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ログパイプライン

パイプラインとプロセッサは受信ログを解析して変換し、クエリしやすい構造化属性にします。

注:

Grok 解析ルールは JSON 出力に影響する場合があり、リクエストで使用する前に返却データの調整が必要になることがあります。 たとえば、あるリクエストの戻り値を別のリクエストのボディに使用する際に、\s のような空白を表す正規表現を使用する解析ルールがある場合は、ボディデータ内のエスケープされた空白を %{space} に置き換える必要があります。

Get pipeline order

GET https://api.ap1.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.ap2.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.datadoghq.eu/api/v1/logs/config/pipeline-orderhttps://api.ddog-gov.com/api/v1/logs/config/pipeline-orderhttps://api.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.us3.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.us5.datadoghq.com/api/v1/logs/config/pipeline-order

概要

パイプラインの現在の順序を取得します。 このエンドポイントは、JSON 引数を受け取りません。 This endpoint requires the logs_read_config permission.

応答

OK

Object containing the ordered list of pipeline IDs.

Expand All

フィールド

種類

説明

pipeline_ids [required]

[string]

Ordered Array of <PIPELINE_ID> strings, the order of pipeline IDs in the array define the overall Pipelines order for Datadog.

{
  "pipeline_ids": [
    "tags",
    "org_ids",
    "products"
  ]
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                  # Curl command
curl -X GET "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipeline-order" \
-H "Accept: application/json" \
-H "DD-API-KEY: ${DD_API_KEY}" \
-H "DD-APPLICATION-KEY: ${DD_APP_KEY}"

                
"""
Get pipeline order returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.get_logs_pipeline_order()

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Get pipeline order returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new
p api_instance.get_logs_pipeline_order()

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Get pipeline order 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/datadogV1"
)

func main() {
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.GetLogsPipelineOrder(ctx)

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

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Get pipeline order returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsPipelinesOrder;

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

    try {
      LogsPipelinesOrder result = apiInstance.getLogsPipelineOrder();
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#getLogsPipelineOrder");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
// Get pipeline order returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;

#[tokio::main]
async fn main() {
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.get_logs_pipeline_order().await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Get pipeline order returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

apiInstance
  .getLogsPipelineOrder()
  .then((data: v1.LogsPipelinesOrder) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"

Update pipeline order

PUT https://api.ap1.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.ap2.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.datadoghq.eu/api/v1/logs/config/pipeline-orderhttps://api.ddog-gov.com/api/v1/logs/config/pipeline-orderhttps://api.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.us3.datadoghq.com/api/v1/logs/config/pipeline-orderhttps://api.us5.datadoghq.com/api/v1/logs/config/pipeline-order

概要

パイプラインの順序を更新します。ログは順番に処理されるため、パイプラインの順序を変更すると、 他のパイプラインで処理されるデータの構造や内容が変化する可能性があります。

注: PUT メソッドを使用すると、現在の順序を Datadog オーガニゼーションに送信される 新しい順序に置き換えてパイプラインの順序が更新されます。

This endpoint requires the logs_write_pipelines permission.

リクエスト

Body Data (required)

新しい順序のパイプライン ID リストを含むオブジェクト。

Expand All

フィールド

種類

説明

pipeline_ids [required]

[string]

Ordered Array of <PIPELINE_ID> strings, the order of pipeline IDs in the array define the overall Pipelines order for Datadog.

{
  "pipeline_ids": [
    "tags",
    "org_ids",
    "products"
  ]
}

応答

OK

Object containing the ordered list of pipeline IDs.

Expand All

フィールド

種類

説明

pipeline_ids [required]

[string]

Ordered Array of <PIPELINE_ID> strings, the order of pipeline IDs in the array define the overall Pipelines order for Datadog.

{
  "pipeline_ids": [
    "tags",
    "org_ids",
    "products"
  ]
}

Bad Request

Response returned by the Logs API when errors occur.

Expand All

フィールド

種類

説明

error

object

Error returned by the Logs API

code

string

Code identifying the error

details

[object]

Additional error details

message

string

Error message

{
  "error": {
    "code": "string",
    "details": [],
    "message": "string"
  }
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Unprocessable Entity

Response returned by the Logs API when errors occur.

Expand All

フィールド

種類

説明

error

object

Error returned by the Logs API

code

string

Code identifying the error

details

[object]

Additional error details

message

string

Error message

{
  "error": {
    "code": "string",
    "details": [],
    "message": "string"
  }
}

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                  # Curl command
curl -X PUT "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipeline-order" \
-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
{
  "pipeline_ids": [
    "tags",
    "org_ids",
    "products"
  ]
}
EOF

                
"""
Update pipeline order returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
from datadog_api_client.v1.model.logs_pipelines_order import LogsPipelinesOrder

body = LogsPipelinesOrder(
    pipeline_ids=[
        "tags",
        "org_ids",
        "products",
    ],
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.update_logs_pipeline_order(body=body)

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Update pipeline order returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new

body = DatadogAPIClient::V1::LogsPipelinesOrder.new({
  pipeline_ids: [
    "tags",
    "org_ids",
    "products",
  ],
})
p api_instance.update_logs_pipeline_order(body)

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Update pipeline order 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/datadogV1"
)

func main() {
	body := datadogV1.LogsPipelinesOrder{
		PipelineIds: []string{
			"tags",
			"org_ids",
			"products",
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.UpdateLogsPipelineOrder(ctx, body)

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

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Update pipeline order returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsPipelinesOrder;
import java.util.Arrays;

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

    LogsPipelinesOrder body =
        new LogsPipelinesOrder().pipelineIds(Arrays.asList("tags", "org_ids", "products"));

    try {
      LogsPipelinesOrder result = apiInstance.updateLogsPipelineOrder(body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#updateLogsPipelineOrder");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
// Update pipeline order returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;
use datadog_api_client::datadogV1::model::LogsPipelinesOrder;

#[tokio::main]
async fn main() {
    let body = LogsPipelinesOrder::new(vec![
        "tags".to_string(),
        "org_ids".to_string(),
        "products".to_string(),
    ]);
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.update_logs_pipeline_order(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Update pipeline order returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiUpdateLogsPipelineOrderRequest = {
  body: {
    pipelineIds: ["tags", "org_ids", "products"],
  },
};

apiInstance
  .updateLogsPipelineOrder(params)
  .then((data: v1.LogsPipelinesOrder) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"

Get all pipelines

GET https://api.ap1.datadoghq.com/api/v1/logs/config/pipelineshttps://api.ap2.datadoghq.com/api/v1/logs/config/pipelineshttps://api.datadoghq.eu/api/v1/logs/config/pipelineshttps://api.ddog-gov.com/api/v1/logs/config/pipelineshttps://api.datadoghq.com/api/v1/logs/config/pipelineshttps://api.us3.datadoghq.com/api/v1/logs/config/pipelineshttps://api.us5.datadoghq.com/api/v1/logs/config/pipelines

概要

オーガニゼーションからすべてのパイプラインを取得します。 このエンドポイントは、JSON 引数を受け取りません。 This endpoint requires the logs_read_config permission.

応答

OK

Array of all log pipeline objects configured for the organization.

Expand All

フィールド

種類

説明

description

string

A description of the pipeline.

filter

object

Filter for logs.

query

string

The filter query.

id

string

ID of the pipeline.

is_enabled

boolean

Whether or not the pipeline is enabled.

is_read_only

boolean

Whether or not the pipeline can be edited.

name

string

Name of the pipeline.

processors

[ <oneOf>]

Ordered list of processors in this pipeline.

Option 1

object

Create custom grok rules to parse the full message or a specific attribute of your raw event. For more information, see the parsing section.

grok [required]

object

Set of rules for the grok parser.

match_rules [required]

string

List of match rules for the grok parser, separated by a new line.

support_rules

string

List of support rules for the grok parser, separated by a new line.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

samples

[string]

List of sample logs to test this grok parser.

source [required]

string

Name of the log attribute to parse.

default: message

type [required]

enum

Type of logs grok parser. Allowed enum values: grok-parser

default: grok-parser

Option 2

object

As Datadog receives logs, it timestamps them using the value(s) from any of these default attributes.

  • timestamp

  • date

  • _timestamp

  • Timestamp

  • eventTime

  • published_date

    If your logs put their dates in an attribute not in this list, use the log date Remapper Processor to define their date attribute as the official log timestamp. The recognized date formats are ISO8601, UNIX (the milliseconds EPOCH format), and RFC3164.

Note: If your logs don’t contain any of the default attributes and you haven’t defined your own date attribute, Datadog timestamps the logs with the date it received them.

If multiple log date remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs date remapper. Allowed enum values: date-remapper

default: date-remapper

Option 3

object

Use this Processor if you want to assign some attributes as the official status.

Each incoming status value is mapped as follows.

  • Integers from 0 to 7 map to the Syslog severity standards
  • Strings beginning with emerg or f (case-insensitive) map to emerg (0)
  • Strings beginning with a (case-insensitive) map to alert (1)
  • Strings beginning with c (case-insensitive) map to critical (2)
  • Strings beginning with err (case-insensitive) map to error (3)
  • Strings beginning with w (case-insensitive) map to warning (4)
  • Strings beginning with n (case-insensitive) map to notice (5)
  • Strings beginning with i (case-insensitive) map to info (6)
  • Strings beginning with d, trace or verbose (case-insensitive) map to debug (7)
  • Strings beginning with o or matching OK or Success (case-insensitive) map to OK
  • All others map to info (6)

Note: If multiple log status remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs status remapper. Allowed enum values: status-remapper

default: status-remapper

Option 4

object

Use this processor if you want to assign one or more attributes as the official service.

Note: If multiple service remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs service remapper. Allowed enum values: service-remapper

default: service-remapper

Option 5

object

The message is a key attribute in Datadog. It is displayed in the message column of the Log Explorer and you can do full string search on it. Use this Processor to define one or more attributes as the official log message.

Note: If multiple log message remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: msg

type [required]

enum

Type of logs message remapper. Allowed enum values: message-remapper

default: message-remapper

Option 6

object

The remapper processor remaps any source attribute(s) or tag to another target attribute or tag. Constraints on the tag/attribute name are explained in the Tag Best Practice documentation. Some additional constraints are applied as : or , are not allowed in the target tag/attribute name.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

override_on_conflict

boolean

Override or not the target element if already set,

preserve_source

boolean

Remove or preserve the remapped source element.

source_type

string

Defines if the sources are from log attribute or tag.

default: attribute

sources [required]

[string]

Array of source attributes.

target [required]

string

Final attribute or tag name to remap the sources to.

target_format

enum

If the target_type of the remapper is attribute, try to cast the value to a new specific type. If the cast is not possible, the original type is kept. string, integer, or double are the possible types. If the target_type is tag, this parameter may not be specified. Allowed enum values: auto,string,integer,double

target_type

string

Defines if the final attribute or tag name is from log attribute or tag.

default: attribute

type [required]

enum

Type of logs attribute remapper. Allowed enum values: attribute-remapper

default: attribute-remapper

Option 7

object

This processor extracts query parameters and other important parameters from a URL.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

normalize_ending_slashes

boolean

Normalize the ending slashes or not.

sources [required]

[string]

Array of source attributes.

default: http.url

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.url_details

type [required]

enum

Type of logs URL parser. Allowed enum values: url-parser

default: url-parser

Option 8

object

The User-Agent parser takes a User-Agent attribute and extracts the OS, browser, device, and other user data. It recognizes major bots like the Google Bot, Yahoo Slurp, and Bing.

is_enabled

boolean

Whether or not the processor is enabled.

is_encoded

boolean

Define if the source attribute is URL encoded or not.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: http.useragent

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.useragent_details

type [required]

enum

Type of logs User-Agent parser. Allowed enum values: user-agent-parser

default: user-agent-parser

Option 9

object

Use the Category Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log matching a provided search query. Use categories to create groups for an analytical view. For example, URL groups, machine groups, environments, and response time buckets.

Notes:

  • The syntax of the query is the one of Logs Explorer search bar. The query can be done on any log attribute or tag, whether it is a facet or not. Wildcards can also be used inside your query.
  • Once the log has matched one of the Processor queries, it stops. Make sure they are properly ordered in case a log could match several queries.
  • The names of the categories must be unique.
  • Once defined in the Category Processor, you can map categories to log status using the Log Status Remapper.

categories [required]

[object]

Array of filters to match or not a log and their corresponding name to assign a custom value to the log.

filter

object

Filter for logs.

query

string

The filter query.

name

string

Value to assign to the target attribute.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

target [required]

string

Name of the target attribute which value is defined by the matching category.

type [required]

enum

Type of logs category processor. Allowed enum values: category-processor

default: category-processor

Option 10

object

Use the Arithmetic Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log with the result of the provided formula. This enables you to remap different time attributes with different units into a single attribute, or to compute operations on attributes within the same log.

The formula can use parentheses and the basic arithmetic operators -, +, *, /.

By default, the calculation is skipped if an attribute is missing. Select “Replace missing attribute by 0” to automatically populate missing attribute values with 0 to ensure that the calculation is done. An attribute is missing if it is not found in the log attributes, or if it cannot be converted to a number.

Notes:

  • The operator - needs to be space split in the formula as it can also be contained in attribute names.
  • If the target attribute already exists, it is overwritten by the result of the formula.
  • Results are rounded up to the 9th decimal. For example, if the result of the formula is 0.1234567891, the actual value stored for the attribute is 0.123456789.
  • If you need to scale a unit of measure, see Scale Filter.

expression [required]

string

Arithmetic operation between one or more log attributes.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of expression by 0, false skip the operation if an attribute is missing.

name

string

Name of the processor.

target [required]

string

Name of the attribute that contains the result of the arithmetic operation.

type [required]

enum

Type of logs arithmetic processor. Allowed enum values: arithmetic-processor

default: arithmetic-processor

Option 11

object

Use the string builder processor to add a new attribute (without spaces or special characters) to a log with the result of the provided template. This enables aggregation of different attributes or raw strings into a single attribute.

The template is defined by both raw text and blocks with the syntax %{attribute_path}.

