여정 스칼라 분석 계산
https://api.datadoghq.com/api/v2/product-analytics/journey/scalarhttps://api.us3.datadoghq.com/api/v2/product-analytics/journey/scalarhttps://api.us5.datadoghq.com/api/v2/product-analytics/journey/scalarhttps://api.datadoghq.eu/api/v2/product-analytics/journey/scalarhttps://api.ap1.datadoghq.com/api/v2/product-analytics/journey/scalarhttps://api.ap2.datadoghq.com/api/v2/product-analytics/journey/scalarhttps://api.uk1.datadoghq.com/api/v2/product-analytics/journey/scalarhttps://api.ddog-gov.com/api/v2/product-analytics/journey/scalarhttps://api.us2.ddog-gov.com/api/v2/product-analytics/journey/scalar개요
여정 쿼리에 대한 스칼라 결과를 계산하며, 전환 수, 전환율 또는 전환 시간(필요시 그룹화 패싯별로 세분화됨)을 예로 들 수 있습니다.
요청
Body Data (required)
data [required]여정 스칼라 쿼리를 위한 요청 본문입니다.
attributes [required]Attributes of a journey scalar request.
from [required]Start of the query window, in epoch milliseconds.
query [required]Query definition for a journey scalar request.
compute [required]Defines the metric computed over the journey for a scalar query.
aggregation [required]Aggregation function: count, cardinality, avg, median, min, max, sum,
or a percentile of the form pc<N> such as pc95. Defaults to cardinality.
metricMetric to aggregate on. Use a facet path such as @view.time_spent, or one of the
journey metrics __dd.conversion, __dd.conversion_rate, __dd.time_to_convert,
or __dd.dropoff_rate. Defaults to __dd.conversion.
targetA reference to a step, or a range of steps, in the journey.
Use a node target to name a single step, or a path target to name the range
between two steps.
<type=node>A reference to a single step of the journey.
type [required]The discriminator identifying a target that references a single step.
nodevalue [required]Alias of the targeted node.
<type=path>A reference to the range of steps between two nodes of the journey.
end [required]Alias of the node the path ends at.
start [required]Alias of the node the path starts at.
type [required]The discriminator identifying a target that references a range of steps.
pathgroup_bySegments the results by the values of one or more facets.
facet [required]Attribute path to group by.
limitMaximum number of groups to return. Omit it to let the service choose.
should_exclude_missingWhether to exclude entities that have no value for this facet.
falsesortSort configuration for group-by results.
aggregationThe aggregation function to sort by.
metricThe metric to sort by.
orderDirection of sort.
descasc, descsourceAudience dimension to group by, instead of an event facet.
product_analytics_audience_filters.users, product_analytics_audience_filters.accountstargetA reference to a step, or a range of steps, in the journey.
Use a node target to name a single step, or a path target to name the range
between two steps.
<type=node>A reference to a single step of the journey.
type [required]The discriminator identifying a target that references a single step.
nodevalue [required]Alias of the targeted node.
<type=path>A reference to the range of steps between two nodes of the journey.
end [required]Alias of the node the path ends at.
start [required]Alias of the node the path starts at.
type [required]The discriminator identifying a target that references a range of steps.
pathvalue_filtersRestricts the results to these facet values.
query_idCaller-defined identifier echoed back in the results.
search [required]Defines the steps of the journey and the filters applied to it.
expression [required]Expression combining the node aliases in order, for example A -> B -> C.
filtersFilters applied on top of the journey step expression.
audience_filtersRestricts the journey to an audience built from named sub-queries.
Sub-query names must be unique across users, segments, and accounts.
accountsNamed account sub-queries.
name [required]Unique name for this sub-query, referenced from formula.
querySearch query selecting the accounts.
formulaBoolean expression combining the sub-query names with AND, OR, and NOT.
When empty, all sub-queries are combined with AND.
segmentsNamed segment sub-queries.
name [required]Unique name for this sub-query, referenced from formula.
segment_id [required]Identifier of the saved segment.
usersNamed user sub-queries.
name [required]Unique name for this sub-query, referenced from formula.
querySearch query selecting the users.
graph_filtersFilters on journey-level metrics such as time to convert.
name [required]The journey-level metric the graph filter applies to.
__dd.time_to_convert, __dd.session, __dd.dropoff_rateoperator [required]Comparison operator applied to the graph filter value.
=, <, >, <=, >=targetA reference to a step, or a range of steps, in the journey.
Use a node target to name a single step, or a path target to name the range
between two steps.
<type=node>A reference to a single step of the journey.
type [required]The discriminator identifying a target that references a single step.
nodevalue [required]Alias of the targeted node.
