Update targeting rules for a flag
PUT
https://api.datadoghq.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.us3.datadoghq.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.us5.datadoghq.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.datadoghq.eu/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.ap1.datadoghq.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.ap2.datadoghq.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.uk1.datadoghq.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.ddog-gov.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationshttps://api.us2.ddog-gov.com/api/v2/feature-flags/{feature_flag_id}/environments/{environment_id}/allocationsPrésentation
Updates targeting rules (allocations) for a specific feature flag in a specific environment. This operation replaces the existing allocation set with the request payload.
OAuth apps require the feature_flag_config_write authorization scope to access this endpoint.
Arguments
Paramètres du chemin
Name
Type
Description
feature_flag_id [required]string
The ID of the feature flag.
environment_id [required]string
The ID of the environment.
Requête
Body Data (required)
Champ
Type
Description
data [required][object]
Targeting rules (allocations) to replace existing ones with.
attributes [required]object
Request to create or update a targeting rule (allocation) for a feature flag environment.
experiment_idstring
The experiment ID for experiment-linked allocations.
exposure_scheduleobject
Progressive release request payload.
absolute_start_timedate-time
The absolute UTC start time for this schedule.
control_variant_idstring
The control variant ID used for experiment comparisons.
control_variant_keystring
The control variant key used during creation workflows.
iduuid
The unique identifier of the progressive rollout.
rollout_options [required]object
Rollout options request payload.
autostartboolean
Whether the schedule should begin automatically.
selection_interval_msint64
Interval in milliseconds for uniform interval strategies.
strategy [required]enum
The progression strategy used by a progressive rollout.
Allowed enum values:
UNIFORM_INTERVALS, NO_ROLLOUTrollout_steps [required][object]
Ordered progression steps for exposure.
exposure_ratio [required]double
The exposure ratio for this step.
grouped_step_index [required]int64
Logical index grouping related steps.
iduuid
The unique identifier of the progression step.
interval_msint64
Step duration in milliseconds.
is_pause_record [required]boolean
Whether this step represents a pause record.
guardrail_metrics[object]
Guardrail metrics used to monitor and auto-pause or abort.
metric_id [required]string
The metric ID to monitor.
trigger_action [required]enum
Action to perform when a guardrail threshold is triggered.
Allowed enum values:
PAUSE, ABORTiduuid
The unique identifier of the targeting rule allocation.
key [required]string
The unique key of the targeting rule allocation.
name [required]string
The display name of the targeting rule.
targeting_rules[object]
Targeting rules that determine audience eligibility.
conditions [required][object]
Conditions that must match for this rule.
attributestring
The user or request attribute to evaluate. Required for inline conditions; omit when saved_filter_id is set.
operatorenum
The operator used in a targeting condition.
Allowed enum values:
LT, LTE, GT, GTE, MATCHES, NOT_MATCHES, ONE_OF, NOT_ONE_OF, IS_NULL, EQUALSsaved_filter_iduuid
The ID of a saved filter to reference as this condition. Mutually exclusive
with operator, attribute, and value. When set, the saved filter's
targeting rules are evaluated in place of an inline predicate.
value[string]
Values used by the selected operator. Required for inline conditions; omit when saved_filter_id is set.
type [required]enum
The type of targeting rule (called allocation in the API model).
Allowed enum values:
FEATURE_GATE, CANARYvariant_weights[object]
Variant distribution weights.
value [required]double
The percentage weight for this variant.
variant_iduuid
The variant ID to assign weight to.
variant_keystring
The variant key to assign weight to.
type [required]enum
The resource type.
Allowed enum values:
allocations{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}Réponse
OK
Response containing a list of targeting rules (allocations).
Champ
Type
Description
data [required][object]
List of targeting rules (allocations).
attributes [required]object
Targeting rule (allocation) details for a feature flag environment.
created_at [required]date-time
The timestamp when the targeting rule allocation was created.
environment_ids [required][string]
Environment IDs associated with this targeting rule allocation.
experiment_idstring
The experiment ID linked to this targeting rule allocation.
exposure_scheduleobject
Progressive release details for a targeting rule allocation.
absolute_start_timedate-time
The absolute UTC start time for this schedule.
allocation_id [required]uuid
The targeting rule allocation ID this progressive rollout belongs to.
control_variant_idstring
The control variant ID used for experiment comparisons.
created_at [required]date-time
The timestamp when the schedule was created.
guardrail_triggered_actionstring
Last guardrail action triggered for this schedule.
guardrail_triggers [required][object]
Guardrail trigger records for this schedule.
allocation_exposure_schedule_id [required]uuid
The progressive rollout ID this trigger belongs to.
created_at [required]date-time
The timestamp when this trigger was created.
flagging_variant_id [required]uuid
The variant ID that triggered this event.
id [required]uuid
The unique identifier of the guardrail trigger.
metric_id [required]string
The metric ID associated with the trigger.
triggered_action [required]string
The action that was triggered.
updated_at [required]date-time
The timestamp when this trigger was last updated.
iduuid
The unique identifier of the progressive rollout.
rollout_options [required]object
Applied progression options for a progressive rollout.
autostart [required]boolean
Whether the schedule starts automatically.
selection_interval_ms [required]int64
Interval in milliseconds for uniform interval strategies.
strategy [required]enum
The progression strategy used by a progressive rollout.
