리텐션 시계열 계산
경고Note: This endpoint is in preview and is subject to change.
If you have any feedback, contact Datadog support.
POST
https://api.datadoghq.com/api/v2/product-analytics/retention/timeserieshttps://api.us3.datadoghq.com/api/v2/product-analytics/retention/timeserieshttps://api.us5.datadoghq.com/api/v2/product-analytics/retention/timeserieshttps://api.datadoghq.eu/api/v2/product-analytics/retention/timeserieshttps://api.ap1.datadoghq.com/api/v2/product-analytics/retention/timeserieshttps://api.ap2.datadoghq.com/api/v2/product-analytics/retention/timeserieshttps://api.uk1.datadoghq.com/api/v2/product-analytics/retention/timeserieshttps://api.ddog-gov.com/api/v2/product-analytics/retention/timeserieshttps://api.us2.ddog-gov.com/api/v2/product-analytics/retention/timeseries개요
리텐션 그리드와 동일한 쿼리 정의를 사용하여 시간 경과에 따른 일련의 값으로 리텐션을 계산합니다.
요청
Body Data (required)
리텐션 시계열 쿼리입니다.
항목
유형
설명
data [required]object
리텐션 스칼라 또는 리텐션 시계열 쿼리를 위한 요청 본문입니다.
attributes [required]object
Attributes of a retention scalar or retention timeseries request.
exclude_anonymous_trafficboolean
Whether to exclude sessions that are not tied to an identified user.
falsefrom [required]int64
Start of the query window, in epoch milliseconds.
query [required]object
Query definition for a retention scalar or retention timeseries request.
computation_scope<oneOf>
Restricts a retention query to part of the grid, so that results can be examined in detail. Omit it to compute the whole grid.
<type=cohort>object
Narrows a retention query to a single cohort row.
target [required]<oneOf>
Selects a cohort, either by index or by the aggregation that rolls all cohorts together.
<type=index>object
Selects a cohort or return period by its zero-based position in the grid.
type [required]enum
The discriminator identifying a target selected by index.
Allowed enum values:
indexvalue [required]int64
Zero-based index of the targeted cohort or return period.
<type=aggregation>object
Selects the rolled-up row that aggregates every cohort, rather than a single cohort.
type [required]enum
The discriminator identifying a target selected by aggregation.
Allowed enum values:
aggregationvalue [required]string
The aggregation that produced the rolled-up row.
type [required]enum
The discriminator identifying a scope narrowed to one cohort.
Allowed enum values:
cohort<type=return_period>object
Narrows a retention query to a single return-period column.
target [required]object
Selects a cohort or return period by its zero-based position in the grid.
type [required]enum
The discriminator identifying a target selected by index.
Allowed enum values:
indexvalue [required]int64
Zero-based index of the targeted cohort or return period.
type [required]enum
The discriminator identifying a scope narrowed to one return period.
Allowed enum values:
return_period<type=cell>object
Narrows a retention query to a single cell, at the intersection of one cohort and one return period.
cohort_target [required]<oneOf>
Selects a cohort, either by index or by the aggregation that rolls all cohorts together.
<type=index>object
Selects a cohort or return period by its zero-based position in the grid.
type [required]enum
The discriminator identifying a target selected by index.
Allowed enum values:
indexvalue [required]int64
Zero-based index of the targeted cohort or return period.
<type=aggregation>object
Selects the rolled-up row that aggregates every cohort, rather than a single cohort.
type [required]enum
The discriminator identifying a target selected by aggregation.
Allowed enum values:
aggregationvalue [required]string
The aggregation that produced the rolled-up row.
return_period_target [required]object
Selects a cohort or return period by its zero-based position in the grid.
type [required]enum
The discriminator identifying a target selected by index.
Allowed enum values:
indexvalue [required]int64
Zero-based index of the targeted cohort or return period.
type [required]enum
The discriminator identifying a scope narrowed to one grid cell.
Allowed enum values:
cellcompute [required]object
The metric and aggregation applied to a retention query.
aggregation [required]string
The aggregation function applied to the metric, such as count or avg.
metric [required]enum
The retention metric to compute, either an absolute count or a rate.
Allowed enum values:
__dd.retention, __dd.retention_rategroup_by[object]
Splits the results by the values of one or more facets.
facet [required]string
The attribute path to group by.
limitint64
Maximum number of groups to return. Omit it to let the service choose.
should_exclude_missingboolean
Whether to drop entities that have no value for the facet.
falsesortobject
Sort configuration for group-by results.
aggregationstring
The aggregation function to sort by.
metricstring
The metric to sort by.
orderenum
Direction of sort.
descAllowed enum values:
asc, descsourcestring
Audience source backing the group-by, when grouping by an audience rather than a facet.
target [required]enum
Which axis of the retention grid a group-by applies to.
Allowed enum values:
cohort, return_periodsearch [required]object
Defines the cohort and return criteria that make up a retention query.
cohort_criteria [required]object
Defines the event that places an entity into a cohort, and how cohorts are bucketed over time.
base_query [required]<oneOf>
A query definition discriminated by the data_source field.
Use product_analytics for standard event queries, or
product_analytics_occurrence for occurrence-filtered queries.
<data_source=product_analytics>object
A standard Product Analytics event query.
data_source [required]enum
The data source identifier.
Allowed enum values:
product_analyticssearch [required]object
Search parameters for an event query.
querystring
The search query using Datadog search syntax.
<data_source=product_analytics_occurrence>object
A Product Analytics occurrence-filtered query.
data_source [required]enum
The data source identifier for occurrence queries.
Allowed enum values:
product_analytics_occurrencesearch [required]object
Search parameters for an occurrence query.
occurrencesobject
Filter for occurrence-based queries.
metaobject
Additional metadata.
<any-key>string
operator [required]string
Comparison operator (=, >=, <=, >, <).
value [required]string
The occurrence count threshold as a string.
querystring
The search query using Datadog search syntax.
time_interval [required]<oneOf>
A retention interval, either aligned to calendar boundaries or of a fixed length. Cohort criteria use calendar intervals; return criteria use fixed intervals.
<type=calendar>object
A retention interval aligned to calendar boundaries.
type [required]enum
The discriminator identifying a calendar-aligned retention interval.
Allowed enum values:
calendarvalue [required]object
A calendar-aligned bucket definition, such as "every 1 week starting on Monday".
alignmentstring
Where each bucket starts within the calendar unit. Use an hour for day (for example 1am or 14),
a day name for week (for example monday), or an ordinal for month (for example 1st).
quantityint64
Number of calendar units per bucket.
timezonestring
Timezone used to align the buckets.
type [required]enum
Calendar unit used to bucket cohorts.
Allowed enum values:
minute, hour, day, week, month, quarter, year<type=fixed>object
A retention interval of fixed length, such as "7 days".
type [required]enum
The discriminator identifying a fixed-length retention interval.
Allowed enum values:
fixedunit [required]enum
Time unit for a fixed-length retention interval.
Allowed enum values:
day, week, monthvalue [required]double
Length of the interval, expressed in unit.
filtersobject
Filters narrowing the events considered by a retention query.
audience_filtersobject
Audience filter definitions for targeting specific user segments.
accounts[object]
Account audience queries.
name [required]string
Name of this query, referenced in the formula.
querystring
Search query for filtering accounts.
formulastring
Boolean formula combining audience queries by name.
segments[object]
Segment audience queries.
name [required]string
Name of this query, referenced in the formula.
segment_id [required]uuid
UUID of the segment to filter by.
users[object]
User audience queries.
name [required]string
Name of this query, referenced in the formula.
querystring
Search query for filtering users.
string_filterstring
Free-text search query applied to the events.
retention_entity [required]enum
The entity whose retention is measured.
Allowed enum values:
@usr.id, @account.idreturn_condition [required]enum
When an entity counts as having returned. Use conversion_on to count only entities that
returned during the period itself, or conversion_on_or_after to also count later returns.
Allowed enum values:
conversion_on, conversion_on_or_afterreturn_criteriaobject
Defines the event that counts as a return, and the window in which it must occur.
base_query [required]<oneOf>
A query definition discriminated by the data_source field.
Use product_analytics for standard event queries, or
product_analytics_occurrence for occurrence-filtered queries.
<data_source=product_analytics>object
A standard Product Analytics event query.
data_source [required]enum
The data source identifier.
Allowed enum values:
product_analyticssearch [required]object
Search parameters for an event query.
querystring
The search query using Datadog search syntax.
<data_source=product_analytics_occurrence>object
A Product Analytics occurrence-filtered query.
data_source [required]enum
The data source identifier for occurrence queries.
Allowed enum values:
product_analytics_occurrencesearch [required]object
Search parameters for an occurrence query.
occurrencesobject
Filter for occurrence-based queries.
metaobject
Additional metadata.
<any-key>string
operator [required]string
Comparison operator (=, >=, <=, >, <).
value [required]string
The occurrence count threshold as a string.
querystring
The search query using Datadog search syntax.
time_interval<oneOf>
A retention interval, either aligned to calendar boundaries or of a fixed length. Cohort criteria use calendar intervals; return criteria use fixed intervals.
<type=calendar>object
A retention interval aligned to calendar boundaries.
type [required]enum
The discriminator identifying a calendar-aligned retention interval.
Allowed enum values:
calendarvalue [required]object
A calendar-aligned bucket definition, such as "every 1 week starting on Monday".
alignmentstring
Where each bucket starts within the calendar unit. Use an hour for day (for example 1am or 14),
a day name for week (for example monday), or an ordinal for month (for example 1st).
quantityint64
Number of calendar units per bucket.
timezonestring
Timezone used to align the buckets.
type [required]enum
Calendar unit used to bucket cohorts.
Allowed enum values:
minute, hour, day, week, month, quarter, year<type=fixed>object
A retention interval of fixed length, such as "7 days".
type [required]enum
The discriminator identifying a fixed-length retention interval.
Allowed enum values:
fixedunit [required]enum
Time unit for a fixed-length retention interval.
Allowed enum values:
day, week, monthvalue [required]double
Length of the interval, expressed in unit.
to [required]int64
End of the query window, in epoch milliseconds.
type [required]enum
The resource type identifier for a retention scalar or retention timeseries request.
Allowed enum values:
formula_retention_request{
"data": {
"attributes": {
"exclude_anonymous_traffic": false,
"from": 1756425600000,
"query": {
"computation_scope": {
"target": {
"type": "index",
"value": 0
},
"type": "cohort"
},
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"group_by": [
{
"facet": "@geo.country",
"limit": 10,
"should_exclude_missing": false,
"sort": {
"aggregation": "count",
"metric": "string",
"order": "string"
},
"source": "string",
"target": "cohort"
}
],
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"filters": {
"audience_filters": {
"accounts": [
{
"name": "",
"query": "string"
}
],
"formula": "u",
"segments": [
{
"name": "",
"segment_id": "00000000-0000-0000-0000-000000000000"
}
],
"users": [
{
"name": "u",
"query": "*"
}
]
},
"string_filter": "string"
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}응답
OK
Response for a timeseries analytics query.
항목
유형
설명
dataobject
Data object for a timeseries analytics response.
attributesobject
Attributes of a timeseries analytics response, containing series data, timestamps, and interval definitions.
intervals[object]
Interval definitions describing the time buckets used in the response.
millisecondsint64
The duration of each time bucket in milliseconds.
start_timeint64
The start of this interval as an epoch timestamp in milliseconds.
times[integer]
Epoch timestamps (in milliseconds) for each bucket in this interval.
typestring
The interval type (e.g., fixed or auto-computed bucket size).
series[object]
The list of series, each corresponding to a query or group-by combination.
group_tags[string]
The group-by tag values that identify this series.
query_indexint64
The index of the query that produced this series.
unit[object]
Unit definitions for the series values.
familystring
The unit family (e.g., time, bytes).
idint64
Numeric identifier for the unit.
namestring
The full name of the unit (e.g., nanosecond).
pluralstring
Plural form of the unit name (e.g., nanoseconds).
scale_factordouble
Conversion factor relative to the base unit of the family.
short_namestring
Abbreviated unit name (e.g., ns).
times[integer]
Timestamps for each data point (epoch milliseconds).
values[array]
Values for each series at each time point.
idstring
Unique identifier for this response data object.
typeenum
The resource type identifier for a timeseries analytics response.
Allowed enum values:
timeseries_responsemetaobject
Metadata for a Product Analytics query response.
request_idstring
Unique identifier of the query.
statusenum
The execution status of a Product Analytics query.
Allowed enum values:
done, running, timeout{
"data": {
"attributes": {
"intervals": [
{
"milliseconds": "integer",
"start_time": "integer",
"times": [],
"type": "string"
}
],
"series": [
{
"group_tags": [],
"query_index": "integer",
"unit": [
{
"family": "time",
"id": "integer",
"name": "nanosecond",
"plural": "string",
"scale_factor": "number",
"short_name": "string"
}
]
}
],
"times": [],
"values": []
},
"id": "string",
"type": "string"
},
"meta": {
"request_id": "string",
"status": "string"
}
}Bad Request
API error response.
항목
유형
설명
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Not Authorized
API error response.
항목
유형
설명
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Too many requests
API error response.
항목
유형
설명
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}코드 예시
리텐션 시계열 계산
# 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 POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/retention/timeseries" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.retention_rate"
},
"search": {
"cohort_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Signup"
}
},
"time_interval": {
"type": "calendar",
"value": {
"alignment": "monday",
"quantity": 1,
"timezone": "UTC",
"type": "week"
}
}
},
"retention_entity": "@usr.id",
"return_condition": "conversion_on_or_after",
"return_criteria": {
"base_query": {
"data_source": "product_analytics",
"search": {
"query": "@type:view"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_retention_request"
}
}
EOF리텐션 시계열 계산
// Compute retention timeseries 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() {
body := datadogV2.ProductAnalyticsFormulaRetentionRequest{
Data: datadogV2.ProductAnalyticsFormulaRetentionRequestData{
Attributes: datadogV2.ProductAnalyticsFormulaRetentionRequestAttributes{
ExcludeAnonymousTraffic: datadog.PtrBool(false),
From: 1756425600000,
Query: datadogV2.ProductAnalyticsFormulaRetentionQuery{
ComputationScope: &datadogV2.ProductAnalyticsRetentionScope{
ProductAnalyticsRetentionCohortScope: &datadogV2.ProductAnalyticsRetentionCohortScope{
Target: datadogV2.ProductAnalyticsRetentionCohortTarget{
ProductAnalyticsRetentionIndexTarget: &datadogV2.ProductAnalyticsRetentionIndexTarget{
Type: datadogV2.PRODUCTANALYTICSRETENTIONINDEXTARGETTYPE_INDEX,
Value: 0,
}},
Type: datadogV2.PRODUCTANALYTICSRETENTIONCOHORTSCOPETYPE_COHORT,
}},
Compute: datadogV2.ProductAnalyticsRetentionCompute{
Aggregation: "count",
Metric: datadogV2.PRODUCTANALYTICSRETENTIONCOMPUTEMETRIC_RETENTION_RATE,
},
GroupBy: []datadogV2.ProductAnalyticsRetentionGroupBy{
{
Facet: "@geo.country",
Limit: datadog.PtrInt64(10),
ShouldExcludeMissing: datadog.PtrBool(false),
Sort: &datadogV2.ProductAnalyticsGroupBySort{
Aggregation: datadog.PtrString("count"),
Order: datadogV2.QUERYSORTORDER_DESC.Ptr(),
},
Target: datadogV2.PRODUCTANALYTICSRETENTIONGROUPBYTARGET_COHORT,
},
},
Search: datadogV2.ProductAnalyticsRetentionSearch{
CohortCriteria: datadogV2.ProductAnalyticsRetentionCohortCriteria{
BaseQuery: datadogV2.ProductAnalyticsBaseQuery{
ProductAnalyticsEventQuery: &datadogV2.ProductAnalyticsEventQuery{
DataSource: datadogV2.PRODUCTANALYTICSEVENTQUERYDATASOURCE_PRODUCT_ANALYTICS,
Search: datadogV2.ProductAnalyticsEventSearch{
Query: datadog.PtrString("@type:view"),
},
}},
TimeInterval: datadogV2.ProductAnalyticsRetentionTimeInterval{
ProductAnalyticsRetentionCalendarTimeInterval: &datadogV2.ProductAnalyticsRetentionCalendarTimeInterval{
Type: datadogV2.PRODUCTANALYTICSRETENTIONCALENDARTIMEINTERVALTYPE_CALENDAR,
Value: datadogV2.ProductAnalyticsCalendarInterval{
Alignment: datadog.PtrString("monday"),
Quantity: datadog.PtrInt64(1),
Timezone: datadog.PtrString("UTC"),
Type: datadogV2.PRODUCTANALYTICSCALENDARINTERVALTYPE_WEEK,
},
}},
},
Filters: &datadogV2.ProductAnalyticsRetentionFilters{
AudienceFilters: &datadogV2.ProductAnalyticsAudienceFilters{
Accounts: []datadogV2.ProductAnalyticsAudienceAccountSubquery{
{
Name: "",
},
},
Formula: datadog.PtrString("u"),
Segments: []datadogV2.ProductAnalyticsAudienceSegmentSubquery{
{
Name: "",
SegmentId: uuid.MustParse("00000000-0000-0000-0000-000000000000"),
},
},
Users: []datadogV2.ProductAnalyticsAudienceUserSubquery{
{
Name: "u",
Query: datadog.PtrString("*"),
},
},
},
},
RetentionEntity: datadogV2.PRODUCTANALYTICSRETENTIONENTITY_USER_ID,
ReturnCondition: datadogV2.PRODUCTANALYTICSRETENTIONRETURNCONDITION_CONVERSION_ON_OR_AFTER,
ReturnCriteria: &datadogV2.ProductAnalyticsRetentionReturnCriteria{
BaseQuery: datadogV2.ProductAnalyticsBaseQuery{
ProductAnalyticsEventQuery: &datadogV2.ProductAnalyticsEventQuery{
DataSource: datadogV2.PRODUCTANALYTICSEVENTQUERYDATASOURCE_PRODUCT_ANALYTICS,
Search: datadogV2.ProductAnalyticsEventSearch{
Query: datadog.PtrString("@type:view"),
},
}},
TimeInterval: &datadogV2.ProductAnalyticsRetentionTimeInterval{
ProductAnalyticsRetentionCalendarTimeInterval: &datadogV2.ProductAnalyticsRetentionCalendarTimeInterval{
Type: datadogV2.PRODUCTANALYTICSRETENTIONCALENDARTIMEINTERVALTYPE_CALENDAR,
Value: datadogV2.ProductAnalyticsCalendarInterval{
Alignment: datadog.PtrString("monday"),
Quantity: datadog.PtrInt64(1),
Timezone: datadog.PtrString("UTC"),
Type: datadogV2.PRODUCTANALYTICSCALENDARINTERVALTYPE_WEEK,
},
}},
},
},
},
To: 1756857600000,
},
Type: datadogV2.PRODUCTANALYTICSFORMULARETENTIONREQUESTTYPE_FORMULA_RETENTION_REQUEST,
},
}
ctx := datadog.NewDefaultContext(context.Background())
ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
configuration := datadog.NewConfiguration()
configuration.SetUnstableOperationEnabled("v2.QueryProductAnalyticsRetentionTimeseries", true)
apiClient := datadog.NewAPIClient(configuration)
api := datadogV2.NewProductAnalyticsApi(apiClient)
resp, r, err := api.QueryProductAnalyticsRetentionTimeseries(ctx, body)
if err != nil {
fmt.Fprintf(os.Stderr, "Error when calling `ProductAnalyticsApi.QueryProductAnalyticsRetentionTimeseries`: %v\n", err)
fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
}
responseContent, _ := json.MarshalIndent(resp, "", " ")
fmt.Fprintf(os.Stdout, "Response from `ProductAnalyticsApi.QueryProductAnalyticsRetentionTimeseries`:\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"리텐션 시계열 계산
// Compute retention timeseries returns "OK" response
import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.ProductAnalyticsApi;
import com.datadog.api.client.v2.model.ProductAnalyticsAudienceAccountSubquery;
import com.datadog.api.client.v2.model.ProductAnalyticsAudienceFilters;
import com.datadog.api.client.v2.model.ProductAnalyticsAudienceSegmentSubquery;
import com.datadog.api.client.v2.model.ProductAnalyticsAudienceUserSubquery;
import com.datadog.api.client.v2.model.ProductAnalyticsBaseQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsCalendarInterval;
import com.datadog.api.client.v2.model.ProductAnalyticsCalendarIntervalType;
import com.datadog.api.client.v2.model.ProductAnalyticsEventQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsEventQueryDataSource;
import com.datadog.api.client.v2.model.ProductAnalyticsEventSearch;
import com.datadog.api.client.v2.model.ProductAnalyticsFormulaRetentionQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsFormulaRetentionRequest;
import com.datadog.api.client.v2.model.ProductAnalyticsFormulaRetentionRequestAttributes;
import com.datadog.api.client.v2.model.ProductAnalyticsFormulaRetentionRequestData;
import com.datadog.api.client.v2.model.ProductAnalyticsFormulaRetentionRequestType;
import com.datadog.api.client.v2.model.ProductAnalyticsGroupBySort;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCalendarTimeInterval;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCalendarTimeIntervalType;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCohortCriteria;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCohortScope;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCohortScopeType;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCohortTarget;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionCompute;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionComputeMetric;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionEntity;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionFilters;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionGroupBy;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionGroupByTarget;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionIndexTarget;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionIndexTargetType;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionReturnCondition;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionReturnCriteria;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionScope;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionSearch;
import com.datadog.api.client.v2.model.ProductAnalyticsRetentionTimeInterval;
import com.datadog.api.client.v2.model.ProductAnalyticsTimeseriesResponse;
import com.datadog.api.client.v2.model.QuerySortOrder;
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"));
defaultClient.setUnstableOperationEnabled("v2.queryProductAnalyticsRetentionTimeseries", true);
ProductAnalyticsApi apiInstance = new ProductAnalyticsApi(defaultClient);
ProductAnalyticsFormulaRetentionRequest body =
new ProductAnalyticsFormulaRetentionRequest()
.data(
new ProductAnalyticsFormulaRetentionRequestData()
.attributes(
new ProductAnalyticsFormulaRetentionRequestAttributes()
.excludeAnonymousTraffic(false)
.from(1756425600000L)
.query(
new ProductAnalyticsFormulaRetentionQuery()
.computationScope(
new ProductAnalyticsRetentionScope(
new ProductAnalyticsRetentionCohortScope()
.target(
new ProductAnalyticsRetentionCohortTarget(
new ProductAnalyticsRetentionIndexTarget()
.type(
ProductAnalyticsRetentionIndexTargetType
.INDEX)
.value(0L)))
.type(
ProductAnalyticsRetentionCohortScopeType
.COHORT)))
.compute(
new ProductAnalyticsRetentionCompute()
.aggregation("count")
.metric(
ProductAnalyticsRetentionComputeMetric
.RETENTION_RATE))
.groupBy(
Collections.singletonList(
new ProductAnalyticsRetentionGroupBy()
.facet("@geo.country")
.limit(10L)
.shouldExcludeMissing(false)
.sort(
new ProductAnalyticsGroupBySort()
.aggregation("count")
.order(QuerySortOrder.DESC))
.target(
ProductAnalyticsRetentionGroupByTarget.COHORT)))
.search(
new ProductAnalyticsRetentionSearch()
.cohortCriteria(
new ProductAnalyticsRetentionCohortCriteria()
.baseQuery(
new ProductAnalyticsBaseQuery(
new ProductAnalyticsEventQuery()
.dataSource(
ProductAnalyticsEventQueryDataSource
.PRODUCT_ANALYTICS)
.search(
new ProductAnalyticsEventSearch()
.query("@type:view"))))
.timeInterval(
new ProductAnalyticsRetentionTimeInterval(
new ProductAnalyticsRetentionCalendarTimeInterval()
.type(
ProductAnalyticsRetentionCalendarTimeIntervalType
.CALENDAR)
.value(
new ProductAnalyticsCalendarInterval()
.alignment("monday")
.quantity(1L)
.timezone("UTC")
.type(
ProductAnalyticsCalendarIntervalType
.WEEK)))))
.filters(
new ProductAnalyticsRetentionFilters()
.audienceFilters(
new ProductAnalyticsAudienceFilters()
.accounts(
Collections.singletonList(
new ProductAnalyticsAudienceAccountSubquery()
.name("")))
.formula("u")
.segments(
Collections.singletonList(
new ProductAnalyticsAudienceSegmentSubquery()
.name("")
.segmentId(
UUID.fromString(
"00000000-0000-0000-0000-000000000000"))))
.users(
Collections.singletonList(
new ProductAnalyticsAudienceUserSubquery()
.name("u")
.query("*")))))
.retentionEntity(
ProductAnalyticsRetentionEntity.USER_ID)
.returnCondition(
ProductAnalyticsRetentionReturnCondition
.CONVERSION_ON_OR_AFTER)
.returnCriteria(
new ProductAnalyticsRetentionReturnCriteria()
.baseQuery(
new ProductAnalyticsBaseQuery(
new ProductAnalyticsEventQuery()
.dataSource(
ProductAnalyticsEventQueryDataSource
.PRODUCT_ANALYTICS)
.search(
new ProductAnalyticsEventSearch()
.query("@type:view"))))
.timeInterval(
new ProductAnalyticsRetentionTimeInterval(
new ProductAnalyticsRetentionCalendarTimeInterval()
.type(
ProductAnalyticsRetentionCalendarTimeIntervalType
.CALENDAR)
.value(
new ProductAnalyticsCalendarInterval()
.alignment("monday")
.quantity(1L)
.timezone("UTC")
.type(
ProductAnalyticsCalendarIntervalType
.WEEK)))))))
.to(1756857600000L))
.type(ProductAnalyticsFormulaRetentionRequestType.FORMULA_RETENTION_REQUEST));
try {
ProductAnalyticsTimeseriesResponse result =
apiInstance.queryProductAnalyticsRetentionTimeseries(body);
System.out.println(result);
} catch (ApiException e) {
System.err.println(
"Exception when calling ProductAnalyticsApi#queryProductAnalyticsRetentionTimeseries");
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"리텐션 시계열 계산
"""
Compute retention timeseries returns "OK" response
"""
from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.product_analytics_api import ProductAnalyticsApi
from datadog_api_client.v2.model.product_analytics_audience_account_subquery import (
ProductAnalyticsAudienceAccountSubquery,
)
from datadog_api_client.v2.model.product_analytics_audience_filters import ProductAnalyticsAudienceFilters
from datadog_api_client.v2.model.product_analytics_audience_segment_subquery import (
ProductAnalyticsAudienceSegmentSubquery,
)
from datadog_api_client.v2.model.product_analytics_audience_user_subquery import ProductAnalyticsAudienceUserSubquery
from datadog_api_client.v2.model.product_analytics_calendar_interval import ProductAnalyticsCalendarInterval
from datadog_api_client.v2.model.product_analytics_calendar_interval_type import ProductAnalyticsCalendarIntervalType
from datadog_api_client.v2.model.product_analytics_event_query import ProductAnalyticsEventQuery
from datadog_api_client.v2.model.product_analytics_event_query_data_source import ProductAnalyticsEventQueryDataSource
from datadog_api_client.v2.model.product_analytics_event_search import ProductAnalyticsEventSearch
from datadog_api_client.v2.model.product_analytics_formula_retention_query import ProductAnalyticsFormulaRetentionQuery
from datadog_api_client.v2.model.product_analytics_formula_retention_request import (
ProductAnalyticsFormulaRetentionRequest,
)
from datadog_api_client.v2.model.product_analytics_formula_retention_request_attributes import (
ProductAnalyticsFormulaRetentionRequestAttributes,
)
from datadog_api_client.v2.model.product_analytics_formula_retention_request_data import (
ProductAnalyticsFormulaRetentionRequestData,
)
from datadog_api_client.v2.model.product_analytics_formula_retention_request_type import (
ProductAnalyticsFormulaRetentionRequestType,
)
from datadog_api_client.v2.model.product_analytics_group_by_sort import ProductAnalyticsGroupBySort
from datadog_api_client.v2.model.product_analytics_retention_calendar_time_interval import (
ProductAnalyticsRetentionCalendarTimeInterval,
)
from datadog_api_client.v2.model.product_analytics_retention_calendar_time_interval_type import (
ProductAnalyticsRetentionCalendarTimeIntervalType,
)
from datadog_api_client.v2.model.product_analytics_retention_cohort_criteria import (
ProductAnalyticsRetentionCohortCriteria,
)
from datadog_api_client.v2.model.product_analytics_retention_cohort_scope import ProductAnalyticsRetentionCohortScope
from datadog_api_client.v2.model.product_analytics_retention_cohort_scope_type import (
ProductAnalyticsRetentionCohortScopeType,
)
from datadog_api_client.v2.model.product_analytics_retention_compute import ProductAnalyticsRetentionCompute
from datadog_api_client.v2.model.product_analytics_retention_compute_metric import (
ProductAnalyticsRetentionComputeMetric,
)
from datadog_api_client.v2.model.product_analytics_retention_entity import ProductAnalyticsRetentionEntity
from datadog_api_client.v2.model.product_analytics_retention_filters import ProductAnalyticsRetentionFilters
from datadog_api_client.v2.model.product_analytics_retention_group_by import ProductAnalyticsRetentionGroupBy
from datadog_api_client.v2.model.product_analytics_retention_group_by_target import (
ProductAnalyticsRetentionGroupByTarget,
)
from datadog_api_client.v2.model.product_analytics_retention_index_target import ProductAnalyticsRetentionIndexTarget
from datadog_api_client.v2.model.product_analytics_retention_index_target_type import (
ProductAnalyticsRetentionIndexTargetType,
)
from datadog_api_client.v2.model.product_analytics_retention_return_condition import (
ProductAnalyticsRetentionReturnCondition,
)
from datadog_api_client.v2.model.product_analytics_retention_return_criteria import (
ProductAnalyticsRetentionReturnCriteria,
)
from datadog_api_client.v2.model.product_analytics_retention_search import ProductAnalyticsRetentionSearch
from datadog_api_client.v2.model.query_sort_order import QuerySortOrder
from uuid import UUID
body = ProductAnalyticsFormulaRetentionRequest(
data=ProductAnalyticsFormulaRetentionRequestData(
attributes=ProductAnalyticsFormulaRetentionRequestAttributes(
exclude_anonymous_traffic=False,
_from=1756425600000,
query=ProductAnalyticsFormulaRetentionQuery(
computation_scope=ProductAnalyticsRetentionCohortScope(
target=ProductAnalyticsRetentionIndexTarget(
type=ProductAnalyticsRetentionIndexTargetType.INDEX,
value=0,
),
type=ProductAnalyticsRetentionCohortScopeType.COHORT,
),
compute=ProductAnalyticsRetentionCompute(
aggregation="count",
metric=ProductAnalyticsRetentionComputeMetric.RETENTION_RATE,
),
group_by=[
ProductAnalyticsRetentionGroupBy(
facet="@geo.country",
limit=10,
should_exclude_missing=False,
sort=ProductAnalyticsGroupBySort(
aggregation="count",
order=QuerySortOrder.DESC,
),
target=ProductAnalyticsRetentionGroupByTarget.COHORT,
),
],
search=ProductAnalyticsRetentionSearch(
cohort_criteria=ProductAnalyticsRetentionCohortCriteria(
base_query=ProductAnalyticsEventQuery(
data_source=ProductAnalyticsEventQueryDataSource.PRODUCT_ANALYTICS,
search=ProductAnalyticsEventSearch(
query="@type:view",
),
),
time_interval=ProductAnalyticsRetentionCalendarTimeInterval(
type=ProductAnalyticsRetentionCalendarTimeIntervalType.CALENDAR,
value=ProductAnalyticsCalendarInterval(
alignment="monday",
quantity=1,
timezone="UTC",
type=ProductAnalyticsCalendarIntervalType.WEEK,
),
),
),
filters=ProductAnalyticsRetentionFilters(
audience_filters=ProductAnalyticsAudienceFilters(
accounts=[
ProductAnalyticsAudienceAccountSubquery(
name="",
),
],
formula="u",
segments=[
ProductAnalyticsAudienceSegmentSubquery(
name="",
segment_id=UUID("00000000-0000-0000-0000-000000000000"),
),
],
users=[
ProductAnalyticsAudienceUserSubquery(
name="u",
query="*",
),
],
),
),
retention_entity=ProductAnalyticsRetentionEntity.USER_ID,
return_condition=ProductAnalyticsRetentionReturnCondition.CONVERSION_ON_OR_AFTER,
return_criteria=ProductAnalyticsRetentionReturnCriteria(
base_query=ProductAnalyticsEventQuery(
data_source=ProductAnalyticsEventQueryDataSource.PRODUCT_ANALYTICS,
search=ProductAnalyticsEventSearch(
query="@type:view",
),
),
time_interval=ProductAnalyticsRetentionCalendarTimeInterval(
type=ProductAnalyticsRetentionCalendarTimeIntervalType.CALENDAR,
value=ProductAnalyticsCalendarInterval(
alignment="monday",
quantity=1,
timezone="UTC",
type=ProductAnalyticsCalendarIntervalType.WEEK,
),
),
),
),
),
to=1756857600000,
),
type=ProductAnalyticsFormulaRetentionRequestType.FORMULA_RETENTION_REQUEST,
),
)
configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
configuration.unstable_operations["query_product_analytics_retention_timeseries"] = True
with ApiClient(configuration) as api_client:
api_instance = ProductAnalyticsApi(api_client)
response = api_instance.query_product_analytics_retention_timeseries(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"리텐션 시계열 계산
# Compute retention timeseries returns "OK" response
require "datadog_api_client"
DatadogAPIClient.configure do |config|
config.access_token = ENV["DD_BEARER_TOKEN"]
config.unstable_operations["v2.query_product_analytics_retention_timeseries".to_sym] = true
end
api_instance = DatadogAPIClient::V2::ProductAnalyticsAPI.new
body = DatadogAPIClient::V2::ProductAnalyticsFormulaRetentionRequest.new({
data: DatadogAPIClient::V2::ProductAnalyticsFormulaRetentionRequestData.new({
attributes: DatadogAPIClient::V2::ProductAnalyticsFormulaRetentionRequestAttributes.new({
exclude_anonymous_traffic: false,
from: 1756425600000,
query: DatadogAPIClient::V2::ProductAnalyticsFormulaRetentionQuery.new({
computation_scope: DatadogAPIClient::V2::ProductAnalyticsRetentionCohortScope.new({
target: DatadogAPIClient::V2::ProductAnalyticsRetentionIndexTarget.new({
type: DatadogAPIClient::V2::ProductAnalyticsRetentionIndexTargetType::INDEX,
value: 0,
}),
type: DatadogAPIClient::V2::ProductAnalyticsRetentionCohortScopeType::COHORT,
}),
compute: DatadogAPIClient::V2::ProductAnalyticsRetentionCompute.new({
aggregation: "count",
metric: DatadogAPIClient::V2::ProductAnalyticsRetentionComputeMetric::RETENTION_RATE,
}),
group_by: [
DatadogAPIClient::V2::ProductAnalyticsRetentionGroupBy.new({
facet: "@geo.country",
limit: 10,
should_exclude_missing: false,
sort: DatadogAPIClient::V2::ProductAnalyticsGroupBySort.new({
aggregation: "count",
order: DatadogAPIClient::V2::QuerySortOrder::DESC,
}),
target: DatadogAPIClient::V2::ProductAnalyticsRetentionGroupByTarget::COHORT,
}),
],
search: DatadogAPIClient::V2::ProductAnalyticsRetentionSearch.new({
cohort_criteria: DatadogAPIClient::V2::ProductAnalyticsRetentionCohortCriteria.new({
base_query: DatadogAPIClient::V2::ProductAnalyticsEventQuery.new({
data_source: DatadogAPIClient::V2::ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
search: DatadogAPIClient::V2::ProductAnalyticsEventSearch.new({
query: "@type:view",
}),
}),
time_interval: DatadogAPIClient::V2::ProductAnalyticsRetentionCalendarTimeInterval.new({
type: DatadogAPIClient::V2::ProductAnalyticsRetentionCalendarTimeIntervalType::CALENDAR,
value: DatadogAPIClient::V2::ProductAnalyticsCalendarInterval.new({
alignment: "monday",
quantity: 1,
timezone: "UTC",
type: DatadogAPIClient::V2::ProductAnalyticsCalendarIntervalType::WEEK,
}),
}),
}),
filters: DatadogAPIClient::V2::ProductAnalyticsRetentionFilters.new({
audience_filters: DatadogAPIClient::V2::ProductAnalyticsAudienceFilters.new({
accounts: [
DatadogAPIClient::V2::ProductAnalyticsAudienceAccountSubquery.new({
name: "",
}),
],
formula: "u",
segments: [
DatadogAPIClient::V2::ProductAnalyticsAudienceSegmentSubquery.new({
name: "",
segment_id: "00000000-0000-0000-0000-000000000000",
}),
],
users: [
DatadogAPIClient::V2::ProductAnalyticsAudienceUserSubquery.new({
name: "u",
query: "*",
}),
],
}),
}),
retention_entity: DatadogAPIClient::V2::ProductAnalyticsRetentionEntity::USER_ID,
return_condition: DatadogAPIClient::V2::ProductAnalyticsRetentionReturnCondition::CONVERSION_ON_OR_AFTER,
return_criteria: DatadogAPIClient::V2::ProductAnalyticsRetentionReturnCriteria.new({
base_query: DatadogAPIClient::V2::ProductAnalyticsEventQuery.new({
data_source: DatadogAPIClient::V2::ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
search: DatadogAPIClient::V2::ProductAnalyticsEventSearch.new({
query: "@type:view",
}),
}),
time_interval: DatadogAPIClient::V2::ProductAnalyticsRetentionCalendarTimeInterval.new({
type: DatadogAPIClient::V2::ProductAnalyticsRetentionCalendarTimeIntervalType::CALENDAR,
value: DatadogAPIClient::V2::ProductAnalyticsCalendarInterval.new({
alignment: "monday",
quantity: 1,
timezone: "UTC",
type: DatadogAPIClient::V2::ProductAnalyticsCalendarIntervalType::WEEK,
}),
}),
}),
}),
}),
to: 1756857600000,
}),
type: DatadogAPIClient::V2::ProductAnalyticsFormulaRetentionRequestType::FORMULA_RETENTION_REQUEST,
}),
})
p api_instance.query_product_analytics_retention_timeseries(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"리텐션 시계열 계산
// Compute retention timeseries returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_product_analytics::ProductAnalyticsAPI;
use datadog_api_client::datadogV2::model::ProductAnalyticsAudienceAccountSubquery;
use datadog_api_client::datadogV2::model::ProductAnalyticsAudienceFilters;
use datadog_api_client::datadogV2::model::ProductAnalyticsAudienceSegmentSubquery;
use datadog_api_client::datadogV2::model::ProductAnalyticsAudienceUserSubquery;
use datadog_api_client::datadogV2::model::ProductAnalyticsBaseQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsCalendarInterval;
use datadog_api_client::datadogV2::model::ProductAnalyticsCalendarIntervalType;
use datadog_api_client::datadogV2::model::ProductAnalyticsEventQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsEventQueryDataSource;
use datadog_api_client::datadogV2::model::ProductAnalyticsEventSearch;
use datadog_api_client::datadogV2::model::ProductAnalyticsFormulaRetentionQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsFormulaRetentionRequest;
use datadog_api_client::datadogV2::model::ProductAnalyticsFormulaRetentionRequestAttributes;
use datadog_api_client::datadogV2::model::ProductAnalyticsFormulaRetentionRequestData;
use datadog_api_client::datadogV2::model::ProductAnalyticsFormulaRetentionRequestType;
use datadog_api_client::datadogV2::model::ProductAnalyticsGroupBySort;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCalendarTimeInterval;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCalendarTimeIntervalType;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCohortCriteria;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCohortScope;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCohortScopeType;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCohortTarget;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionCompute;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionComputeMetric;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionEntity;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionFilters;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionGroupBy;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionGroupByTarget;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionIndexTarget;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionIndexTargetType;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionReturnCondition;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionReturnCriteria;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionScope;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionSearch;
use datadog_api_client::datadogV2::model::ProductAnalyticsRetentionTimeInterval;
use datadog_api_client::datadogV2::model::QuerySortOrder;
use uuid::Uuid;
#[tokio::main]
async fn main() {
let body =
ProductAnalyticsFormulaRetentionRequest::new(
ProductAnalyticsFormulaRetentionRequestData::new(
ProductAnalyticsFormulaRetentionRequestAttributes::new(
1756425600000,
ProductAnalyticsFormulaRetentionQuery::new(
ProductAnalyticsRetentionCompute::new(
"count".to_string(),
ProductAnalyticsRetentionComputeMetric::RETENTION_RATE,
),
ProductAnalyticsRetentionSearch::new(
ProductAnalyticsRetentionCohortCriteria::new(
ProductAnalyticsBaseQuery::ProductAnalyticsEventQuery(
Box::new(
ProductAnalyticsEventQuery::new(
ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
ProductAnalyticsEventSearch::new().query("@type:view".to_string()),
),
),
),
ProductAnalyticsRetentionTimeInterval::ProductAnalyticsRetentionCalendarTimeInterval(
Box::new(
ProductAnalyticsRetentionCalendarTimeInterval::new(
ProductAnalyticsRetentionCalendarTimeIntervalType::CALENDAR,
ProductAnalyticsCalendarInterval::new(
ProductAnalyticsCalendarIntervalType::WEEK,
)
.alignment("monday".to_string())
.quantity(1)
.timezone("UTC".to_string()),
),
),
),
),
ProductAnalyticsRetentionEntity::USER_ID,
ProductAnalyticsRetentionReturnCondition::CONVERSION_ON_OR_AFTER,
)
.filters(
ProductAnalyticsRetentionFilters
::new().audience_filters(
ProductAnalyticsAudienceFilters::new()
.accounts(vec![ProductAnalyticsAudienceAccountSubquery::new("".to_string())])
.formula("u".to_string())
.segments(
vec![
ProductAnalyticsAudienceSegmentSubquery::new(
"".to_string(),
Uuid::parse_str(
"00000000-0000-0000-0000-000000000000",
).expect("invalid UUID"),
)
],
)
.users(
vec![
ProductAnalyticsAudienceUserSubquery::new(
"u".to_string(),
).query("*".to_string())
],
),
),
)
.return_criteria(
ProductAnalyticsRetentionReturnCriteria::new(
ProductAnalyticsBaseQuery::ProductAnalyticsEventQuery(
Box::new(
ProductAnalyticsEventQuery::new(
ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
ProductAnalyticsEventSearch::new().query("@type:view".to_string()),
),
),
),
).time_interval(
ProductAnalyticsRetentionTimeInterval
::ProductAnalyticsRetentionCalendarTimeInterval(
Box::new(
ProductAnalyticsRetentionCalendarTimeInterval::new(
ProductAnalyticsRetentionCalendarTimeIntervalType::CALENDAR,
ProductAnalyticsCalendarInterval::new(
ProductAnalyticsCalendarIntervalType::WEEK,
)
.alignment("monday".to_string())
.quantity(1)
.timezone("UTC".to_string()),
),
),
),
),
),
)
.computation_scope(
ProductAnalyticsRetentionScope::ProductAnalyticsRetentionCohortScope(
Box::new(
ProductAnalyticsRetentionCohortScope::new(
ProductAnalyticsRetentionCohortTarget::ProductAnalyticsRetentionIndexTarget(
Box::new(
ProductAnalyticsRetentionIndexTarget::new(
ProductAnalyticsRetentionIndexTargetType::INDEX,
0,
),
),
),
ProductAnalyticsRetentionCohortScopeType::COHORT,
),
),
),
)
.group_by(
vec![
ProductAnalyticsRetentionGroupBy::new(
"@geo.country".to_string(),
ProductAnalyticsRetentionGroupByTarget::COHORT,
)
.limit(10)
.should_exclude_missing(false)
.sort(
ProductAnalyticsGroupBySort::new()
.aggregation("count".to_string())
.order(QuerySortOrder::DESC),
)
],
),
1756857600000,
).exclude_anonymous_traffic(false),
ProductAnalyticsFormulaRetentionRequestType::FORMULA_RETENTION_REQUEST,
),
);
let mut configuration = datadog::Configuration::new();
configuration
.set_unstable_operation_enabled("v2.query_product_analytics_retention_timeseries", true);
let api = ProductAnalyticsAPI::with_config(configuration);
let resp = api.query_product_analytics_retention_timeseries(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="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="us3.datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="us5.datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="datadoghq.eu" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="ap1.datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="ap2.datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="uk1.datadoghq.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="ddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo runDD_SITE="us2.ddog-gov.com" DD_API_KEY="<API-KEY>" DD_APP_KEY="<APP-KEY>" cargo run리텐션 시계열 계산
/**
* Compute retention timeseries 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,
},
},
});
configuration.unstableOperations[
"v2.queryProductAnalyticsRetentionTimeseries"
] = true;
const apiInstance = new v2.ProductAnalyticsApi(configuration);
const params: v2.ProductAnalyticsApiQueryProductAnalyticsRetentionTimeseriesRequest =
{
body: {
data: {
attributes: {
excludeAnonymousTraffic: false,
from: 1756425600000,
query: {
computationScope: {
target: {
type: "index",
value: 0,
},
type: "cohort",
},
compute: {
aggregation: "count",
metric: "__dd.retention_rate",
},
groupBy: [
{
facet: "@geo.country",
limit: 10,
shouldExcludeMissing: false,
sort: {
aggregation: "count",
order: "desc",
},
target: "cohort",
},
],
search: {
cohortCriteria: {
baseQuery: {
dataSource: "product_analytics",
search: {
query: "@type:view",
},
},
timeInterval: {
type: "calendar",
value: {
alignment: "monday",
quantity: 1,
timezone: "UTC",
type: "week",
},
},
},
filters: {
audienceFilters: {
accounts: [
{
name: "",
},
],
formula: "u",
segments: [
{
name: "",
segmentId: "00000000-0000-0000-0000-000000000000",
},
],
users: [
{
name: "u",
query: "*",
},
],
},
},
retentionEntity: "@usr.id",
returnCondition: "conversion_on_or_after",
returnCriteria: {
baseQuery: {
dataSource: "product_analytics",
search: {
query: "@type:view",
},
},
timeInterval: {
type: "calendar",
value: {
alignment: "monday",
quantity: 1,
timezone: "UTC",
type: "week",
},
},
},
},
},
to: 1756857600000,
},
type: "formula_retention_request",
},
},
};
apiInstance
.queryProductAnalyticsRetentionTimeseries(params)
.then((data: v2.ProductAnalyticsTimeseriesResponse) => {
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"