<type=path>A reference to the range of steps between two nodes of the journey.
end [required]Alias of the node the path ends at.
start [required]Alias of the node the path starts at.
type [required]The discriminator identifying a target that references a range of steps.
pathvalue [required]Value compared against the metric. Durations are expressed in milliseconds.
string_filterFree-text search query applied to the whole journey.
join_keysIdentity join keys used to stitch events belonging to the same user or session.
primaryPrimary identity join key. Defaults to @session.id.
secondaryAdditional identity join keys.
node_objects [required]Map of node alias to the query matching that step of the journey.
Every alias used in expression must have an entry here.
<any-key>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>A standard Product Analytics event query.
data_source [required]The data source identifier.
product_analyticssearch [required]Search parameters for an event query.
queryThe search query using Datadog search syntax.
<data_source=product_analytics_occurrence>A Product Analytics occurrence-filtered query.
data_source [required]The data source identifier for occurrence queries.
product_analytics_occurrencesearch [required]Search parameters for an occurrence query.
occurrencesFilter for occurrence-based queries.
metaAdditional metadata.
<any-key>operator [required]Comparison operator (=, >=, <=, >, <).
value [required]The occurrence count threshold as a string.
queryThe search query using Datadog search syntax.
to [required]End of the query window, in epoch milliseconds.
type [required]The resource type identifier for a journey timeseries or scalar request.
formula_journey_request{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "string",
"target": {
"type": "node",
"value": "A"
}
},
"group_by": [
{
"facet": "@geo.country",
"limit": "integer",
"should_exclude_missing": false,
"sort": {
"aggregation": "count",
"metric": "string",
"order": "string"
},
"source": "product_analytics_audience_filters.users",
"target": {
"type": "node",
"value": "A"
},
"value_filters": []
}
],
"query_id": "string",
"search": {
"expression": "A -> B",
"filters": {
"audience_filters": {
"accounts": [
{
"name": "enterprise_accounts",
"query": "string"
}
],
"formula": "power_users AND NOT trial_segment",
"segments": [
{
"name": "trial_segment",
"segment_id": "00000000-0000-0000-0000-000000000000"
}
],
"users": [
{
"name": "power_users",
"query": "string"
}
]
},
"graph_filters": [
{
"name": "__dd.time_to_convert",
"operator": "<=",
"target": {
"type": "node",
"value": "A"
},
"value": 60000
}
],
"string_filter": "string"
},
"join_keys": {
"primary": "@session.id",
"secondary": []
},
"node_objects": {
"<any-key>": "undefined"
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}응답
OK
Response for a journey scalar query.
data [required]The single JSON:API resource holding journey scalar results. Its attributes contain one value per group, suitable for a query value or top list widget.
attributes [required]Attributes of a scalar analytics response, containing the result columns.
columnsThe list of result columns, each containing values and metadata.
metaMetadata associated with a scalar response column, including optional unit information.
unitUnit definitions for the column values, if applicable.
familyThe unit family (e.g., time, bytes).
idNumeric identifier for the unit.
nameThe full name of the unit (e.g., nanosecond).
pluralPlural form of the unit name (e.g., nanoseconds).
scale_factorConversion factor relative to the base unit of the family.
short_nameAbbreviated unit name (e.g., ns).
nameColumn name (facet name for group-by, or "query").
typeColumn type.
number, groupvaluesColumn values.
id [required]Identifier of this result.
type [required]The resource type identifier for a journey scalar response.
journey_scalar_response{
"data": {
"attributes": {
"columns": [
{
"meta": {
"unit": [
{
"family": "time",
"id": "integer",
"name": "nanosecond",
"plural": "string",
"scale_factor": "number",
"short_name": "string"
}
]
},
"name": "string",
"type": "string",
"values": []
}
]
},
"id": "00000000-0000-0000-0000-000000000000",
"type": "journey_scalar_response"
}
}Bad Request
API error response.
errors [required]A list of errors.
{
"errors": [
"Bad Request"
]
}Not Authorized
API error response.
errors [required]A list of errors.
{
"errors": [
"Bad Request"
]
}Too many requests
API error response.
errors [required]A list of errors.