Allowed enum values:
UNIFORM_INTERVALS, NO_ROLLOUTrollout_steps [required][object]
Ordered progression steps for exposure.
allocation_exposure_schedule_id [required]uuid
The progressive rollout ID this step belongs to.
created_at [required]date-time
The timestamp when the progression step was created.
exposure_ratio [required]double
The exposure ratio for this step.
grouped_step_index [required]int64
Logical index grouping related steps.
id [required]uuid
The unique identifier of the progression step.
interval_msint64
Step duration in milliseconds.
is_pause_record [required]boolean
Whether this step represents a pause record.
order_position [required]int64
Sort order for the progression step.
updated_at [required]date-time
The timestamp when the progression step was last updated.
updated_at [required]date-time
The timestamp when the schedule was last updated.
guardrail_metrics [required][object]
Guardrail metrics associated with this targeting rule allocation.
metric_id [required]string
The metric ID to monitor.
trigger_action [required]enum
Action to perform when a guardrail threshold is triggered.
Allowed enum values:
PAUSE, ABORTtriggered_bystring
The signal or system that triggered the action.
iduuid
The unique identifier of the targeting rule allocation.
key [required]string
The unique key of the targeting rule allocation.
name [required]string
The display name of the targeting rule.
order_position [required]int64
Sort order position within the environment.
targeting_rules [required][object]
Conditions associated with this targeting rule allocation.
conditions [required][object]
Conditions evaluated by this targeting rule.
attributestring
The user or request attribute to evaluate. Omitted for saved-filter references.
created_at [required]date-time
The timestamp when the condition was created.
id [required]uuid
The unique identifier of the condition.
operatorenum
The operator used in a targeting condition.
Allowed enum values:
LT, LTE, GT, GTE, MATCHES, NOT_MATCHES, ONE_OF, NOT_ONE_OF, IS_NULL, EQUALSsaved_filter_iduuid
The ID of the saved filter referenced by this condition, or null for inline conditions.
updated_at [required]date-time
The timestamp when the condition was last updated.
value[string]
Values used by the selected operator. Omitted for saved-filter references.
created_at [required]date-time
The timestamp when the targeting rule was created.
id [required]uuid
The unique identifier of the targeting rule.
updated_at [required]date-time
The timestamp when the targeting rule was last updated.
type [required]enum
The type of targeting rule (called allocation in the API model).
Allowed enum values:
FEATURE_GATE, CANARYupdated_at [required]date-time
The timestamp when the targeting rule allocation was last updated.
variant_weights [required][object]
Weighted variant assignments for this targeting rule allocation.
created_atdate-time
The timestamp when the variant weight was created.
iduuid
Unique identifier of the variant weight assignment.
updated_atdate-time
The timestamp when the variant weight was last updated.
value [required]double
The percentage weight for the variant.
variantobject
A variant of a feature flag.
created_atdate-time
The timestamp when the variant was created.
id [required]uuid
The unique identifier of the variant.
key [required]string
The unique key of the variant.
name [required]string
The name of the variant.
updated_atdate-time
The timestamp when the variant was last updated.
value [required]string
The value of the variant as a string.
variant_id [required]uuid
The variant ID.
id [required]uuid
The unique identifier of the targeting rule allocation.
type [required]enum
The resource type.
Allowed enum values:
allocations{
"data": [
{
"attributes": {
"created_at": "2024-01-01T12:00:00Z",
"environment_ids": [
"550e8400-e29b-41d4-a716-446655440001"
],
"experiment_id": "550e8400-e29b-41d4-a716-446655440030",
"exposure_schedule": {
"absolute_start_time": "2025-06-13T12:00:00Z",
"allocation_id": "550e8400-e29b-41d4-a716-446655440020",
"control_variant_id": "550e8400-e29b-41d4-a716-446655440012",
"created_at": "2024-01-01T12:00:00Z",
"guardrail_triggered_action": "PAUSE",
"guardrail_triggers": [
{
"allocation_exposure_schedule_id": "550e8400-e29b-41d4-a716-446655440010",
"created_at": "2024-01-01T12:00:00Z",
"flagging_variant_id": "550e8400-e29b-41d4-a716-446655440001",
"id": "550e8400-e29b-41d4-a716-446655440080",
"metric_id": "metric-error-rate",
"triggered_action": "PAUSE",
"updated_at": "2024-01-01T12:00:00Z"
}
],
"id": "550e8400-e29b-41d4-a716-446655440010",
"rollout_options": {
"autostart": false,
"selection_interval_ms": 3600000,
"strategy": "UNIFORM_INTERVALS"
},
"rollout_steps": [
{