Notes:

  • The processor only accepts attributes with values or an array of values in the blocks.
  • If an attribute cannot be used (object or array of object), it is replaced by an empty string or the entire operation is skipped depending on your selection.
  • If the target attribute already exists, it is overwritten by the result of the template.
  • Results of the template cannot exceed 256 characters.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of template by an empty string. If false (default), skips the operation for missing attributes.

name

string

Name of the processor.

target [required]

string

The name of the attribute that contains the result of the template.

template [required]

string

A formula with one or more attributes and raw text.

type [required]

enum

Type of logs string builder processor. Allowed enum values: string-builder-processor

default: string-builder-processor

Option 12

object

Nested Pipelines are pipelines within a pipeline. Use Nested Pipelines to split the processing into two steps. For example, first use a high-level filtering such as team and then a second level of filtering based on the integration, service, or any other tag or attribute.

A pipeline can contain Nested Pipelines and Processors whereas a Nested Pipeline can only contain Processors.

filter

object

Filter for logs.

query

string

The filter query.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

processors

[object]

Ordered list of processors in this pipeline.

type [required]

enum

Type of logs pipeline processor. Allowed enum values: pipeline

default: pipeline

Option 13

object

The GeoIP parser takes an IP address attribute and extracts if available the Continent, Country, Subdivision, and City information in the target attribute path.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: network.client.ip

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: network.client.geoip

type [required]

enum

Type of GeoIP parser. Allowed enum values: geo-ip-parser

default: geo-ip-parser

Option 14

object

Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in the processors mapping table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

default_lookup

string

Value to set the target attribute if the source value is not found in the list.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_table [required]

[string]

Mapping table of values for the source attribute and their associated target attribute values, formatted as ["source_key1,target_value1", "source_key2,target_value2"]

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list or the default_lookup if not found in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 15

object

Note: Reference Tables are in public beta. Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in a Reference Table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_enrichment_table [required]

string

Name of the Reference Table for the source attribute and their associated target attribute values.

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 16

object

There are two ways to improve correlation between application traces and logs.

  1. Follow the documentation on how to inject a trace ID in the application logs and by default log integrations take care of all the rest of the setup.

  2. Use the Trace remapper processor to define a log attribute as its associated trace ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.trace_id

type [required]

enum

Type of logs trace remapper. Allowed enum values: trace-id-remapper

default: trace-id-remapper

Option 17

object

There are two ways to define correlation between application spans and logs:

  1. Follow the documentation on how to inject a span ID in the application logs. Log integrations automatically handle all remaining setup steps by default.

  2. Use the span remapper processor to define a log attribute as its associated span ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.span_id

type [required]

enum

Type of logs span remapper. Allowed enum values: span-id-remapper

default: span-id-remapper

Option 18

object

A processor for extracting, aggregating, or transforming values from JSON arrays within your logs. Supported operations are:

  • Select value from matching element
  • Compute array length
  • Append a value to an array

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

operation [required]

 <oneOf>

Configuration of the array processor operation to perform.

Option 1

object

Operation that appends a value to a target array attribute.

preserve_source

boolean

Remove or preserve the remapped source element.

default: true

source [required]

string

Attribute path containing the value to append.

target [required]

string

Attribute path of the array to append to.

type [required]

enum

Operation type. Allowed enum values: append

Option 2

object

Operation that computes the length of a source array and stores the result in the target attribute.

source [required]

string

Attribute path of the array to measure.

target [required]

string

Attribute that receives the computed length.

type [required]

enum

Operation type. Allowed enum values: length

Option 3

object

Operation that finds an object in a source array using a filter, and then extracts a specific value into the target attribute.

filter [required]

string

Filter condition expressed as key:value used to find the matching element.

source [required]

string

Attribute path of the array to search into.

target [required]

string

Attribute that receives the extracted value.

type [required]

enum

Operation type. Allowed enum values: select

value_to_extract [required]

string

Key of the value to extract from the matching element.

type [required]

enum

Type of logs array processor. Allowed enum values: array-processor

default: array-processor

tags

[string]

A list of tags associated with the pipeline.

type

string

Type of pipeline.

{
  "description": "string",
  "filter": {
    "query": "source:python"
  },
  "id": "string",
  "is_enabled": false,
  "is_read_only": false,
  "name": "",
  "processors": [
    {
      "grok": {
        "match_rules": "rule_name_1 foo\nrule_name_2 bar\n",
        "support_rules": "rule_name_1 foo\nrule_name_2 bar\n"
      },
      "is_enabled": false,
      "name": "string",
      "samples": [],
      "source": "message",
      "type": "grok-parser"
    }
  ],
  "tags": [],
  "type": "pipeline"
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                  # Curl command
curl -X GET "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines" \
-H "Accept: application/json" \
-H "DD-API-KEY: ${DD_API_KEY}" \
-H "DD-APPLICATION-KEY: ${DD_APP_KEY}"

                
"""
Get all pipelines returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.list_logs_pipelines()

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Get all pipelines returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new
p api_instance.list_logs_pipelines()

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Get all pipelines 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/datadogV1"
)

func main() {
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.ListLogsPipelines(ctx)

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

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Get all pipelines returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsPipeline;
import java.util.List;

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

    try {
      List<LogsPipeline> result = apiInstance.listLogsPipelines();
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#listLogsPipelines");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
// Get all pipelines returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;

#[tokio::main]
async fn main() {
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.list_logs_pipelines().await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Get all pipelines returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

apiInstance
  .listLogsPipelines()
  .then((data: v1.LogsPipeline[]) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"

Create a pipeline

POST https://api.ap1.datadoghq.com/api/v1/logs/config/pipelineshttps://api.ap2.datadoghq.com/api/v1/logs/config/pipelineshttps://api.datadoghq.eu/api/v1/logs/config/pipelineshttps://api.ddog-gov.com/api/v1/logs/config/pipelineshttps://api.datadoghq.com/api/v1/logs/config/pipelineshttps://api.us3.datadoghq.com/api/v1/logs/config/pipelineshttps://api.us5.datadoghq.com/api/v1/logs/config/pipelines

概要

オーガニゼーションにパイプラインを作成します。 This endpoint requires the logs_write_pipelines permission.

リクエスト

Body Data (required)

新しいパイプラインの定義。

Expand All

フィールド

種類

説明

description

string

A description of the pipeline.

filter

object

Filter for logs.

query

string

The filter query.

id

string

ID of the pipeline.

is_enabled

boolean

Whether or not the pipeline is enabled.

is_read_only

boolean

Whether or not the pipeline can be edited.

name [required]

string

Name of the pipeline.

processors

[ <oneOf>]

Ordered list of processors in this pipeline.

Option 1

object

Create custom grok rules to parse the full message or a specific attribute of your raw event. For more information, see the parsing section.

grok [required]

object

Set of rules for the grok parser.

match_rules [required]

string

List of match rules for the grok parser, separated by a new line.

support_rules

string

List of support rules for the grok parser, separated by a new line.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

samples

[string]

List of sample logs to test this grok parser.

source [required]

string

Name of the log attribute to parse.

default: message

type [required]

enum

Type of logs grok parser. Allowed enum values: grok-parser

default: grok-parser

Option 2

object

As Datadog receives logs, it timestamps them using the value(s) from any of these default attributes.

  • timestamp

  • date

  • _timestamp

  • Timestamp

  • eventTime

  • published_date

    If your logs put their dates in an attribute not in this list, use the log date Remapper Processor to define their date attribute as the official log timestamp. The recognized date formats are ISO8601, UNIX (the milliseconds EPOCH format), and RFC3164.

Note: If your logs don’t contain any of the default attributes and you haven’t defined your own date attribute, Datadog timestamps the logs with the date it received them.

If multiple log date remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs date remapper. Allowed enum values: date-remapper

default: date-remapper

Option 3

object

Use this Processor if you want to assign some attributes as the official status.

Each incoming status value is mapped as follows.

  • Integers from 0 to 7 map to the Syslog severity standards
  • Strings beginning with emerg or f (case-insensitive) map to emerg (0)
  • Strings beginning with a (case-insensitive) map to alert (1)
  • Strings beginning with c (case-insensitive) map to critical (2)
  • Strings beginning with err (case-insensitive) map to error (3)
  • Strings beginning with w (case-insensitive) map to warning (4)
  • Strings beginning with n (case-insensitive) map to notice (5)
  • Strings beginning with i (case-insensitive) map to info (6)
  • Strings beginning with d, trace or verbose (case-insensitive) map to debug (7)
  • Strings beginning with o or matching OK or Success (case-insensitive) map to OK
  • All others map to info (6)

Note: If multiple log status remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs status remapper. Allowed enum values: status-remapper

default: status-remapper

Option 4

object

Use this processor if you want to assign one or more attributes as the official service.

Note: If multiple service remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs service remapper. Allowed enum values: service-remapper

default: service-remapper

Option 5

object

The message is a key attribute in Datadog. It is displayed in the message column of the Log Explorer and you can do full string search on it. Use this Processor to define one or more attributes as the official log message.

Note: If multiple log message remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: msg

type [required]

enum

Type of logs message remapper. Allowed enum values: message-remapper

default: message-remapper

Option 6

object

The remapper processor remaps any source attribute(s) or tag to another target attribute or tag. Constraints on the tag/attribute name are explained in the Tag Best Practice documentation. Some additional constraints are applied as : or , are not allowed in the target tag/attribute name.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

override_on_conflict

boolean

Override or not the target element if already set,

preserve_source

boolean

Remove or preserve the remapped source element.

source_type

string

Defines if the sources are from log attribute or tag.

default: attribute

sources [required]

[string]

Array of source attributes.

target [required]

string

Final attribute or tag name to remap the sources to.

target_format

enum

If the target_type of the remapper is attribute, try to cast the value to a new specific type. If the cast is not possible, the original type is kept. string, integer, or double are the possible types. If the target_type is tag, this parameter may not be specified. Allowed enum values: auto,string,integer,double

target_type

string

Defines if the final attribute or tag name is from log attribute or tag.

default: attribute

type [required]

enum

Type of logs attribute remapper. Allowed enum values: attribute-remapper

default: attribute-remapper

Option 7

object

This processor extracts query parameters and other important parameters from a URL.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

normalize_ending_slashes

boolean

Normalize the ending slashes or not.

sources [required]

[string]

Array of source attributes.

default: http.url

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.url_details

type [required]

enum

Type of logs URL parser. Allowed enum values: url-parser

default: url-parser

Option 8

object

The User-Agent parser takes a User-Agent attribute and extracts the OS, browser, device, and other user data. It recognizes major bots like the Google Bot, Yahoo Slurp, and Bing.

is_enabled

boolean

Whether or not the processor is enabled.

is_encoded

boolean

Define if the source attribute is URL encoded or not.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: http.useragent

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.useragent_details

type [required]

enum

Type of logs User-Agent parser. Allowed enum values: user-agent-parser

default: user-agent-parser

Option 9

object

Use the Category Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log matching a provided search query. Use categories to create groups for an analytical view. For example, URL groups, machine groups, environments, and response time buckets.

Notes:

  • The syntax of the query is the one of Logs Explorer search bar. The query can be done on any log attribute or tag, whether it is a facet or not. Wildcards can also be used inside your query.
  • Once the log has matched one of the Processor queries, it stops. Make sure they are properly ordered in case a log could match several queries.
  • The names of the categories must be unique.
  • Once defined in the Category Processor, you can map categories to log status using the Log Status Remapper.

categories [required]

[object]

Array of filters to match or not a log and their corresponding name to assign a custom value to the log.

filter

object

Filter for logs.

query

string

The filter query.

name

string

Value to assign to the target attribute.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

target [required]

string

Name of the target attribute which value is defined by the matching category.

type [required]

enum

Type of logs category processor. Allowed enum values: category-processor

default: category-processor

Option 10

object

Use the Arithmetic Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log with the result of the provided formula. This enables you to remap different time attributes with different units into a single attribute, or to compute operations on attributes within the same log.

The formula can use parentheses and the basic arithmetic operators -, +, *, /.

By default, the calculation is skipped if an attribute is missing. Select “Replace missing attribute by 0” to automatically populate missing attribute values with 0 to ensure that the calculation is done. An attribute is missing if it is not found in the log attributes, or if it cannot be converted to a number.

Notes:

  • The operator - needs to be space split in the formula as it can also be contained in attribute names.
  • If the target attribute already exists, it is overwritten by the result of the formula.
  • Results are rounded up to the 9th decimal. For example, if the result of the formula is 0.1234567891, the actual value stored for the attribute is 0.123456789.
  • If you need to scale a unit of measure, see Scale Filter.

expression [required]

string

Arithmetic operation between one or more log attributes.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of expression by 0, false skip the operation if an attribute is missing.

name

string

Name of the processor.

target [required]

string

Name of the attribute that contains the result of the arithmetic operation.

type [required]

enum

Type of logs arithmetic processor. Allowed enum values: arithmetic-processor

default: arithmetic-processor

Option 11

object

Use the string builder processor to add a new attribute (without spaces or special characters) to a log with the result of the provided template. This enables aggregation of different attributes or raw strings into a single attribute.

The template is defined by both raw text and blocks with the syntax %{attribute_path}.

Notes:

  • The processor only accepts attributes with values or an array of values in the blocks.
  • If an attribute cannot be used (object or array of object), it is replaced by an empty string or the entire operation is skipped depending on your selection.
  • If the target attribute already exists, it is overwritten by the result of the template.
  • Results of the template cannot exceed 256 characters.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of template by an empty string. If false (default), skips the operation for missing attributes.

name

string

Name of the processor.

target [required]

string

The name of the attribute that contains the result of the template.

template [required]

string

A formula with one or more attributes and raw text.

type [required]

enum

Type of logs string builder processor. Allowed enum values: string-builder-processor

default: string-builder-processor

Option 12

object

Nested Pipelines are pipelines within a pipeline. Use Nested Pipelines to split the processing into two steps. For example, first use a high-level filtering such as team and then a second level of filtering based on the integration, service, or any other tag or attribute.

