--- title: Créer un monitor description: Créez un monitor à l'aide des options suivantes. breadcrumbs: Documentation > API > Créer un monitor --- > For the complete documentation index, see [llms.txt](https://dnd37xeb23o8n.cloudfront.net/stefon.simmons/docs-astro-web-10435/llms.txt). # Créer un monitor ## v1 (latest) | Datadog site | API endpoint | | ----------------- | ------------------------------------------------- | | app.datadoghq.com | POST https://api.datadoghq.com/api/v1/monitor | | us3.datadoghq.com | POST https://api.us3.datadoghq.com/api/v1/monitor | | us5.datadoghq.com | POST https://api.us5.datadoghq.com/api/v1/monitor | | app.datadoghq.eu | POST https://api.datadoghq.eu/api/v1/monitor | | ap1.datadoghq.com | POST https://api.ap1.datadoghq.com/api/v1/monitor | | ap2.datadoghq.com | POST https://api.ap2.datadoghq.com/api/v1/monitor | | uk1.datadoghq.com | POST https://api.uk1.datadoghq.com/api/v1/monitor | | app.ddog-gov.com | POST https://api.ddog-gov.com/api/v1/monitor | | us2.ddog-gov.com | POST https://api.us2.ddog-gov.com/api/v1/monitor | ### Overview Créez un monitor à l'aide des options suivantes. #### Types de monitors Voici les types de monitors qui peuvent être sélectionnés : - Monitor d'anomalies : `query alert` - Monitor APM : `query alert` ou `trace-analytics alert` - Monitor composite : `composite` - Monitor personnalisé : `service check` - Monitor de prévisions : `query alert` - Monitor de host : `service check` - Monitor d'intégration : `query alert` ou `service check` - Monitor de live process : `process alert` - Monitor de log : `log alert` - Monitor de métrique : `query alert` - Monitor réseau : `service check` - Monitor outlier : `query alert` - Monitor de processus : `service check` - Monitor RUM : `rum alert` - Monitor de SLO : `slo alert` - Monitor Watchdog : `event-v2 alert` - Monitor d'événement v2 : `event-v2 alert` - Monitor d'audit : `audit alert` - Monitor Error Tracking : `error-tracking alert` - Monitor Database Monitoring : `database-monitoring alert` - Monitor de performance réseau : `network-performance alert` - Monitor Cloud Cost : `cost alert` - Monitor Network Path : `network-path alert` **Remarques** : - Les monitors Synthetic sont créés par l'intermédiaire de l'API Synthetics. Consultez la [documentation dédiée à cette API](https://docs.datadoghq.com/api/latest/synthetics.md) pour en savoir plus. - Les monitors de log nécessitent une clé d'application sans restriction. #### Types de requêtes ##### Requête d'alerte de métrique Exemple : `time_aggr(time_window):space_aggr:metric{tags} [by {key}] operator #` - `time_aggr` : avg, sum, max, min, change ou pct_change. - `time_window` : `last_#m` (`#` doit être compris entre 1 et 10080 selon le type de monitor), `last_#h`(`#` doit être compris entre 1 et 168 selon le type de monitor), `last_1d` ou `last_1w`. - `space_aggr` : avg, sum, min ou max. - `tags` : un ou plusieurs tags (séparés par des virgules), ou * - `key` : la « key » de la syntaxe de tag key:value. Définit une alerte distincte pour chaque tag du groupe (alertes multiples). - `operator` : <, <=, >, >=, == ou !=. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. Si vous utilisez l'agrégateur temporel `_change_` ou `_pct_change_`, utilisez plutôt `change_aggr(time_aggr(time_window), timeshift):space_aggr:metric{tags} [by {key}] operator #` avec : - `change_aggr` change ou pct_change. - `time_aggr` avg, sum, max ou min [en savoir plus](https://docs.datadoghq.com/monitors/create/types.md#definir-les-conditions). - `time_window` last_#m (entre 1 et 2880 selon le type de monitor), last_#h (entre 1 et 48 selon le type de monitor) ou last_#d (1 ou 2). - `timeshift` #m_ago (5, 10, 15 ou 30), #h_ago (1, 2 ou 4) ou 1d_ago. Pour créer un monitor outlier, utilisez la requête suivante : `avg(last_30m):outliers(avg:system.cpu.user{role:es-events-data} by {host}, 'dbscan', 7) > 0` ##### Requête de check de service Exemple : `"check".over(tags).last(count).by(group).count_by_status()` - `check` : le nom du check, par exemple `datadog.agent.up`. - `tags` : un ou plusieurs tags entre guillemets (séparés par des virgules) ou "*". Exemple : `.over("env:prod", "role:db")` ; `over` ne peut pas être vide. - `count` : doit être supérieur ou égal à votre seuil maximum (défini dans `options`). Valeur maximale : 100. Par exemple, si vous souhaitez recevoir une notification à partir d'un statut critical, de trois statuts ok, et de deux statuts warn, `count` doit être supérieur ou égal à 3. - `group` : doit être défini pour les monitors de check. Le regroupement par check est déjà explicitement connu pour certains checks de service. Par exemple, les monitors de l'intégration Postgres possèdent les tags `db`, `host` et `port`, tandis que les monitors réseau possèdent les tags `host`, `instance` et `url`. Consultez la section [Checks de service](https://docs.datadoghq.com/api/latest/service-checks.md) pour en savoir plus. ##### Requête d'alerte d'événement **Remarque :** la requête d'alerte d'événement a été remplacée par la requête d'alerte d'événement v2. Pour en savoir plus, consultez le [guide de migration des événements](https://docs.datadoghq.com/service_management/events/guides/migrating_to_new_events_features.md). ##### Requête d'alerte d'événement v2 Exemple : `events(query).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. ##### Requête d'alerte de processus Exemple : `processes(search).over(tags).rollup('count').last(timeframe) operator #` - `search` : chaîne de recherche en texte libre pour interroger les processus. Les processus renvoyés correspondent aux résultats de la page [Live Processes](https://docs.datadoghq.com/infrastructure/process.md?tab=linuxwindows). - `tags` : un ou plusieurs tags (séparés par des virgules). - `timeframe` : l'intervalle sur lequel les totaux doivent être cumulés. Exemples : 10m ou 4h. Intervalles pris en charge : s, m, h et d - `operator` : <, <=, >, >=, == ou != - `#` : un nombre entier ou décimal utilisé pour définir le seuil. ##### Requête d'alerte de log Exemple : `logs(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `index_name` : pour les organisations avec plusieurs index, l'index de log sur lequel la requête porte. - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. ##### Requête de monitor composite Exemple : `12345 && 67890`, où `12345` et `67890` sont les identifiants de monitors non composites. * `name` [*requis*, *valeur par défaut* = **dynamique, en fonction de la requête**] : le nom de l'alerte. * `message` [*requis*, *valeur par défaut* = **dynamique, en fonction de la requête**] : un message à inclure avec les notifications pour ce monitor. Des notifications par e-mail peuvent être envoyées à des utilisateurs spécifiques, en utilisant la même notation « @nomutilisateur » que pour les événements. * `tags` [*facultatif*, *valeur par défaut* = **liste vide**] : la liste des tags à associer à votre monitor. Lorsque vous récupérez tous les détails des monitors via l'API, utilisez l'argument `monitor_tags` pour filtrer les résultats en fonction de ces tags. Ces valeurs ne peuvent être obtenues qu'avec l'API et ne sont ni visibles ni modifiables dans l'interface Datadog. ##### Requête d'alerte SLO Exemple : `error_budget("slo_id").over("time_window") operator #` - `slo_id` : l'ID alphanumérique du SLO pour lequel vous configurez l'alerte. - `time_window` : l'intervalle du SLO cible pour lequel vous souhaitez envoyer une alerte. Valeurs autorisées : `7d`, `30d` ou `90d`. - `operator` : `>=` ou `>` ##### Requête d'alerte d'audit Exemple : `audits(query).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. ##### Requêtes d'alerte de pipelines CI Exemple : `ci-pipelines(query).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. ##### Requête d'alerte de tests CI Exemple : `ci-tests(query).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. ##### Requête d'alerte Error Tracking Exemple de nouveau problème : `error-tracking(query).source(issue_source).new().rollup(rollup_method[, measure]).by(group_by).last(time_window) operator #` Exemple de problème à fort impact : `error-tracking(query).source(issue_source).impact().rollup(rollup_method[, measure]).by(group_by).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `issue_source` : la source du problème ; les sources `all`, `browser`, `mobile` et `backend` sont prises en charge. Si aucune source n'est spécifiée, la valeur `all` est appliquée par défaut. - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. Si aucune méthode n'est spécifiée, la valeur `count` est définie par défaut. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `group by` : la liste d'attributs séparés par des virgules à partir de laquelle effectuer le regroupement. Elle doit contenir au moins `issue.id`. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. **Requête d'alerte Database Monitoring** Exemple : `database-monitoring(query).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. **Requête d'alerte de performance réseau** Exemple : `network-performance(query).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. **Requête d'alerte sur les coûts** Exemple : `formula(query).timeframe_type(time_window).function(parameter) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `timeframe_type` : le type d'intervalle à appliquer pour évaluer le coût. - Les monitors `forecast` prennent en charge l'intervalle `current`. - Les monitors `change`, `anomaly` et `threshold` prennent en charge l'intervalle `last`. - `time_window` : prend en charge le cumul quotidien, par exemple `7d`. - `function` [facultatif, défini par défaut sur le monitor `threshold` si aucune valeur n'est spécifiée] : prend en charge les monitors `change`, `anomaly` et `forecast`. - `parameter` : spécifiez le paramètre du type. - Pour les monitors `change` : - Prise en charge de `relative` ou `absolute`. - [facultatif] Prise en charge de `#`, où `#` correspond à un nombre entier ou décimal utilisé pour définir le seuil. - Pour les monitors `anomaly` : - Prise en charge de `direction=both`, `direction=above` ou `direction=below`. - [facultatif] Prise en charge de `threshold=#`, où `#` correspond à un nombre entier ou décimal utilisé pour définir le seuil. - `operator` - Pour les monitors `threshold` : prend en charge `<`, `<=`, `>`, `>=`, `==` ou `!