"allocation_exposure_schedule_id": "550e8400-e29b-41d4-a716-446655440010",
"created_at": "2024-01-01T12:00:00Z",
"exposure_ratio": 0.1,
"grouped_step_index": 0,
"id": "550e8400-e29b-41d4-a716-446655440040",
"interval_ms": 3600000,
"is_pause_record": false,
"order_position": 0,
"updated_at": "2024-01-01T12:00:00Z"
}
],
"updated_at": "2024-01-01T12:00:00Z"
},
"guardrail_metrics": [
{
"metric_id": "metric-error-rate",
"trigger_action": "PAUSE",
"triggered_by": "guardrail_monitor"
}
],
"id": "550e8400-e29b-41d4-a716-446655440020",
"key": "prod-rollout",
"name": "Production Rollout",
"order_position": 0,
"targeting_rules": [
{
"conditions": [
{
"attribute": "country",
"created_at": "2024-01-01T12:00:00Z",
"id": "550e8400-e29b-41d4-a716-446655440070",
"operator": "ONE_OF",
"saved_filter_id": "550e8400-e29b-41d4-a716-446655440090",
"updated_at": "2024-01-01T12:00:00Z",
"value": [
"US",
"CA"
]
}
],
"created_at": "2024-01-01T12:00:00Z",
"id": "550e8400-e29b-41d4-a716-446655440060",
"updated_at": "2024-01-01T12:00:00Z"
}
],
"type": "FEATURE_GATE",
"updated_at": "2024-01-01T12:00:00Z",
"variant_weights": [
{
"created_at": "2024-01-01T12:00:00Z",
"id": "59061199-e2ff-46e9-8b40-2193e3b21687",
"updated_at": "2024-01-01T12:00:00Z",
"value": 50,
"variant": {
"created_at": "2023-01-01T00:00:00Z",
"id": "550e8400-e29b-41d4-a716-446655440002",
"key": "variant-abc123",
"name": "Variant ABC123",
"updated_at": "2023-01-01T00:00:00Z",
"value": "true"
},
"variant_id": "550e8400-e29b-41d4-a716-446655440001"
}
]
},
"id": "550e8400-e29b-41d4-a716-446655440020",
"type": "allocations"
}
]
}Accepted - Approval required for this change
Response containing a list of targeting rules (allocations).
Champ
Type
Description
data [required][object]
List of targeting rules (allocations).
attributes [required]object
Targeting rule (allocation) details for a feature flag environment.
created_at [required]date-time
The timestamp when the targeting rule allocation was created.
environment_ids [required][string]
Environment IDs associated with this targeting rule allocation.
experiment_idstring
The experiment ID linked to this targeting rule allocation.
exposure_scheduleobject
Progressive release details for a targeting rule allocation.
absolute_start_timedate-time
The absolute UTC start time for this schedule.
allocation_id [required]uuid
The targeting rule allocation ID this progressive rollout belongs to.
control_variant_idstring
The control variant ID used for experiment comparisons.
created_at [required]date-time
The timestamp when the schedule was created.
guardrail_triggered_actionstring
Last guardrail action triggered for this schedule.
guardrail_triggers [required][object]
Guardrail trigger records for this schedule.
allocation_exposure_schedule_id [required]uuid
The progressive rollout ID this trigger belongs to.
created_at [required]date-time
The timestamp when this trigger was created.
flagging_variant_id [required]uuid
The variant ID that triggered this event.
id [required]uuid
The unique identifier of the guardrail trigger.
metric_id [required]string
The metric ID associated with the trigger.
triggered_action [required]string
The action that was triggered.
updated_at [required]date-time
The timestamp when this trigger was last updated.
iduuid
The unique identifier of the progressive rollout.
rollout_options [required]object
Applied progression options for a progressive rollout.
autostart [required]boolean
Whether the schedule starts automatically.
selection_interval_ms [required]int64
Interval in milliseconds for uniform interval strategies.
strategy [required]enum
The progression strategy used by a progressive rollout.
Allowed enum values:
UNIFORM_INTERVALS, NO_ROLLOUTrollout_steps [required][object]
Ordered progression steps for exposure.
allocation_exposure_schedule_id [required]uuid
The progressive rollout ID this step belongs to.
created_at [required]date-time
The timestamp when the progression step was created.
exposure_ratio [required]double
The exposure ratio for this step.
grouped_step_index [required]int64
Logical index grouping related steps.
id [required]uuid
The unique identifier of the progression step.
interval_msint64
Step duration in milliseconds.
is_pause_record [required]boolean
Whether this step represents a pause record.
order_position [required]int64
Sort order for the progression step.
updated_at [required]date-time
The timestamp when the progression step was last updated.
updated_at [required]date-time
The timestamp when the schedule was last updated.
guardrail_metrics [required][object]
Guardrail metrics associated with this targeting rule allocation.
metric_id [required]string
The metric ID to monitor.
trigger_action [required]enum
Action to perform when a guardrail threshold is triggered.