A pipeline can contain Nested Pipelines and Processors whereas a Nested Pipeline can only contain Processors.

filter

object

Filter for logs.

query

string

The filter query.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

processors

[object]

Ordered list of processors in this pipeline.

type [required]

enum

Type of logs pipeline processor. Allowed enum values: pipeline

default: pipeline

Option 13

object

The GeoIP parser takes an IP address attribute and extracts if available the Continent, Country, Subdivision, and City information in the target attribute path.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: network.client.ip

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: network.client.geoip

type [required]

enum

Type of GeoIP parser. Allowed enum values: geo-ip-parser

default: geo-ip-parser

Option 14

object

Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in the processors mapping table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

default_lookup

string

Value to set the target attribute if the source value is not found in the list.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_table [required]

[string]

Mapping table of values for the source attribute and their associated target attribute values, formatted as ["source_key1,target_value1", "source_key2,target_value2"]

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list or the default_lookup if not found in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 15

object

Note: Reference Tables are in public beta. Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in a Reference Table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_enrichment_table [required]

string

Name of the Reference Table for the source attribute and their associated target attribute values.

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 16

object

There are two ways to improve correlation between application traces and logs.

  1. Follow the documentation on how to inject a trace ID in the application logs and by default log integrations take care of all the rest of the setup.

  2. Use the Trace remapper processor to define a log attribute as its associated trace ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.trace_id

type [required]

enum

Type of logs trace remapper. Allowed enum values: trace-id-remapper

default: trace-id-remapper

Option 17

object

There are two ways to define correlation between application spans and logs:

  1. Follow the documentation on how to inject a span ID in the application logs. Log integrations automatically handle all remaining setup steps by default.

  2. Use the span remapper processor to define a log attribute as its associated span ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.span_id

type [required]

enum

Type of logs span remapper. Allowed enum values: span-id-remapper

default: span-id-remapper

Option 18

object

A processor for extracting, aggregating, or transforming values from JSON arrays within your logs. Supported operations are:

  • Select value from matching element
  • Compute array length
  • Append a value to an array

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

operation [required]

 <oneOf>

Configuration of the array processor operation to perform.

Option 1

object

Operation that appends a value to a target array attribute.

preserve_source

boolean

Remove or preserve the remapped source element.

default: true

source [required]

string

Attribute path containing the value to append.

target [required]

string

Attribute path of the array to append to.

type [required]

enum

Operation type. Allowed enum values: append

Option 2

object

Operation that computes the length of a source array and stores the result in the target attribute.

source [required]

string

Attribute path of the array to measure.

target [required]

string

Attribute that receives the computed length.

type [required]

enum

Operation type. Allowed enum values: length

Option 3

object

Operation that finds an object in a source array using a filter, and then extracts a specific value into the target attribute.

filter [required]

string

Filter condition expressed as key:value used to find the matching element.

source [required]

string

Attribute path of the array to search into.

target [required]

string

Attribute that receives the extracted value.

type [required]

enum

Operation type. Allowed enum values: select

value_to_extract [required]

string

Key of the value to extract from the matching element.

type [required]

enum

Type of logs array processor. Allowed enum values: array-processor

default: array-processor

tags

[string]

A list of tags associated with the pipeline.

type

string

Type of pipeline.

{
  "filter": {
    "query": "source:python"
  },
  "name": "testPipelineArrayAppend",
  "processors": [
    {
      "type": "array-processor",
      "is_enabled": true,
      "name": "append_ip_to_array",
      "operation": {
        "type": "append",
        "source": "network.client.ip",
        "target": "sourceIps"
      }
    }
  ],
  "tags": []
}
{
  "filter": {
    "query": "source:python"
  },
  "name": "testPipelineArrayAppendNoPreserve",
  "processors": [
    {
      "type": "array-processor",
      "is_enabled": true,
      "name": "append_ip_and_remove_source",
      "operation": {
        "type": "append",
        "source": "network.client.ip",
        "target": "sourceIps",
        "preserve_source": false
      }
    }
  ],
  "tags": []
}
{
  "filter": {
    "query": "source:python"
  },
  "name": "testPipelineArrayAppendPreserve",
  "processors": [
    {
      "type": "array-processor",
      "is_enabled": true,
      "name": "append_ip_and_keep_source",
      "operation": {
        "type": "append",
        "source": "network.client.ip",
        "target": "sourceIps",
        "preserve_source": true
      }
    }
  ],
  "tags": []
}

応答

OK

Pipelines and processors operate on incoming logs, parsing and transforming them into structured attributes for easier querying.

Note: These endpoints are only available for admin users. Make sure to use an application key created by an admin.

Expand All

フィールド

種類

説明

description

string

A description of the pipeline.

filter

object

Filter for logs.

query

string

The filter query.

id

string

ID of the pipeline.

is_enabled

boolean

Whether or not the pipeline is enabled.

is_read_only

boolean

Whether or not the pipeline can be edited.

name [required]

string

Name of the pipeline.

processors

[ <oneOf>]

Ordered list of processors in this pipeline.

Option 1

object

Create custom grok rules to parse the full message or a specific attribute of your raw event. For more information, see the parsing section.

grok [required]

object

Set of rules for the grok parser.

match_rules [required]

string

List of match rules for the grok parser, separated by a new line.

support_rules

string

List of support rules for the grok parser, separated by a new line.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

samples

[string]

List of sample logs to test this grok parser.

source [required]

string

Name of the log attribute to parse.

default: message

type [required]

enum

Type of logs grok parser. Allowed enum values: grok-parser

default: grok-parser

Option 2

object

As Datadog receives logs, it timestamps them using the value(s) from any of these default attributes.

  • timestamp

  • date

  • _timestamp

  • Timestamp

  • eventTime

  • published_date

    If your logs put their dates in an attribute not in this list, use the log date Remapper Processor to define their date attribute as the official log timestamp. The recognized date formats are ISO8601, UNIX (the milliseconds EPOCH format), and RFC3164.

Note: If your logs don’t contain any of the default attributes and you haven’t defined your own date attribute, Datadog timestamps the logs with the date it received them.

If multiple log date remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs date remapper. Allowed enum values: date-remapper

default: date-remapper

Option 3

object

Use this Processor if you want to assign some attributes as the official status.

Each incoming status value is mapped as follows.

  • Integers from 0 to 7 map to the Syslog severity standards
  • Strings beginning with emerg or f (case-insensitive) map to emerg (0)
  • Strings beginning with a (case-insensitive) map to alert (1)
  • Strings beginning with c (case-insensitive) map to critical (2)
  • Strings beginning with err (case-insensitive) map to error (3)
  • Strings beginning with w (case-insensitive) map to warning (4)
  • Strings beginning with n (case-insensitive) map to notice (5)
  • Strings beginning with i (case-insensitive) map to info (6)
  • Strings beginning with d, trace or verbose (case-insensitive) map to debug (7)
  • Strings beginning with o or matching OK or Success (case-insensitive) map to OK
  • All others map to info (6)

Note: If multiple log status remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs status remapper. Allowed enum values: status-remapper

default: status-remapper

Option 4

object

Use this processor if you want to assign one or more attributes as the official service.

Note: If multiple service remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs service remapper. Allowed enum values: service-remapper

default: service-remapper

Option 5

object

The message is a key attribute in Datadog. It is displayed in the message column of the Log Explorer and you can do full string search on it. Use this Processor to define one or more attributes as the official log message.

Note: If multiple log message remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: msg

type [required]

enum

Type of logs message remapper. Allowed enum values: message-remapper

default: message-remapper

Option 6

object

The remapper processor remaps any source attribute(s) or tag to another target attribute or tag. Constraints on the tag/attribute name are explained in the Tag Best Practice documentation. Some additional constraints are applied as : or , are not allowed in the target tag/attribute name.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

override_on_conflict

boolean

Override or not the target element if already set,

preserve_source

boolean

Remove or preserve the remapped source element.

source_type

string

Defines if the sources are from log attribute or tag.

default: attribute

sources [required]

[string]

Array of source attributes.

target [required]

string

Final attribute or tag name to remap the sources to.

target_format

enum

If the target_type of the remapper is attribute, try to cast the value to a new specific type. If the cast is not possible, the original type is kept. string, integer, or double are the possible types. If the target_type is tag, this parameter may not be specified. Allowed enum values: auto,string,integer,double

target_type

string

Defines if the final attribute or tag name is from log attribute or tag.

default: attribute

type [required]

enum

Type of logs attribute remapper. Allowed enum values: attribute-remapper

default: attribute-remapper

Option 7

object

This processor extracts query parameters and other important parameters from a URL.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

normalize_ending_slashes

boolean

Normalize the ending slashes or not.

sources [required]

[string]

Array of source attributes.

default: http.url

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.url_details

type [required]

enum

Type of logs URL parser. Allowed enum values: url-parser

default: url-parser

Option 8

object

The User-Agent parser takes a User-Agent attribute and extracts the OS, browser, device, and other user data. It recognizes major bots like the Google Bot, Yahoo Slurp, and Bing.

is_enabled

boolean

Whether or not the processor is enabled.

is_encoded

boolean

Define if the source attribute is URL encoded or not.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: http.useragent

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.useragent_details

type [required]

enum

Type of logs User-Agent parser. Allowed enum values: user-agent-parser

default: user-agent-parser

Option 9

object

Use the Category Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log matching a provided search query. Use categories to create groups for an analytical view. For example, URL groups, machine groups, environments, and response time buckets.

Notes:

  • The syntax of the query is the one of Logs Explorer search bar. The query can be done on any log attribute or tag, whether it is a facet or not. Wildcards can also be used inside your query.
  • Once the log has matched one of the Processor queries, it stops. Make sure they are properly ordered in case a log could match several queries.
  • The names of the categories must be unique.
  • Once defined in the Category Processor, you can map categories to log status using the Log Status Remapper.

categories [required]

[object]

Array of filters to match or not a log and their corresponding name to assign a custom value to the log.

filter

object

Filter for logs.

query

string

The filter query.

name

string

Value to assign to the target attribute.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

target [required]

string

Name of the target attribute which value is defined by the matching category.

type [required]

enum

Type of logs category processor. Allowed enum values: category-processor

default: category-processor

Option 10

object

Use the Arithmetic Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log with the result of the provided formula. This enables you to remap different time attributes with different units into a single attribute, or to compute operations on attributes within the same log.

The formula can use parentheses and the basic arithmetic operators -, +, *, /.

By default, the calculation is skipped if an attribute is missing. Select “Replace missing attribute by 0” to automatically populate missing attribute values with 0 to ensure that the calculation is done. An attribute is missing if it is not found in the log attributes, or if it cannot be converted to a number.

Notes:

  • The operator - needs to be space split in the formula as it can also be contained in attribute names.
  • If the target attribute already exists, it is overwritten by the result of the formula.
  • Results are rounded up to the 9th decimal. For example, if the result of the formula is 0.1234567891, the actual value stored for the attribute is 0.123456789.
  • If you need to scale a unit of measure, see Scale Filter.

expression [required]

string

Arithmetic operation between one or more log attributes.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of expression by 0, false skip the operation if an attribute is missing.

name

string

Name of the processor.

target [required]

string

Name of the attribute that contains the result of the arithmetic operation.

type [required]

enum

Type of logs arithmetic processor. Allowed enum values: arithmetic-processor

default: arithmetic-processor

Option 11

object

Use the string builder processor to add a new attribute (without spaces or special characters) to a log with the result of the provided template. This enables aggregation of different attributes or raw strings into a single attribute.

The template is defined by both raw text and blocks with the syntax %{attribute_path}.

Notes:

  • The processor only accepts attributes with values or an array of values in the blocks.
  • If an attribute cannot be used (object or array of object), it is replaced by an empty string or the entire operation is skipped depending on your selection.
  • If the target attribute already exists, it is overwritten by the result of the template.
  • Results of the template cannot exceed 256 characters.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of template by an empty string. If false (default), skips the operation for missing attributes.

name

string

Name of the processor.

target [required]

string

The name of the attribute that contains the result of the template.

template [required]

string

A formula with one or more attributes and raw text.

type [required]

enum

Type of logs string builder processor. Allowed enum values: string-builder-processor

default: string-builder-processor

Option 12

object

Nested Pipelines are pipelines within a pipeline. Use Nested Pipelines to split the processing into two steps. For example, first use a high-level filtering such as team and then a second level of filtering based on the integration, service, or any other tag or attribute.

A pipeline can contain Nested Pipelines and Processors whereas a Nested Pipeline can only contain Processors.

filter

object

Filter for logs.

query

string

The filter query.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

processors

[object]

Ordered list of processors in this pipeline.

type [required]

enum

Type of logs pipeline processor. Allowed enum values: pipeline

default: pipeline

Option 13

object

The GeoIP parser takes an IP address attribute and extracts if available the Continent, Country, Subdivision, and City information in the target attribute path.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: network.client.ip

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: network.client.geoip

type [required]

enum

Type of GeoIP parser. Allowed enum values: geo-ip-parser

default: geo-ip-parser

Option 14

object

Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in the processors mapping table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

default_lookup

string

Value to set the target attribute if the source value is not found in the list.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_table [required]

[string]

Mapping table of values for the source attribute and their associated target attribute values, formatted as ["source_key1,target_value1", "source_key2,target_value2"]

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list or the default_lookup if not found in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 15

object

Note: Reference Tables are in public beta. Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in a Reference Table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_enrichment_table [required]

string

Name of the Reference Table for the source attribute and their associated target attribute values.

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 16

object

There are two ways to improve correlation between application traces and logs.

  1. Follow the documentation on how to inject a trace ID in the application logs and by default log integrations take care of all the rest of the setup.

  2. Use the Trace remapper processor to define a log attribute as its associated trace ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.trace_id

type [required]

enum

Type of logs trace remapper. Allowed enum values: trace-id-remapper

default: trace-id-remapper

Option 17

object

There are two ways to define correlation between application spans and logs:

  1. Follow the documentation on how to inject a span ID in the application logs. Log integrations automatically handle all remaining setup steps by default.

  2. Use the span remapper processor to define a log attribute as its associated span ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.span_id

type [required]

enum

Type of logs span remapper. Allowed enum values: span-id-remapper

default: span-id-remapper

Option 18

object

A processor for extracting, aggregating, or transforming values from JSON arrays within your logs. Supported operations are:

  • Select value from matching element
  • Compute array length
  • Append a value to an array

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

operation [required]

 <oneOf>

Configuration of the array processor operation to perform.

