--- title: Compute journey scalar analytics description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > API Reference > Product Analytics --- > For the complete documentation index, see [llms.txt](https://docs.datadoghq.com/llms.txt). # Compute journey scalar analytics{% #compute-journey-scalar-analytics %} Copy pageCopied {% tab title="v2" %} **Note**: This endpoint is in preview and is subject to change. If you have any feedback, contact [Datadog support](https://docs.datadoghq.com/help/). | Datadog site | API endpoint | | ----------------- | -------------------------------------------------------------------------- | | ap1.datadoghq.com | POST https://api.ap1.datadoghq.com/api/v2/product-analytics/journey/scalar | | ap2.datadoghq.com | POST https://api.ap2.datadoghq.com/api/v2/product-analytics/journey/scalar | | app.datadoghq.eu | POST https://api.datadoghq.eu/api/v2/product-analytics/journey/scalar | | app.ddog-gov.com | POST https://api.ddog-gov.com/api/v2/product-analytics/journey/scalar | | us2.ddog-gov.com | POST https://api.us2.ddog-gov.com/api/v2/product-analytics/journey/scalar | | uk1.datadoghq.com | POST https://api.uk1.datadoghq.com/api/v2/product-analytics/journey/scalar | | app.datadoghq.com | POST https://api.datadoghq.com/api/v2/product-analytics/journey/scalar | | us3.datadoghq.com | POST https://api.us3.datadoghq.com/api/v2/product-analytics/journey/scalar | | us5.datadoghq.com | POST https://api.us5.datadoghq.com/api/v2/product-analytics/journey/scalar | ### Overview Compute scalar results for a journey query, such as the conversion count, the conversion rate, or the time to convert, optionally segmented by group-by facets. This endpoint requires the `rum_apps_read` permission. ### Request #### Body Data (required) {% tab title="Model" %} | Parent field | Field | Type | Description | | ------------------------------------------ | ------------------------------------------ | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data [*required*] | object | The single JSON:API resource carrying a journey scalar query. Its attributes hold the time window and the journey metric to reduce to one value over that window. | | data | attributes [*required*] | object | Attributes of a journey scalar request. | | attributes | from [*required*] | int64 | Start of the query window, in epoch milliseconds. | | attributes | query [*required*] | object | Query definition for a journey scalar request. | | query | compute [*required*] | object | Defines the metric computed over the journey for a scalar query. | | compute | aggregation [*required*] | string | Aggregation function: `count`, `cardinality`, `avg`, `median`, `min`, `max`, `sum`, or a percentile of the form `pc` such as `pc95`. Defaults to `cardinality`. | | compute | metric | string | Metric 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`. | | compute | target | | A 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. | | target | | object | A reference to a single step of the journey. | | | type [*required*] | enum | The discriminator identifying a target that references a single step. Allowed enum values: `node` | | | value [*required*] | string | Alias of the targeted node. | | target | | object | A reference to the range of steps between two nodes of the journey. | | | end [*required*] | string | Alias of the node the path ends at. | | | start [*required*] | string | Alias of the node the path starts at. | | | type [*required*] | enum | The discriminator identifying a target that references a range of steps. Allowed enum values: `path` | | query | group_by | [object] | Segments the results by the values of one or more facets. | | group_by | facet [*required*] | string | Attribute path to group by. | | group_by | limit | int64 | Maximum number of groups to return. Omit it to let the service choose. | | group_by | should_exclude_missing | boolean | Whether to exclude entities that have no value for this facet. | | group_by | sort | object | Sort configuration for group-by results. | | sort | aggregation | string | The aggregation function to sort by. | | sort | metric | string | The metric to sort by. | | sort | order | enum | Direction of sort. Allowed enum values: `asc,desc` | | group_by | source | enum | Audience dimension to group by, instead of an event facet. Allowed enum values: `product_analytics_audience_filters.users,product_analytics_audience_filters.accounts` | | group_by | target | | A 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. | | target | | object | A reference to a single step of the journey. | | | type [*required*] | enum | The discriminator identifying a target that references a single step. Allowed enum values: `node` | | | value [*required*] | string | Alias of the targeted node. | | target | | object | A reference to the range of steps between two nodes of the journey. | | | end [*required*] | string | Alias of the node the path ends at. | | | start [*required*] | string | Alias of the node the path starts at. | | | type [*required*] | enum | The discriminator identifying a target that references a range of steps. Allowed enum values: `path` | | group_by | value_filters | [string] | Restricts the results to these