=`. - Pour les monitors `change` : prend en charge `>` ou `<`. - Pour les monitors `anomaly` : prend en charge `>=`. - Pour les monitors `forecast` : prend en charge `>`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. **Requête d'alerte Network Path** Exemple : `network-path(query).index(index_name).rollup(rollup_method[, measure]).last(time_window) operator #` - `query` : la requête de recherche respectant la [syntaxe de recherche de logs](https://docs.datadoghq.com/logs/search_syntax.md). - `index_name` : le type de données à surveiller ; les types `netpath-path` et `netpath-hop` sont pris en charge. - `rollup_method` : la méthode de cumul des statistiques ; les méthodes `count`, `avg` et `cardinality` sont prises en charge. - `measure` - pour les `rollup_method` `avg` et `cardinality` : indiquez le nom de la mesure ou de la facette à utiliser. - `time_window` : #m (entre 1 et 2880) ou #h (entre 1 et 48). - `operator` : `<`, `<=`, `>`, `>=`, `==` ou `!=`. - `#` : un nombre entier ou décimal utilisé pour définir le seuil. This endpoint requires any of the following permissions: - `monitors_write` - `monitors_draft_write` OAuth apps require one of the following authorization [scopes](https://dnd37xeb23o8n.cloudfront.net/stefon.simmons/docs-astro-web-10435/fr/api/latest/scopes.md#monitors) to access this endpoint: `monitors_write`, `monitors_draft_write`. ### Request #### Body Data (required) Corps de requête pour la création d'un monitor. {% tabs %} {% tab label="Model" %} | Parent field | Field | Type | Description | | ----------------------------- | ------------------------------ | --------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | assets | [object] | Objet décrivant un monitor. | | assets | category [*required*] | enum | Indicates the type of asset this entity represents on a monitor. Allowed enum values: `runbook`. | | assets | name [*required*] | string | Name for the monitor asset | | assets | resource_key | string | Represents the identifier of the internal Datadog resource that this asset represents. IDs in this field should be passed in as strings. | | assets | resource_type | enum | Type of internal Datadog resource associated with a monitor asset. Allowed enum values: `notebook`. | | assets | url [*required*] | string | URL link for the asset. For links with an internal resource type set, this should be the relative path to where the Datadog domain is appended internally. For external links, this should be the full URL path. | | | 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`. | | | matching_downtimes | [object] | A list of active v1 downtimes that match this monitor. | | matching_downtimes | end | int64 | POSIX timestamp to end the downtime. | | matching_downtimes | scope | [string] | The scope(s) to which the downtime applies. Must be in `key:value` format. For example, `host:app2`. Provide multiple scopes as a comma-separated list like `env:dev,env:prod`. The resulting downtime applies to sources that matches ALL provided scopes (`env:dev` **AND** `env:prod`). | | matching_downtimes | start | int64 | POSIX timestamp to start the downtime. | | | message | string | A message to include with notifications for this monitor. | | | name | string | The monitor name. | | | options | object | List of options associated with your monitor. | | options | enable_logs_sample | boolean | Whether or not to send a log sample when the log monitor triggers. | | options | enable_samples | boolean | Whether or not to send a list of samples when the monitor triggers. This is only used by CI Test and Pipeline monitors. | | options | escalation_message | string | We recommend using the [is_renotify](https://docs.datadoghq.com/monitors/notify.md?tab=is_alert#renotify), block in the original message instead. A message to include with a re-notification. Supports the `@username` notification we allow elsewhere. Not applicable if `renotify_interval` is `None`. | | options | evaluation_delay | int64 | Time (in seconds) to delay evaluation, as a non-negative integer. For example, if the value is set to `300` (5min), the timeframe is set to `last_5m` and the time is 7:00, the monitor evaluates data from 6:50 to 6:55. This is useful for AWS CloudWatch and other backfilled metrics to ensure the monitor always has data during evaluation. | | options | group_retention_duration | string | The time span after which groups with missing data are dropped from the monitor state. The minimum value is one hour, and the maximum value is 72 hours. Example values are: "60m", "1h", and "2d". This option is only available for APM Trace Analytics, Audit Trail, CI, Error Tracking, Event, Logs, and RUM monitors. | | options | groupby_simple_monitor | boolean | **DEPRECATED**: Whether the log alert monitor triggers a single alert or multiple alerts when any group breaches a threshold. Use `notify_by` instead. | | options | include_tags | boolean | A Boolean indicating whether notifications from this monitor automatically inserts its triggering tags into the title. **Examples** - If `True`, `[Triggered on {host:h1}] Monitor Title` - If `False`, `[Triggered] Monitor Title` Default: `true`. | | options | locked | boolean | **DEPRECATED**: Whether or not the monitor is locked (only editable by creator and admins). Use `restricted_roles` instead. | | options | min_failure_duration | int64 | How long the test should be in failure before alerting (integer, number of seconds, max 7200). Default: `0`. | | options | min_location_failed | int64 | The minimum number of locations in failure at the same time during at least one moment in the `min_failure_duration` period (`min_location_failed` and `min_failure_duration` are part of the advanced alerting rules - integer, >= 1). Default: `1`. | | options | new_group_delay | int64 | Time (in seconds) to skip evaluations for new groups. For example, this option can be used to skip evaluations for new hosts while they initialize. Must be a non negative integer. | | options | new_host_delay | int64 | **DEPRECATED**: Time (in seconds) to allow a host to boot and applications to fully start before starting the evaluation of monitor results. Should be a non negative integer. Use new_group_delay instead. Default: `300`. | | options | no_data_timeframe | int64 | The number of minutes before a monitor notifies after data stops reporting. Datadog recommends at least 2x the monitor timeframe for query alerts or 2 minutes for service checks. If omitted, 2x the evaluation timeframe is used for query alerts, and 24 hours is used for service checks. | | options | notification_preset_name | enum | Toggles the display of additional content sent in the monitor notification. Allowed enum values: `show_all`, `hide_query`, `hide_handles`, `hide_all`, `hide_query_and_handles`, `show_only_snapshot`, `hide_handles_and_footer`. Default: `show_all`. | | options | notify_audit | boolean | A Boolean indicating whether tagged users is notified on changes to this monitor. Default: `false`. | | options | notify_by | [string] | Controls what granularity a monitor alerts on. Only available for monitors with groupings. For instance, a monitor grouped by `cluster`, `namespace`, and `pod` can be configured to only notify on each new `cluster` violating the alert conditions by setting `notify_by` to `["cluster"]`. Tags mentioned in `notify_by` must be a subset of the grouping tags in the query. For example, a query grouped by `cluster` and `namespace` cannot notify on `region`. Setting `notify_by` to `["*"]` configures the monitor to notify as a simple-alert. | | options | notify_no_data | boolean | A Boolean indicating whether this monitor notifies when data stops reporting. Defaults to `false`. | | options | on_missing_data | enum | Controls how groups or monitors are treated if an evaluation does not return any data points. The default option results in different behavior depending on the monitor query type. For monitors using Count queries, an empty monitor evaluation is treated as 0 and is compared to the threshold conditions. For monitors using any query type other than Count, for example Gauge, Measure, or Rate, the monitor shows the last known status. This option is available for APM Trace Analytics, Audit Trail, CI, Error Tracking, Event, Logs, and RUM monitors. It is also required for metric monitors that use `scheduling_options.custom_schedule`. Allowed enum values: `default`, `show_no_data`, `show_and_notify_no_data`, `resolve`. | | options | renotify_interval | int64 | The number of minutes after the last notification before a monitor re-notifies on the current status. It only re-notifies if it’s not resolved. Default: `null`. | | options | renotify_occurrences | int64 | The number of times re-notification messages should be sent on the current status at the provided re-notification interval. | | options | renotify_statuses | [string] | The types of monitor statuses for which re-notification messages are sent. Default: **null** if `renotify_interval` is **null**. If `renotify_interval` is set, defaults to renotify on `Alert` and `No Data`. | | options | require_full_window | boolean | A Boolean indicating whether this monitor needs a full window of data before it’s evaluated. We highly recommend you set this to `false` for sparse metrics, otherwise some evaluations are skipped. Default is false. This setting only applies to metric monitors. | | options | scheduling_options | object | Configuration options for scheduling. | | scheduling_options | custom_schedule | object | Configuration options for the custom schedule. **This feature is in private beta.