Option 1

object

Operation that appends a value to a target array attribute.

preserve_source

boolean

Remove or preserve the remapped source element.

default: true

source [required]

string

Attribute path containing the value to append.

target [required]

string

Attribute path of the array to append to.

type [required]

enum

Operation type. Allowed enum values: append

Option 2

object

Operation that computes the length of a source array and stores the result in the target attribute.

source [required]

string

Attribute path of the array to measure.

target [required]

string

Attribute that receives the computed length.

type [required]

enum

Operation type. Allowed enum values: length

Option 3

object

Operation that finds an object in a source array using a filter, and then extracts a specific value into the target attribute.

filter [required]

string

Filter condition expressed as key:value used to find the matching element.

source [required]

string

Attribute path of the array to search into.

target [required]

string

Attribute that receives the extracted value.

type [required]

enum

Operation type. Allowed enum values: select

value_to_extract [required]

string

Key of the value to extract from the matching element.

type [required]

enum

Type of logs array processor. Allowed enum values: array-processor

default: array-processor

tags

[string]

A list of tags associated with the pipeline.

type

string

Type of pipeline.

{
  "description": "string",
  "filter": {
    "query": "source:python"
  },
  "id": "string",
  "is_enabled": false,
  "is_read_only": false,
  "name": "",
  "processors": [
    {
      "grok": {
        "match_rules": "rule_name_1 foo\nrule_name_2 bar\n",
        "support_rules": "rule_name_1 foo\nrule_name_2 bar\n"
      },
      "is_enabled": false,
      "name": "string",
      "samples": [],
      "source": "message",
      "type": "grok-parser"
    }
  ],
  "tags": [],
  "type": "pipeline"
}

Bad Request

Response returned by the Logs API when errors occur.

Expand All

フィールド

種類

説明

error

object

Error returned by the Logs API

code

string

Code identifying the error

details

[object]

Additional error details

message

string

Error message

{
  "error": {
    "code": "string",
    "details": [],
    "message": "string"
  }
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                          # Curl command
curl -X POST "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines" \
-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
{
  "filter": {
    "query": "source:python"
  },
  "name": "testPipelineArrayAppend",
  "processors": [
    {
      "type": "array-processor",
      "is_enabled": true,
      "name": "append_ip_to_array",
      "operation": {
        "type": "append",
        "source": "network.client.ip",
        "target": "sourceIps"
      }
    }
  ],
  "tags": []
}
EOF

                        
                          # Curl command
curl -X POST "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines" \
-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
{
  "filter": {
    "query": "source:python"
  },
  "name": "testPipelineArrayAppendNoPreserve",
  "processors": [
    {
      "type": "array-processor",
      "is_enabled": true,
      "name": "append_ip_and_remove_source",
      "operation": {
        "type": "append",
        "source": "network.client.ip",
        "target": "sourceIps",
        "preserve_source": false
      }
    }
  ],
  "tags": []
}
EOF

                        
                          # Curl command
curl -X POST "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines" \
-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
{
  "filter": {
    "query": "source:python"
  },
  "name": "testPipelineArrayAppendPreserve",
  "processors": [
    {
      "type": "array-processor",
      "is_enabled": true,
      "name": "append_ip_and_keep_source",
      "operation": {
        "type": "append",
        "source": "network.client.ip",
        "target": "sourceIps",
        "preserve_source": true
      }
    }
  ],
  "tags": []
}
EOF

                        
// Create a pipeline with Array Processor Append Operation 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/datadogV1"
)

func main() {
	body := datadogV1.LogsPipeline{
		Filter: &datadogV1.LogsFilter{
			Query: datadog.PtrString("source:python"),
		},
		Name: "testPipelineArrayAppend",
		Processors: []datadogV1.LogsProcessor{
			datadogV1.LogsProcessor{
				LogsArrayProcessor: &datadogV1.LogsArrayProcessor{
					Type:      datadogV1.LOGSARRAYPROCESSORTYPE_ARRAY_PROCESSOR,
					IsEnabled: datadog.PtrBool(true),
					Name:      datadog.PtrString("append_ip_to_array"),
					Operation: datadogV1.LogsArrayProcessorOperation{
						LogsArrayProcessorOperationAppend: &datadogV1.LogsArrayProcessorOperationAppend{
							Type:   datadogV1.LOGSARRAYPROCESSOROPERATIONAPPENDTYPE_APPEND,
							Source: "network.client.ip",
							Target: "sourceIps",
						}},
				}},
		},
		Tags: []string{},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.CreateLogsPipeline(ctx, body)

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

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

// Create a pipeline with Array Processor Append Operation with preserve_source false 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/datadogV1"
)

func main() {
	body := datadogV1.LogsPipeline{
		Filter: &datadogV1.LogsFilter{
			Query: datadog.PtrString("source:python"),
		},
		Name: "testPipelineArrayAppendNoPreserve",
		Processors: []datadogV1.LogsProcessor{
			datadogV1.LogsProcessor{
				LogsArrayProcessor: &datadogV1.LogsArrayProcessor{
					Type:      datadogV1.LOGSARRAYPROCESSORTYPE_ARRAY_PROCESSOR,
					IsEnabled: datadog.PtrBool(true),
					Name:      datadog.PtrString("append_ip_and_remove_source"),
					Operation: datadogV1.LogsArrayProcessorOperation{
						LogsArrayProcessorOperationAppend: &datadogV1.LogsArrayProcessorOperationAppend{
							Type:           datadogV1.LOGSARRAYPROCESSOROPERATIONAPPENDTYPE_APPEND,
							Source:         "network.client.ip",
							Target:         "sourceIps",
							PreserveSource: datadog.PtrBool(false),
						}},
				}},
		},
		Tags: []string{},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.CreateLogsPipeline(ctx, body)

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

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

// Create a pipeline with Array Processor Append Operation with preserve_source true 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/datadogV1"
)

func main() {
	body := datadogV1.LogsPipeline{
		Filter: &datadogV1.LogsFilter{
			Query: datadog.PtrString("source:python"),
		},
		Name: "testPipelineArrayAppendPreserve",
		Processors: []datadogV1.LogsProcessor{
			datadogV1.LogsProcessor{
				LogsArrayProcessor: &datadogV1.LogsArrayProcessor{
					Type:      datadogV1.LOGSARRAYPROCESSORTYPE_ARRAY_PROCESSOR,
					IsEnabled: datadog.PtrBool(true),
					Name:      datadog.PtrString("append_ip_and_keep_source"),
					Operation: datadogV1.LogsArrayProcessorOperation{
						LogsArrayProcessorOperationAppend: &datadogV1.LogsArrayProcessorOperationAppend{
							Type:           datadogV1.LOGSARRAYPROCESSOROPERATIONAPPENDTYPE_APPEND,
							Source:         "network.client.ip",
							Target:         "sourceIps",
							PreserveSource: datadog.PtrBool(true),
						}},
				}},
		},
		Tags: []string{},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.CreateLogsPipeline(ctx, body)

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

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Create a pipeline with Array Processor Append Operation returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsArrayProcessor;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperation;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperationAppend;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperationAppendType;
import com.datadog.api.client.v1.model.LogsArrayProcessorType;
import com.datadog.api.client.v1.model.LogsFilter;
import com.datadog.api.client.v1.model.LogsPipeline;
import com.datadog.api.client.v1.model.LogsProcessor;
import java.util.Collections;

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

    LogsPipeline body =
        new LogsPipeline()
            .filter(new LogsFilter().query("source:python"))
            .name("testPipelineArrayAppend")
            .processors(
                Collections.singletonList(
                    new LogsProcessor(
                        new LogsArrayProcessor()
                            .type(LogsArrayProcessorType.ARRAY_PROCESSOR)
                            .isEnabled(true)
                            .name("append_ip_to_array")
                            .operation(
                                new LogsArrayProcessorOperation(
                                    new LogsArrayProcessorOperationAppend()
                                        .type(LogsArrayProcessorOperationAppendType.APPEND)
                                        .source("network.client.ip")
                                        .target("sourceIps"))))));

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

// Create a pipeline with Array Processor Append Operation with preserve_source false returns "OK"
// response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsArrayProcessor;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperation;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperationAppend;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperationAppendType;
import com.datadog.api.client.v1.model.LogsArrayProcessorType;
import com.datadog.api.client.v1.model.LogsFilter;
import com.datadog.api.client.v1.model.LogsPipeline;
import com.datadog.api.client.v1.model.LogsProcessor;
import java.util.Collections;

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

    LogsPipeline body =
        new LogsPipeline()
            .filter(new LogsFilter().query("source:python"))
            .name("testPipelineArrayAppendNoPreserve")
            .processors(
                Collections.singletonList(
                    new LogsProcessor(
                        new LogsArrayProcessor()
                            .type(LogsArrayProcessorType.ARRAY_PROCESSOR)
                            .isEnabled(true)
                            .name("append_ip_and_remove_source")
                            .operation(
                                new LogsArrayProcessorOperation(
                                    new LogsArrayProcessorOperationAppend()
                                        .type(LogsArrayProcessorOperationAppendType.APPEND)
                                        .source("network.client.ip")
                                        .target("sourceIps")
                                        .preserveSource(false))))));

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

// Create a pipeline with Array Processor Append Operation with preserve_source true returns "OK"
// response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsArrayProcessor;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperation;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperationAppend;
import com.datadog.api.client.v1.model.LogsArrayProcessorOperationAppendType;
import com.datadog.api.client.v1.model.LogsArrayProcessorType;
import com.datadog.api.client.v1.model.LogsFilter;
import com.datadog.api.client.v1.model.LogsPipeline;
import com.datadog.api.client.v1.model.LogsProcessor;
import java.util.Collections;

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

    LogsPipeline body =
        new LogsPipeline()
            .filter(new LogsFilter().query("source:python"))
            .name("testPipelineArrayAppendPreserve")
            .processors(
                Collections.singletonList(
                    new LogsProcessor(
                        new LogsArrayProcessor()
                            .type(LogsArrayProcessorType.ARRAY_PROCESSOR)
                            .isEnabled(true)
                            .name("append_ip_and_keep_source")
                            .operation(
                                new LogsArrayProcessorOperation(
                                    new LogsArrayProcessorOperationAppend()
                                        .type(LogsArrayProcessorOperationAppendType.APPEND)
                                        .source("network.client.ip")
                                        .target("sourceIps")
                                        .preserveSource(true))))));

    try {
      LogsPipeline result = apiInstance.createLogsPipeline(body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#createLogsPipeline");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
"""
Create a pipeline with Array Processor Append Operation returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
from datadog_api_client.v1.model.logs_array_processor import LogsArrayProcessor
from datadog_api_client.v1.model.logs_array_processor_operation_append import LogsArrayProcessorOperationAppend
from datadog_api_client.v1.model.logs_array_processor_operation_append_type import LogsArrayProcessorOperationAppendType
from datadog_api_client.v1.model.logs_array_processor_type import LogsArrayProcessorType
from datadog_api_client.v1.model.logs_filter import LogsFilter
from datadog_api_client.v1.model.logs_pipeline import LogsPipeline

body = LogsPipeline(
    filter=LogsFilter(
        query="source:python",
    ),
    name="testPipelineArrayAppend",
    processors=[
        LogsArrayProcessor(
            type=LogsArrayProcessorType.ARRAY_PROCESSOR,
            is_enabled=True,
            name="append_ip_to_array",
            operation=LogsArrayProcessorOperationAppend(
                type=LogsArrayProcessorOperationAppendType.APPEND,
                source="network.client.ip",
                target="sourceIps",
            ),
        ),
    ],
    tags=[],
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.create_logs_pipeline(body=body)

    print(response)

"""
Create a pipeline with Array Processor Append Operation with preserve_source false returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
from datadog_api_client.v1.model.logs_array_processor import LogsArrayProcessor
from datadog_api_client.v1.model.logs_array_processor_operation_append import LogsArrayProcessorOperationAppend
from datadog_api_client.v1.model.logs_array_processor_operation_append_type import LogsArrayProcessorOperationAppendType
from datadog_api_client.v1.model.logs_array_processor_type import LogsArrayProcessorType
from datadog_api_client.v1.model.logs_filter import LogsFilter
from datadog_api_client.v1.model.logs_pipeline import LogsPipeline

body = LogsPipeline(
    filter=LogsFilter(
        query="source:python",
    ),
    name="testPipelineArrayAppendNoPreserve",
    processors=[
        LogsArrayProcessor(
            type=LogsArrayProcessorType.ARRAY_PROCESSOR,
            is_enabled=True,
            name="append_ip_and_remove_source",
            operation=LogsArrayProcessorOperationAppend(
                type=LogsArrayProcessorOperationAppendType.APPEND,
                source="network.client.ip",
                target="sourceIps",
                preserve_source=False,
            ),
        ),
    ],
    tags=[],
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.create_logs_pipeline(body=body)

    print(response)

"""
Create a pipeline with Array Processor Append Operation with preserve_source true returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
from datadog_api_client.v1.model.logs_array_processor import LogsArrayProcessor
from datadog_api_client.v1.model.logs_array_processor_operation_append import LogsArrayProcessorOperationAppend
from datadog_api_client.v1.model.logs_array_processor_operation_append_type import LogsArrayProcessorOperationAppendType
from datadog_api_client.v1.model.logs_array_processor_type import LogsArrayProcessorType
from datadog_api_client.v1.model.logs_filter import LogsFilter
from datadog_api_client.v1.model.logs_pipeline import LogsPipeline

body = LogsPipeline(
    filter=LogsFilter(
        query="source:python",
    ),
    name="testPipelineArrayAppendPreserve",
    processors=[
        LogsArrayProcessor(
            type=LogsArrayProcessorType.ARRAY_PROCESSOR,
            is_enabled=True,
            name="append_ip_and_keep_source",
            operation=LogsArrayProcessorOperationAppend(
                type=LogsArrayProcessorOperationAppendType.APPEND,
                source="network.client.ip",
                target="sourceIps",
                preserve_source=True,
            ),
        ),
    ],
    tags=[],
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.create_logs_pipeline(body=body)

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Create a pipeline with Array Processor Append Operation returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new

body = DatadogAPIClient::V1::LogsPipeline.new({
  filter: DatadogAPIClient::V1::LogsFilter.new({
    query: "source:python",
  }),
  name: "testPipelineArrayAppend",
  processors: [
    DatadogAPIClient::V1::LogsArrayProcessor.new({
      type: DatadogAPIClient::V1::LogsArrayProcessorType::ARRAY_PROCESSOR,
      is_enabled: true,
      name: "append_ip_to_array",
      operation: DatadogAPIClient::V1::LogsArrayProcessorOperationAppend.new({
        type: DatadogAPIClient::V1::LogsArrayProcessorOperationAppendType::APPEND,
        source: "network.client.ip",
        target: "sourceIps",
      }),
    }),
  ],
  tags: [],
})
p api_instance.create_logs_pipeline(body)

# Create a pipeline with Array Processor Append Operation with preserve_source false returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new

body = DatadogAPIClient::V1::LogsPipeline.new({
  filter: DatadogAPIClient::V1::LogsFilter.new({
    query: "source:python",
  }),
  name: "testPipelineArrayAppendNoPreserve",
  processors: [
    DatadogAPIClient::V1::LogsArrayProcessor.new({
      type: DatadogAPIClient::V1::LogsArrayProcessorType::ARRAY_PROCESSOR,
      is_enabled: true,
      name: "append_ip_and_remove_source",
      operation: DatadogAPIClient::V1::LogsArrayProcessorOperationAppend.new({
        type: DatadogAPIClient::V1::LogsArrayProcessorOperationAppendType::APPEND,
        source: "network.client.ip",
        target: "sourceIps",
        preserve_source: false,
      }),
    }),
  ],
  tags: [],
})
p api_instance.create_logs_pipeline(body)

# Create a pipeline with Array Processor Append Operation with preserve_source true returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new

body = DatadogAPIClient::V1::LogsPipeline.new({
  filter: DatadogAPIClient::V1::LogsFilter.new({
    query: "source:python",
  }),
  name: "testPipelineArrayAppendPreserve",
  processors: [
    DatadogAPIClient::V1::LogsArrayProcessor.new({
      type: DatadogAPIClient::V1::LogsArrayProcessorType::ARRAY_PROCESSOR,
      is_enabled: true,
      name: "append_ip_and_keep_source",
      operation: DatadogAPIClient::V1::LogsArrayProcessorOperationAppend.new({
        type: DatadogAPIClient::V1::LogsArrayProcessorOperationAppendType::APPEND,
        source: "network.client.ip",
        target: "sourceIps",
        preserve_source: true,
      }),
    }),
  ],
  tags: [],
})
p api_instance.create_logs_pipeline(body)

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Create a pipeline with Array Processor Append Operation returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;
use datadog_api_client::datadogV1::model::LogsArrayProcessor;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperation;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperationAppend;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperationAppendType;
use datadog_api_client::datadogV1::model::LogsArrayProcessorType;
use datadog_api_client::datadogV1::model::LogsFilter;
use datadog_api_client::datadogV1::model::LogsPipeline;
use datadog_api_client::datadogV1::model::LogsProcessor;

#[tokio::main]
async fn main() {
    let body = LogsPipeline::new("testPipelineArrayAppend".to_string())
        .filter(LogsFilter::new().query("source:python".to_string()))
        .processors(vec![LogsProcessor::LogsArrayProcessor(Box::new(
            LogsArrayProcessor::new(
                LogsArrayProcessorOperation::LogsArrayProcessorOperationAppend(Box::new(
                    LogsArrayProcessorOperationAppend::new(
                        "network.client.ip".to_string(),
                        "sourceIps".to_string(),
                        LogsArrayProcessorOperationAppendType::APPEND,
                    ),
                )),
                LogsArrayProcessorType::ARRAY_PROCESSOR,
            )
            .is_enabled(true)
            .name("append_ip_to_array".to_string()),
        ))])
        .tags(vec![]);
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.create_logs_pipeline(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

// Create a pipeline with Array Processor Append Operation with preserve_source
// false returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;
use datadog_api_client::datadogV1::model::LogsArrayProcessor;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperation;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperationAppend;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperationAppendType;
use datadog_api_client::datadogV1::model::LogsArrayProcessorType;
use datadog_api_client::datadogV1::model::LogsFilter;
use datadog_api_client::datadogV1::model::LogsPipeline;
use datadog_api_client::datadogV1::model::LogsProcessor;

#[tokio::main]
async fn main() {
    let body = LogsPipeline::new("testPipelineArrayAppendNoPreserve".to_string())
        .filter(LogsFilter::new().query("source:python".to_string()))
        .processors(vec![LogsProcessor::LogsArrayProcessor(Box::new(
            LogsArrayProcessor::new(
                LogsArrayProcessorOperation::LogsArrayProcessorOperationAppend(Box::new(
                    LogsArrayProcessorOperationAppend::new(
                        "network.client.ip".to_string(),
                        "sourceIps".to_string(),
                        LogsArrayProcessorOperationAppendType::APPEND,
                    )
                    .preserve_source(false),
                )),
                LogsArrayProcessorType::ARRAY_PROCESSOR,
            )
            .is_enabled(true)
            .name("append_ip_and_remove_source".to_string()),
        ))])
        .tags(vec![]);
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.create_logs_pipeline(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

// Create a pipeline with Array Processor Append Operation with preserve_source
// true returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;
use datadog_api_client::datadogV1::model::LogsArrayProcessor;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperation;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperationAppend;
use datadog_api_client::datadogV1::model::LogsArrayProcessorOperationAppendType;
use datadog_api_client::datadogV1::model::LogsArrayProcessorType;
use datadog_api_client::datadogV1::model::LogsFilter;
use datadog_api_client::datadogV1::model::LogsPipeline;
use datadog_api_client::datadogV1::model::LogsProcessor;

#[tokio::main]
async fn main() {
    let body = LogsPipeline::new("testPipelineArrayAppendPreserve".to_string())
        .filter(LogsFilter::new().query("source:python".to_string()))
        .processors(vec![LogsProcessor::LogsArrayProcessor(Box::new(
            LogsArrayProcessor::new(
                LogsArrayProcessorOperation::LogsArrayProcessorOperationAppend(Box::new(
                    LogsArrayProcessorOperationAppend::new(
                        "network.client.ip".to_string(),
                        "sourceIps".to_string(),
                        LogsArrayProcessorOperationAppendType::APPEND,
                    )
                    .preserve_source(true),
                )),
                LogsArrayProcessorType::ARRAY_PROCESSOR,
            )
            .is_enabled(true)
            .name("append_ip_and_keep_source".to_string()),
        ))])
        .tags(vec![]);
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.create_logs_pipeline(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Create a pipeline with Array Processor Append Operation returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiCreateLogsPipelineRequest = {
  body: {
    filter: {
      query: "source:python",
    },
    name: "testPipelineArrayAppend",
    processors: [
      {
        type: "array-processor",
        isEnabled: true,
        name: "append_ip_to_array",
        operation: {
          type: "append",
          source: "network.client.ip",
          target: "sourceIps",
        },
      },
    ],
    tags: [],
  },
};

apiInstance
  .createLogsPipeline(params)
  .then((data: v1.LogsPipeline) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

/**
 * Create a pipeline with Array Processor Append Operation with preserve_source false returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiCreateLogsPipelineRequest = {
  body: {
    filter: {
      query: "source:python",
    },
    name: "testPipelineArrayAppendNoPreserve",
    processors: [
      {
        type: "array-processor",
        isEnabled: true,
        name: "append_ip_and_remove_source",
        operation: {
          type: "append",
          source: "network.client.ip",
          target: "sourceIps",
          preserveSource: false,
        },
      },
    ],
    tags: [],
  },
};

apiInstance
  .createLogsPipeline(params)
  .then((data: v1.LogsPipeline) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

/**
 * Create a pipeline with Array Processor Append Operation with preserve_source true returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiCreateLogsPipelineRequest = {
  body: {
    filter: {
      query: "source:python",
    },
    name: "testPipelineArrayAppendPreserve",
    processors: [
      {
        type: "array-processor",
        isEnabled: true,
        name: "append_ip_and_keep_source",
        operation: {
          type: "append",
          source: "network.client.ip",
          target: "sourceIps",
          preserveSource: true,
        },
      },
    ],
    tags: [],
  },
};

apiInstance
  .createLogsPipeline(params)
  .then((data: v1.LogsPipeline) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"

Get a pipeline

GET https://api.ap1.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.ap2.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.datadoghq.eu/api/v1/logs/config/pipelines/{pipeline_id}https://api.ddog-gov.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.us3.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.us5.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}

概要

オーガニゼーションから指定されたパイプラインを取得します。 このエンドポイントは、JSON 引数を受け取りません。 This endpoint requires the logs_read_config permission.

引数

パスパラメーター

名前

種類

説明

pipeline_id [required]

string

ID of the pipeline to get.

応答

OK

Pipelines and processors operate on incoming logs, parsing and transforming them into structured attributes for easier querying.

Note: These endpoints are only available for admin users. Make sure to use an application key created by an admin.

Expand All

フィールド

種類

説明

description

string

A description of the pipeline.

filter

object

Filter for logs.

query

string

The filter query.

id

string

ID of the pipeline.

is_enabled

boolean

Whether or not the pipeline is enabled.

is_read_only

boolean

Whether or not the pipeline can be edited.

name [required]

string

Name of the pipeline.

processors

[ <oneOf>]

Ordered list of processors in this pipeline.

Option 1

object

Create custom grok rules to parse the full message or a specific attribute of your raw event. For more information, see the parsing section.

grok [required]

object

Set of rules for the grok parser.

match_rules [required]

string

List of match rules for the grok parser, separated by a new line.

support_rules

string

List of support rules for the grok parser, separated by a new line.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

samples

[string]

List of sample logs to test this grok parser.

source [required]

string

Name of the log attribute to parse.

default: message

type [required]

enum

Type of logs grok parser. Allowed enum values: grok-parser

default: grok-parser

Option 2

object

As Datadog receives logs, it timestamps them using the value(s) from any of these default attributes.

  • timestamp

  • date

  • _timestamp

  • Timestamp

  • eventTime

  • published_date

    If your logs put their dates in an attribute not in this list, use the log date Remapper Processor to define their date attribute as the official log timestamp. The recognized date formats are ISO8601, UNIX (the milliseconds EPOCH format), and RFC3164.

Note: If your logs don’t contain any of the default attributes and you haven’t defined your own date attribute, Datadog timestamps the logs with the date it received them.

If multiple log date remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs date remapper. Allowed enum values: date-remapper

default: date-remapper

Option 3

object

Use this Processor if you want to assign some attributes as the official status.

Each incoming status value is mapped as follows.

  • Integers from 0 to 7 map to the Syslog severity standards
  • Strings beginning with emerg or f (case-insensitive) map to emerg (0)
  • Strings beginning with a (case-insensitive) map to alert (1)
  • Strings beginning with c (case-insensitive) map to critical (2)
  • Strings beginning with err (case-insensitive) map to error (3)
  • Strings beginning with w (case-insensitive) map to warning (4)
  • Strings beginning with n (case-insensitive) map to notice (5)
  • Strings beginning with i (case-insensitive) map to info (6)
  • Strings beginning with d, trace or verbose (case-insensitive) map to debug (7)
  • Strings beginning with o or matching OK or Success (case-insensitive) map to OK
  • All others map to info (6)

Note: If multiple log status remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs status remapper. Allowed enum values: status-remapper

default: status-remapper

Option 4

object

Use this processor if you want to assign one or more attributes as the official service.

Note: If multiple service remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs service remapper. Allowed enum values: service-remapper

default: service-remapper

Option 5

object

The message is a key attribute in Datadog. It is displayed in the message column of the Log Explorer and you can do full string search on it. Use this Processor to define one or more attributes as the official log message.

Note: If multiple log message remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: msg

type [required]

enum

Type of logs message remapper. Allowed enum values: message-remapper

default: message-remapper

Option 6

object

The remapper processor remaps any source attribute(s) or tag to another target attribute or tag. Constraints on the tag/attribute name are explained in the Tag Best Practice documentation. Some additional constraints are applied as : or , are not allowed in the target tag/attribute name.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

override_on_conflict

boolean

Override or not the target element if already set,

preserve_source

boolean

Remove or preserve the remapped source element.

source_type

string

Defines if the sources are from log attribute or tag.

default: attribute

sources [required]

[string]

Array of source attributes.

target [required]

string

Final attribute or tag name to remap the sources to.

target_format

enum

If the target_type of the remapper is attribute, try to cast the value to a new specific type. If the cast is not possible, the original type is kept. string, integer, or double are the possible types. If the target_type is tag, this parameter may not be specified. Allowed enum values: auto,string,integer,double

target_type

string

Defines if the final attribute or tag name is from log attribute or tag.

default: attribute

type [required]

enum

Type of logs attribute remapper. Allowed enum values: attribute-remapper

default: attribute-remapper

Option 7

object

This processor extracts query parameters and other important parameters from a URL.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

normalize_ending_slashes

boolean

Normalize the ending slashes or not.

sources [required]

[string]

Array of source attributes.

default: http.url

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.url_details

type [required]

enum

Type of logs URL parser. Allowed enum values: url-parser

default: url-parser

Option 8

object

The User-Agent parser takes a User-Agent attribute and extracts the OS, browser, device, and other user data. It recognizes major bots like the Google Bot, Yahoo Slurp, and Bing.

is_enabled

boolean

Whether or not the processor is enabled.

is_encoded

boolean

Define if the source attribute is URL encoded or not.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: http.useragent

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.useragent_details

type [required]

enum

Type of logs User-Agent parser. Allowed enum values: user-agent-parser

default: user-agent-parser

Option 9

object

Use the Category Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log matching a provided search query. Use categories to create groups for an analytical view. For example, URL groups, machine groups, environments, and response time buckets.

Notes:

  • The syntax of the query is the one of Logs Explorer search bar. The query can be done on any log attribute or tag, whether it is a facet or not. Wildcards can also be used inside your query.
  • Once the log has matched one of the Processor queries, it stops. Make sure they are properly ordered in case a log could match several queries.
  • The names of the categories must be unique.
  • Once defined in the Category Processor, you can map categories to log status using the Log Status Remapper.

categories [required]

[object]

Array of filters to match or not a log and their corresponding name to assign a custom value to the log.

filter

object

Filter for logs.

query

string

The filter query.

name

string

Value to assign to the target attribute.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

target [required]

string

Name of the target attribute which value is defined by the matching category.

type [required]

enum

Type of logs category processor. Allowed enum values: category-processor

default: category-processor

Option 10

object

Use the Arithmetic Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log with the result of the provided formula. This enables you to remap different time attributes with different units into a single attribute, or to compute operations on attributes within the same log.

The formula can use parentheses and the basic arithmetic operators -, +, *, /.

By default, the calculation is skipped if an attribute is missing. Select “Replace missing attribute by 0” to automatically populate missing attribute values with 0 to ensure that the calculation is done. An attribute is missing if it is not found in the log attributes, or if it cannot be converted to a number.

Notes:

  • The operator - needs to be space split in the formula as it can also be contained in attribute names.
  • If the target attribute already exists, it is overwritten by the result of the formula.
  • Results are rounded up to the 9th decimal. For example, if the result of the formula is 0.1234567891, the actual value stored for the attribute is 0.123456789.
  • If you need to scale a unit of measure, see Scale Filter.

expression [required]

string

Arithmetic operation between one or more log attributes.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of expression by 0, false skip the operation if an attribute is missing.

name

string

Name of the processor.

target [required]

string

Name of the attribute that contains the result of the arithmetic operation.

type [required]

enum

Type of logs arithmetic processor. Allowed enum values: arithmetic-processor

default: arithmetic-processor

Option 11

object

Use the string builder processor to add a new attribute (without spaces or special characters) to a log with the result of the provided template. This enables aggregation of different attributes or raw strings into a single attribute.

The template is defined by both raw text and blocks with the syntax %{attribute_path}.

Notes:

  • The processor only accepts attributes with values or an array of values in the blocks.
  • If an attribute cannot be used (object or array of object), it is replaced by an empty string or the entire operation is skipped depending on your selection.
  • If the target attribute already exists, it is overwritten by the result of the template.
  • Results of the template cannot exceed 256 characters.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of template by an empty string. If false (default), skips the operation for missing attributes.

name

string

Name of the processor.

target [required]

string

The name of the attribute that contains the result of the template.

template [required]

string

A formula with one or more attributes and raw text.

type [required]

enum

Type of logs string builder processor. Allowed enum values: string-builder-processor

default: string-builder-processor

Option 12

object

Nested Pipelines are pipelines within a pipeline. Use Nested Pipelines to split the processing into two steps. For example, first use a high-level filtering such as team and then a second level of filtering based on the integration, service, or any other tag or attribute.

A pipeline can contain Nested Pipelines and Processors whereas a Nested Pipeline can only contain Processors.

filter

object

Filter for logs.

query

string

The filter query.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

processors

[object]

Ordered list of processors in this pipeline.

type [required]

enum

Type of logs pipeline processor. Allowed enum values: pipeline

default: pipeline

Option 13

object

The GeoIP parser takes an IP address attribute and extracts if available the Continent, Country, Subdivision, and City information in the target attribute path.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: network.client.ip

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: network.client.geoip

type [required]

enum

Type of GeoIP parser. Allowed enum values: geo-ip-parser

default: geo-ip-parser

Option 14

object

Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in the processors mapping table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

default_lookup

string

Value to set the target attribute if the source value is not found in the list.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_table [required]

[string]

Mapping table of values for the source attribute and their associated target attribute values, formatted as ["source_key1,target_value1", "source_key2,target_value2"]

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list or the default_lookup if not found in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 15

object

Note: Reference Tables are in public beta. Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in a Reference Table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_enrichment_table [required]

string

Name of the Reference Table for the source attribute and their associated target attribute values.

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 16

object

There are two ways to improve correlation between application traces and logs.

  1. Follow the documentation on how to inject a trace ID in the application logs and by default log integrations take care of all the rest of the setup.

  2. Use the Trace remapper processor to define a log attribute as its associated trace ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.trace_id

type [required]

enum

Type of logs trace remapper. Allowed enum values: trace-id-remapper

default: trace-id-remapper

Option 17

object

There are two ways to define correlation between application spans and logs:

  1. Follow the documentation on how to inject a span ID in the application logs. Log integrations automatically handle all remaining setup steps by default.

  2. Use the span remapper processor to define a log attribute as its associated span ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.span_id

type [required]

enum

Type of logs span remapper. Allowed enum values: span-id-remapper

default: span-id-remapper

Option 18

object

A processor for extracting, aggregating, or transforming values from JSON arrays within your logs. Supported operations are:

  • Select value from matching element
  • Compute array length
  • Append a value to an array

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

operation [required]

 <oneOf>

Configuration of the array processor operation to perform.

Option 1

object

Operation that appends a value to a target array attribute.

preserve_source

boolean

Remove or preserve the remapped source element.

default: true

source [required]

string

Attribute path containing the value to append.

target [required]

string

Attribute path of the array to append to.

type [required]

enum

Operation type. Allowed enum values: append

Option 2

object

Operation that computes the length of a source array and stores the result in the target attribute.

source [required]

string

Attribute path of the array to measure.

target [required]

string

Attribute that receives the computed length.

type [required]

enum

Operation type. Allowed enum values: length

Option 3

object

Operation that finds an object in a source array using a filter, and then extracts a specific value into the target attribute.

filter [required]

string

Filter condition expressed as key:value used to find the matching element.

source [required]

string

Attribute path of the array to search into.

target [required]

string

Attribute that receives the extracted value.

type [required]

enum

Operation type. Allowed enum values: select

value_to_extract [required]

string

Key of the value to extract from the matching element.

type [required]

enum

Type of logs array processor. Allowed enum values: array-processor

default: array-processor

tags

[string]

A list of tags associated with the pipeline.

type

string

Type of pipeline.

{
  "description": "string",
  "filter": {
    "query": "source:python"
  },
  "id": "string",
  "is_enabled": false,
  "is_read_only": false,
  "name": "",
  "processors": [
    {
      "grok": {
        "match_rules": "rule_name_1 foo\nrule_name_2 bar\n",
        "support_rules": "rule_name_1 foo\nrule_name_2 bar\n"
      },
      "is_enabled": false,
      "name": "string",
      "samples": [],
      "source": "message",
      "type": "grok-parser"
    }
  ],
  "tags": [],
  "type": "pipeline"
}

Bad Request

Response returned by the Logs API when errors occur.

Expand All

フィールド

種類

説明

error

object

Error returned by the Logs API

code

string

Code identifying the error

details

[object]

Additional error details

message

string

Error message

{
  "error": {
    "code": "string",
    "details": [],
    "message": "string"
  }
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                  # Path parameters
export pipeline_id="CHANGE_ME"
# Curl command
curl -X GET "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines/${pipeline_id}" \
-H "Accept: application/json" \
-H "DD-API-KEY: ${DD_API_KEY}" \
-H "DD-APPLICATION-KEY: ${DD_APP_KEY}"

                
"""
Get a pipeline returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.get_logs_pipeline(
        pipeline_id="pipeline_id",
    )

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Get a pipeline returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new
p api_instance.get_logs_pipeline("pipeline_id")

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Get a pipeline 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/datadogV1"
)

func main() {
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.GetLogsPipeline(ctx, "pipeline_id")

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

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Get a pipeline returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsPipeline;

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

    try {
      LogsPipeline result = apiInstance.getLogsPipeline("pipeline_id");
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#getLogsPipeline");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
// Get a pipeline returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;

#[tokio::main]
async fn main() {
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.get_logs_pipeline("pipeline_id".to_string()).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Get a pipeline returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiGetLogsPipelineRequest = {
  pipelineId: "pipeline_id",
};

apiInstance
  .getLogsPipeline(params)
  .then((data: v1.LogsPipeline) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"

Delete a pipeline

DELETE https://api.ap1.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.ap2.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.datadoghq.eu/api/v1/logs/config/pipelines/{pipeline_id}https://api.ddog-gov.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.us3.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.us5.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}

概要

オーガニゼーションから特定のパイプラインを取得します。 このエンドポイントは、JSON 引数を受け取りません。 This endpoint requires the logs_write_pipelines permission.

引数

パスパラメーター

名前

種類

説明

pipeline_id [required]

string

ID of the pipeline to delete.

応答

OK

Bad Request

Response returned by the Logs API when errors occur.

Expand All

フィールド

種類

説明

error

object

Error returned by the Logs API

code

string

Code identifying the error

details

[object]

Additional error details

message

string

Error message

{
  "error": {
    "code": "string",
    "details": [],
    "message": "string"
  }
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                  # Path parameters
export pipeline_id="CHANGE_ME"
# Curl command
curl -X DELETE "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines/${pipeline_id}" \
-H "Accept: application/json" \
-H "DD-API-KEY: ${DD_API_KEY}" \
-H "DD-APPLICATION-KEY: ${DD_APP_KEY}"

                
"""
Delete a pipeline returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    api_instance.delete_logs_pipeline(
        pipeline_id="pipeline_id",
    )

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Delete a pipeline returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new
p api_instance.delete_logs_pipeline("pipeline_id")

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Delete a pipeline returns "OK" response

package main

import (
	"context"
	"fmt"
	"os"

	"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
	"github.com/DataDog/datadog-api-client-go/v2/api/datadogV1"
)

func main() {
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	r, err := api.DeleteLogsPipeline(ctx, "pipeline_id")

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Delete a pipeline returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;

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

    try {
      apiInstance.deleteLogsPipeline("pipeline_id");
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#deleteLogsPipeline");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
// Delete a pipeline returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;

#[tokio::main]
async fn main() {
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api.delete_logs_pipeline("pipeline_id".to_string()).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Delete a pipeline returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiDeleteLogsPipelineRequest = {
  pipelineId: "pipeline_id",
};

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

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"

Update a pipeline

PUT https://api.ap1.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.ap2.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.datadoghq.eu/api/v1/logs/config/pipelines/{pipeline_id}https://api.ddog-gov.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.us3.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}https://api.us5.datadoghq.com/api/v1/logs/config/pipelines/{pipeline_id}

概要

特定のパイプライン構成を更新し、プロセッサーまたは順序を変更します。

注: このメソッドを使用すると、現在の構成を Datadog Organization に送信された新しい構成に置き換えて、パイプライン構成が更新されます。

This endpoint requires the logs_write_pipelines permission.

引数

パスパラメーター

名前

種類

説明

pipeline_id [required]

string

ID of the pipeline to delete.

リクエスト

Body Data (required)

パイプラインの新しい定義。

Expand All

フィールド

種類

説明

description

string

A description of the pipeline.

filter

object

Filter for logs.

query

string

The filter query.

id

string

ID of the pipeline.

is_enabled

boolean

Whether or not the pipeline is enabled.

is_read_only

boolean

Whether or not the pipeline can be edited.

name [required]

string

Name of the pipeline.

processors

[ <oneOf>]

Ordered list of processors in this pipeline.

Option 1

object

Create custom grok rules to parse the full message or a specific attribute of your raw event. For more information, see the parsing section.

grok [required]

object

Set of rules for the grok parser.

match_rules [required]

string

List of match rules for the grok parser, separated by a new line.

support_rules

string

List of support rules for the grok parser, separated by a new line.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

samples

[string]

List of sample logs to test this grok parser.

source [required]

string

Name of the log attribute to parse.

default: message

type [required]

enum

Type of logs grok parser. Allowed enum values: grok-parser

default: grok-parser

Option 2

object

As Datadog receives logs, it timestamps them using the value(s) from any of these default attributes.

  • timestamp

  • date

  • _timestamp

  • Timestamp

  • eventTime

  • published_date

    If your logs put their dates in an attribute not in this list, use the log date Remapper Processor to define their date attribute as the official log timestamp. The recognized date formats are ISO8601, UNIX (the milliseconds EPOCH format), and RFC3164.

Note: If your logs don’t contain any of the default attributes and you haven’t defined your own date attribute, Datadog timestamps the logs with the date it received them.

If multiple log date remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs date remapper. Allowed enum values: date-remapper

default: date-remapper

Option 3

object

Use this Processor if you want to assign some attributes as the official status.

Each incoming status value is mapped as follows.

  • Integers from 0 to 7 map to the Syslog severity standards
  • Strings beginning with emerg or f (case-insensitive) map to emerg (0)
  • Strings beginning with a (case-insensitive) map to alert (1)
  • Strings beginning with c (case-insensitive) map to critical (2)
  • Strings beginning with err (case-insensitive) map to error (3)
  • Strings beginning with w (case-insensitive) map to warning (4)
  • Strings beginning with n (case-insensitive) map to notice (5)
  • Strings beginning with i (case-insensitive) map to info (6)
  • Strings beginning with d, trace or verbose (case-insensitive) map to debug (7)
  • Strings beginning with o or matching OK or Success (case-insensitive) map to OK
  • All others map to info (6)

Note: If multiple log status remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs status remapper. Allowed enum values: status-remapper

default: status-remapper

Option 4

object

Use this processor if you want to assign one or more attributes as the official service.

Note: If multiple service remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs service remapper. Allowed enum values: service-remapper

default: service-remapper

Option 5

object

The message is a key attribute in Datadog. It is displayed in the message column of the Log Explorer and you can do full string search on it. Use this Processor to define one or more attributes as the official log message.