{
"errors": [
"Bad Request"
]
}코드 예시
# 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/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"from": 1756425600000,
"query": {
"compute": {
"aggregation": "count",
"metric": "__dd.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/product-analytics/journey/scalar" \
-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.conversion_rate"
},
"search": {
"expression": "A -> B",
"node_objects": {
"A": {
"data_source": "product_analytics",
"search": {
"query": "@type:view @view.name:Login"
}
},
"B": {
"data_source": "product_analytics",
"search": {
"query": "@type:action @action.target.name:Submit"
}
}
}
}
},
"to": 1756857600000
},
"type": "formula_journey_request"
}
}
EOF// Compute journey scalar analytics 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"
)
func main() {
body := datadogV2.ProductAnalyticsJourneyScalarRequest{
Data: datadogV2.ProductAnalyticsJourneyScalarRequestData{
Attributes: datadogV2.ProductAnalyticsJourneyScalarRequestAttributes{
From: 1756425600000,
Query: datadogV2.ProductAnalyticsJourneyScalarQuery{
Compute: datadogV2.ProductAnalyticsJourneyScalarCompute{
Aggregation: "count",
Target: &datadogV2.ProductAnalyticsJourneyTarget{
ProductAnalyticsJourneyNodeTarget: &datadogV2.ProductAnalyticsJourneyNodeTarget{
Type: datadogV2.PRODUCTANALYTICSJOURNEYNODETARGETTYPE_NODE,
Value: "A",
}},
},
GroupBy: []datadogV2.ProductAnalyticsGraphQueryGroupBy{
{
Facet: "@geo.country",
ShouldExcludeMissing: datadog.PtrBool(false),
Sort: &datadogV2.ProductAnalyticsGroupBySort{
Aggregation: datadog.PtrString("count"),
Order: datadogV2.QUERYSORTORDER_DESC.Ptr(),
},
Source: datadogV2.PRODUCTANALYTICSGRAPHQUERYGROUPBYSOURCE_USERS.Ptr(),
Target: &datadogV2.ProductAnalyticsJourneyTarget{
ProductAnalyticsJourneyNodeTarget: &datadogV2.ProductAnalyticsJourneyNodeTarget{
Type: datadogV2.PRODUCTANALYTICSJOURNEYNODETARGETTYPE_NODE,
Value: "A",
}},
ValueFilters: []string{},
},
},
Search: datadogV2.ProductAnalyticsJourneySearch{
Expression: "A -> B",
Filters: &datadogV2.ProductAnalyticsJourneySearchFilters{
AudienceFilters: &datadogV2.ProductAnalyticsJourneyAudienceFilters{
Accounts: []datadogV2.ProductAnalyticsJourneyAudienceAccountQuery{
{
Name: "enterprise_accounts",
},
},
Formula: datadog.PtrString("power_users AND NOT trial_segment"),
Segments: []datadogV2.ProductAnalyticsJourneyAudienceSegmentQuery{
{
Name: "trial_segment",
SegmentId: "00000000-0000-0000-0000-000000000000",
},
},
Users: []datadogV2.ProductAnalyticsJourneyAudienceUserQuery{
{
Name: "power_users",
},
},
},
GraphFilters: []datadogV2.ProductAnalyticsJourneySearchGraphFilter{
{
Name: datadogV2.PRODUCTANALYTICSJOURNEYSEARCHGRAPHFILTERNAME_TIME_TO_CONVERT,
Operator: datadogV2.PRODUCTANALYTICSJOURNEYSEARCHGRAPHFILTEROPERATOR_LESS_THAN_OR_EQUAL,
Target: &datadogV2.ProductAnalyticsJourneyTarget{
ProductAnalyticsJourneyNodeTarget: &datadogV2.ProductAnalyticsJourneyNodeTarget{
Type: datadogV2.PRODUCTANALYTICSJOURNEYNODETARGETTYPE_NODE,
Value: "A",
}},
Value: 60000,
},
},
},
JoinKeys: &datadogV2.ProductAnalyticsJoinKeys{
Primary: datadog.PtrString("@session.id"),
Secondary: []string{},
},
NodeObjects: map[string]datadogV2.ProductAnalyticsBaseQuery{
"A": datadogV2.ProductAnalyticsBaseQuery{
ProductAnalyticsEventQuery: &datadogV2.ProductAnalyticsEventQuery{
DataSource: datadogV2.PRODUCTANALYTICSEVENTQUERYDATASOURCE_PRODUCT_ANALYTICS,
Search: datadogV2.ProductAnalyticsEventSearch{
Query: datadog.PtrString("@type:view @view.name:Login"),
},
}},
"B": datadogV2.ProductAnalyticsBaseQuery{
ProductAnalyticsEventQuery: &datadogV2.ProductAnalyticsEventQuery{
DataSource: datadogV2.PRODUCTANALYTICSEVENTQUERYDATASOURCE_PRODUCT_ANALYTICS,
Search: datadogV2.ProductAnalyticsEventSearch{
Query: datadog.PtrString("@type:action @action.target.name:Submit"),
},
}},
},
},
},
To: 1756857600000,
},
Type: datadogV2.PRODUCTANALYTICSFORMULAJOURNEYREQUESTTYPE_FORMULA_JOURNEY_REQUEST,
},
}
ctx := datadog.NewDefaultContext(context.Background())
ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
configuration := datadog.NewConfiguration()
configuration.SetUnstableOperationEnabled("v2.QueryProductAnalyticsJourneyScalar", true)