** | | custom_schedule | recurrences | [object] | Array of custom schedule recurrences. | | recurrences | rrule | string | Defines the recurrence rule (RRULE) for a given schedule. | | recurrences | start | string | Defines the start date and time of the recurring schedule. | | recurrences | timezone | string | Defines the timezone the schedule runs on. | | scheduling_options | evaluation_window | object | Configuration options for the evaluation window. If `hour_starts` is set, no other fields may be set. Otherwise, `day_starts` and `month_starts` must be set together. | | evaluation_window | day_starts | string | The time of the day at which a one day cumulative evaluation window starts. | | evaluation_window | hour_starts | int32 | The minute of the hour at which a one hour cumulative evaluation window starts. | | evaluation_window | month_starts | int32 | The day of the month at which a one month cumulative evaluation window starts. | | evaluation_window | timezone | string | The timezone of the time of the day of the cumulative evaluation window start. | | options | silenced | object | **DEPRECATED**: Information about the downtime applied to the monitor. Only shows v1 downtimes. | | additionalProperties | | int64 | UTC epoch timestamp in seconds when the downtime for the group expires. | | options | synthetics_check_id | string | **DEPRECATED**: ID of the corresponding Synthetic check. | | options | threshold_windows | object | Alerting time window options. | | threshold_windows | recovery_window | string | Describes how long an anomalous metric must be normal before the alert recovers. | | threshold_windows | trigger_window | string | Describes how long a metric must be anomalous before an alert triggers. | | options | thresholds | object | List of the different monitor threshold available. | | thresholds | critical | double | The monitor `CRITICAL` threshold. | | thresholds | critical_query | string | Query evaluated as a dynamic `CRITICAL` threshold. Only supported on metric monitors with a formula query and options['variables']. Cannot be combined with static thresholds. This field is in preview. | | thresholds | critical_recovery | double | The monitor `CRITICAL` recovery threshold. | | thresholds | critical_recovery_query | string | Query evaluated as a dynamic `CRITICAL` recovery threshold. Only supported on metric monitors with a formula query and options['variables']. Cannot be combined with static thresholds. This field is in preview. | | thresholds | ok | double | The monitor `OK` threshold. | | thresholds | unknown | double | The monitor UNKNOWN threshold. | | thresholds | warning | double | The monitor `WARNING` threshold. | | thresholds | warning_recovery | double | The monitor `WARNING` recovery threshold. | | options | timeout_h | int64 | The number of hours of the monitor not reporting data before it automatically resolves from a triggered state. The minimum allowed value is 0 hours. The maximum allowed value is 24 hours. Default: `null`. | | options | variables | [] | List of requests that can be used in the monitor query. **This feature is currently in beta.** | | variables | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | variables | Object 2 | object | A formula and functions cost query. | | Object 2 | aggregator | enum | Aggregation methods for metric queries. Allowed enum values: `avg`, `sum`, `max`, `min`, `last`, `area`, `l2norm`, `percentile`, `stddev`. | | Object 2 | data_source [*required*] | enum | Data source for cost queries. Allowed enum values: `metrics`, `cloud_cost`, `datadog_usage`. | | Object 2 | name [*required*] | string | Name of the query for use in formulas. | | Object 2 | query [*required*] | string | The monitor query. | | variables | Object 3 | object | A formula and functions data quality query. | | Object 3 | data_source [*required*] | enum | Data source for data quality queries. Allowed enum values: `data_quality_metrics`. | | Object 3 | filter [*required*] | string | Filter expression used to match on data entities. Uses Aastra query syntax. | | Object 3 | group_by | [string] | Optional grouping fields for aggregation. | | Object 3 | measure [*required*] | string | The data quality measure to query. Common values include: `bytes`, `cardinality`, `custom`, `freshness`, `max`, `mean`, `min`, `nullness`, `percent_negative`, `percent_zero`, `row_count`, `stddev`, `sum`, `uniqueness`. Additional values may be supported. | | Object 3 | monitor_options | object | Monitor configuration options for data quality queries. | | monitor_options | crontab_override | string | Crontab expression to override the default schedule. | | monitor_options | custom_sql | string | Custom SQL query for the monitor. | | monitor_options | custom_where | string | Custom WHERE clause for the query. | | monitor_options | group_by_columns | [string] | Columns to group results by. | | monitor_options | model_configuration | object | Tuning options for the anomaly detection model used by the monitor. | | model_configuration | auto_resolve_days | int32 | Number of days after which an open alert is automatically resolved. When unset, alerts stay open until the measure returns within bounds. | | model_configuration | enable_flatline_detection | boolean | Whether to alert when the measure stops changing entirely. Defaults to `true`. | | model_configuration | function | enum | Function applied to the measure before it is compared against the predicted bounds. Allowed enum values: `DIFF`, `DIFF_PERCENT`. | | model_configuration | min_lower_bound_size | double | Minimum distance between the predicted value and the lower bound. Widening the lower bound to at least this size suppresses alerts on small downward deviations. When unset, no minimum is enforced. | | model_configuration | min_upper_bound_size | double | Minimum distance between the predicted value and the upper bound. Widening the upper bound to at least this size suppresses alerts on small upward deviations. When unset, no minimum is enforced. | | model_configuration | model_bounds_override | enum | Restricts which predicted bound the monitor alerts on. `UPPER_ONLY` alerts only when the measure rises above the upper bound, `LOWER_ONLY` only when it falls below the lower bound. When unset, the monitor alerts on both. Allowed enum values: `UPPER_ONLY`, `LOWER_ONLY`. | | monitor_options | model_type_override | enum | Override for the model type used in anomaly detection. Allowed enum values: `freshness`, `percentage`, `any`. | | monitor_options | sensitivity | double | Sensitivity of the anomaly detection model, expressed as a multiplier on the width of the predicted bounds. Higher values widen the bounds and produce fewer alerts; lower values tighten them and produce more alerts. Defaults to `3.0`. | | monitor_options | source_to_target_config | object | Configuration for a source to target monitor, which compares the same measure across two data entities and alerts on the difference between them. | | source_to_target_config | diff_type [*required*] | enum | How the difference between the source and target measures is computed. `absolute` subtracts the two values, `diff_percent` expresses the difference as a percentage of the source value. Allowed enum values: `absolute`, `diff_percent`. | | source_to_target_config | entity_type [*required*] | string | Type of the data entities being compared. | | source_to_target_config | source [*required*] | object | Measure configuration for one side of a source to target comparison. | | source | custom_sql | string | Custom SQL query used to compute the measure for this entity. | | source | custom_where | string | Custom WHERE clause applied when computing the measure for this entity. | | source | entity_id [*required*] | string | Identifier of the data entity to measure. | | source | entity_type [*required*] | string | Type of the data entity to measure. | | source | group_by_columns | [string] | Columns to group results by when computing the measure for this entity. | | source_to_target_config | target [*required*] | object | Measure configuration for one side of a source to target comparison. | | target | custom_sql | string | Custom SQL query used to compute the measure for this entity. | | target | custom_where | string | Custom WHERE clause applied when computing the measure for this entity. | | target | entity_id [*required*] | string | Identifier of the data entity to measure. | | target | entity_type [*required*] | string | Type of the data entity to measure. | | target | group_by_columns | [string] | Columns to group results by when computing the measure for this entity. | | Object 3 | name [*required*] | string | Name of the query for use in formulas. | | Object 3 | schema_version | string | Schema version for the data quality query. | | Object 3 | scope | string | Optional scoping expression to further filter metrics. Uses metrics filter syntax. This is useful when an entity has been configured to emit metrics with additional tags. | | variables | Object 4 | object | A formula and functions data jobs query. | | Object 4 | job_type [*required*] | string | The type of job being monitored. Valid values include: `databricks.job`, `spark.application`, `airflow.dag`, `dbt.job`, `dbt.model`, `dbt.test`, `glue.job`. Custom job types are supported with the `custom.ol.