facet values. | | query | query_id | string | Caller-defined identifier echoed back in the results. | | query | search [*required*] | object | Defines the steps of the journey and the filters applied to it. | | search | expression [*required*] | string | Expression combining the node aliases in order, for example `A -> B -> C`. | | search | filters | object | Filters applied on top of the journey step expression. | | filters | audience_filters | object | Restricts the journey to an audience built from named sub-queries. Sub-query names must be unique across `users`, `segments`, and `accounts`. | | audience_filters | accounts | [object] | Named account sub-queries. | | accounts | name [*required*] | string | Unique name for this sub-query, referenced from `formula`. | | accounts | query | string | Search query selecting the accounts. | | audience_filters | formula | string | Boolean expression combining the sub-query names with `AND`, `OR`, and `NOT`. When empty, all sub-queries are combined with `AND`. | | audience_filters | segments | [object] | Named segment sub-queries. | | segments | name [*required*] | string | Unique name for this sub-query, referenced from `formula`. | | segments | segment_id [*required*] | string | Identifier of the saved segment. | | audience_filters | users | [object] | Named user sub-queries. | | users | name [*required*] | string | Unique name for this sub-query, referenced from `formula`. | | users | query | string | Search query selecting the users. | | filters | graph_filters | [object] | Filters on journey-level metrics such as time to convert. | | graph_filters | name [*required*] | enum | The journey-level metric the graph filter applies to. Allowed enum values: `__dd.time_to_convert,__dd.session,__dd.dropoff_rate` | | graph_filters | operator [*required*] | enum | Comparison operator applied to the graph filter value. Allowed enum values: `=,<,>,<=,>=` | | graph_filters | target | | A 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. | | target | | object | A reference to a single step of the journey. | | | type [*required*] | enum | The discriminator identifying a target that references a single step. Allowed enum values: `node` | | | value [*required*] | string | Alias of the targeted node. | | target | | object | A reference to the range of steps between two nodes of the journey. | | | end [*required*] | string | Alias of the node the path ends at. | | | start [*required*] | string | Alias of the node the path starts at. | | | type [*required*] | enum | The discriminator identifying a target that references a range of steps. Allowed enum values: `path` | | graph_filters | value [*required*] | int64 | Value compared against the metric. Durations are expressed in milliseconds. | | filters | string_filter | string | Free-text search query applied to the whole journey. | | search | join_keys | object | Identity join keys used to stitch events belonging to the same user or session. | | join_keys | primary | string | Primary identity join key. Defaults to `@session.id`. | | join_keys | secondary | [string] | Additional identity join keys. | | search | node_objects [*required*] | object | Map of node alias to the query matching that step of the journey. Every alias used in `expression` must have an entry here. | | additionalProperties | | | A query definition discriminated by the `data_source` field. Use `product_analytics` for standard event queries, or `product_analytics_occurrence` for occurrence-filtered queries. | | | | object | A standard Product Analytics event query. | | | data_source [*required*] | enum | The data source identifier. Allowed enum values: `product_analytics` | | | search [*required*] | object | Search parameters for an event query. | | search | query | string | The search query using Datadog search syntax. | | | | object | A Product Analytics occurrence-filtered query. | | | data_source [*required*] | enum | The data source identifier for occurrence queries. Allowed enum values: `product_analytics_occurrence` | | | search [*required*] | object | Search parameters for an occurrence query. | | search | occurrences | object | Filter for occurrence-based queries. | | occurrences | meta | object | Additional metadata. | | additionalProperties | | string | | occurrences | operator [*required*] | string | Comparison operator (=, >=, <=, >, <). | | occurrences | value [*required*] | string | The occurrence count threshold as a string. | | search | query | string | The search query using Datadog search syntax. | | attributes | to [*required*] | int64 | End of the query window, in epoch milliseconds. | | data | type [*required*] | enum | The resource type identifier for a journey timeseries or scalar request. Allowed enum values: `formula_journey_request` | {% /tab %} {% tab title="Example" %} ```json { "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": { "": "undefined" } } }, "to": 1756857600000 }, "type": "formula_journey_request" } } ``` {% /tab %} ### Response {% tab title="200" %} OK {% tab title="Model" %} Response for a journey scalar query. | Parent field | Field | Type | Description | | ------------ | ---------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data [*required*] | object | 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. | | data | attributes [*required*] | object | Attributes of a scalar analytics response, containing the result columns. | | attributes | columns | [object] | The list of result columns, each containing values and metadata. | | columns | meta | object | Metadata associated with a scalar response column, including optional unit information. | | meta | unit | [object] | Unit definitions for the column values, if applicable. | | unit | family | string | The unit family (e.g., time, bytes). | | unit | id | int64 | Numeric identifier for the unit. | | unit | name | string | The full name of the unit (e.g., nanosecond). | | unit | plural | string | Plural form of the unit name (e.g., nanoseconds). | | unit | scale_factor | double | Conversion factor relative to the base unit of the family. | | unit | short_name | string | Abbreviated unit name (e.g., ns). | | columns | name | string | Column name (facet name for group-by, or "query"). | | columns | type | enum | Column type. Allowed enum values: `number,group` | | columns | values | [] | Column values. | | data | id [*required*] | string | Identifier of this result. | | data | type [*required*] | enum | The resource type identifier for a journey scalar response. Allowed enum values: `journey_scalar_response` | {% /tab %} {% tab title="Example" %} ```json { "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" } } ``` {% /tab %} {% /tab %} {% tab title="400" %} Bad Request {% tab title="Model" %} API error response. | Field | Type | Description | | ------------------------ | -------- | ----------------- | | errors [*required*] | [string] | A list of errors. | {% /tab %} {% tab title="Example" %} ```json { "errors": [ "Bad Request" ] } ``` {% /tab %} {% /tab %} {% tab title="403" %} Not Authorized {% tab title="Model" %} API error response. | Field | Type | Description | | ------------------------ | -------- | ----------------- | | errors [*required*] | [string] | A list of errors. | {% /tab %} {% tab title="Example" %} ```json { "errors": [ "Bad Request" ] } ``` {% /tab %} {% /tab %} {% tab title="429" %} Too many requests {% tab title="Model" %} API error response. | Field | Type | Description | | ------------------------ | -------- | ----------------- | | errors [*required*] | [string] | A list of errors. | {% /tab %} {% tab title="Example" %} ```json { "errors": [ "Bad Request" ] } ``` {% /tab %} {% /tab %} ### Code Example ##### \## default # \# Curl command curl -X POST "https://api.datadoghq.com/api/v2/product-analytics/journey/scalar" \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -H "DD-API-KEY: ${DD_API_KEY}" \ -H "DD-APPLICATION-KEY: ${DD_APP_KEY}" \ -d @- << EOF { "data": { "attributes": { "from": 1756425600000, "query": { "compute": { "aggregation": "count", "metric": "__dd.conversion_rate" }, "search": { "expression": "A -\u003e 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 ##### ```python """ Compute journey scalar analytics returns "OK" response """ 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.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](https://docs.datadoghq.com/api/latest.md?code-lang=python) and then save the example to `example.py` and run following commands: DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" python3 "example.py" ##### ```ruby # Compute journey scalar analytics returns "OK" response require "datadog_api_client" DatadogAPIClient.configure do |config| 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](https://docs.datadoghq.com/api/latest.md?code-lang=ruby) and then save the example to `example.rb` and run following commands: DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" rb "example.rb" ##### ```go // 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()) 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](https://docs.datadoghq.com/api/latest.md?code-lang=go) and then save the example to `main.go` and run following commands: DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" go run "main.go" ##### ```java // 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.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](https://docs.datadoghq.com/api/latest.md?code-lang=java) and then save the example to `Example.java` and run following commands: DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" java "Example.java" ##### ```rust // 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.QueryProductAnalyticsJourneyScalar", 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](https://docs.datadoghq.com/api/latest.md?code-lang=rust) and then save the example to `src/main.rs` and run following commands: DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" cargo run ##### ```typescript /** * Compute journey scalar analytics returns "OK" response */ import { client, v2 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration(); 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](https://docs.datadoghq.com/api/latest.md?code-lang=typescript) and then save the example to `example.ts` and run following commands: DD_SITE="datadoghq.com" DD_API_KEY="" DD_APP_KEY="" tsc "example.ts" {% /tab %}