apiClient := datadog.NewAPIClient(configuration)
api := datadogV2.NewProductAnalyticsApi(apiClient)
resp, r, err := api.QueryProductAnalyticsJourneyScalar(ctx, body)
if err != nil {
fmt.Fprintf(os.Stderr, "Error when calling `ProductAnalyticsApi.QueryProductAnalyticsJourneyScalar`: %v\n", err)
fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
}
responseContent, _ := json.MarshalIndent(resp, "", " ")
fmt.Fprintf(os.Stdout, "Response from `ProductAnalyticsApi.QueryProductAnalyticsJourneyScalar`:\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"// Compute journey scalar analytics 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.ProductAnalyticsBaseQuery;
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.ProductAnalyticsFormulaJourneyRequestType;
import com.datadog.api.client.v2.model.ProductAnalyticsGraphQueryGroupBy;
import com.datadog.api.client.v2.model.ProductAnalyticsGraphQueryGroupBySource;
import com.datadog.api.client.v2.model.ProductAnalyticsGroupBySort;
import com.datadog.api.client.v2.model.ProductAnalyticsJoinKeys;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyAudienceAccountQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyAudienceFilters;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyAudienceSegmentQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyAudienceUserQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyNodeTarget;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyNodeTargetType;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyScalarCompute;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyScalarQuery;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyScalarRequest;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyScalarRequestAttributes;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyScalarRequestData;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyScalarResponse;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneySearch;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneySearchFilters;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneySearchGraphFilter;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneySearchGraphFilterName;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneySearchGraphFilterOperator;
import com.datadog.api.client.v2.model.ProductAnalyticsJourneyTarget;
import com.datadog.api.client.v2.model.QuerySortOrder;
import java.util.Collections;
import java.util.Map;
public class Example {
public static void main(String[] args) {
ApiClient defaultClient = ApiClient.getDefaultApiClient();
defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
defaultClient.setUnstableOperationEnabled("v2.queryProductAnalyticsJourneyScalar", true);
ProductAnalyticsApi apiInstance = new ProductAnalyticsApi(defaultClient);
ProductAnalyticsJourneyScalarRequest body =
new ProductAnalyticsJourneyScalarRequest()
.data(
new ProductAnalyticsJourneyScalarRequestData()
.attributes(
new ProductAnalyticsJourneyScalarRequestAttributes()
.from(1756425600000L)
.query(
new ProductAnalyticsJourneyScalarQuery()
.compute(
new ProductAnalyticsJourneyScalarCompute()
.aggregation("count")
.target(
new ProductAnalyticsJourneyTarget(
new ProductAnalyticsJourneyNodeTarget()
.type(
ProductAnalyticsJourneyNodeTargetType
.NODE)
.value("A"))))
.groupBy(
Collections.singletonList(
new ProductAnalyticsGraphQueryGroupBy()
.facet("@geo.country")
.shouldExcludeMissing(false)
.sort(
new ProductAnalyticsGroupBySort()
.aggregation("count")
.order(QuerySortOrder.DESC))
.source(
ProductAnalyticsGraphQueryGroupBySource.USERS)
.target(
new ProductAnalyticsJourneyTarget(
new ProductAnalyticsJourneyNodeTarget()
.type(
ProductAnalyticsJourneyNodeTargetType
.NODE)
.value("A")))))
.search(
new ProductAnalyticsJourneySearch()
.expression("A -> B")
.filters(
new ProductAnalyticsJourneySearchFilters()
.audienceFilters(
new ProductAnalyticsJourneyAudienceFilters()
.accounts(
Collections.singletonList(
new ProductAnalyticsJourneyAudienceAccountQuery()
.name(
"enterprise_accounts")))
.formula(
"power_users AND NOT trial_segment")
.segments(
Collections.singletonList(
new ProductAnalyticsJourneyAudienceSegmentQuery()
.name("trial_segment")
.segmentId(
"00000000-0000-0000-0000-000000000000")))
.users(
Collections.singletonList(
new ProductAnalyticsJourneyAudienceUserQuery()
.name("power_users"))))
.graphFilters(
Collections.singletonList(
new ProductAnalyticsJourneySearchGraphFilter()
.name(
ProductAnalyticsJourneySearchGraphFilterName
.TIME_TO_CONVERT)
.operator(
ProductAnalyticsJourneySearchGraphFilterOperator
.LESS_THAN_OR_EQUAL)
.target(
new ProductAnalyticsJourneyTarget(
new ProductAnalyticsJourneyNodeTarget()
.type(
ProductAnalyticsJourneyNodeTargetType
.NODE)
.value("A")))
.value(60000L))))
.joinKeys(
new ProductAnalyticsJoinKeys()
.primary("@session.id"))
.nodeObjects(
Map.ofEntries(
Map.entry(
"A",
new ProductAnalyticsBaseQuery(
new ProductAnalyticsEventQuery()
.dataSource(
ProductAnalyticsEventQueryDataSource
.PRODUCT_ANALYTICS)
.search(
new ProductAnalyticsEventSearch()
.query(
"@type:view"
+ " @view.name:Login")))),
Map.entry(
"B",
new ProductAnalyticsBaseQuery(
new ProductAnalyticsEventQuery()
.dataSource(
ProductAnalyticsEventQueryDataSource
.PRODUCT_ANALYTICS)
.search(
new ProductAnalyticsEventSearch()
.query(
"@type:action"
+ " @action.target.name:Submit"))))))))
.to(1756857600000L))
.type(ProductAnalyticsFormulaJourneyRequestType.FORMULA_JOURNEY_REQUEST));
try {
ProductAnalyticsJourneyScalarResponse result =
apiInstance.queryProductAnalyticsJourneyScalar(body);
System.out.println(result);
} catch (ApiException e) {
System.err.println(
"Exception when calling ProductAnalyticsApi#queryProductAnalyticsJourneyScalar");
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""""
Compute journey scalar analytics 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_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_journey_request_type import (
ProductAnalyticsFormulaJourneyRequestType,
)
from datadog_api_client.v2.model.product_analytics_graph_query_group_by import ProductAnalyticsGraphQueryGroupBy
from datadog_api_client.v2.model.product_analytics_graph_query_group_by_source import (
ProductAnalyticsGraphQueryGroupBySource,
)
from datadog_api_client.v2.model.product_analytics_group_by_sort import ProductAnalyticsGroupBySort
from datadog_api_client.v2.model.product_analytics_join_keys import ProductAnalyticsJoinKeys
from datadog_api_client.v2.model.product_analytics_journey_audience_account_query import (
ProductAnalyticsJourneyAudienceAccountQuery,
)
from datadog_api_client.v2.model.product_analytics_journey_audience_filters import (
ProductAnalyticsJourneyAudienceFilters,
)
from datadog_api_client.v2.model.product_analytics_journey_audience_segment_query import (
ProductAnalyticsJourneyAudienceSegmentQuery,
)
from datadog_api_client.v2.model.product_analytics_journey_audience_user_query import (
ProductAnalyticsJourneyAudienceUserQuery,
)
from datadog_api_client.v2.model.product_analytics_journey_node_target import ProductAnalyticsJourneyNodeTarget
from datadog_api_client.v2.model.product_analytics_journey_node_target_type import ProductAnalyticsJourneyNodeTargetType
from datadog_api_client.v2.model.product_analytics_journey_scalar_compute import ProductAnalyticsJourneyScalarCompute
from datadog_api_client.v2.model.product_analytics_journey_scalar_query import ProductAnalyticsJourneyScalarQuery
from datadog_api_client.v2.model.product_analytics_journey_scalar_request import ProductAnalyticsJourneyScalarRequest
from datadog_api_client.v2.model.product_analytics_journey_scalar_request_attributes import (
ProductAnalyticsJourneyScalarRequestAttributes,
)
from datadog_api_client.v2.model.product_analytics_journey_scalar_request_data import (
ProductAnalyticsJourneyScalarRequestData,
)
from datadog_api_client.v2.model.product_analytics_journey_search import ProductAnalyticsJourneySearch
from datadog_api_client.v2.model.product_analytics_journey_search_filters import ProductAnalyticsJourneySearchFilters
from datadog_api_client.v2.model.product_analytics_journey_search_graph_filter import (
ProductAnalyticsJourneySearchGraphFilter,
)
from datadog_api_client.v2.model.product_analytics_journey_search_graph_filter_name import (
ProductAnalyticsJourneySearchGraphFilterName,
)
from datadog_api_client.v2.model.product_analytics_journey_search_graph_filter_operator import (
