Create a monitor

v1 (latest)

POST https://api.ap1.datadoghq.com/api/v1/monitorhttps://api.ap2.datadoghq.com/api/v1/monitorhttps://api.datadoghq.eu/api/v1/monitorhttps://api.ddog-gov.com/api/v1/monitorhttps://api.us2.ddog-gov.com/api/v1/monitorhttps://api.uk1.datadoghq.com/api/v1/monitorhttps://api.datadoghq.com/api/v1/monitorhttps://api.us3.datadoghq.com/api/v1/monitorhttps://api.us5.datadoghq.com/api/v1/monitor

概要

指定されたオプションを使用してモニターを作成します。

モニタータイプ

次から選択したモニターのタイプ:

異常検知: query alert
APM: query alert または trace-analytics alert
複合条件: composite
カスタム: service check
予測: query alert
ホスト: service check
インテグレーション: query alert または service check
ライブプロセス: process alert
ログ: log alert
メトリクス: query alert
ネットワーク: service check
外れ値: query alert
プロセス: service check
rum: rum alert
SLO: slo alert
watchdog: event-v2 alert
event-v2: event-v2 alert
監査: audit alert
エラー追跡: error-tracking alert
データベースモニタリング: database-monitoring alert
ネットワークパフォーマンス: network-performance alert
クラウドコスト: cost alert

注:

Synthetic モニターは Synthetics API を使って作成します。詳しくは、Synthetics API ドキュメントを参照してください。
ログモニターには、スコープされていないアプリキーが必要です。

クエリタイプ

メトリクスアラートクエリ

例: time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #

time_aggr: avg、sum、max、min、change、または pct_change
time_window: last_#m (モニタータイプに応じて、1〜10080 の #) または last_#h(モニタータイプに応じて、1〜168 の #) または last_1d、または last_1w
space_aggr: avg、sum、min、または max
tags: 1 つまたは複数のタグ (カンマ区切り)、または *
key: key:value タグ構文における ‘key’、グループ内の各タグに対して個別のアラートを定義します (マルチアラート)
operator: <、<=、>、>=、==、または !=
#: しきい値の設定に用いる整数または 10 進数

_change_ または _pct_change_ の時間アグリゲータを使用している場合は、代わりに change_aggr(time_aggr(time_window), timeshift):space_aggr:metric{tags} [by {key}] operator # を使用します。

change_aggr change、pct_change
time_aggr avg、sum、max、min 詳細
time_window last_#m (モニタータイプに応じて、1〜2880)、last_#h (モニタータイプに応じて、1〜48)、または last_#d (1 or 2)
timeshift #m_ago (5、10、15、または 30)、#h_ago (1、2、または 4)、または 1d_ago

これを利用して、以下のクエリで外れ値モニターを作成します。 avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0

サービスチェッククエリ

例: "check".over(tags).last(count).by(group).count_by_status()

check チェックの名前。例: datadog.agent.up
tags 1 つ以上の引用符付きタグ (カンマ区切り)、または “*"。例: .over("env:prod", "role:db")、over を空白にすることはできません。
count 最大しきい値 (options で定義) 以上でなければなりません。最大値は 100 です。例えば、1 つの critical、3 つの ok、2 つの warn のステータスに対して通知するように指定した場合、count は最低でも 3 でなければなりません。
group チェックモニターに指定する必要があります。一部のサービスチェックでは、チェックごとのグループ化がすでに明示的に知られています。例えば、Postgres インテグレーションモニターは db、host、port でタグ付けされ、ネットワークモニターは host、instance、url でタグ付けされます。詳しくはサービスチェックのドキュメントを参照してください。

イベントアラートクエリ

注イベントアラートクエリは、イベント V2 アラートクエリに置き換わりました。詳細については、イベント移行ガイドを参照してください。

イベント V2 アラートクエリ

例: events(query).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ - ログ検索構文に従います。
rollup_method 統計情報のロールアップの方法 - count、avg、cardinality をサポートします。
measure avg とカーディナリティ rollup_method の場合 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48).
operator <、<=、>、>=、==、または !=
# しきい値の設定に使用する整数または 10 進数。

プロセスアラートクエリ

例: processes(search).over(tags).rollup('count').last(timeframe) operator #

search プロセスをクエリするためのフリーテキスト検索文字列。一致したプロセスは、Live Processes ページの結果と一致します。
tags 1 つ以上のタグ (カンマ区切り)
timeframe カウントをロールアップする時間枠。例: 10m、4h。サポートされているタイムフレーム: S、M、H、D
operator <、<=、>、>=、==、または !=
# しきい値の設定に用いる整数または 10 進数。

ログアラートクエリ

例: logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ - ログ検索構文に従います。
index_name 複数インデックスの組織の場合、リクエストが実行されるログインデックス。
rollup_method 統計情報のロールアップの方法 - count、avg、cardinality をサポートします。
measure avg とカーディナリティ rollup_method の場合 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48)。
operator <、<=、>、>=、==、または !=
# しきい値の設定に用いる整数または 10 進数。