Allowed enum values:
PAUSE, ABORTtriggered_bystring
The signal or system that triggered the action.
iduuid
The unique identifier of the targeting rule allocation.
key [required]string
The unique key of the targeting rule allocation.
name [required]string
The display name of the targeting rule.
order_position [required]int64
Sort order position within the environment.
targeting_rules [required][object]
Conditions associated with this targeting rule allocation.
conditions [required][object]
Conditions evaluated by this targeting rule.
attributestring
The user or request attribute to evaluate. Omitted for saved-filter references.
created_at [required]date-time
The timestamp when the condition was created.
id [required]uuid
The unique identifier of the condition.
operatorenum
The operator used in a targeting condition.
Allowed enum values:
LT, LTE, GT, GTE, MATCHES, NOT_MATCHES, ONE_OF, NOT_ONE_OF, IS_NULL, EQUALSsaved_filter_iduuid
The ID of the saved filter referenced by this condition, or null for inline conditions.
updated_at [required]date-time
The timestamp when the condition was last updated.
value[string]
Values used by the selected operator. Omitted for saved-filter references.
created_at [required]date-time
The timestamp when the targeting rule was created.
id [required]uuid
The unique identifier of the targeting rule.
updated_at [required]date-time
The timestamp when the targeting rule was last updated.
type [required]enum
The type of targeting rule (called allocation in the API model).
Allowed enum values:
FEATURE_GATE, CANARYupdated_at [required]date-time
The timestamp when the targeting rule allocation was last updated.
variant_weights [required][object]
Weighted variant assignments for this targeting rule allocation.
created_atdate-time
The timestamp when the variant weight was created.
iduuid
Unique identifier of the variant weight assignment.
updated_atdate-time
The timestamp when the variant weight was last updated.
value [required]double
The percentage weight for the variant.
variantobject
A variant of a feature flag.
created_atdate-time
The timestamp when the variant was created.
id [required]uuid
The unique identifier of the variant.
key [required]string
The unique key of the variant.
name [required]string
The name of the variant.
updated_atdate-time
The timestamp when the variant was last updated.
value [required]string
The value of the variant as a string.
variant_id [required]uuid
The variant ID.
id [required]uuid
The unique identifier of the targeting rule allocation.
type [required]enum
The resource type.
Allowed enum values:
allocations{
"data": [
{
"attributes": {
"created_at": "2024-01-01T12:00:00Z",
"environment_ids": [
"550e8400-e29b-41d4-a716-446655440001"
],
"experiment_id": "550e8400-e29b-41d4-a716-446655440030",
"exposure_schedule": {
"absolute_start_time": "2025-06-13T12:00:00Z",
"allocation_id": "550e8400-e29b-41d4-a716-446655440020",
"control_variant_id": "550e8400-e29b-41d4-a716-446655440012",
"created_at": "2024-01-01T12:00:00Z",
"guardrail_triggered_action": "PAUSE",
"guardrail_triggers": [
{
"allocation_exposure_schedule_id": "550e8400-e29b-41d4-a716-446655440010",
"created_at": "2024-01-01T12:00:00Z",
"flagging_variant_id": "550e8400-e29b-41d4-a716-446655440001",
"id": "550e8400-e29b-41d4-a716-446655440080",
"metric_id": "metric-error-rate",
"triggered_action": "PAUSE",
"updated_at": "2024-01-01T12:00:00Z"
}
],
"id": "550e8400-e29b-41d4-a716-446655440010",
"rollout_options": {
"autostart": false,
"selection_interval_ms": 3600000,
"strategy": "UNIFORM_INTERVALS"
},
"rollout_steps": [
{
"allocation_exposure_schedule_id": "550e8400-e29b-41d4-a716-446655440010",
"created_at": "2024-01-01T12:00:00Z",
"exposure_ratio": 0.1,
"grouped_step_index": 0,
"id": "550e8400-e29b-41d4-a716-446655440040",
"interval_ms": 3600000,
"is_pause_record": false,
"order_position": 0,
"updated_at": "2024-01-01T12:00:00Z"
}
],
"updated_at": "2024-01-01T12:00:00Z"
},
"guardrail_metrics": [
{
"metric_id": "metric-error-rate",
"trigger_action": "PAUSE",
"triggered_by": "guardrail_monitor"
}
],
"id": "550e8400-e29b-41d4-a716-446655440020",
"key": "prod-rollout",
"name": "Production Rollout",
"order_position": 0,
"targeting_rules": [
{
"conditions": [
{
"attribute": "country",
"created_at": "2024-01-01T12:00:00Z",
"id": "550e8400-e29b-41d4-a716-446655440070",
"operator": "ONE_OF",
"saved_filter_id": "550e8400-e29b-41d4-a716-446655440090",
"updated_at": "2024-01-01T12:00:00Z",
"value": [
"US",
"CA"
]
}
],
"created_at": "2024-01-01T12:00:00Z",
"id": "550e8400-e29b-41d4-a716-446655440060",
"updated_at": "2024-01-01T12:00:00Z"
}
],
"type": "FEATURE_GATE",
"updated_at": "2024-01-01T12:00:00Z",
"variant_weights": [
{
"created_at": "2024-01-01T12:00:00Z",
"id": "59061199-e2ff-46e9-8b40-2193e3b21687",
"updated_at": "2024-01-01T12:00:00Z",
"value": 50,
"variant": {
"created_at": "2023-01-01T00:00:00Z",
"id": "550e8400-e29b-41d4-a716-446655440002",
"key": "variant-abc123",
"name": "Variant ABC123",
"updated_at": "2023-01-01T00:00:00Z",
"value": "true"
},
"variant_id": "550e8400-e29b-41d4-a716-446655440001"
}
]
},
"id": "550e8400-e29b-41d4-a716-446655440020",
"type": "allocations"
}
]
}Bad Request
API error response.
