab %}