--- title: Query timeseries data across multiple products description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > API Reference > Metrics --- # Query timeseries data across multiple products{% #query-timeseries-data-across-multiple-products %} Copy pageCopied {% tab title="v2" %} | Datadog site | API endpoint | | ----------------- | ---------------------------------------------------------- | | ap1.datadoghq.com | POST https://api.ap1.datadoghq.com/api/v2/query/timeseries | | ap2.datadoghq.com | POST https://api.ap2.datadoghq.com/api/v2/query/timeseries | | app.datadoghq.eu | POST https://api.datadoghq.eu/api/v2/query/timeseries | | app.ddog-gov.com | POST https://api.ddog-gov.com/api/v2/query/timeseries | | us2.ddog-gov.com | POST https://api.us2.ddog-gov.com/api/v2/query/timeseries | | app.datadoghq.com | POST https://api.datadoghq.com/api/v2/query/timeseries | | us3.datadoghq.com | POST https://api.us3.datadoghq.com/api/v2/query/timeseries | | us5.datadoghq.com | POST https://api.us5.datadoghq.com/api/v2/query/timeseries | ### Overview Query timeseries data across various data sources and process the data by applying formulas and functions. This endpoint requires the `timeseries_query` permission. OAuth apps require the `timeseries_query` authorization [scope](https://docs.datadoghq.com/api/latest/scopes.md#metrics) to access this endpoint. ### Request #### Body Data (required) {% tab title="Model" %} | Parent field | Field | Type | Description | | ------------ | -------------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data [*required*] | object | A single timeseries query to be executed. | | data | attributes [*required*] | object | The object describing a timeseries formula request. | | attributes | formulas | [object] | List of formulas to be calculated and returned as responses. | | formulas | formula [*required*] | string | Formula string, referencing one or more queries with their name property. | | formulas | limit | object | Message for specifying limits to the number of values returned by a query. This limit is only for scalar queries and has no effect on timeseries queries. | | limit | count | int32 | The number of results to which to limit. | | limit | order | enum | Direction of sort. Allowed enum values: `asc,desc` | | attributes | from [*required*] | int64 | Start date (inclusive) of the query in milliseconds since the Unix epoch. | | attributes | interval | int64 | A time interval in milliseconds. May be overridden by a larger interval if the query would result in too many points for the specified timeframe. Defaults to a reasonable interval for the given timeframe. | | attributes | queries [*required*] | [ ] | List of queries to be run and used as inputs to the formulas. | | queries | Option 1 | object | A query against Datadog custom metrics or Cloud Cost data sources. | | Option 1 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 1 | data_source [*required*] | enum | A data source that is powered by the Metrics platform. Allowed enum values: `metrics,cloud_cost` | | Option 1 | name | string | The variable name for use in formulas. | | Option 1 | query [*required*] | string | A classic metrics query string. | | queries | Option 2 | object | An individual timeseries query for logs, RUM, traces, CI pipelines, security signals, and other event-based data sources. Use this query type for any data source powered by the Events Platform. See the data_source field for the full list of supported sources. | | Option 2 | compute [*required*] | object | The instructions for what to compute for this query. | | compute | aggregation [*required*] | enum | The type of aggregation that can be performed on events-based queries. Allowed enum values: `count,cardinality,pc75,pc90,pc95,pc98,pc99,sum,min,max` | | compute | interval | int64 | Interval for compute in milliseconds. | | compute | metric | string | The "measure" attribute on which to perform the computation. | | Option 2 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 2 | data_source [*required*] | enum | A data source that is powered by the Events Platform. Allowed enum values: `logs,spans,network,rum,security_signals,profiles,audit,events,ci_tests,ci_pipelines` | | Option 2 | group_by | [object] | The list of facets on which to split results. | | group_by | facet [*required*] | string | The facet by which to split groups. | | group_by | limit | int32 | The maximum buckets to return for this group by. Note: at