ProductAnalyticsJourneySearchGraphFilterOperator,
)
from datadog_api_client.v2.model.query_sort_order import QuerySortOrder
body = ProductAnalyticsJourneyScalarRequest(
data=ProductAnalyticsJourneyScalarRequestData(
attributes=ProductAnalyticsJourneyScalarRequestAttributes(
_from=1756425600000,
query=ProductAnalyticsJourneyScalarQuery(
compute=ProductAnalyticsJourneyScalarCompute(
aggregation="count",
target=ProductAnalyticsJourneyNodeTarget(
type=ProductAnalyticsJourneyNodeTargetType.NODE,
value="A",
),
),
group_by=[
ProductAnalyticsGraphQueryGroupBy(
facet="@geo.country",
should_exclude_missing=False,
sort=ProductAnalyticsGroupBySort(
aggregation="count",
order=QuerySortOrder.DESC,
),
source=ProductAnalyticsGraphQueryGroupBySource.USERS,
target=ProductAnalyticsJourneyNodeTarget(
type=ProductAnalyticsJourneyNodeTargetType.NODE,
value="A",
),
value_filters=[],
),
],
search=ProductAnalyticsJourneySearch(
expression="A -> B",
filters=ProductAnalyticsJourneySearchFilters(
audience_filters=ProductAnalyticsJourneyAudienceFilters(
accounts=[
ProductAnalyticsJourneyAudienceAccountQuery(
name="enterprise_accounts",
),
],
formula="power_users AND NOT trial_segment",
segments=[
ProductAnalyticsJourneyAudienceSegmentQuery(
name="trial_segment",
segment_id="00000000-0000-0000-0000-000000000000",
),
],
users=[
ProductAnalyticsJourneyAudienceUserQuery(
name="power_users",
),
],
),
graph_filters=[
ProductAnalyticsJourneySearchGraphFilter(
name=ProductAnalyticsJourneySearchGraphFilterName.TIME_TO_CONVERT,
operator=ProductAnalyticsJourneySearchGraphFilterOperator.LESS_THAN_OR_EQUAL,
target=ProductAnalyticsJourneyNodeTarget(
type=ProductAnalyticsJourneyNodeTargetType.NODE,
value="A",
),
value=60000,
),
],
),
join_keys=ProductAnalyticsJoinKeys(
primary="@session.id",
secondary=[],
),
node_objects=dict(
A=ProductAnalyticsEventQuery(
data_source=ProductAnalyticsEventQueryDataSource.PRODUCT_ANALYTICS,
search=ProductAnalyticsEventSearch(
query="@type:view @view.name:Login",
),
),
B=ProductAnalyticsEventQuery(
data_source=ProductAnalyticsEventQueryDataSource.PRODUCT_ANALYTICS,
search=ProductAnalyticsEventSearch(
query="@type:action @action.target.name:Submit",
),
),
),
),
),
to=1756857600000,
),
type=ProductAnalyticsFormulaJourneyRequestType.FORMULA_JOURNEY_REQUEST,
),
)
configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
configuration.unstable_operations["query_product_analytics_journey_scalar"] = True
with ApiClient(configuration) as api_client:
api_instance = ProductAnalyticsApi(api_client)
response = api_instance.query_product_analytics_journey_scalar(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"# Compute journey scalar analytics 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_journey_scalar".to_sym] = true
end
api_instance = DatadogAPIClient::V2::ProductAnalyticsAPI.new
body = DatadogAPIClient::V2::ProductAnalyticsJourneyScalarRequest.new({
data: DatadogAPIClient::V2::ProductAnalyticsJourneyScalarRequestData.new({
attributes: DatadogAPIClient::V2::ProductAnalyticsJourneyScalarRequestAttributes.new({
from: 1756425600000,
query: DatadogAPIClient::V2::ProductAnalyticsJourneyScalarQuery.new({
compute: DatadogAPIClient::V2::ProductAnalyticsJourneyScalarCompute.new({
aggregation: "count",
target: DatadogAPIClient::V2::ProductAnalyticsJourneyNodeTarget.new({
type: DatadogAPIClient::V2::ProductAnalyticsJourneyNodeTargetType::NODE,
value: "A",
}),
}),
group_by: [
DatadogAPIClient::V2::ProductAnalyticsGraphQueryGroupBy.new({
facet: "@geo.country",
should_exclude_missing: false,
sort: DatadogAPIClient::V2::ProductAnalyticsGroupBySort.new({
aggregation: "count",
order: DatadogAPIClient::V2::QuerySortOrder::DESC,
}),
source: DatadogAPIClient::V2::ProductAnalyticsGraphQueryGroupBySource::USERS,
target: DatadogAPIClient::V2::ProductAnalyticsJourneyNodeTarget.new({
type: DatadogAPIClient::V2::ProductAnalyticsJourneyNodeTargetType::NODE,
value: "A",
}),
value_filters: [],
}),
],
search: DatadogAPIClient::V2::ProductAnalyticsJourneySearch.new({
expression: "A -> B",
filters: DatadogAPIClient::V2::ProductAnalyticsJourneySearchFilters.new({
audience_filters: DatadogAPIClient::V2::ProductAnalyticsJourneyAudienceFilters.new({
accounts: [