` prefix. | | Object 4 | jobs_query [*required*] | string | Filter expression used to select the jobs to monitor. | | Object 4 | name [*required*] | string | Name of the query for use in formulas. Must be `run_query`. | | Object 4 | query_dialect [*required*] | string | Query dialect for data jobs queries. Currently only `metric` is supported. | | variables | Object 5 | object | A formula and functions aggregate augmented query. Used to enrich base query results with data from a reference table. | | Object 5 | augment_query [*required*] | | Augment query for aggregate augmented queries. Can be an events query or a reference table query. | | augment_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | augment_query | | object | A reference table query for use in aggregate queries. | | | columns | [object] | List of columns to retrieve from the reference table. | | columns | alias | string | Optional alias for the column. | | columns | name [*required*] | string | Name of the column. | | | data_source [*required*] | enum | Data source for reference table queries. Allowed enum values: `reference_table`. | | | name | string | Name of the query. | | | query_filter | string | Optional filter expression for the reference table query. | | | table_name [*required*] | string | Name of the reference table. | | Object 5 | base_query [*required*] | | Base query for aggregate queries. Can be an events query or a metrics query. | | base_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | base_query | Object 2 | object | A formula and functions metrics query for use in aggregate queries. | | Object 2 | aggregator | enum | Aggregator for metrics queries. Allowed enum values: `avg`, `min`, `max`, `sum`, `last`, `mean`, `area`, `l2norm`, `percentile`, `stddev`, `count_unique`. | | Object 2 | data_source [*required*] | enum | Data source for metrics queries. Allowed enum values: `metrics`, `cloud_cost`, `datadog_usage`. | | Object 2 | name | string | Name of the query for use in formulas. | | Object 2 | query [*required*] | string | The metrics query definition. | | Object 5 | compute [*required*] | [object] | Compute options for the query. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 5 | data_source [*required*] | enum | Data source for aggregate augmented queries. Allowed enum values: `aggregate_augmented_query`. | | Object 5 | group_by [*required*] | [object] | Group by options for the query. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 5 | join_condition [*required*] | object | Join condition for aggregate augmented queries. | | join_condition | augment_attribute [*required*] | string | Attribute from the augment query to join on. | | join_condition | base_attribute [*required*] | string | Attribute from the base query to join on. | | join_condition | join_type [*required*] | enum | Join type for aggregate query join conditions. Allowed enum values: `inner`, `left`. | | Object 5 | name | string | Name of the query for use in formulas. | | variables | Object 6 | object | A formula and functions aggregate filtered query. Used to filter base query results using data from another source. | | Object 6 | base_query [*required*] | | Base query for aggregate queries. Can be an events query or a metrics query. | | base_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | base_query | Object 2 | object | A formula and functions metrics query for use in aggregate queries. | | Object 2 | aggregator | enum | Aggregator for metrics queries. Allowed enum values: `avg`, `min`, `max`, `sum`, `last`, `mean`, `area`, `l2norm`, `percentile`, `stddev`, `count_unique`. | | Object 2 | data_source [*required*] | enum | Data source for metrics queries. Allowed enum values: `metrics`, `cloud_cost`, `datadog_usage`. | | Object 2 | name | string | Name of the query for use in formulas. | | Object 2 | query [*required*] | string | The metrics query definition. | | Object 6 | compute | [object] | Compute options for the query. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 6 | data_source [*required*] | enum | Data source for aggregate filtered queries. Allowed enum values: `aggregate_filtered_query`. | | Object 6 | filter_query [*required*] | | Filter query for aggregate filtered queries. Can be an events query or a reference table query. | | filter_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | filter_query | | object | A reference table query for use in aggregate queries. | | | columns | [object] | List of columns to retrieve from the reference table. | | columns | alias | string | Optional alias for the column. | | columns | name [*required*] | string | Name of the column. | | | data_source [*required*] | enum | Data source for reference table queries. Allowed enum values: `reference_table`. | | | name | string | Name of the query. | | | query_filter | string | Optional filter expression for the reference table query. | | | table_name [*required*] | string | Name of the reference table. | | Object 6 | filters [*required*] | [object] | Filter conditions for the query. | | filters | base_attribute [*required*] | string | Attribute from the base query to filter on. | | filters | exclude | boolean | Whether to exclude matching records instead of including them. Default: `false`. | | filters | filter_attribute [*required*] | string | Attribute from the filter query to match against. | | Object 6 | group_by | [object] | Group by options for the query. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 6 | name | string | Name of the query for use in formulas. | | | 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](https://docs.datadoghq.com/api/latest/roles.md#list-roles) 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](https://docs.datadoghq.com/api/latest/restriction-policies.md) to manage write authorization for individual monitors by teams and users, in addition to roles. | | | tags | [string] | Tags associated to your monitor. | | | type [*required*] | enum | The type of the monitor. For more information about `type`, see the [monitor options](https://docs.datadoghq.com/monitors/guide/monitor_api_options.md) docs. Allowed enum values: `composite`, `event alert`, `log alert`, `metric alert`, `process alert`, `query alert`, `rum alert`, `service check`, `synthetics alert`, `trace-analytics alert`, `slo alert`, `event-v2 alert`, `audit alert`, `ci-pipelines alert`, `ci-tests alert`, `error-tracking alert`, `database-monitoring alert`, `network-performance alert`, `cost alert`, `data-quality alert`, `network-path alert`, `data-jobs alert`, `llm-observability alert`. | {% /tab %} {% tab label="Example" %} **Create a Cost Monitor returns "OK" response** ```json { "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": [ { "data_source": "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" } ], "include_tags": true } } ``` **Create a Data Jobs monitor returns "OK" response** ```json { "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", "jobs_query": "job_name:*", "job_type": "databricks.job", "query_dialect": "metric" } ] } } ``` **Create a Data Quality monitor returns "OK" response** ```json { "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", "data_source": "data_quality_metrics", "measure": "row_count", "filter": "search for column where `database:production AND table:users`", "group_by": [ "entity_id" ] } ] } } ``` {% /tab %} {% /tabs %} ### Response {% tabs %} {% tab label="200" %} OK {% tabs %} {% tab label="Model" %} Object describing a monitor. | Parent field | Field | Type | Description | | ----------------------------- | ------------------------------ | --------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | 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). | | assets | category [*required*] | enum | Indicates the type of asset this entity represents on a monitor. Allowed enum values: `runbook`. | | assets | name [*required*] | string | Name for the monitor asset | | assets | resource_key | string | Represents the identifier of the internal Datadog resource that this asset represents. IDs in this field should be passed in as strings. | | assets | resource_type | enum | Type of internal Datadog resource associated with a monitor asset. Allowed enum values: `notebook`. | | assets | url [*required*] | string | URL link for the asset. For links with an internal resource type set, this should be the relative path to where the Datadog domain is appended internally. For external links, this should be the full URL path. | | | created | date-time | Timestamp of the monitor creation. Read-only. | | | creator | object | Object describing the creator of the shared element. Read-only. | | creator | email | string | Email of the creator. | | creator | handle | string | Handle of the creator. | | creator | name | string | Name of the creator. | | | deleted | date-time | Whether or not the monitor is deleted. (Always `null`) Read-only. | | | 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`. | | | id | int64 | ID of this monitor. Read-only. | | | matching_downtimes | [object] | A list of active v1 downtimes that match this monitor. | | matching_downtimes | end | int64 | POSIX timestamp to end the downtime. | | matching_downtimes | id [*required*] | int64 | The downtime ID. Read-only. | | matching_downtimes | scope | [string] | The scope(s) to which the downtime applies. Must be in `key:value` format. For example, `host:app2`. Provide multiple scopes as a comma-separated list like `env:dev,env:prod`. The resulting