Champ
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Forbidden
API error response.
Champ
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Not Found
API error response.
Champ
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Conflict
API error response.
Champ
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Too many requests
API error response.
Champ
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Exemple de code
Update targeting rules for a flag in an environment returns "OK" response
# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOF# Path parameters
export feature_flag_id="550e8400-e29b-41d4-a716-446655440000"
export environment_id="550e8400-e29b-41d4-a716-446655440001"
# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X PUT "https://api.${DD_SITE}/api/v2/feature-flags/${feature_flag_id}/environments/${environment_id}/allocations" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": [
{
"type": "allocations",
"attributes": {
"key": "overwrite-allocation-example-feature-flag",
"name": "New targeting rule Example-Feature-Flag",
"targeting_rules": [],
"variant_weights": [
{
"variant_id": "550e8400-e29b-41d4-a716-446655440002",
"value": 100
}
],
"exposure_schedule": {
"rollout_options": {
"strategy": "UNIFORM_INTERVALS",
"autostart": false,
"selection_interval_ms": 86400000
},
"rollout_steps": [
{
"exposure_ratio": 0.05,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 0
},
{
"exposure_ratio": 0.25,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 1
},
{
"exposure_ratio": 1,
"interval_ms": null,
"is_pause_record": false,
"grouped_step_index": 2
}
]
},
"guardrail_metrics": [],
"type": "CANARY"
}
}
]
}
EOFUpdate targeting rules for a flag in an environment returns "OK" response
// Update targeting rules for a flag in an environment returns "OK" response
package main
import (
"context"
"encoding/json"
"fmt"
"os"
"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
"github.com/google/uuid"
)
func main() {
// there is a valid "feature_flag" in the system
FeatureFlagDataAttributesVariants0ID := uuid.MustParse(os.Getenv("FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID"))
FeatureFlagDataID := uuid.MustParse(os.Getenv("FEATURE_FLAG_DATA_ID"))
// there is a valid "environment" in the system
EnvironmentDataID := uuid.MustParse(os.Getenv("ENVIRONMENT_DATA_ID"))
body := datadogV2.OverwriteAllocationsRequest{
Data: []datadogV2.AllocationDataRequest{
{
Type: datadogV2.ALLOCATIONDATATYPE_ALLOCATIONS,
Attributes: datadogV2.UpsertAllocationRequest{
Key: "overwrite-allocation-example-feature-flag",
Name: "New targeting rule Example-Feature-Flag",
TargetingRules: []datadogV2.TargetingRuleRequest{},
VariantWeights: []datadogV2.VariantWeightRequest{
{
VariantId: datadog.PtrUUID(FeatureFlagDataAttributesVariants0ID),
Value: 100,
},
},
ExposureSchedule: &datadogV2.ExposureScheduleRequest{
RolloutOptions: datadogV2.RolloutOptionsRequest{
Strategy: datadogV2.ROLLOUTSTRATEGY_UNIFORM_INTERVALS,
Autostart: *datadog.NewNullableBool(datadog.PtrBool(false)),
SelectionIntervalMs: datadog.PtrInt64(86400000),
},
RolloutSteps: []datadogV2.ExposureRolloutStepRequest{
{
ExposureRatio: 0.05,
IntervalMs: *datadog.NewNullableInt64(nil),
IsPauseRecord: false,
GroupedStepIndex: 0,
},
{
ExposureRatio: 0.25,
IntervalMs: *datadog.NewNullableInt64(nil),
IsPauseRecord: false,
GroupedStepIndex: 1,
},
{
ExposureRatio: 1,
IntervalMs: *datadog.NewNullableInt64(nil),
IsPauseRecord: false,
GroupedStepIndex: 2,
},
},
},
GuardrailMetrics: []datadogV2.GuardrailMetricRequest{},
Type: datadogV2.ALLOCATIONTYPE_CANARY,
},
},
},
}
ctx := datadog.NewDefaultContext(context.Background())
ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
configuration := datadog.NewConfiguration()
apiClient := datadog.NewAPIClient(configuration)
api := datadogV2.NewFeatureFlagsApi(apiClient)
resp, r, err := api.UpdateAllocationsForFeatureFlagInEnvironment(ctx, FeatureFlagDataID, EnvironmentDataID, body)
if err != nil {
fmt.Fprintf(os.Stderr, "Error when calling `FeatureFlagsApi.UpdateAllocationsForFeatureFlagInEnvironment`: %v\n", err)
fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
}
responseContent, _ := json.MarshalIndent(resp, "", " ")
fmt.Fprintf(os.Stdout, "Response from `FeatureFlagsApi.UpdateAllocationsForFeatureFlagInEnvironment`:\n%s\n", responseContent)
}
Instructions
First install the library and its dependencies and then save the example to main.go and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"Update targeting rules for a flag in an environment returns "OK" response
// Update targeting rules for a flag in an environment returns "OK" response
import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.FeatureFlagsApi;