DatadogAPIClient::V2::ProductAnalyticsJourneyAudienceAccountQuery.new({
name: "enterprise_accounts",
}),
],
formula: "power_users AND NOT trial_segment",
segments: [
DatadogAPIClient::V2::ProductAnalyticsJourneyAudienceSegmentQuery.new({
name: "trial_segment",
segment_id: "00000000-0000-0000-0000-000000000000",
}),
],
users: [
DatadogAPIClient::V2::ProductAnalyticsJourneyAudienceUserQuery.new({
name: "power_users",
}),
],
}),
graph_filters: [
DatadogAPIClient::V2::ProductAnalyticsJourneySearchGraphFilter.new({
name: DatadogAPIClient::V2::ProductAnalyticsJourneySearchGraphFilterName::TIME_TO_CONVERT,
operator: DatadogAPIClient::V2::ProductAnalyticsJourneySearchGraphFilterOperator::LESS_THAN_OR_EQUAL,
target: DatadogAPIClient::V2::ProductAnalyticsJourneyNodeTarget.new({
type: DatadogAPIClient::V2::ProductAnalyticsJourneyNodeTargetType::NODE,
value: "A",
}),
value: 60000,
}),
],
}),
join_keys: DatadogAPIClient::V2::ProductAnalyticsJoinKeys.new({
primary: "@session.id",
secondary: [],
}),
node_objects: {
A: DatadogAPIClient::V2::ProductAnalyticsEventQuery.new({
data_source: DatadogAPIClient::V2::ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
search: DatadogAPIClient::V2::ProductAnalyticsEventSearch.new({
query: "@type:view @view.name:Login",
}),
}), B: DatadogAPIClient::V2::ProductAnalyticsEventQuery.new({
data_source: DatadogAPIClient::V2::ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
search: DatadogAPIClient::V2::ProductAnalyticsEventSearch.new({
query: "@type:action @action.target.name:Submit",
}),
}),
},
}),
}),
to: 1756857600000,
}),
type: DatadogAPIClient::V2::ProductAnalyticsFormulaJourneyRequestType::FORMULA_JOURNEY_REQUEST,
}),
})
p api_instance.query_product_analytics_journey_scalar(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"// Compute journey scalar analytics returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_product_analytics::ProductAnalyticsAPI;
use datadog_api_client::datadogV2::model::ProductAnalyticsBaseQuery;
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::ProductAnalyticsFormulaJourneyRequestType;
use datadog_api_client::datadogV2::model::ProductAnalyticsGraphQueryGroupBy;
use datadog_api_client::datadogV2::model::ProductAnalyticsGraphQueryGroupBySource;
use datadog_api_client::datadogV2::model::ProductAnalyticsGroupBySort;
use datadog_api_client::datadogV2::model::ProductAnalyticsJoinKeys;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyAudienceAccountQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyAudienceFilters;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyAudienceSegmentQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyAudienceUserQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyNodeTarget;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyNodeTargetType;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyScalarCompute;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyScalarQuery;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyScalarRequest;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyScalarRequestAttributes;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyScalarRequestData;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneySearch;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneySearchFilters;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneySearchGraphFilter;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneySearchGraphFilterName;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneySearchGraphFilterOperator;
use datadog_api_client::datadogV2::model::ProductAnalyticsJourneyTarget;
use datadog_api_client::datadogV2::model::QuerySortOrder;
use std::collections::BTreeMap;
#[tokio::main]
async fn main() {
let body =
ProductAnalyticsJourneyScalarRequest::new(
ProductAnalyticsJourneyScalarRequestData::new(
ProductAnalyticsJourneyScalarRequestAttributes::new(
1756425600000,
ProductAnalyticsJourneyScalarQuery::new(
ProductAnalyticsJourneyScalarCompute::new(
"count".to_string(),
).target(
ProductAnalyticsJourneyTarget::ProductAnalyticsJourneyNodeTarget(
Box::new(
ProductAnalyticsJourneyNodeTarget::new(
ProductAnalyticsJourneyNodeTargetType::NODE,
"A".to_string(),
),
),
),