downtime applies to sources that matches ALL provided scopes (`env:dev` **AND** `env:prod`). | | matching_downtimes | start | int64 | POSIX timestamp to start the downtime. | | | message | string | A message to include with notifications for this monitor. | | | modified | date-time | Last timestamp when the monitor was edited. Read-only. | | | multi | boolean | Whether or not the monitor is broken down on different groups. Read-only. | | | name | string | The monitor name. | | | options | object | List of options associated with your monitor. | | options | aggregation | object | Type of aggregation performed in the monitor query. Read-only. | | aggregation | group_by | string | Group to break down the monitor on. | | aggregation | metric | string | Metric name used in the monitor. | | aggregation | type | string | Metric type used in the monitor. | | options | device_ids | [string] | **DEPRECATED**: IDs of the device the Synthetics monitor is running on. Read-only. | | options | enable_logs_sample | boolean | Whether or not to send a log sample when the log monitor triggers. | | options | enable_samples | boolean | Whether or not to send a list of samples when the monitor triggers. This is only used by CI Test and Pipeline monitors. | | options | escalation_message | string | We recommend using the [is_renotify](https://docs.datadoghq.com/monitors/notify.md?tab=is_alert#renotify), block in the original message instead. A message to include with a re-notification. Supports the `@username` notification we allow elsewhere. Not applicable if `renotify_interval` is `None`. | | options | evaluation_delay | int64 | Time (in seconds) to delay evaluation, as a non-negative integer. For example, if the value is set to `300` (5min), the timeframe is set to `last_5m` and the time is 7:00, the monitor evaluates data from 6:50 to 6:55. This is useful for AWS CloudWatch and other backfilled metrics to ensure the monitor always has data during evaluation. | | options | group_retention_duration | string | The time span after which groups with missing data are dropped from the monitor state. The minimum value is one hour, and the maximum value is 72 hours. Example values are: "60m", "1h", and "2d". This option is only available for APM Trace Analytics, Audit Trail, CI, Error Tracking, Event, Logs, and RUM monitors. | | options | groupby_simple_monitor | boolean | **DEPRECATED**: Whether the log alert monitor triggers a single alert or multiple alerts when any group breaches a threshold. Use `notify_by` instead. | | options | include_tags | boolean | A Boolean indicating whether notifications from this monitor automatically inserts its triggering tags into the title. **Examples** - If `True`, `[Triggered on {host:h1}] Monitor Title` - If `False`, `[Triggered] Monitor Title` Default: `true`. | | options | locked | boolean | **DEPRECATED**: Whether or not the monitor is locked (only editable by creator and admins). Use `restricted_roles` instead. | | options | min_failure_duration | int64 | How long the test should be in failure before alerting (integer, number of seconds, max 7200). Default: `0`. | | options | min_location_failed | int64 | The minimum number of locations in failure at the same time during at least one moment in the `min_failure_duration` period (`min_location_failed` and `min_failure_duration` are part of the advanced alerting rules - integer, >= 1). Default: `1`. | | options | new_group_delay | int64 | Time (in seconds) to skip evaluations for new groups. For example, this option can be used to skip evaluations for new hosts while they initialize. Must be a non negative integer. | | options | new_host_delay | int64 | **DEPRECATED**: Time (in seconds) to allow a host to boot and applications to fully start before starting the evaluation of monitor results. Should be a non negative integer. Use new_group_delay instead. Default: `300`. | | options | no_data_timeframe | int64 | The number of minutes before a monitor notifies after data stops reporting. Datadog recommends at least 2x the monitor timeframe for query alerts or 2 minutes for service checks. If omitted, 2x the evaluation timeframe is used for query alerts, and 24 hours is used for service checks. | | options | notification_preset_name | enum | Toggles the display of additional content sent in the monitor notification. Allowed enum values: `show_all`, `hide_query`, `hide_handles`, `hide_all`, `hide_query_and_handles`, `show_only_snapshot`, `hide_handles_and_footer`. Default: `show_all`. | | options | notify_audit | boolean | A Boolean indicating whether tagged users is notified on changes to this monitor. Default: `false`. | | options | notify_by | [string] | Controls what granularity a monitor alerts on. Only available for monitors with groupings. For instance, a monitor grouped by `cluster`, `namespace`, and `pod` can be configured to only notify on each new `cluster` violating the alert conditions by setting `notify_by` to `["cluster"]`. Tags mentioned in `notify_by` must be a subset of the grouping tags in the query. For example, a query grouped by `cluster` and `namespace` cannot notify on `region`. Setting `notify_by` to `["*"]` configures the monitor to notify as a simple-alert. | | options | notify_no_data | boolean | A Boolean indicating whether this monitor notifies when data stops reporting. Defaults to `false`. | | options | on_missing_data | enum | Controls how groups or monitors are treated if an evaluation does not return any data points. The default option results in different behavior depending on the monitor query type. For monitors using Count queries, an empty monitor evaluation is treated as 0 and is compared to the threshold conditions. For monitors using any query type other than Count, for example Gauge, Measure, or Rate, the monitor shows the last known status. This option is available for APM Trace Analytics, Audit Trail, CI, Error Tracking, Event, Logs, and RUM monitors. It is also required for metric monitors that use `scheduling_options.custom_schedule`. Allowed enum values: `default`, `show_no_data`, `show_and_notify_no_data`, `resolve`. | | options | renotify_interval | int64 | The number of minutes after the last notification before a monitor re-notifies on the current status. It only re-notifies if it’s not resolved. Default: `null`. | | options | renotify_occurrences | int64 | The number of times re-notification messages should be sent on the current status at the provided re-notification interval. | | options | renotify_statuses | [string] | The types of monitor statuses for which re-notification messages are sent. Default: **null** if `renotify_interval` is **null**. If `renotify_interval` is set, defaults to renotify on `Alert` and `No Data`. | | options | require_full_window | boolean | A Boolean indicating whether this monitor needs a full window of data before it’s evaluated. We highly recommend you set this to `false` for sparse metrics, otherwise some evaluations are skipped. Default is false. This setting only applies to metric monitors. | | options | scheduling_options | object | Configuration options for scheduling. | | scheduling_options | custom_schedule | object | Configuration options for the custom schedule. **This feature is in private beta.** | | custom_schedule | recurrences | [object] | Array of custom schedule recurrences. | | recurrences | rrule | string | Defines the recurrence rule (RRULE) for a given schedule. | | recurrences | start | string | Defines the start date and time of the recurring schedule. | | recurrences | timezone | string | Defines the timezone the schedule runs on. | | scheduling_options | evaluation_window | object | Configuration options for the evaluation window. If `hour_starts` is set, no other fields may be set. Otherwise, `day_starts` and `month_starts` must be set together. | | evaluation_window | day_starts | string | The time of the day at which a one day cumulative evaluation window starts. | | evaluation_window | hour_starts | int32 | The minute of the hour at which a one hour cumulative evaluation window starts. | | evaluation_window | month_starts | int32 | The day of the month at which a one month cumulative evaluation window starts. | | evaluation_window | timezone | string | The timezone of the time of the day of the cumulative evaluation window start. | | options | silenced | object | **DEPRECATED**: Information about the downtime applied to the monitor. Only shows v1 downtimes. | | additionalProperties | | int64 | UTC epoch timestamp in seconds when the downtime for the group expires. | | options | synthetics_check_id | string | **DEPRECATED**: ID of the corresponding Synthetic check. | | options | threshold_windows | object | Alerting time window options. | | threshold_windows | recovery_window | string | Describes how long an anomalous metric must be normal before the alert recovers. | | threshold_windows | trigger_window | string | Describes how long a metric must be anomalous before an alert triggers. | | options | thresholds | object | List of the different monitor threshold available. | | thresholds | critical | double | The monitor `CRITICAL` threshold. | | thresholds | critical_query | string | Query evaluated as a dynamic `CRITICAL` threshold. Only supported on metric monitors with a formula query and options['variables']. Cannot be combined with static thresholds. This field is in preview. | | thresholds | critical_recovery | double | The monitor `CRITICAL` recovery threshold. | | thresholds | critical_recovery_query | string | Query evaluated as a dynamic `CRITICAL` recovery threshold. Only supported on metric monitors with a formula query and options['variables']. Cannot be combined with static thresholds. This field is in preview. | | thresholds | ok | double | The monitor `OK` threshold. | | thresholds | unknown | double | The monitor UNKNOWN threshold. | | thresholds | warning | double | The monitor `WARNING` threshold. | | thresholds | warning_recovery | double | The monitor `WARNING` recovery threshold. | | options | timeout_h | int64 | The number of hours of the monitor not reporting data before it automatically resolves from a triggered state. The minimum allowed value is 0 hours. The maximum allowed value is 24 hours. Default: `null`. | | options | variables | [] | List of requests that can be used in the monitor query. **This feature is currently in beta.