import com.datadog.api.client.v2.model.AllocationDataRequest;
import com.datadog.api.client.v2.model.AllocationDataType;
import com.datadog.api.client.v2.model.AllocationType;
import com.datadog.api.client.v2.model.ExposureRolloutStepRequest;
import com.datadog.api.client.v2.model.ExposureScheduleRequest;
import com.datadog.api.client.v2.model.ListAllocationsResponse;
import com.datadog.api.client.v2.model.OverwriteAllocationsRequest;
import com.datadog.api.client.v2.model.RolloutOptionsRequest;
import com.datadog.api.client.v2.model.RolloutStrategy;
import com.datadog.api.client.v2.model.UpsertAllocationRequest;
import com.datadog.api.client.v2.model.VariantWeightRequest;
import java.util.Arrays;
import java.util.Collections;
import java.util.UUID;
public class Example {
public static void main(String[] args) {
ApiClient defaultClient = ApiClient.getDefaultApiClient();
defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
FeatureFlagsApi apiInstance = new FeatureFlagsApi(defaultClient);
// there is a valid "feature_flag" in the system
UUID FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID = null;
try {
FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID =
UUID.fromString(System.getenv("FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID"));
} catch (IllegalArgumentException e) {
System.err.println("Error parsing UUID: " + e.getMessage());
}
UUID FEATURE_FLAG_DATA_ID = null;
try {
FEATURE_FLAG_DATA_ID = UUID.fromString(System.getenv("FEATURE_FLAG_DATA_ID"));
} catch (IllegalArgumentException e) {
System.err.println("Error parsing UUID: " + e.getMessage());
}
// there is a valid "environment" in the system
UUID ENVIRONMENT_DATA_ID = null;
try {
ENVIRONMENT_DATA_ID = UUID.fromString(System.getenv("ENVIRONMENT_DATA_ID"));
} catch (IllegalArgumentException e) {
System.err.println("Error parsing UUID: " + e.getMessage());
}
OverwriteAllocationsRequest body =
new OverwriteAllocationsRequest()
.data(
Collections.singletonList(
new AllocationDataRequest()
.type(AllocationDataType.ALLOCATIONS)
.attributes(
new UpsertAllocationRequest()
.key("overwrite-allocation-example-feature-flag")
.name("New targeting rule Example-Feature-Flag")
.variantWeights(
Collections.singletonList(
new VariantWeightRequest()
.variantId(FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID)
.value(100.0)))
.exposureSchedule(
new ExposureScheduleRequest()
.rolloutOptions(
new RolloutOptionsRequest()
.strategy(RolloutStrategy.UNIFORM_INTERVALS)
.autostart(false)
.selectionIntervalMs(86400000L))
.rolloutSteps(
Arrays.asList(
new ExposureRolloutStepRequest()
.exposureRatio(0.05)
.intervalMs(null)
.isPauseRecord(false)
.groupedStepIndex(0L),
new ExposureRolloutStepRequest()
.exposureRatio(0.25)
.intervalMs(null)
.isPauseRecord(false)
.groupedStepIndex(1L),
new ExposureRolloutStepRequest()
.exposureRatio(1.0)
.intervalMs(null)
.isPauseRecord(false)
.groupedStepIndex(2L))))
.type(AllocationType.CANARY))));
try {
ListAllocationsResponse result =
apiInstance.updateAllocationsForFeatureFlagInEnvironment(
FEATURE_FLAG_DATA_ID, ENVIRONMENT_DATA_ID, body);
System.out.println(result);
} catch (ApiException e) {
System.err.println(
"Exception when calling FeatureFlagsApi#updateAllocationsForFeatureFlagInEnvironment");
System.err.println("Status code: " + e.getCode());
System.err.println("Reason: " + e.getResponseBody());
System.err.println("Response headers: " + e.getResponseHeaders());
e.printStackTrace();
}
}
}
Instructions
First install the library and its dependencies and then save the example to Example.java and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"Update targeting rules for a flag in an environment returns "OK" response
"""
Update targeting rules for a flag in an environment returns "OK" response
"""
from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.feature_flags_api import FeatureFlagsApi
from datadog_api_client.v2.model.allocation_data_request import AllocationDataRequest
from datadog_api_client.v2.model.allocation_data_type import AllocationDataType
from datadog_api_client.v2.model.allocation_type import AllocationType
from datadog_api_client.v2.model.exposure_rollout_step_request import ExposureRolloutStepRequest
from datadog_api_client.v2.model.exposure_schedule_request import ExposureScheduleRequest
from datadog_api_client.v2.model.overwrite_allocations_request import OverwriteAllocationsRequest
from datadog_api_client.v2.model.rollout_options_request import RolloutOptionsRequest
from datadog_api_client.v2.model.rollout_strategy import RolloutStrategy
from datadog_api_client.v2.model.upsert_allocation_request import UpsertAllocationRequest
from datadog_api_client.v2.model.variant_weight_request import VariantWeightRequest