Note: If multiple log message remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: msg

type [required]

enum

Type of logs message remapper. Allowed enum values: message-remapper

default: message-remapper

Option 6

object

The remapper processor remaps any source attribute(s) or tag to another target attribute or tag. Constraints on the tag/attribute name are explained in the Tag Best Practice documentation. Some additional constraints are applied as : or , are not allowed in the target tag/attribute name.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

override_on_conflict

boolean

Override or not the target element if already set,

preserve_source

boolean

Remove or preserve the remapped source element.

source_type

string

Defines if the sources are from log attribute or tag.

default: attribute

sources [required]

[string]

Array of source attributes.

target [required]

string

Final attribute or tag name to remap the sources to.

target_format

enum

If the target_type of the remapper is attribute, try to cast the value to a new specific type. If the cast is not possible, the original type is kept. string, integer, or double are the possible types. If the target_type is tag, this parameter may not be specified. Allowed enum values: auto,string,integer,double

target_type

string

Defines if the final attribute or tag name is from log attribute or tag.

default: attribute

type [required]

enum

Type of logs attribute remapper. Allowed enum values: attribute-remapper

default: attribute-remapper

Option 7

object

This processor extracts query parameters and other important parameters from a URL.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

normalize_ending_slashes

boolean

Normalize the ending slashes or not.

sources [required]

[string]

Array of source attributes.

default: http.url

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.url_details

type [required]

enum

Type of logs URL parser. Allowed enum values: url-parser

default: url-parser

Option 8

object

The User-Agent parser takes a User-Agent attribute and extracts the OS, browser, device, and other user data. It recognizes major bots like the Google Bot, Yahoo Slurp, and Bing.

is_enabled

boolean

Whether or not the processor is enabled.

is_encoded

boolean

Define if the source attribute is URL encoded or not.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: http.useragent

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.useragent_details

type [required]

enum

Type of logs User-Agent parser. Allowed enum values: user-agent-parser

default: user-agent-parser

Option 9

object

Use the Category Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log matching a provided search query. Use categories to create groups for an analytical view. For example, URL groups, machine groups, environments, and response time buckets.

Notes:

  • The syntax of the query is the one of Logs Explorer search bar. The query can be done on any log attribute or tag, whether it is a facet or not. Wildcards can also be used inside your query.
  • Once the log has matched one of the Processor queries, it stops. Make sure they are properly ordered in case a log could match several queries.
  • The names of the categories must be unique.
  • Once defined in the Category Processor, you can map categories to log status using the Log Status Remapper.

categories [required]

[object]

Array of filters to match or not a log and their corresponding name to assign a custom value to the log.

filter

object

Filter for logs.

query

string

The filter query.

name

string

Value to assign to the target attribute.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

target [required]

string

Name of the target attribute which value is defined by the matching category.

type [required]

enum

Type of logs category processor. Allowed enum values: category-processor

default: category-processor

Option 10

object

Use the Arithmetic Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log with the result of the provided formula. This enables you to remap different time attributes with different units into a single attribute, or to compute operations on attributes within the same log.

The formula can use parentheses and the basic arithmetic operators -, +, *, /.

By default, the calculation is skipped if an attribute is missing. Select “Replace missing attribute by 0” to automatically populate missing attribute values with 0 to ensure that the calculation is done. An attribute is missing if it is not found in the log attributes, or if it cannot be converted to a number.

Notes:

  • The operator - needs to be space split in the formula as it can also be contained in attribute names.
  • If the target attribute already exists, it is overwritten by the result of the formula.
  • Results are rounded up to the 9th decimal. For example, if the result of the formula is 0.1234567891, the actual value stored for the attribute is 0.123456789.
  • If you need to scale a unit of measure, see Scale Filter.

expression [required]

string

Arithmetic operation between one or more log attributes.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of expression by 0, false skip the operation if an attribute is missing.

name

string

Name of the processor.

target [required]

string

Name of the attribute that contains the result of the arithmetic operation.

type [required]

enum

Type of logs arithmetic processor. Allowed enum values: arithmetic-processor

default: arithmetic-processor

Option 11

object

Use the string builder processor to add a new attribute (without spaces or special characters) to a log with the result of the provided template. This enables aggregation of different attributes or raw strings into a single attribute.

The template is defined by both raw text and blocks with the syntax %{attribute_path}.

Notes:

  • The processor only accepts attributes with values or an array of values in the blocks.
  • If an attribute cannot be used (object or array of object), it is replaced by an empty string or the entire operation is skipped depending on your selection.
  • If the target attribute already exists, it is overwritten by the result of the template.
  • Results of the template cannot exceed 256 characters.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of template by an empty string. If false (default), skips the operation for missing attributes.

name

string

Name of the processor.

target [required]

string

The name of the attribute that contains the result of the template.

template [required]

string

A formula with one or more attributes and raw text.

type [required]

enum

Type of logs string builder processor. Allowed enum values: string-builder-processor

default: string-builder-processor

Option 12

object

Nested Pipelines are pipelines within a pipeline. Use Nested Pipelines to split the processing into two steps. For example, first use a high-level filtering such as team and then a second level of filtering based on the integration, service, or any other tag or attribute.

A pipeline can contain Nested Pipelines and Processors whereas a Nested Pipeline can only contain Processors.

filter

object

Filter for logs.

query

string

The filter query.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

processors

[object]

Ordered list of processors in this pipeline.

type [required]

enum

Type of logs pipeline processor. Allowed enum values: pipeline

default: pipeline

Option 13

object

The GeoIP parser takes an IP address attribute and extracts if available the Continent, Country, Subdivision, and City information in the target attribute path.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: network.client.ip

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: network.client.geoip

type [required]

enum

Type of GeoIP parser. Allowed enum values: geo-ip-parser

default: geo-ip-parser

Option 14

object

Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in the processors mapping table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

default_lookup

string

Value to set the target attribute if the source value is not found in the list.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_table [required]

[string]

Mapping table of values for the source attribute and their associated target attribute values, formatted as ["source_key1,target_value1", "source_key2,target_value2"]

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list or the default_lookup if not found in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 15

object

Note: Reference Tables are in public beta. Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in a Reference Table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_enrichment_table [required]

string

Name of the Reference Table for the source attribute and their associated target attribute values.

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 16

object

There are two ways to improve correlation between application traces and logs.

  1. Follow the documentation on how to inject a trace ID in the application logs and by default log integrations take care of all the rest of the setup.

  2. Use the Trace remapper processor to define a log attribute as its associated trace ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.trace_id

type [required]

enum

Type of logs trace remapper. Allowed enum values: trace-id-remapper

default: trace-id-remapper

Option 17

object

There are two ways to define correlation between application spans and logs:

  1. Follow the documentation on how to inject a span ID in the application logs. Log integrations automatically handle all remaining setup steps by default.

  2. Use the span remapper processor to define a log attribute as its associated span ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.span_id

type [required]

enum

Type of logs span remapper. Allowed enum values: span-id-remapper

default: span-id-remapper

Option 18

object

A processor for extracting, aggregating, or transforming values from JSON arrays within your logs. Supported operations are:

  • Select value from matching element
  • Compute array length
  • Append a value to an array

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

operation [required]

 <oneOf>

Configuration of the array processor operation to perform.

Option 1

object

Operation that appends a value to a target array attribute.

preserve_source

boolean

Remove or preserve the remapped source element.

default: true

source [required]

string

Attribute path containing the value to append.

target [required]

string

Attribute path of the array to append to.

type [required]

enum

Operation type. Allowed enum values: append

Option 2

object

Operation that computes the length of a source array and stores the result in the target attribute.

source [required]

string

Attribute path of the array to measure.

target [required]

string

Attribute that receives the computed length.

type [required]

enum

Operation type. Allowed enum values: length

Option 3

object

Operation that finds an object in a source array using a filter, and then extracts a specific value into the target attribute.

filter [required]

string

Filter condition expressed as key:value used to find the matching element.

source [required]

string

Attribute path of the array to search into.

target [required]

string

Attribute that receives the extracted value.

type [required]

enum

Operation type. Allowed enum values: select

value_to_extract [required]

string

Key of the value to extract from the matching element.

type [required]

enum

Type of logs array processor. Allowed enum values: array-processor

default: array-processor

tags

[string]

A list of tags associated with the pipeline.

type

string

Type of pipeline.

{
  "description": "string",
  "filter": {
    "query": "source:python"
  },
  "is_enabled": false,
  "name": "",
  "processors": [
    {
      "grok": {
        "match_rules": "rule_name_1 foo\nrule_name_2 bar\n",
        "support_rules": "rule_name_1 foo\nrule_name_2 bar\n"
      },
      "is_enabled": false,
      "name": "string",
      "samples": [],
      "source": "message",
      "type": "grok-parser"
    }
  ],
  "tags": []
}

応答

OK

Pipelines and processors operate on incoming logs, parsing and transforming them into structured attributes for easier querying.

Note: These endpoints are only available for admin users. Make sure to use an application key created by an admin.

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フィールド

種類

説明

description

string

A description of the pipeline.

filter

object

Filter for logs.

query

string

The filter query.

id

string

ID of the pipeline.

is_enabled

boolean

Whether or not the pipeline is enabled.

is_read_only

boolean

Whether or not the pipeline can be edited.

name [required]

string

Name of the pipeline.

processors

[ <oneOf>]

Ordered list of processors in this pipeline.

Option 1

object

Create custom grok rules to parse the full message or a specific attribute of your raw event. For more information, see the parsing section.

grok [required]

object

Set of rules for the grok parser.

match_rules [required]

string

List of match rules for the grok parser, separated by a new line.

support_rules

string

List of support rules for the grok parser, separated by a new line.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

samples

[string]

List of sample logs to test this grok parser.

source [required]

string

Name of the log attribute to parse.

default: message

type [required]

enum

Type of logs grok parser. Allowed enum values: grok-parser

default: grok-parser

Option 2

object

As Datadog receives logs, it timestamps them using the value(s) from any of these default attributes.

  • timestamp

  • date

  • _timestamp

  • Timestamp

  • eventTime

  • published_date

    If your logs put their dates in an attribute not in this list, use the log date Remapper Processor to define their date attribute as the official log timestamp. The recognized date formats are ISO8601, UNIX (the milliseconds EPOCH format), and RFC3164.

Note: If your logs don’t contain any of the default attributes and you haven’t defined your own date attribute, Datadog timestamps the logs with the date it received them.

If multiple log date remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs date remapper. Allowed enum values: date-remapper

default: date-remapper

Option 3

object

Use this Processor if you want to assign some attributes as the official status.

Each incoming status value is mapped as follows.

  • Integers from 0 to 7 map to the Syslog severity standards
  • Strings beginning with emerg or f (case-insensitive) map to emerg (0)
  • Strings beginning with a (case-insensitive) map to alert (1)
  • Strings beginning with c (case-insensitive) map to critical (2)
  • Strings beginning with err (case-insensitive) map to error (3)
  • Strings beginning with w (case-insensitive) map to warning (4)
  • Strings beginning with n (case-insensitive) map to notice (5)
  • Strings beginning with i (case-insensitive) map to info (6)
  • Strings beginning with d, trace or verbose (case-insensitive) map to debug (7)
  • Strings beginning with o or matching OK or Success (case-insensitive) map to OK
  • All others map to info (6)

Note: If multiple log status remapper processors can be applied to a given log, only the first one (according to the pipelines order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs status remapper. Allowed enum values: status-remapper

default: status-remapper

Option 4

object

Use this processor if you want to assign one or more attributes as the official service.

Note: If multiple service remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

type [required]

enum

Type of logs service remapper. Allowed enum values: service-remapper

default: service-remapper

Option 5

object

The message is a key attribute in Datadog. It is displayed in the message column of the Log Explorer and you can do full string search on it. Use this Processor to define one or more attributes as the official log message.

Note: If multiple log message remapper processors can be applied to a given log, only the first one (according to the pipeline order) is taken into account.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: msg

type [required]

enum

Type of logs message remapper. Allowed enum values: message-remapper

default: message-remapper

Option 6

object

The remapper processor remaps any source attribute(s) or tag to another target attribute or tag. Constraints on the tag/attribute name are explained in the Tag Best Practice documentation. Some additional constraints are applied as : or , are not allowed in the target tag/attribute name.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

override_on_conflict

boolean

Override or not the target element if already set,

preserve_source

boolean

Remove or preserve the remapped source element.

source_type

string

Defines if the sources are from log attribute or tag.

default: attribute

sources [required]

[string]

Array of source attributes.

target [required]

string

Final attribute or tag name to remap the sources to.

target_format

enum

If the target_type of the remapper is attribute, try to cast the value to a new specific type. If the cast is not possible, the original type is kept. string, integer, or double are the possible types. If the target_type is tag, this parameter may not be specified. Allowed enum values: auto,string,integer,double

target_type

string

Defines if the final attribute or tag name is from log attribute or tag.

default: attribute

type [required]

enum

Type of logs attribute remapper. Allowed enum values: attribute-remapper

default: attribute-remapper

Option 7

object

This processor extracts query parameters and other important parameters from a URL.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

normalize_ending_slashes

boolean

Normalize the ending slashes or not.

sources [required]

[string]

Array of source attributes.

default: http.url

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.url_details

type [required]

enum

Type of logs URL parser. Allowed enum values: url-parser

default: url-parser

Option 8

object

The User-Agent parser takes a User-Agent attribute and extracts the OS, browser, device, and other user data. It recognizes major bots like the Google Bot, Yahoo Slurp, and Bing.

is_enabled

boolean

Whether or not the processor is enabled.

is_encoded

boolean

Define if the source attribute is URL encoded or not.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: http.useragent

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: http.useragent_details

type [required]

enum

Type of logs User-Agent parser. Allowed enum values: user-agent-parser

default: user-agent-parser

Option 9

object

Use the Category Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log matching a provided search query. Use categories to create groups for an analytical view. For example, URL groups, machine groups, environments, and response time buckets.

Notes:

  • The syntax of the query is the one of Logs Explorer search bar. The query can be done on any log attribute or tag, whether it is a facet or not. Wildcards can also be used inside your query.
  • Once the log has matched one of the Processor queries, it stops. Make sure they are properly ordered in case a log could match several queries.
  • The names of the categories must be unique.
  • Once defined in the Category Processor, you can map categories to log status using the Log Status Remapper.

categories [required]

[object]

Array of filters to match or not a log and their corresponding name to assign a custom value to the log.

filter

object

Filter for logs.

query

string

The filter query.

name

string

Value to assign to the target attribute.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

target [required]

string

Name of the target attribute which value is defined by the matching category.

type [required]

enum

Type of logs category processor. Allowed enum values: category-processor

default: category-processor

Option 10

object

Use the Arithmetic Processor to add a new attribute (without spaces or special characters in the new attribute name) to a log with the result of the provided formula. This enables you to remap different time attributes with different units into a single attribute, or to compute operations on attributes within the same log.

The formula can use parentheses and the basic arithmetic operators -, +, *, /.