** | | variables | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | variables | Object 2 | object | A formula and functions cost query. | | Object 2 | aggregator | enum | Aggregation methods for metric queries. Allowed enum values: `avg`, `sum`, `max`, `min`, `last`, `area`, `l2norm`, `percentile`, `stddev`. | | Object 2 | data_source [*required*] | enum | Data source for cost queries. Allowed enum values: `metrics`, `cloud_cost`, `datadog_usage`. | | Object 2 | name [*required*] | string | Name of the query for use in formulas. | | Object 2 | query [*required*] | string | The monitor query. | | variables | Object 3 | object | A formula and functions data quality query. | | Object 3 | data_source [*required*] | enum | Data source for data quality queries. Allowed enum values: `data_quality_metrics`. | | Object 3 | filter [*required*] | string | Filter expression used to match on data entities. Uses Aastra query syntax. | | Object 3 | group_by | [string] | Optional grouping fields for aggregation. | | Object 3 | measure [*required*] | string | The data quality measure to query. Common values include: `bytes`, `cardinality`, `custom`, `freshness`, `max`, `mean`, `min`, `nullness`, `percent_negative`, `percent_zero`, `row_count`, `stddev`, `sum`, `uniqueness`. Additional values may be supported. | | Object 3 | monitor_options | object | Monitor configuration options for data quality queries. | | monitor_options | crontab_override | string | Crontab expression to override the default schedule. | | monitor_options | custom_sql | string | Custom SQL query for the monitor. | | monitor_options | custom_where | string | Custom WHERE clause for the query. | | monitor_options | group_by_columns | [string] | Columns to group results by. | | monitor_options | model_configuration | object | Tuning options for the anomaly detection model used by the monitor. | | model_configuration | auto_resolve_days | int32 | Number of days after which an open alert is automatically resolved. When unset, alerts stay open until the measure returns within bounds. | | model_configuration | enable_flatline_detection | boolean | Whether to alert when the measure stops changing entirely. Defaults to `true`. | | model_configuration | function | enum | Function applied to the measure before it is compared against the predicted bounds. Allowed enum values: `DIFF`, `DIFF_PERCENT`. | | model_configuration | min_lower_bound_size | double | Minimum distance between the predicted value and the lower bound. Widening the lower bound to at least this size suppresses alerts on small downward deviations. When unset, no minimum is enforced. | | model_configuration | min_upper_bound_size | double | Minimum distance between the predicted value and the upper bound. Widening the upper bound to at least this size suppresses alerts on small upward deviations. When unset, no minimum is enforced. | | model_configuration | model_bounds_override | enum | Restricts which predicted bound the monitor alerts on. `UPPER_ONLY` alerts only when the measure rises above the upper bound, `LOWER_ONLY` only when it falls below the lower bound. When unset, the monitor alerts on both. Allowed enum values: `UPPER_ONLY`, `LOWER_ONLY`. | | monitor_options | model_type_override | enum | Override for the model type used in anomaly detection. Allowed enum values: `freshness`, `percentage`, `any`. | | monitor_options | sensitivity | double | Sensitivity of the anomaly detection model, expressed as a multiplier on the width of the predicted bounds. Higher values widen the bounds and produce fewer alerts; lower values tighten them and produce more alerts. Defaults to `3.0`. | | monitor_options | source_to_target_config | object | Configuration for a source to target monitor, which compares the same measure across two data entities and alerts on the difference between them. | | source_to_target_config | diff_type [*required*] | enum | How the difference between the source and target measures is computed. `absolute` subtracts the two values, `diff_percent` expresses the difference as a percentage of the source value. Allowed enum values: `absolute`, `diff_percent`. | | source_to_target_config | entity_type [*required*] | string | Type of the data entities being compared. | | source_to_target_config | source [*required*] | object | Measure configuration for one side of a source to target comparison. | | source | custom_sql | string | Custom SQL query used to compute the measure for this entity. | | source | custom_where | string | Custom WHERE clause applied when computing the measure for this entity. | | source | entity_id [*required*] | string | Identifier of the data entity to measure. | | source | entity_type [*required*] | string | Type of the data entity to measure. | | source | group_by_columns | [string] | Columns to group results by when computing the measure for this entity. | | source_to_target_config | target [*required*] | object | Measure configuration for one side of a source to target comparison. | | target | custom_sql | string | Custom SQL query used to compute the measure for this entity. | | target | custom_where | string | Custom WHERE clause applied when computing the measure for this entity. | | target | entity_id [*required*] | string | Identifier of the data entity to measure. | | target | entity_type [*required*] | string | Type of the data entity to measure. | | target | group_by_columns | [string] | Columns to group results by when computing the measure for this entity. | | Object 3 | name [*required*] | string | Name of the query for use in formulas. | | Object 3 | schema_version | string | Schema version for the data quality query. | | Object 3 | scope | string | Optional scoping expression to further filter metrics. Uses metrics filter syntax. This is useful when an entity has been configured to emit metrics with additional tags. | | variables | Object 4 | object | A formula and functions data jobs query. | | Object 4 | job_type [*required*] | string | The type of job being monitored. Valid values include: `databricks.job`, `spark.application`, `airflow.dag`, `dbt.job`, `dbt.model`, `dbt.test`, `glue.job`. Custom job types are supported with the `custom.ol.` prefix. | | Object 4 | jobs_query [*required*] | string | Filter expression used to select the jobs to monitor. | | Object 4 | name [*required*] | string | Name of the query for use in formulas. Must be `run_query`. | | Object 4 | query_dialect [*required*] | string | Query dialect for data jobs queries. Currently only `metric` is supported. | | variables | Object 5 | object | A formula and functions aggregate augmented query. Used to enrich base query results with data from a reference table. | | Object 5 | augment_query [*required*] | | Augment query for aggregate augmented queries. Can be an events query or a reference table query. | | augment_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | augment_query | | object | A reference table query for use in aggregate queries. | | | columns | [object] | List of columns to retrieve from the reference table. | | columns | alias | string | Optional alias for the column. | | columns | name [*required*] | string | Name of the column. | | | data_source [*required*] | enum | Data source for reference table queries. Allowed enum values: `reference_table`. | | | name | string | Name of the query. | | | query_filter | string | Optional filter expression for the reference table query. | | | table_name [*required*] | string | Name of the reference table. | | Object 5 | base_query [*required*] | | Base query for aggregate queries. Can be an events query or a metrics query. | | base_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | base_query | Object 2 | object | A formula and functions metrics query for use in aggregate queries. | | Object 2 | aggregator | enum | Aggregator for metrics queries. Allowed enum values: `avg`, `min`, `max`, `sum`, `last`, `mean`, `area`, `l2norm`, `percentile`, `stddev`, `count_unique`. | | Object 2 | data_source [*required*] | enum | Data source for metrics queries. Allowed enum values: `metrics`, `cloud_cost`, `datadog_usage`. | | Object 2 | name | string | Name of the query for use in formulas. | | Object 2 | query [*required*] | string | The metrics query definition. | | Object 5 | compute [*required*] | [object] | Compute options for the query. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 5 | data_source [*required*] | enum | Data source for aggregate augmented queries. Allowed enum values: `aggregate_augmented_query`. | | Object 5 | group_by [*required*] | [object] | Group by options for the query. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 5 | join_condition [*required*] | object | Join condition for aggregate augmented queries. | | join_condition | augment_attribute [*required*] | string | Attribute from the augment query to join on. | | join_condition | base_attribute [*required*] | string | Attribute from the base query to join on. | | join_condition | join_type [*required*] | enum | Join type for aggregate query join conditions. Allowed enum values: `inner`, `left`. | | Object 5 | name | string | Name of the query for use in formulas. | | variables | Object 6 | object | A formula and functions aggregate filtered query. Used to filter base query results using data from another source. | | Object 6 | base_query [*required*] | | Base query for aggregate queries. Can be an events query or a metrics query. | | base_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | base_query | Object 2 | object | A formula and functions metrics query for use in aggregate queries. | | Object 2 | aggregator | enum | Aggregator for metrics queries. Allowed enum values: `avg`, `min`, `max`, `sum`, `last`, `mean`, `area`, `l2norm`, `percentile`, `stddev`, `count_unique`. | | Object 2 | data_source [*required*] | enum | Data source for metrics queries. Allowed enum values: `metrics`, `cloud_cost`, `datadog_usage`. | | Object 2 | name | string | Name of the query for use in formulas. | | Object 2 | query [*required*] | string | The metrics query definition. | | Object 6 | compute | [object] | Compute options for the query. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 6 | data_source [*required*] | enum | Data source for aggregate filtered queries. Allowed enum values: `aggregate_filtered_query`. | | Object 6 | filter_query [*required*] | | Filter query for aggregate filtered queries. Can be an events query or a reference table query. | | filter_query | Object 1 | object | A formula and functions events query. | | Object 1 | compute [*required*] | object | Compute options. | | compute | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | compute | interval | int64 | A time interval in milliseconds. | | compute | metric | string | Measurable attribute to compute. | | compute | name | string | The name assigned to this aggregation, when multiple aggregations are defined for a query. | | compute | source | string | Source reference for composite query payloads. | | Object 1 | data_source [*required*] | enum | Data source for event platform-based queries. Allowed enum values: `rum`, `ci_pipelines`, `ci_tests`, `audit`, `events`, `logs`, `spans`, `database_queries`, `network`, `network_path`. | | Object 1 | group_by | [object] | Group by options. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 1 | indexes | [string] | An array of index names to query in the stream. Omit or use `[]` to query all indexes at once. | | Object 1 | name [*required*] | string | Name of the query for use in formulas. | | Object 1 | search | object | Search options. | | search | query [*required*] | string | Events search string. | | filter_query | | object | A reference table query for use in aggregate queries. | | | columns | [object] | List of columns to retrieve from the reference table. | | columns | alias | string | Optional alias for the column. | | columns | name [*required*] | string | Name of the column. | | | data_source [*required*] | enum | Data source for reference table queries. Allowed enum values: `reference_table`. | | | name | string | Name of the query. | | | query_filter | string | Optional filter expression for the reference table query. | | | table_name [*required*] | string | Name of the reference table. | | Object 6 | filters [*required*] | [object] | Filter conditions for the query. | | filters | base_attribute [*required*] | string | Attribute from the base query to filter on. | | filters | exclude | boolean | Whether to exclude matching records instead of including them. Default: `false`. | | filters | filter_attribute [*required*] | string | Attribute from the filter query to match against. | | Object 6 | group_by | [object] | Group by options for the query. | | group_by | facet [*required*] | string | Event facet. | | group_by | limit | int64 | Number of groups to return. | | group_by | sort | object | Options for sorting group by results. | | sort | aggregation [*required*] | enum | Aggregation methods for event platform queries. Allowed enum values: `count`, `cardinality`, `median`, `pc75`, `pc90`, `pc95`, `pc98`, `pc99`, `sum`, `min`, `max`, `avg`. | | sort | metric | string | Metric used for sorting group by results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc`, `desc`. Default: `desc`. | | group_by | source | string | Source reference for composite query payloads. | | Object 6 | name | string | Name of the query for use in formulas. | | | overall_state | enum | The different states your monitor can be in. Read-only. 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](https://docs.datadoghq.com/api/latest/roles.md#list-roles) 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](https://docs.datadoghq.com/api/latest/restriction-policies.md) to manage write authorization for individual monitors by teams and users, in addition to roles. | | | state | object | Wrapper object with the different monitor states. Read-only. | | state | groups | object | Dictionary where the keys are groups (comma separated lists of tags) and the values are the list of groups your monitor is broken down on. | | additionalProperties | | object | Monitor state for a single group. | | | last_nodata_ts | int64 | Latest timestamp the monitor was in NO_DATA state. | | | last_notified_ts | int64 | Latest timestamp of the notification sent for this monitor group. | | | last_resolved_ts | int64 | Latest timestamp the monitor group was resolved. | | | last_triggered_ts | int64 | Latest timestamp the monitor group triggered. | | | name | string | The name of the monitor. | | | status | enum | The different states your monitor can be in. Read-only. Allowed enum values: `Alert`, `Ignored`, `No Data`, `OK`, `Skipped`, `Unknown`, `Warn`. | | | tags | [string] | Tags associated to your monitor. | | | type [*required*] | enum | The type of the monitor. For more information about `type`, see the [monitor options](https://docs.datadoghq.com/monitors/guide/monitor_api_options.md) docs. Allowed enum values: `composite`, `event alert`, `log alert`, `metric alert`, `process alert`, `query alert`, `rum alert`, `service check`, `synthetics alert`, `trace-analytics alert`, `slo alert`, `event-v2 alert`, `audit alert`, `ci-pipelines alert`, `ci-tests alert`, `error-tracking alert`, `database-monitoring alert`, `network-performance alert`, `cost alert`, `data-quality alert`, `network-path alert`, `data-jobs alert`, `llm-observability alert`. | {% /tab %} {% tab label="Example" %} ```json { "assets": [ { "category": "runbook", "name": "Monitor Runbook", "resource_key": "12345", "resource_type": "string", "url": "/notebooks/12345" } ], "created": "2019-09-19T10:00:00.000Z", "creator": { "email": "string", "handle": "string", "name": "string" }, "deleted": "2019-09-19T10:00:00.000Z", "draft_status": "string", "id": "integer", "matching_downtimes": [ { "end": 1412792983, "id": 1625, "scope": [ "env:staging" ], "start": 1412792983 } ], "message": "string", "modified": "2019-09-19T10:00:00.000Z", "multi": false, "name": "My monitor", "options": { "aggregation": { "group_by": "host", "metric": "metrics.name", "type": "count" }, "device_ids": [], "enable_logs_sample": false, "enable_samples": false, "escalation_message": "string", "evaluation_delay": "integer", "group_retention_duration": "string", "groupby_simple_monitor": false, "include_tags": false, "locked": false, "min_failure_duration": "integer", "min_location_failed": "integer", "new_group_delay": "integer", "new_host_delay": "integer", "no_data_timeframe": "integer", "notification_preset_name": "string", "notify_audit": false, "notify_by": [], "notify_no_data": false, "on_missing_data": "string", "renotify_interval": "integer", "renotify_occurrences": "integer", "renotify_statuses": [], "require_full_window": false, "scheduling_options": { "custom_schedule": { "recurrences": [ { "rrule": "FREQ=WEEKLY;BYDAY=MO,TU,WE,TH,FR", "start": "2023-08-31T16:30:00", "timezone": "Europe/Paris" } ] }, "evaluation_window": { "day_starts": "04:00", "hour_starts": 0, "month_starts": 1, "timezone": "Europe/Paris" } }, "silenced": { "": "integer" }, "synthetics_check_id": "string", "threshold_windows": { "recovery_window": "string", "trigger_window": "string" }, "thresholds": { "critical": "number", "critical_query": "formula(\"2 * query1\").rollup(\"avg\").last(\"6mo\")", "critical_recovery": "number", "critical_recovery_query": "formula(\"1.5 * query1\").rollup(\"avg\").last(\"3mo\")", "ok": "number", "unknown": "number", "warning": "number", "warning_recovery": "number" }, "timeout_h": "integer", "variables": [ { "compute": { "aggregation": "avg", "interval": 60000, "metric": "@duration", "name": "compute_result", "source": "filter_query" }, "data_source": "rum", "group_by": [ { "facet": "status", "limit": 10, "sort": { "aggregation": "avg", "metric": "string", "order": "asc" }, "source": "filter_query" } ], "indexes": [ "days-3", "days-7" ], "name": "query_errors", "search": { "query": "service:query" } } ] }, "overall_state": "string", "priority": "integer", "query": "avg(last_5m):sum:system.net.bytes_rcvd{host:host0} > 100", "restricted_roles": [], "state": { "groups": { "": { "last_nodata_ts": "integer", "last_notified_ts": "integer", "last_resolved_ts": "integer", "last_triggered_ts": "integer", "name": "string", "status": "string" } } }, "tags": [], "type": "query alert" } ``` {% /tab %} {% /tabs %} {% /tab %} {% tab label="400" %} Bad Request {% tabs %} {% tab label="Model" %} Error response object. | Field | Type | Description | | ------------------- | -------- | ------------------------------------ | | errors [*required*] | [string] | Array of errors returned by the API. | {% /tab %} {% tab label="Example" %} ```json { "errors": [ "Bad Request" ] } ``` {% /tab %} {% /tabs %} {% /tab %} {% tab label="403" %} Forbidden {% tabs %} {% tab label="Model" %} Error response object. | Field | Type | Description | | ------------------- | -------- | ------------------------------------ | | errors [*required*] | [string] | Array of errors returned by the API. | {% /tab %} {% tab label="Example" %} ```json { "errors": [ "Bad Request" ] } ``` {% /tab %} {% /tabs %} {% /tab %} {% tab label="429" %} Too many requests {% tabs %} {% tab label="Model" %} Error response object. | Field | Type | Description | | ------------------- | -------- | ------------------------------------ | | errors [*required*] | [string] | Array of errors returned by the API. | {% /tab %} {% tab label="Example" %} ```json { "errors": [ "Bad Request" ] } ``` {% /tab %} {% /tabs %} {% /tab %} {% /tabs %} ### Code Example {% tabs %} {% tab label="Curl" %} ```bash # Set your Datadog site and credentials export DD_SITE="datadoghq.com" # Or us3.datadoghq.com, us5.datadoghq.com, datadoghq.eu, ap1.datadoghq.com, ap2.datadoghq.com, uk1.datadoghq.com, ddog-gov.com, us2.ddog-gov.com # Use a Personal Access Token or Service Access Token export DD_BEARER_TOKEN="" curl -X POST "https://api.