),
ProductAnalyticsJourneySearch::new(
"A -> B".to_string(),
BTreeMap::from(
[
(
"A".to_string(),
ProductAnalyticsBaseQuery::ProductAnalyticsEventQuery(
Box::new(
ProductAnalyticsEventQuery::new(
ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
ProductAnalyticsEventSearch
::new().query("@type:view @view.name:Login".to_string()),
),
),
),
),
(
"B".to_string(),
ProductAnalyticsBaseQuery::ProductAnalyticsEventQuery(
Box::new(
ProductAnalyticsEventQuery::new(
ProductAnalyticsEventQueryDataSource::PRODUCT_ANALYTICS,
ProductAnalyticsEventSearch
::new().query(
"@type:action @action.target.name:Submit".to_string(),
),
),
),
),
),
],
),
)
.filters(
ProductAnalyticsJourneySearchFilters::new()
.audience_filters(
ProductAnalyticsJourneyAudienceFilters::new()
.accounts(
vec![
ProductAnalyticsJourneyAudienceAccountQuery::new(
"enterprise_accounts".to_string(),
)
],
)
.formula("power_users AND NOT trial_segment".to_string())
.segments(
vec![
ProductAnalyticsJourneyAudienceSegmentQuery::new(
"trial_segment".to_string(),
"00000000-0000-0000-0000-000000000000".to_string(),
)
],
)
.users(
vec![
ProductAnalyticsJourneyAudienceUserQuery::new(
"power_users".to_string(),
)
],
),
)
.graph_filters(
vec![
ProductAnalyticsJourneySearchGraphFilter::new(
ProductAnalyticsJourneySearchGraphFilterName::TIME_TO_CONVERT,
ProductAnalyticsJourneySearchGraphFilterOperator::LESS_THAN_OR_EQUAL,
60000,
).target(
ProductAnalyticsJourneyTarget::ProductAnalyticsJourneyNodeTarget(
Box::new(
ProductAnalyticsJourneyNodeTarget::new(
ProductAnalyticsJourneyNodeTargetType::NODE,
"A".to_string(),
),
),
),
)
],
),
)
.join_keys(
ProductAnalyticsJoinKeys::new().primary("@session.id".to_string()).secondary(vec![]),
),
).group_by(
vec![
ProductAnalyticsGraphQueryGroupBy::new("@geo.country".to_string())
.should_exclude_missing(false)
.sort(
ProductAnalyticsGroupBySort::new()
.aggregation("count".to_string())
.order(QuerySortOrder::DESC),
)
.source(ProductAnalyticsGraphQueryGroupBySource::USERS)
.target(
ProductAnalyticsJourneyTarget::ProductAnalyticsJourneyNodeTarget(
Box::new(
ProductAnalyticsJourneyNodeTarget::new(
ProductAnalyticsJourneyNodeTargetType::NODE,
"A".to_string(),
),
),
),
)
.value_filters(vec![])
],
),
1756857600000,
),
ProductAnalyticsFormulaJourneyRequestType::FORMULA_JOURNEY_REQUEST,
),
);
let mut configuration = datadog::Configuration::new();
configuration.set_unstable_operation_enabled("v2.query_product_analytics_journey_scalar", true);
let api = ProductAnalyticsAPI::with_config(configuration);
let resp = api.query_product_analytics_journey_scalar(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 run/**
* Compute journey scalar analytics 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.queryProductAnalyticsJourneyScalar"] =
true;
const apiInstance = new v2.ProductAnalyticsApi(configuration);
const params: v2.ProductAnalyticsApiQueryProductAnalyticsJourneyScalarRequest =
{
body: {
data: {
attributes: {
from: 1756425600000,
query: {
compute: {
aggregation: "count",
target: {
type: "node",
value: "A",
},
},
groupBy: [
{
facet: "@geo.country",
shouldExcludeMissing: false,
sort: {
aggregation: "count",
order: "desc",
},
source: "product_analytics_audience_filters.users",
target: {
type: "node",
value: "A",
},
valueFilters: [],
},
],
search: {
expression: "A -> B",
filters: {
audienceFilters: {
accounts: [
{
name: "enterprise_accounts",
},
],
formula: "power_users AND NOT trial_segment",
segments: [
{
name: "trial_segment",
segmentId: "00000000-0000-0000-0000-000000000000",
},
],
users: [
{
name: "power_users",
},
],
},
graphFilters: [
{
name: "__dd.time_to_convert",
operator: "<=",
target: {
type: "node",
value: "A",
},
value: 60000,
},
],
},
joinKeys: {
primary: "@session.id",
secondary: [],
},
nodeObjects: {
A: {
dataSource: "product_analytics",
search: {
query: "@type:view @view.name:Login",
},
},
B: {
dataSource: "product_analytics",
search: {
query: "@type:action @action.target.name:Submit",
},
},
},
},
},
to: 1756857600000,
},
type: "formula_journey_request",
},
},
};
apiInstance
.queryProductAnalyticsJourneyScalar(params)
.then((data: v2.ProductAnalyticsJourneyScalarResponse) => {
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"