By default, the calculation is skipped if an attribute is missing. Select “Replace missing attribute by 0” to automatically populate missing attribute values with 0 to ensure that the calculation is done. An attribute is missing if it is not found in the log attributes, or if it cannot be converted to a number.

Notes:

  • The operator - needs to be space split in the formula as it can also be contained in attribute names.
  • If the target attribute already exists, it is overwritten by the result of the formula.
  • Results are rounded up to the 9th decimal. For example, if the result of the formula is 0.1234567891, the actual value stored for the attribute is 0.123456789.
  • If you need to scale a unit of measure, see Scale Filter.

expression [required]

string

Arithmetic operation between one or more log attributes.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of expression by 0, false skip the operation if an attribute is missing.

name

string

Name of the processor.

target [required]

string

Name of the attribute that contains the result of the arithmetic operation.

type [required]

enum

Type of logs arithmetic processor. Allowed enum values: arithmetic-processor

default: arithmetic-processor

Option 11

object

Use the string builder processor to add a new attribute (without spaces or special characters) to a log with the result of the provided template. This enables aggregation of different attributes or raw strings into a single attribute.

The template is defined by both raw text and blocks with the syntax %{attribute_path}.

Notes:

  • The processor only accepts attributes with values or an array of values in the blocks.
  • If an attribute cannot be used (object or array of object), it is replaced by an empty string or the entire operation is skipped depending on your selection.
  • If the target attribute already exists, it is overwritten by the result of the template.
  • Results of the template cannot exceed 256 characters.

is_enabled

boolean

Whether or not the processor is enabled.

is_replace_missing

boolean

If true, it replaces all missing attributes of template by an empty string. If false (default), skips the operation for missing attributes.

name

string

Name of the processor.

target [required]

string

The name of the attribute that contains the result of the template.

template [required]

string

A formula with one or more attributes and raw text.

type [required]

enum

Type of logs string builder processor. Allowed enum values: string-builder-processor

default: string-builder-processor

Option 12

object

Nested Pipelines are pipelines within a pipeline. Use Nested Pipelines to split the processing into two steps. For example, first use a high-level filtering such as team and then a second level of filtering based on the integration, service, or any other tag or attribute.

A pipeline can contain Nested Pipelines and Processors whereas a Nested Pipeline can only contain Processors.

filter

object

Filter for logs.

query

string

The filter query.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

processors

[object]

Ordered list of processors in this pipeline.

type [required]

enum

Type of logs pipeline processor. Allowed enum values: pipeline

default: pipeline

Option 13

object

The GeoIP parser takes an IP address attribute and extracts if available the Continent, Country, Subdivision, and City information in the target attribute path.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources [required]

[string]

Array of source attributes.

default: network.client.ip

target [required]

string

Name of the parent attribute that contains all the extracted details from the sources.

default: network.client.geoip

type [required]

enum

Type of GeoIP parser. Allowed enum values: geo-ip-parser

default: geo-ip-parser

Option 14

object

Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in the processors mapping table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

default_lookup

string

Value to set the target attribute if the source value is not found in the list.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_table [required]

[string]

Mapping table of values for the source attribute and their associated target attribute values, formatted as ["source_key1,target_value1", "source_key2,target_value2"]

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list or the default_lookup if not found in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 15

object

Note: Reference Tables are in public beta. Use the Lookup Processor to define a mapping between a log attribute and a human readable value saved in a Reference Table. For example, you can use the Lookup Processor to map an internal service ID into a human readable service name. Alternatively, you could also use it to check if the MAC address that just attempted to connect to the production environment belongs to your list of stolen machines.

is_enabled

boolean

Whether or not the processor is enabled.

lookup_enrichment_table [required]

string

Name of the Reference Table for the source attribute and their associated target attribute values.

name

string

Name of the processor.

source [required]

string

Source attribute used to perform the lookup.

target [required]

string

Name of the attribute that contains the corresponding value in the mapping list.

type [required]

enum

Type of logs lookup processor. Allowed enum values: lookup-processor

default: lookup-processor

Option 16

object

There are two ways to improve correlation between application traces and logs.

  1. Follow the documentation on how to inject a trace ID in the application logs and by default log integrations take care of all the rest of the setup.

  2. Use the Trace remapper processor to define a log attribute as its associated trace ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.trace_id

type [required]

enum

Type of logs trace remapper. Allowed enum values: trace-id-remapper

default: trace-id-remapper

Option 17

object

There are two ways to define correlation between application spans and logs:

  1. Follow the documentation on how to inject a span ID in the application logs. Log integrations automatically handle all remaining setup steps by default.

  2. Use the span remapper processor to define a log attribute as its associated span ID.

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

sources

[string]

Array of source attributes.

default: dd.span_id

type [required]

enum

Type of logs span remapper. Allowed enum values: span-id-remapper

default: span-id-remapper

Option 18

object

A processor for extracting, aggregating, or transforming values from JSON arrays within your logs. Supported operations are:

  • Select value from matching element
  • Compute array length
  • Append a value to an array

is_enabled

boolean

Whether or not the processor is enabled.

name

string

Name of the processor.

operation [required]

 <oneOf>

Configuration of the array processor operation to perform.

Option 1

object

Operation that appends a value to a target array attribute.

preserve_source

boolean

Remove or preserve the remapped source element.

default: true

source [required]

string

Attribute path containing the value to append.

target [required]

string

Attribute path of the array to append to.

type [required]

enum

Operation type. Allowed enum values: append

Option 2

object

Operation that computes the length of a source array and stores the result in the target attribute.

source [required]

string

Attribute path of the array to measure.

target [required]

string

Attribute that receives the computed length.

type [required]

enum

Operation type. Allowed enum values: length

Option 3

object

Operation that finds an object in a source array using a filter, and then extracts a specific value into the target attribute.

filter [required]

string

Filter condition expressed as key:value used to find the matching element.

source [required]

string

Attribute path of the array to search into.

target [required]

string

Attribute that receives the extracted value.

type [required]

enum

Operation type. Allowed enum values: select

value_to_extract [required]

string

Key of the value to extract from the matching element.

type [required]

enum

Type of logs array processor. Allowed enum values: array-processor

default: array-processor

tags

[string]

A list of tags associated with the pipeline.

type

string

Type of pipeline.

{
  "description": "string",
  "filter": {
    "query": "source:python"
  },
  "id": "string",
  "is_enabled": false,
  "is_read_only": false,
  "name": "",
  "processors": [
    {
      "grok": {
        "match_rules": "rule_name_1 foo\nrule_name_2 bar\n",
        "support_rules": "rule_name_1 foo\nrule_name_2 bar\n"
      },
      "is_enabled": false,
      "name": "string",
      "samples": [],
      "source": "message",
      "type": "grok-parser"
    }
  ],
  "tags": [],
  "type": "pipeline"
}

Bad Request

Response returned by the Logs API when errors occur.

Expand All

フィールド

種類

説明

error

object

Error returned by the Logs API

code

string

Code identifying the error

details

[object]

Additional error details

message

string

Error message

{
  "error": {
    "code": "string",
    "details": [],
    "message": "string"
  }
}

Forbidden

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

Too many requests

Error response object.

Expand All

フィールド

種類

説明

errors [required]

[string]

Array of errors returned by the API.

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

コード例

                  # Path parameters
export pipeline_id="CHANGE_ME"
# Curl command
curl -X PUT "https://api.ap1.datadoghq.com"https://api.ap2.datadoghq.com"https://api.datadoghq.eu"https://api.ddog-gov.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/logs/config/pipelines/${pipeline_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
{
  "name": "",
  "processors": [
    {
      "grok": {
        "match_rules": "rule_name_1 foo\nrule_name_2 bar\n"
      }
    }
  ]
}
EOF

                
"""
Update a pipeline returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
from datadog_api_client.v1.model.logs_filter import LogsFilter
from datadog_api_client.v1.model.logs_grok_parser import LogsGrokParser
from datadog_api_client.v1.model.logs_grok_parser_rules import LogsGrokParserRules
from datadog_api_client.v1.model.logs_grok_parser_type import LogsGrokParserType
from datadog_api_client.v1.model.logs_pipeline import LogsPipeline

body = LogsPipeline(
    filter=LogsFilter(
        query="source:python",
    ),
    name="",
    processors=[
        LogsGrokParser(
            grok=LogsGrokParserRules(
                match_rules="rule_name_1 foo\nrule_name_2 bar\n",
                support_rules="rule_name_1 foo\nrule_name_2 bar\n",
            ),
            is_enabled=False,
            samples=[],
            source="message",
            type=LogsGrokParserType.GROK_PARSER,
        ),
    ],
    tags=[],
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = LogsPipelinesApi(api_client)
    response = api_instance.update_logs_pipeline(pipeline_id="pipeline_id", body=body)

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" python3 "example.py"
# Update a pipeline returns "OK" response

require "datadog_api_client"
api_instance = DatadogAPIClient::V1::LogsPipelinesAPI.new

body = DatadogAPIClient::V1::LogsPipeline.new({
  filter: DatadogAPIClient::V1::LogsFilter.new({
    query: "source:python",
  }),
  name: "",
  processors: [
    DatadogAPIClient::V1::LogsGrokParser.new({
      grok: DatadogAPIClient::V1::LogsGrokParserRules.new({
        match_rules: 'rule_name_1 foo\nrule_name_2 bar\n',
        support_rules: 'rule_name_1 foo\nrule_name_2 bar\n',
      }),
      is_enabled: false,
      samples: [],
      source: "message",
      type: DatadogAPIClient::V1::LogsGrokParserType::GROK_PARSER,
    }),
  ],
  tags: [],
})
p api_instance.update_logs_pipeline("pipeline_id", body)

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" rb "example.rb"
// Update a pipeline 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/datadogV1"
)

func main() {
	body := datadogV1.LogsPipeline{
		Filter: &datadogV1.LogsFilter{
			Query: datadog.PtrString("source:python"),
		},
		Name: "",
		Processors: []datadogV1.LogsProcessor{
			datadogV1.LogsProcessor{
				LogsGrokParser: &datadogV1.LogsGrokParser{
					Grok: datadogV1.LogsGrokParserRules{
						MatchRules: `rule_name_1 foo
rule_name_2 bar
`,
						SupportRules: datadog.PtrString(`rule_name_1 foo
rule_name_2 bar
`),
					},
					IsEnabled: datadog.PtrBool(false),
					Samples:   []string{},
					Source:    "message",
					Type:      datadogV1.LOGSGROKPARSERTYPE_GROK_PARSER,
				}},
		},
		Tags: []string{},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewLogsPipelinesApi(apiClient)
	resp, r, err := api.UpdateLogsPipeline(ctx, "pipeline_id", body)

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

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

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" go run "main.go"
// Update a pipeline returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.LogsPipelinesApi;
import com.datadog.api.client.v1.model.LogsFilter;
import com.datadog.api.client.v1.model.LogsGrokParser;
import com.datadog.api.client.v1.model.LogsGrokParserRules;
import com.datadog.api.client.v1.model.LogsGrokParserType;
import com.datadog.api.client.v1.model.LogsPipeline;
import com.datadog.api.client.v1.model.LogsProcessor;
import java.util.Collections;

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

    LogsPipeline body =
        new LogsPipeline()
            .filter(new LogsFilter().query("source:python"))
            .name("")
            .processors(
                Collections.singletonList(
                    new LogsProcessor(
                        new LogsGrokParser()
                            .grok(
                                new LogsGrokParserRules()
                                    .matchRules("""
rule_name_1 foo
rule_name_2 bar

""")
                                    .supportRules("""
rule_name_1 foo
rule_name_2 bar

"""))
                            .isEnabled(false)
                            .source("message")
                            .type(LogsGrokParserType.GROK_PARSER))));

    try {
      LogsPipeline result = apiInstance.updateLogsPipeline("pipeline_id", body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling LogsPipelinesApi#updateLogsPipeline");
      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 and then save the example to Example.java and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" java "Example.java"
// Update a pipeline returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_logs_pipelines::LogsPipelinesAPI;
use datadog_api_client::datadogV1::model::LogsFilter;
use datadog_api_client::datadogV1::model::LogsGrokParser;
use datadog_api_client::datadogV1::model::LogsGrokParserRules;
use datadog_api_client::datadogV1::model::LogsGrokParserType;
use datadog_api_client::datadogV1::model::LogsPipeline;
use datadog_api_client::datadogV1::model::LogsProcessor;

#[tokio::main]
async fn main() {
    let body = LogsPipeline::new("".to_string())
        .filter(LogsFilter::new().query("source:python".to_string()))
        .processors(vec![LogsProcessor::LogsGrokParser(Box::new(
            LogsGrokParser::new(
                LogsGrokParserRules::new(
                    r#"rule_name_1 foo
rule_name_2 bar
"#
                    .to_string(),
                )
                .support_rules(
                    r#"rule_name_1 foo
rule_name_2 bar
"#
                    .to_string(),
                ),
                "message".to_string(),
                LogsGrokParserType::GROK_PARSER,
            )
            .is_enabled(false)
            .samples(vec![]),
        ))])
        .tags(vec![]);
    let configuration = datadog::Configuration::new();
    let api = LogsPipelinesAPI::with_config(configuration);
    let resp = api
        .update_logs_pipeline("pipeline_id".to_string(), body)
        .await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run
/**
 * Update a pipeline returns "OK" response
 */

import { client, v1 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration();
const apiInstance = new v1.LogsPipelinesApi(configuration);

const params: v1.LogsPipelinesApiUpdateLogsPipelineRequest = {
  body: {
    filter: {
      query: "source:python",
    },
    name: "",
    processors: [
      {
        grok: {
          matchRules: "rule_name_1 foo\nrule_name_2 bar\n",
          supportRules: "rule_name_1 foo\nrule_name_2 bar\n",
        },
        isEnabled: false,
        samples: [],
        source: "message",
        type: "grok-parser",
      },
    ],
    tags: [],
  },
  pipelineId: "pipeline_id",
};

apiInstance
  .updateLogsPipeline(params)
  .then((data: v1.LogsPipeline) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

    
DD_SITE="datadoghq.comus3.datadoghq.comus5.datadoghq.comdatadoghq.euap1.datadoghq.comap2.datadoghq.comddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" tsc "example.ts"