${DD_SITE}/api/v1/monitor" \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer ${DD_BEARER_TOKEN}" \ -d @- << EOF { "name": "Example Monitor", "type": "cost alert", "query": "formula(\"exclude_null(query1)\").last(\"7d\").anomaly(direction=\"above\", threshold=10) >= 5", "message": "some message Notify: @hipchat-channel", "tags": [ "test:examplemonitor", "env:ci" ], "priority": 3, "options": { "thresholds": { "critical": 5, "warning": 3 }, "variables": [ { "data_source": "cloud_cost", "query": "sum:aws.cost.net.amortized.shared.resources.allocated{aws_product IN (amplify ,athena, backup, bedrock ) } by {aws_product}.rollup(sum, 86400)", "name": "query1", "aggregator": "sum" } ], "include_tags": true } } EOF ``` ```bash # Set your Datadog site and credentials export DD_SITE="datadoghq.com" # Or us3.datadoghq.com, us5.datadoghq.com, datadoghq.eu, ap1.datadoghq.com, ap2.datadoghq.com, uk1.datadoghq.com, ddog-gov.com, us2.ddog-gov.com # Use a Personal Access Token or Service Access Token export DD_BEARER_TOKEN="" curl -X POST "https://api.${DD_SITE}/api/v1/monitor" \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer ${DD_BEARER_TOKEN}" \ -d @- << EOF { "name": "Example-Monitor", "type": "data-jobs alert", "query": "formula(\"failed_runs(run_query)\").by(job_name,workspace_name).last(10d) > 0", "message": "Data jobs alert triggered", "tags": [ "test:examplemonitor", "env:ci" ], "options": { "thresholds": { "critical": 0 }, "variables": [ { "name": "run_query", "jobs_query": "job_name:*", "job_type": "databricks.job", "query_dialect": "metric" } ] } } EOF ``` ```bash # Set your Datadog site and credentials export DD_SITE="datadoghq.com" # Or us3.datadoghq.com, us5.datadoghq.com, datadoghq.eu, ap1.datadoghq.com, ap2.datadoghq.com, uk1.datadoghq.com, ddog-gov.com, us2.ddog-gov.com # Use a Personal Access Token or Service Access Token export DD_BEARER_TOKEN="" curl -X POST "https://api.${DD_SITE}/api/v1/monitor" \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer ${DD_BEARER_TOKEN}" \ -d @- << EOF { "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", "data_source": "data_quality_metrics", "measure": "row_count", "filter": "search for column where `database:production AND table:users`", "group_by": [ "entity_id" ] } ] } } EOF ``` {% /tab %} {% tab label="Go" %} ```go // 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()) ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN")) 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) } ``` ```go // 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()) ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN")) 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) } ``` ```go // 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()) ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN")) 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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=go) and then save the example to `main.go` and run following commands: ```bash DD_SITE="datadoghq.com" DD_BEARER_TOKEN="" go run "main.go" ``` {% /tab %} {% tab label="Java" %} ```java // 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(); defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN")); 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(); } } } ``` ```java // 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(); defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN")); 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(); } } } ``` ```java // 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(); defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN")); 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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=java) and then save the example to `Example.java` and run following commands: ```bash DD_SITE="datadoghq.com" DD_BEARER_TOKEN="" java "Example.java" ``` {% /tab %} {% tab label="Python" %} ```python """ Create a Cost Monitor returns "OK" response """ from os import environ 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() configuration.access_token = environ["DD_BEARER_TOKEN"] with ApiClient(configuration) as api_client: api_instance = MonitorsApi(api_client) response = api_instance.create_monitor(body=body) print(response) ``` ```python """ Create a Data Jobs monitor returns "OK" response """ from os import environ 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() configuration.access_token = environ["DD_BEARER_TOKEN"] with ApiClient(configuration) as api_client: api_instance = MonitorsApi(api_client) response = api_instance.create_monitor(body=body) print(response) ``` ```python """ Create a Data Quality monitor returns "OK" response """ from os import environ 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() configuration.access_token = environ["DD_BEARER_TOKEN"] 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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=python) and then save the example to `example.py` and run following commands: ```bash DD_SITE="datadoghq.com" DD_BEARER_TOKEN="" python3 "example.py" ``` {% /tab %} {% tab label="Ruby" %} ```ruby # Create a Cost Monitor returns "OK" response require "datadog_api_client" DatadogAPIClient.configure do |config| config.access_token = ENV["DD_BEARER_TOKEN"] end 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) ``` ```ruby # Create a Data Jobs monitor returns "OK" response require "datadog_api_client" DatadogAPIClient.configure do |config| config.access_token = ENV["DD_BEARER_TOKEN"] end 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) ``` ```ruby # Create a Data Quality monitor returns "OK" response require "datadog_api_client" DatadogAPIClient.configure do |config| config.access_token = ENV["DD_BEARER_TOKEN"] end 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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=ruby) and then save the example to `example.rb` and run following commands: ```bash DD_SITE="datadoghq.com" DD_BEARER_TOKEN="" rb "example.rb" ``` {% /tab %} {% tab label="Rust" %} ```rust // 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()); } } ``` ```rust // 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()); } } ``` ```rust // 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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=rust) and then save the example to `src/main.rs` and run following commands: ```bash DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" cargo run ``` {% /tab %} {% tab label="TypeScript" %} ```typescript /** * Create a Cost Monitor returns "OK" response */ import { client, v1 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration({ authMethods: { AuthZ: { accessToken: process.env.DD_BEARER_TOKEN as string, }, }, }); 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)); ``` ```typescript /** * Create a Data Jobs monitor returns "OK" response */ import { client, v1 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration({ authMethods: { AuthZ: { accessToken: process.env.DD_BEARER_TOKEN as string, }, }, }); 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)); ``` ```typescript /** * Create a Data Quality monitor returns "OK" response */ import { client, v1 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration({ authMethods: { AuthZ: { accessToken: process.env.DD_BEARER_TOKEN as string, }, }, }); 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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=typescript) and then save the example to `example.ts` and run following commands: ```bash DD_SITE="datadoghq.com" DD_BEARER_TOKEN="" tsc "example.ts" ``` {% /tab %} {% tab label="Python [legacy]" %} ```python from datadog import initialize, api options = { 'api_key': '', 'app_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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=python-legacy) and then save the example to `example.py` and run following commands: ```bash DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" python "example.py" ``` {% /tab %} {% tab label="Ruby [legacy]" %} ```ruby require 'dogapi' api_key = '' app_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](https://dnd37xeb23o8n.cloudfront.net/api/latest.md?code-lang=ruby-legacy) and then save the example to `example.rb` and run following commands: ```bash DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" rb "example.rb" ``` {% /tab %} {% /tabs %}