複合条件クエリ

例: 12345 && 67890、ここで 12345 と 67890 は非複合条件モニターの ID

name [必須、デフォルト = 動的、クエリに基づく]: アラートの名前。
message [必須、デフォルト = 動的、クエリに基づく]: このモニターに対する通知に含めるメッセージ。イベントと同じ「@ユーザー名」表記で、特定のユーザーに対してメール通知を行うことができます。
tags [オプション、デフォルト = 空リスト]: モニターに関連付けるタグの一覧。 API 経由ですべてのモニターの詳細を取得する場合、monitor_tags 引数を使用して、これらのタグで結果をフィルタリングします。これは、API 経由でのみ利用可能で、Datadog UI では表示も編集もできません。

SLO アラートクエリ

例: error_budget("slo_id").over("time_window") operator #

slo_id: 警告を構成する SLO の英数字の SLO ID。
time_window: 警告したい SLO ターゲットのタイムウィンドウ。有効なオプション: 7d、30d、90d`。
operator: >= または >

監査アラートクエリ

例: audits(query).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ - ログ検索構文に従います。
rollup_method 統計情報のロールアップの方法 - count、avg、cardinality をサポートします。
measure avg とカーディナリティ rollup_method の場合 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48)。
operator <、<=、>、>=、==、または !=
# しきい値の設定に用いる整数または 10 進数。

CI パイプラインのアラートクエリ

例: ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ - ログ検索構文に従います。
rollup_method 統計情報のロールアップの方法 - count、avg、cardinality をサポートします。
measure avg とカーディナリティ rollup_method の場合 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48)。
operator <、<=、>、>=、==、または !=
# しきい値の設定に用いる整数または 10 進数。

CI テストアラートクエリ

例: ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ - ログ検索構文に従います。
rollup_method 統計情報のロールアップメソッド - count、avg、cardinality をサポートします。
measure avg とカーディナリティ rollup_method の場合 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48)
operator <、<=、>、>=、==、または !=
# しきい値の設定に使用する整数または 10 進数。

エラー追跡アラートクエリ

“New issue” の例: error-tracking(query).source(issue_source).new().rollup(rollup_method[, measure]).by(group_by).last(time_window) operator # “High impact issue” の例: error-tracking(query).source(issue_source).impact().rollup(rollup_method[, measure]).by(group_by).last(time_window) operator #

query は検索クエリで、ログ検索構文に従います。
issue_source はイシューソースです、all、browser、mobile、backend をサポートし、省略された場合は all がデフォルトになります。
rollup_method は統計ロールアップメソッドです。count、avg、cardinality をサポートし、省略された場合は count がデフォルトになります。
measure は avg および cardinality の rollup_method に対して使用するメジャーまたは利用したいファセット名を指定します。
group by はグループ化する属性のカンマ区切りリストで、少なくとも issue.id を含める必要があります。
time_window は #m (1 から 2880 の間) 、 #h (1 から 48 の間) です。
operator は <、<=、>、>=、==、!= のいずれかを指定します。
# はしきい値を設定するために使用する整数または小数値です。

データベースモニタリングアラートクエリ

例: database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ - ログ検索構文に従います。
rollup_method 統計情報のロールアップの方法 - count、avg、cardinality をサポートします。
measure avg とカーディナリティ rollup_method の場合 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48)。
operator <、<=、>、>=、==、または !=
# しきい値の設定に用いる整数または 10 進数。

ネットワークパフォーマンスアラートクエリ

例: network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #

query 検索クエリ -ログ検索構文に従います。
rollup_method 統計ロールアップ方法 - count、avg、cardinality をサポートしています。
measure avg およびカーディナリティ rollup_method 用 - 使用するメジャーまたはファセット名を指定します。
time_window #m (1〜2880)、#h (1〜48)
operator <、<=、>、>=、==、または !=
# しきい値を設定するための整数または 10 進数。

This endpoint requires the monitors_write permission.

OAuth apps require the monitors_write authorization scope to access this endpoint.

リクエスト

Body Data (required)

モニターのリクエスト本文を作成します。

Expand All

フィールド

種類

説明

assets

[object]

The list of monitor assets tied to a monitor, which represents key links for users to take action on monitor alerts (for example, runbooks).

created

date-time

Timestamp of the monitor creation.

creator

object

Object describing the creator of the shared element.

deleted

date-time

Whether or not the monitor is deleted. (Always null)

draft_status

enum

Indicates whether the monitor is in a draft or published state.

draft: The monitor appears as Draft and does not send notifications. published: The monitor is active and evaluates conditions and notify as configured.

This field is in preview. The draft value is only available to customers with the feature enabled. Allowed enum values: draft,published

default: published

int64

ID of this monitor.

matching_downtimes

[object]

A list of active v1 downtimes that match this monitor.

message

string

A message to include with notifications for this monitor.

modified

date-time

Last timestamp when the monitor was edited.

multi

boolean

Whether or not the monitor is broken down on different groups.

name

string

The monitor name.

options

object

List of options associated with your monitor.

variables

[ <oneOf>]

List of requests that can be used in the monitor query. This feature is currently in beta.

Option 5

object

A formula and functions aggregate augmented query. Used to enrich base query results with data from a reference table.

augment_query [required]

<oneOf>

Augment query for aggregate augmented queries. Can be an events query or a reference table query.

Option 6

object

A formula and functions aggregate filtered query. Used to filter base query results using data from another source.

filter_query [required]

<oneOf>

Filter query for aggregate filtered queries. Can be an events query or a reference table query.

overall_state

enum

The different states your monitor can be in. Allowed enum values: Alert,Ignored,No Data,OK,Skipped,Unknown,Warn

priority

int64

Integer from 1 (high) to 5 (low) indicating alert severity.

query [required]

string

The monitor query.

restricted_roles

[string]

A list of unique role identifiers to define which roles are allowed to edit the monitor. The unique identifiers for all roles can be pulled from the Roles API and are located in the data.id field. Editing a monitor includes any updates to the monitor configuration, monitor deletion, and muting of the monitor for any amount of time. You can use the Restriction Policies API to manage write authorization for individual monitors by teams and users, in addition to roles.

state

object

Wrapper object with the different monitor states.

応答

Object describing a monitor.

Expand All

フィールド

種類

説明

assets

[object]

The list of monitor assets tied to a monitor, which represents key links for users to take action on monitor alerts (for example, runbooks).

created

date-time

Timestamp of the monitor creation.

creator

object

Object describing the creator of the shared element.

deleted

date-time

Whether or not the monitor is deleted. (Always null)

draft_status

enum

Indicates whether the monitor is in a draft or published state.

draft: The monitor appears as Draft and does not send notifications. published: The monitor is active and evaluates conditions and notify as configured.

This field is in preview. The draft value is only available to customers with the feature enabled. Allowed enum values: draft,published

default: published

int64

ID of this monitor.

matching_downtimes

[object]

A list of active v1 downtimes that match this monitor.

message

string

A message to include with notifications for this monitor.

modified

date-time

Last timestamp when the monitor was edited.

multi

boolean

Whether or not the monitor is broken down on different groups.

name

string

The monitor name.

options

object

List of options associated with your monitor.

variables

[ <oneOf>]

List of requests that can be used in the monitor query. This feature is currently in beta.

Option 5

object

A formula and functions aggregate augmented query. Used to enrich base query results with data from a reference table.

augment_query [required]

<oneOf>

Augment query for aggregate augmented queries. Can be an events query or a reference table query.

Option 6

object

A formula and functions aggregate filtered query. Used to filter base query results using data from another source.

filter_query [required]

<oneOf>

Filter query for aggregate filtered queries. Can be an events query or a reference table query.

overall_state

enum

The different states your monitor can be in. Allowed enum values: Alert,Ignored,No Data,OK,Skipped,Unknown,Warn

priority

int64

Integer from 1 (high) to 5 (low) indicating alert severity.

query [required]

string

The monitor query.

restricted_roles

[string]

state

object

Wrapper object with the different monitor states.

コード例

                          ## default
# 

# 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.us2.ddog-gov.com"https://api.uk1.datadoghq.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/monitor" \
-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
{
  "message": "You may need to add web hosts if this is consistently high.",
  "name": "Bytes received on host0",
  "options": {
    "no_data_timeframe": 20,
    "notify_no_data": true
  },
  "priority": 3,
  "query": "avg(last_5m):sum:system.net.bytes_rcvd{host:host0} \u003e 100",
  "tags": [
    "app:webserver",
    "frontend"
  ],
  "type": "query alert"
}
EOF

                        
                          ## default
# 

# 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.us2.ddog-gov.com"https://api.uk1.datadoghq.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/monitor" \
-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
{
  "message": "You may need to add web hosts if this is consistently high.",
  "name": "Bytes received on host0",
  "options": {
    "no_data_timeframe": 20,
    "notify_no_data": true
  },
  "priority": 3,
  "query": "avg(last_5m):sum:system.net.bytes_rcvd{host:host0} \u003e 100",
  "tags": [
    "app:webserver",
    "frontend"
  ],
  "type": "query alert"
}
EOF

                        
                          ## default
# 

# 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.us2.ddog-gov.com"https://api.uk1.datadoghq.com"https://api.datadoghq.com"https://api.us3.datadoghq.com"https://api.us5.datadoghq.com/api/v1/monitor" \
-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
{
  "message": "You may need to add web hosts if this is consistently high.",
  "name": "Bytes received on host0",
  "options": {
    "no_data_timeframe": 20,
    "notify_no_data": true
  },
  "priority": 3,
  "query": "avg(last_5m):sum:system.net.bytes_rcvd{host:host0} \u003e 100",
  "tags": [
    "app:webserver",
    "frontend"
  ],
  "type": "query alert"
}
EOF

                        

// Create a Cost Monitor 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.Monitor{
		Name:    datadog.PtrString("Example Monitor"),
		Type:    datadogV1.MONITORTYPE_COST_ALERT,
		Query:   `formula("exclude_null(query1)").last("7d").anomaly(direction="above", threshold=10) >= 5`,
		Message: datadog.PtrString("some message Notify: @hipchat-channel"),
		Tags: []string{
			"test:examplemonitor",
			"env:ci",
		},
		Priority: *datadog.NewNullableInt64(datadog.PtrInt64(3)),
		Options: &datadogV1.MonitorOptions{
			Thresholds: &datadogV1.MonitorThresholds{
				Critical: datadog.PtrFloat64(5),
				Warning:  *datadog.NewNullableFloat64(datadog.PtrFloat64(3)),
			},
			Variables: []datadogV1.MonitorFormulaAndFunctionQueryDefinition{
				datadogV1.MonitorFormulaAndFunctionQueryDefinition{
					MonitorFormulaAndFunctionCostQueryDefinition: &datadogV1.MonitorFormulaAndFunctionCostQueryDefinition{
						DataSource: datadogV1.MONITORFORMULAANDFUNCTIONCOSTDATASOURCE_CLOUD_COST,
						Query:      "sum:aws.cost.net.amortized.shared.resources.allocated{aws_product IN (amplify ,athena, backup, bedrock ) } by {aws_product}.rollup(sum, 86400)",
						Name:       "query1",
						Aggregator: datadogV1.MONITORFORMULAANDFUNCTIONCOSTAGGREGATOR_SUM.Ptr(),
					}},
			},
			IncludeTags: datadog.PtrBool(true),
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewMonitorsApi(apiClient)
	resp, r, err := api.CreateMonitor(ctx, body)

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

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

// Create a Data Jobs monitor 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.Monitor{
		Name:    datadog.PtrString("Example-Monitor"),
		Type:    datadogV1.MONITORTYPE_DATA_JOBS_ALERT,
		Query:   `formula("failed_runs(run_query)").by(job_name,workspace_name).last(10d) > 0`,
		Message: datadog.PtrString("Data jobs alert triggered"),
		Tags: []string{
			"test:examplemonitor",
			"env:ci",
		},
		Options: &datadogV1.MonitorOptions{
			Thresholds: &datadogV1.MonitorThresholds{
				Critical: datadog.PtrFloat64(0),
			},
			Variables: []datadogV1.MonitorFormulaAndFunctionQueryDefinition{
				datadogV1.MonitorFormulaAndFunctionQueryDefinition{
					MonitorFormulaAndFunctionDataJobsQueryDefinition: &datadogV1.MonitorFormulaAndFunctionDataJobsQueryDefinition{
						Name:         "run_query",
						JobsQuery:    "job_name:*",
						JobType:      "databricks.job",
						QueryDialect: "metric",
					}},
			},
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewMonitorsApi(apiClient)
	resp, r, err := api.CreateMonitor(ctx, body)

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

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

// Create a Data Quality monitor 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.Monitor{
		Name:    datadog.PtrString("Example-Monitor"),
		Type:    datadogV1.MONITORTYPE_DATA_QUALITY_ALERT,
		Query:   `formula("query1").last("5m") > 100`,
		Message: datadog.PtrString("Data quality alert triggered"),
		Tags: []string{
			"test:examplemonitor",
			"env:ci",
		},
		Priority: *datadog.NewNullableInt64(datadog.PtrInt64(3)),
		Options: &datadogV1.MonitorOptions{
			Thresholds: &datadogV1.MonitorThresholds{
				Critical: datadog.PtrFloat64(100),
			},
			Variables: []datadogV1.MonitorFormulaAndFunctionQueryDefinition{
				datadogV1.MonitorFormulaAndFunctionQueryDefinition{
					MonitorFormulaAndFunctionDataQualityQueryDefinition: &datadogV1.MonitorFormulaAndFunctionDataQualityQueryDefinition{
						Name:       "query1",
						DataSource: datadogV1.MONITORFORMULAANDFUNCTIONDATAQUALITYDATASOURCE_DATA_QUALITY_METRICS,
						Measure:    "row_count",
						Filter:     `search for column where ` + "`" + `database:production AND table:users` + "`",
						GroupBy: []string{
							"entity_id",
						},
					}},
			},
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV1.NewMonitorsApi(apiClient)
	resp, r, err := api.CreateMonitor(ctx, body)

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

	responseContent, _ := json.MarshalIndent(resp, "", "  ")
	fmt.Fprintf(os.Stdout, "Response from `MonitorsApi.CreateMonitor`:\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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" go run "main.go"

// Create a Cost Monitor returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.MonitorsApi;
import com.datadog.api.client.v1.model.Monitor;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionCostAggregator;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionCostDataSource;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionCostQueryDefinition;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionQueryDefinition;
import com.datadog.api.client.v1.model.MonitorOptions;
import com.datadog.api.client.v1.model.MonitorThresholds;
import com.datadog.api.client.v1.model.MonitorType;
import java.util.Arrays;
import java.util.Collections;

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

    Monitor body =
        new Monitor()
            .name("Example Monitor")
            .type(MonitorType.COST_ALERT)
            .query(
                """
formula("exclude_null(query1)").last("7d").anomaly(direction="above", threshold=10) >= 5
""")
            .message("some message Notify: @hipchat-channel")
            .tags(Arrays.asList("test:examplemonitor", "env:ci"))
            .priority(3L)
            .options(
                new MonitorOptions()
                    .thresholds(new MonitorThresholds().critical(5.0).warning(3.0))
                    .variables(
                        Collections.singletonList(
                            new MonitorFormulaAndFunctionQueryDefinition(
                                new MonitorFormulaAndFunctionCostQueryDefinition()
                                    .dataSource(MonitorFormulaAndFunctionCostDataSource.CLOUD_COST)
                                    .query(
                                        "sum:aws.cost.net.amortized.shared.resources.allocated{aws_product"
                                            + " IN (amplify ,athena, backup, bedrock ) } by"
                                            + " {aws_product}.rollup(sum, 86400)")
                                    .name("query1")
                                    .aggregator(MonitorFormulaAndFunctionCostAggregator.SUM))))
                    .includeTags(true));

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

// Create a Data Jobs monitor returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.MonitorsApi;
import com.datadog.api.client.v1.model.Monitor;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionDataJobsQueryDefinition;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionQueryDefinition;
import com.datadog.api.client.v1.model.MonitorOptions;
import com.datadog.api.client.v1.model.MonitorThresholds;
import com.datadog.api.client.v1.model.MonitorType;
import java.util.Arrays;
import java.util.Collections;

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

    Monitor body =
        new Monitor()
            .name("Example-Monitor")
            .type(MonitorType.DATA_JOBS_ALERT)
            .query(
                """
formula("failed_runs(run_query)").by(job_name,workspace_name).last(10d) > 0
""")
            .message("Data jobs alert triggered")
            .tags(Arrays.asList("test:examplemonitor", "env:ci"))
            .options(
                new MonitorOptions()
                    .thresholds(new MonitorThresholds())
                    .variables(
                        Collections.singletonList(
                            new MonitorFormulaAndFunctionQueryDefinition(
                                new MonitorFormulaAndFunctionDataJobsQueryDefinition()
                                    .name("run_query")
                                    .jobsQuery("job_name:*")
                                    .jobType("databricks.job")
                                    .queryDialect("metric")))));

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

// Create a Data Quality monitor returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v1.api.MonitorsApi;
import com.datadog.api.client.v1.model.Monitor;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionDataQualityDataSource;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionDataQualityQueryDefinition;
import com.datadog.api.client.v1.model.MonitorFormulaAndFunctionQueryDefinition;
import com.datadog.api.client.v1.model.MonitorOptions;
import com.datadog.api.client.v1.model.MonitorThresholds;
import com.datadog.api.client.v1.model.MonitorType;
import java.util.Arrays;
import java.util.Collections;

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

    Monitor body =
        new Monitor()
            .name("Example-Monitor")
            .type(MonitorType.DATA_QUALITY_ALERT)
            .query("""
formula("query1").last("5m") > 100
""")
            .message("Data quality alert triggered")
            .tags(Arrays.asList("test:examplemonitor", "env:ci"))
            .priority(3L)
            .options(
                new MonitorOptions()
                    .thresholds(new MonitorThresholds().critical(100.0))
                    .variables(
                        Collections.singletonList(
                            new MonitorFormulaAndFunctionQueryDefinition(
                                new MonitorFormulaAndFunctionDataQualityQueryDefinition()
                                    .name("query1")
                                    .dataSource(
                                        MonitorFormulaAndFunctionDataQualityDataSource
                                            .DATA_QUALITY_METRICS)
                                    .measure("row_count")
                                    .filter(
                                        "search for column where `database:production AND"
                                            + " table:users`")
                                    .groupBy(Collections.singletonList("entity_id"))))));

    try {
      Monitor result = apiInstance.createMonitor(body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling MonitorsApi#createMonitor");
      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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" java "Example.java"

from datadog import initialize, api

options = {
    'api_key': '<DATADOG_API_KEY>',
    'app_key': '<DATADOG_APPLICATION_KEY>'
}

initialize(**options)

# Create a new monitor
monitor_options = {
    "notify_no_data": True,
    "no_data_timeframe": 20
}
tags = ["app:webserver", "frontend"]
api.Monitor.create(
    type="query alert",
    query="avg(last_5m):sum:system.net.bytes_rcvd{host:host0} > 100",
    name="Bytes received on host0",
    message="We may need to add web hosts if this is consistently high.",
    tags=tags,
    options=monitor_options
)

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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" python "example.py"

"""
Create a Cost Monitor returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.monitors_api import MonitorsApi
from datadog_api_client.v1.model.monitor import Monitor
from datadog_api_client.v1.model.monitor_formula_and_function_cost_aggregator import (
    MonitorFormulaAndFunctionCostAggregator,
)
from datadog_api_client.v1.model.monitor_formula_and_function_cost_data_source import (
    MonitorFormulaAndFunctionCostDataSource,
)
from datadog_api_client.v1.model.monitor_formula_and_function_cost_query_definition import (
    MonitorFormulaAndFunctionCostQueryDefinition,
)
from datadog_api_client.v1.model.monitor_options import MonitorOptions
from datadog_api_client.v1.model.monitor_thresholds import MonitorThresholds
from datadog_api_client.v1.model.monitor_type import MonitorType

body = Monitor(
    name="Example Monitor",
    type=MonitorType.COST_ALERT,
    query='formula("exclude_null(query1)").last("7d").anomaly(direction="above", threshold=10) >= 5',
    message="some message Notify: @hipchat-channel",
    tags=[
        "test:examplemonitor",
        "env:ci",
    ],
    priority=3,
    options=MonitorOptions(
        thresholds=MonitorThresholds(
            critical=5.0,
            warning=3.0,
        ),
        variables=[
            MonitorFormulaAndFunctionCostQueryDefinition(
                data_source=MonitorFormulaAndFunctionCostDataSource.CLOUD_COST,
                query="sum:aws.cost.net.amortized.shared.resources.allocated{aws_product IN (amplify ,athena, backup, bedrock ) } by {aws_product}.rollup(sum, 86400)",
                name="query1",
                aggregator=MonitorFormulaAndFunctionCostAggregator.SUM,
            ),
        ],
        include_tags=True,
    ),
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = MonitorsApi(api_client)
    response = api_instance.create_monitor(body=body)

    print(response)

"""
Create a Data Jobs monitor returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.monitors_api import MonitorsApi
from datadog_api_client.v1.model.monitor import Monitor
from datadog_api_client.v1.model.monitor_formula_and_function_data_jobs_query_definition import (
    MonitorFormulaAndFunctionDataJobsQueryDefinition,
)
from datadog_api_client.v1.model.monitor_options import MonitorOptions
from datadog_api_client.v1.model.monitor_thresholds import MonitorThresholds
from datadog_api_client.v1.model.monitor_type import MonitorType

body = Monitor(
    name="Example-Monitor",
    type=MonitorType.DATA_JOBS_ALERT,
    query='formula("failed_runs(run_query)").by(job_name,workspace_name).last(10d) > 0',
    message="Data jobs alert triggered",
    tags=[
        "test:examplemonitor",
        "env:ci",
    ],
    options=MonitorOptions(
        thresholds=MonitorThresholds(
            critical=0.0,
        ),
        variables=[
            MonitorFormulaAndFunctionDataJobsQueryDefinition(
                name="run_query",
                jobs_query="job_name:*",
                job_type="databricks.job",
                query_dialect="metric",
            ),
        ],
    ),
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = MonitorsApi(api_client)
    response = api_instance.create_monitor(body=body)

    print(response)

"""
Create a Data Quality monitor returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.monitors_api import MonitorsApi
from datadog_api_client.v1.model.monitor import Monitor
from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_data_source import (
    MonitorFormulaAndFunctionDataQualityDataSource,
)
from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_query_definition import (
    MonitorFormulaAndFunctionDataQualityQueryDefinition,
)
from datadog_api_client.v1.model.monitor_options import MonitorOptions
from datadog_api_client.v1.model.monitor_thresholds import MonitorThresholds
from datadog_api_client.v1.model.monitor_type import MonitorType

body = Monitor(
    name="Example-Monitor",
    type=MonitorType.DATA_QUALITY_ALERT,
    query='formula("query1").last("5m") > 100',
    message="Data quality alert triggered",
    tags=[
        "test:examplemonitor",
        "env:ci",
    ],
    priority=3,
    options=MonitorOptions(
        thresholds=MonitorThresholds(
            critical=100.0,
        ),
        variables=[
            MonitorFormulaAndFunctionDataQualityQueryDefinition(
                name="query1",
                data_source=MonitorFormulaAndFunctionDataQualityDataSource.DATA_QUALITY_METRICS,
                measure="row_count",
                filter="search for column where `database:production AND table:users`",
                group_by=[
                    "entity_id",
                ],
            ),
        ],
    ),
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = MonitorsApi(api_client)
    response = api_instance.create_monitor(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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" python3 "example.py"

require 'dogapi'

api_key = '<DATADOG_API_KEY>'
app_key = '<DATADOG_APPLICATION_KEY>'

dog = Dogapi::Client.new(api_key, app_key)

# Create a new monitor
options = {
  'notify_no_data' => true,
  'no_data_timeframe' => 20
}
tags = ['app:webserver', 'frontend']
dog.monitor(
  'query alert',
  'avg(last_5m):sum:system.net.bytes_rcvd{host:host0} > 100',
  name: 'Bytes received on host0',
  message: 'We may need to add web hosts if this is consistently high.',
  tags: tags,
  options: options
)

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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" rb "example.rb"

# Create a Cost Monitor returns "OK" response

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

body = DatadogAPIClient::V1::Monitor.new({
  name: "Example Monitor",
  type: DatadogAPIClient::V1::MonitorType::COST_ALERT,
  query: 'formula("exclude_null(query1)").last("7d").anomaly(direction="above", threshold=10) >= 5',
  message: "some message Notify: @hipchat-channel",
  tags: [
    "test:examplemonitor",
    "env:ci",
  ],
  priority: 3,
  options: DatadogAPIClient::V1::MonitorOptions.new({
    thresholds: DatadogAPIClient::V1::MonitorThresholds.new({
      critical: 5,
      warning: 3,
    }),
    variables: [
      DatadogAPIClient::V1::MonitorFormulaAndFunctionCostQueryDefinition.new({
        data_source: DatadogAPIClient::V1::MonitorFormulaAndFunctionCostDataSource::CLOUD_COST,
        query: "sum:aws.cost.net.amortized.shared.resources.allocated{aws_product IN (amplify ,athena, backup, bedrock ) } by {aws_product}.rollup(sum, 86400)",
        name: "query1",
        aggregator: DatadogAPIClient::V1::MonitorFormulaAndFunctionCostAggregator::SUM,
      }),
    ],
    include_tags: true,
  }),
})
p api_instance.create_monitor(body)

# Create a Data Jobs monitor returns "OK" response

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

body = DatadogAPIClient::V1::Monitor.new({
  name: "Example-Monitor",
  type: DatadogAPIClient::V1::MonitorType::DATA_JOBS_ALERT,
  query: 'formula("failed_runs(run_query)").by(job_name,workspace_name).last(10d) > 0',
  message: "Data jobs alert triggered",
  tags: [
    "test:examplemonitor",
    "env:ci",
  ],
  options: DatadogAPIClient::V1::MonitorOptions.new({
    thresholds: DatadogAPIClient::V1::MonitorThresholds.new({
      critical: 0,
    }),
    variables: [
      DatadogAPIClient::V1::MonitorFormulaAndFunctionDataJobsQueryDefinition.new({
        name: "run_query",
        jobs_query: "job_name:*",
        job_type: "databricks.job",
        query_dialect: "metric",
      }),
    ],
  }),
})
p api_instance.create_monitor(body)

# Create a Data Quality monitor returns "OK" response

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

body = DatadogAPIClient::V1::Monitor.new({
  name: "Example-Monitor",
  type: DatadogAPIClient::V1::MonitorType::DATA_QUALITY_ALERT,
  query: 'formula("query1").last("5m") > 100',
  message: "Data quality alert triggered",
  tags: [
    "test:examplemonitor",
    "env:ci",
  ],
  priority: 3,
  options: DatadogAPIClient::V1::MonitorOptions.new({
    thresholds: DatadogAPIClient::V1::MonitorThresholds.new({
      critical: 100,
    }),
    variables: [
      DatadogAPIClient::V1::MonitorFormulaAndFunctionDataQualityQueryDefinition.new({
        name: "query1",
        data_source: DatadogAPIClient::V1::MonitorFormulaAndFunctionDataQualityDataSource::DATA_QUALITY_METRICS,
        measure: "row_count",
        filter: "search for column where `database:production AND table:users`",
        group_by: [
          "entity_id",
        ],
      }),
    ],
  }),
})
p api_instance.create_monitor(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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" rb "example.rb"

// Create a Cost Monitor returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_monitors::MonitorsAPI;
use datadog_api_client::datadogV1::model::Monitor;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionCostAggregator;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionCostDataSource;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionCostQueryDefinition;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionQueryDefinition;
use datadog_api_client::datadogV1::model::MonitorOptions;
use datadog_api_client::datadogV1::model::MonitorThresholds;
use datadog_api_client::datadogV1::model::MonitorType;

#[tokio::main]
async fn main() {
    let body =
        Monitor::new(
            r#"formula("exclude_null(query1)").last("7d").anomaly(direction="above", threshold=10) >= 5"#.to_string(),
            MonitorType::COST_ALERT,
        )
            .message("some message Notify: @hipchat-channel".to_string())
            .name("Example Monitor".to_string())
            .options(
                MonitorOptions::new()
                    .include_tags(true)
                    .thresholds(MonitorThresholds::new().critical(5.0 as f64).warning(Some(3.0 as f64)))
                    .variables(
                        vec![
                            MonitorFormulaAndFunctionQueryDefinition::MonitorFormulaAndFunctionCostQueryDefinition(
                                Box::new(
                                    MonitorFormulaAndFunctionCostQueryDefinition::new(
                                        MonitorFormulaAndFunctionCostDataSource::CLOUD_COST,
                                        "query1".to_string(),
                                        "sum:aws.cost.net.amortized.shared.resources.allocated{aws_product IN (amplify ,athena, backup, bedrock ) } by {aws_product}.rollup(sum, 86400)".to_string(),
                                    ).aggregator(MonitorFormulaAndFunctionCostAggregator::SUM),
                                ),
                            )
                        ],
                    ),
            )
            .priority(Some(3))
            .tags(vec!["test:examplemonitor".to_string(), "env:ci".to_string()]);
    let configuration = datadog::Configuration::new();
    let api = MonitorsAPI::with_config(configuration);
    let resp = api.create_monitor(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

// Create a Data Jobs monitor returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_monitors::MonitorsAPI;
use datadog_api_client::datadogV1::model::Monitor;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionDataJobsQueryDefinition;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionQueryDefinition;
use datadog_api_client::datadogV1::model::MonitorOptions;
use datadog_api_client::datadogV1::model::MonitorThresholds;
use datadog_api_client::datadogV1::model::MonitorType;

#[tokio::main]
async fn main() {
    let body =
        Monitor::new(
            r#"formula("failed_runs(run_query)").by(job_name,workspace_name).last(10d) > 0"#.to_string(),
            MonitorType::DATA_JOBS_ALERT,
        )
            .message("Data jobs alert triggered".to_string())
            .name("Example-Monitor".to_string())
            .options(
                MonitorOptions::new()
                    .thresholds(MonitorThresholds::new().critical(0.0 as f64))
                    .variables(
                        vec![
                            MonitorFormulaAndFunctionQueryDefinition::MonitorFormulaAndFunctionDataJobsQueryDefinition(
                                Box::new(
                                    MonitorFormulaAndFunctionDataJobsQueryDefinition::new(
                                        "databricks.job".to_string(),
                                        "job_name:*".to_string(),
                                        "run_query".to_string(),
                                        "metric".to_string(),
                                    ),
                                ),
                            )
                        ],
                    ),
            )
            .tags(vec!["test:examplemonitor".to_string(), "env:ci".to_string()]);
    let configuration = datadog::Configuration::new();
    let api = MonitorsAPI::with_config(configuration);
    let resp = api.create_monitor(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