# there is a valid "feature_flag" in the system
FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID = environ["FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID"]
FEATURE_FLAG_DATA_ID = environ["FEATURE_FLAG_DATA_ID"]
# there is a valid "environment" in the system
ENVIRONMENT_DATA_ID = environ["ENVIRONMENT_DATA_ID"]
body = OverwriteAllocationsRequest(
data=[
AllocationDataRequest(
type=AllocationDataType.ALLOCATIONS,
attributes=UpsertAllocationRequest(
key="overwrite-allocation-example-feature-flag",
name="New targeting rule Example-Feature-Flag",
targeting_rules=[],
variant_weights=[
VariantWeightRequest(
variant_id=FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID,
value=100.0,
),
],
exposure_schedule=ExposureScheduleRequest(
rollout_options=RolloutOptionsRequest(
strategy=RolloutStrategy.UNIFORM_INTERVALS,
autostart=False,
selection_interval_ms=86400000,
),
rollout_steps=[
ExposureRolloutStepRequest(
exposure_ratio=0.05,
interval_ms=None,
is_pause_record=False,
grouped_step_index=0,
),
ExposureRolloutStepRequest(
exposure_ratio=0.25,
interval_ms=None,
is_pause_record=False,
grouped_step_index=1,
),
ExposureRolloutStepRequest(
exposure_ratio=1.0,
interval_ms=None,
is_pause_record=False,
grouped_step_index=2,
),
],
),
guardrail_metrics=[],
type=AllocationType.CANARY,
),
),
],
)
configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
with ApiClient(configuration) as api_client:
api_instance = FeatureFlagsApi(api_client)
response = api_instance.update_allocations_for_feature_flag_in_environment(
feature_flag_id=FEATURE_FLAG_DATA_ID, environment_id=ENVIRONMENT_DATA_ID, body=body
)
print(response)
Instructions
First install the library and its dependencies and then save the example to example.py and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"Update targeting rules for a flag in an environment returns "OK" response
# Update targeting rules for a flag in an environment returns "OK" response
require "datadog_api_client"
DatadogAPIClient.configure do |config|
config.access_token = ENV["DD_BEARER_TOKEN"]
end
api_instance = DatadogAPIClient::V2::FeatureFlagsAPI.new
# there is a valid "feature_flag" in the system
FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID = ENV["FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID"]
FEATURE_FLAG_DATA_ID = ENV["FEATURE_FLAG_DATA_ID"]
# there is a valid "environment" in the system
ENVIRONMENT_DATA_ID = ENV["ENVIRONMENT_DATA_ID"]
body = DatadogAPIClient::V2::OverwriteAllocationsRequest.new({
data: [
DatadogAPIClient::V2::AllocationDataRequest.new({
type: DatadogAPIClient::V2::AllocationDataType::ALLOCATIONS,
attributes: DatadogAPIClient::V2::UpsertAllocationRequest.new({
key: "overwrite-allocation-example-feature-flag",
name: "New targeting rule Example-Feature-Flag",
targeting_rules: [],
variant_weights: [
DatadogAPIClient::V2::VariantWeightRequest.new({
variant_id: FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID,
value: 100,
}),
],
exposure_schedule: DatadogAPIClient::V2::ExposureScheduleRequest.new({
rollout_options: DatadogAPIClient::V2::RolloutOptionsRequest.new({
strategy: DatadogAPIClient::V2::RolloutStrategy::UNIFORM_INTERVALS,
autostart: false,
selection_interval_ms: 86400000,
}),
rollout_steps: [
DatadogAPIClient::V2::ExposureRolloutStepRequest.new({
exposure_ratio: 0.05,
interval_ms: nil,
is_pause_record: false,
grouped_step_index: 0,
}),
DatadogAPIClient::V2::ExposureRolloutStepRequest.new({
exposure_ratio: 0.25,
interval_ms: nil,
is_pause_record: false,
grouped_step_index: 1,
}),
DatadogAPIClient::V2::ExposureRolloutStepRequest.new({
exposure_ratio: 1,
interval_ms: nil,
is_pause_record: false,
grouped_step_index: 2,
}),
],
}),
guardrail_metrics: [],
type: DatadogAPIClient::V2::AllocationType::CANARY,
}),
}),
],
})
p api_instance.update_allocations_for_feature_flag_in_environment(FEATURE_FLAG_DATA_ID, ENVIRONMENT_DATA_ID, body)
Instructions
First install the library and its dependencies and then save the example to example.rb and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"Update targeting rules for a flag in an environment returns "OK" response
// Update targeting rules for a flag in an environment returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_feature_flags::FeatureFlagsAPI;
use datadog_api_client::datadogV2::model::AllocationDataRequest;
use datadog_api_client::datadogV2::model::AllocationDataType;
use datadog_api_client::datadogV2::model::AllocationType;
use datadog_api_client::datadogV2::model::ExposureRolloutStepRequest;
use datadog_api_client::datadogV2::model::ExposureScheduleRequest;
use datadog_api_client::datadogV2::model::OverwriteAllocationsRequest;