// Create a Data Quality monitor returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV1::api_monitors::MonitorsAPI;
use datadog_api_client::datadogV1::model::Monitor;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionDataQualityDataSource;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionDataQualityQueryDefinition;
use datadog_api_client::datadogV1::model::MonitorFormulaAndFunctionQueryDefinition;
use datadog_api_client::datadogV1::model::MonitorOptions;
use datadog_api_client::datadogV1::model::MonitorThresholds;
use datadog_api_client::datadogV1::model::MonitorType;

#[tokio::main]
async fn main() {
    let body =
        Monitor::new(r#"formula("query1").last("5m") > 100"#.to_string(), MonitorType::DATA_QUALITY_ALERT)
            .message("Data quality alert triggered".to_string())
            .name("Example-Monitor".to_string())
            .options(
                MonitorOptions::new()
                    .thresholds(MonitorThresholds::new().critical(100.0 as f64))
                    .variables(
                        vec![
                            MonitorFormulaAndFunctionQueryDefinition
                            ::MonitorFormulaAndFunctionDataQualityQueryDefinition(
                                Box::new(
                                    MonitorFormulaAndFunctionDataQualityQueryDefinition::new(
                                        MonitorFormulaAndFunctionDataQualityDataSource::DATA_QUALITY_METRICS,
                                        r#"search for column where `database:production AND table:users`"#.to_string(),
                                        "row_count".to_string(),
                                        "query1".to_string(),
                                    ).group_by(vec!["entity_id".to_string()]),
                                ),
                            )
                        ],
                    ),
            )
            .priority(Some(3))
            .tags(vec!["test:examplemonitor".to_string(), "env:ci".to_string()]);
    let configuration = datadog::Configuration::new();
    let api = MonitorsAPI::with_config(configuration);
    let resp = api.create_monitor(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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

/**
 * Create a Cost Monitor returns "OK" response
 */

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

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

const params: v1.MonitorsApiCreateMonitorRequest = {
  body: {
    name: "Example Monitor",
    type: "cost alert",
    query: `formula("exclude_null(query1)").last("7d").anomaly(direction="above", threshold=10) >= 5`,
    message: "some message Notify: @hipchat-channel",
    tags: ["test:examplemonitor", "env:ci"],
    priority: 3,
    options: {
      thresholds: {
        critical: 5,
        warning: 3,
      },
      variables: [
        {
          dataSource: "cloud_cost",
          query:
            "sum:aws.cost.net.amortized.shared.resources.allocated{aws_product IN (amplify ,athena, backup, bedrock ) } by {aws_product}.rollup(sum, 86400)",
          name: "query1",
          aggregator: "sum",
        },
      ],
      includeTags: true,
    },
  },
};

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

/**
 * Create a Data Jobs monitor returns "OK" response
 */

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

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

const params: v1.MonitorsApiCreateMonitorRequest = {
  body: {
    name: "Example-Monitor",
    type: "data-jobs alert",
    query: `formula("failed_runs(run_query)").by(job_name,workspace_name).last(10d) > 0`,
    message: "Data jobs alert triggered",
    tags: ["test:examplemonitor", "env:ci"],
    options: {
      thresholds: {
        critical: 0,
      },
      variables: [
        {
          name: "run_query",
          jobsQuery: "job_name:*",
          jobType: "databricks.job",
          queryDialect: "metric",
        },
      ],
    },
  },
};

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

/**
 * Create a Data Quality monitor returns "OK" response
 */

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

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

const params: v1.MonitorsApiCreateMonitorRequest = {
  body: {
    name: "Example-Monitor",
    type: "data-quality alert",
    query: `formula("query1").last("5m") > 100`,
    message: "Data quality alert triggered",
    tags: ["test:examplemonitor", "env:ci"],
    priority: 3,
    options: {
      thresholds: {
        critical: 100,
      },
      variables: [
        {
          name: "query1",
          dataSource: "data_quality_metrics",
          measure: "row_count",
          filter:
            `search for column where ` +
            "`" +
            `database:production AND table:users` +
            "`",
          groupBy: ["entity_id"],
        },
      ],
    },
  },
};

apiInstance
  .createMonitor(params)
  .then((data: v1.Monitor) => {
    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.comuk1.datadoghq.comddog-gov.comus2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" tsc "example.ts"

Create a monitor

概要

リクエスト

Body Data (required)

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

応答

コード例

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Instructions

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Instructions

Create a monitor returns "OK" response

Instructions

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Instructions

Create a monitor returns "OK" response

Instructions

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Instructions

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Instructions

Create a Cost Monitor returns "OK" response

Create a Data Jobs monitor returns "OK" response

Create a Data Quality monitor returns "OK" response

Instructions

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