use datadog_api_client::datadogV2::model::RolloutOptionsRequest;
use datadog_api_client::datadogV2::model::RolloutStrategy;
use datadog_api_client::datadogV2::model::UpsertAllocationRequest;
use datadog_api_client::datadogV2::model::VariantWeightRequest;
#[tokio::main]
async fn main() {
// there is a valid "feature_flag" in the system
let feature_flag_data_attributes_variants_0_id = uuid::Uuid::parse_str(
&std::env::var("FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID").unwrap(),
)
.expect("Invalid UUID");
let feature_flag_data_id =
uuid::Uuid::parse_str(&std::env::var("FEATURE_FLAG_DATA_ID").unwrap())
.expect("Invalid UUID");
// there is a valid "environment" in the system
let environment_data_id = uuid::Uuid::parse_str(&std::env::var("ENVIRONMENT_DATA_ID").unwrap())
.expect("Invalid UUID");
let body = OverwriteAllocationsRequest::new(vec![AllocationDataRequest::new(
UpsertAllocationRequest::new(
"overwrite-allocation-example-feature-flag".to_string(),
"New targeting rule Example-Feature-Flag".to_string(),
AllocationType::CANARY,
)
.exposure_schedule(ExposureScheduleRequest::new(
RolloutOptionsRequest::new(RolloutStrategy::UNIFORM_INTERVALS)
.autostart(Some(false))
.selection_interval_ms(86400000),
vec![
ExposureRolloutStepRequest::new(0.05, 0, false).interval_ms(None),
ExposureRolloutStepRequest::new(0.25, 1, false).interval_ms(None),
ExposureRolloutStepRequest::new(1.0, 2, false).interval_ms(None),
],
))
.guardrail_metrics(vec![])
.targeting_rules(vec![])
.variant_weights(vec![VariantWeightRequest::new(100.0)
.variant_id(feature_flag_data_attributes_variants_0_id.clone())]),
AllocationDataType::ALLOCATIONS,
)]);
let configuration = datadog::Configuration::new();
let api = FeatureFlagsAPI::with_config(configuration);
let resp = api
.update_allocations_for_feature_flag_in_environment(
feature_flag_data_id.clone(),
environment_data_id.clone(),
body,
)
.await;
if let Ok(value) = resp {
println!("{:#?}", value);
} else {
println!("{:#?}", resp.unwrap_err());
}
}
Instructions
First install the library and its dependencies and then save the example to src/main.rs and run following commands:
DD_SITE="datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="us3.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="us5.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="datadoghq.eu" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="ap1.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="ap2.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="uk1.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="us2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runUpdate targeting rules for a flag in an environment returns "OK" response
/**
* Update targeting rules for a flag in an environment returns "OK" response
*/
import { client, v2 } from "@datadog/datadog-api-client";
const configuration = client.createConfiguration({
authMethods: {
AuthZ: {
accessToken: process.env.DD_BEARER_TOKEN as string,
},
},
});
const apiInstance = new v2.FeatureFlagsApi(configuration);
// there is a valid "feature_flag" in the system
const FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID = process.env
.FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID as string;
const FEATURE_FLAG_DATA_ID = process.env.FEATURE_FLAG_DATA_ID as string;
// there is a valid "environment" in the system
const ENVIRONMENT_DATA_ID = process.env.ENVIRONMENT_DATA_ID as string;
const params: v2.FeatureFlagsApiUpdateAllocationsForFeatureFlagInEnvironmentRequest =
{
body: {
data: [
{
type: "allocations",
attributes: {
key: "overwrite-allocation-example-feature-flag",
name: "New targeting rule Example-Feature-Flag",
targetingRules: [],
variantWeights: [
{
variantId: FEATURE_FLAG_DATA_ATTRIBUTES_VARIANTS_0_ID,
value: 100,
},
],
exposureSchedule: {
rolloutOptions: {
strategy: "UNIFORM_INTERVALS",
autostart: false,
selectionIntervalMs: 86400000,
},
rolloutSteps: [
{
exposureRatio: 0.05,
intervalMs: undefined,
isPauseRecord: false,
groupedStepIndex: 0,
},
{
exposureRatio: 0.25,
intervalMs: undefined,
isPauseRecord: false,
groupedStepIndex: 1,
},
{
exposureRatio: 1,
intervalMs: undefined,
isPauseRecord: false,
groupedStepIndex: 2,
},
],
},
guardrailMetrics: [],
type: "CANARY",
},
},
],
},
featureFlagId: FEATURE_FLAG_DATA_ID,
environmentId: ENVIRONMENT_DATA_ID,
};
apiInstance
.updateAllocationsForFeatureFlagInEnvironment(params)
.then((data: v2.ListAllocationsResponse) => {
console.log(
"API called successfully. Returned data: " + JSON.stringify(data)
);
})
.catch((error: any) => console.error(error));
Instructions
First install the library and its dependencies and then save the example to example.ts and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"