most 10000 buckets are allowed. If grouping by multiple facets, the product of limits must not exceed 10000. | | group_by | sort | object | The dimension by which to sort a query's results. | | sort | aggregation [*required*] | enum | The type of aggregation that can be performed on events-based queries. Allowed enum values: `count,cardinality,pc75,pc90,pc95,pc98,pc99,sum,min,max` | | sort | metric | string | The metric's calculated value which should be used to define the sort order of a query's results. | | sort | order | enum | Direction of sort. Allowed enum values: `asc,desc` | | sort | type | enum | The type of sort to use on the calculated value. Allowed enum values: `alphabetical,measure` | | Option 2 | indexes | [string] | The indexes in which to search. | | Option 2 | name | string | The variable name for use in formulas. | | Option 2 | search | object | Configuration of the search/filter for an events query. | | search | query | string | The search/filter string for an events query. | | queries | Option 3 | object | A query for APM resource statistics such as latency, error rate, and hit count, grouped by resource name. | | Option 3 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 3 | data_source [*required*] | enum | A data source for APM resource statistics queries. Allowed enum values: `apm_resource_stats` | | Option 3 | env [*required*] | string | The environment to query. | | Option 3 | group_by | [string] | Tag keys to group results by. | | Option 3 | name [*required*] | string | The variable name for use in formulas. | | Option 3 | operation_name | string | The APM operation name. | | Option 3 | primary_tag_name | string | Name of the second primary tag used within APM. Required when `primary_tag_value` is specified. See [https://docs.datadoghq.com/tracing/guide/setting_primary_tags_to_scope/#add-a-second-primary-tag-in-datadog](https://docs.datadoghq.com/tracing/guide/setting_primary_tags_to_scope.md#add-a-second-primary-tag-in-datadog) | | Option 3 | primary_tag_value | string | Value of the second primary tag by which to filter APM data. `primary_tag_name` must also be specified. | | Option 3 | resource_name | string | The resource name to filter by. | | Option 3 | service [*required*] | string | The service name to filter by. | | Option 3 | stat [*required*] | enum | The APM resource statistic to query. Allowed enum values: `error_rate,errors,hits,latency_avg,latency_max,latency_p50,latency_p75,latency_p90,latency_p95,latency_p99` | | queries | Option 4 | object | A query for APM trace metrics such as hits, errors, and latency percentiles, aggregated across services. | | Option 4 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 4 | data_source [*required*] | enum | A data source for APM metrics queries. Allowed enum values: `apm_metrics` | | Option 4 | group_by | [string] | Optional fields to group the query results by. | | Option 4 | name [*required*] | string | The variable name for use in formulas. | | Option 4 | operation_mode | string | Optional operation mode to aggregate across operation names. | | Option 4 | operation_name | string | Name of operation on service. If not provided, the primary operation name is used. | | Option 4 | peer_tags | [string] | Tags to query for a specific downstream entity (peer.service, peer.db_instance, peer.s3, peer.s3.bucket, etc.). | | Option 4 | query_filter | string | Additional filters for the query using metrics query syntax (for example, env, primary_tag). | | Option 4 | resource_hash | string | The resource hash for exact matching. | | Option 4 | resource_name | string | The full name of a specific resource to filter by. | | Option 4 | service | string | The service name to filter by. | | Option 4 | span_kind | enum | Describes the relationship between the span, its parents, and its children in a trace. Allowed enum values: `consumer,server,client,producer,internal` | | Option 4 | stat [*required*] | enum | The APM metric statistic to query. Allowed enum values: `error_rate,errors,errors_per_second,hits,hits_per_second,apdex,latency_avg,latency_max,latency_p50,latency_p75` | | queries | Option 5 | object | A query for APM dependency statistics between services, such as call latency and error rates. | | Option 5 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 5 | data_source [*required*] | enum | A data source for APM dependency statistics queries. Allowed enum values: `apm_dependency_stats` | | Option 5 | env [*required*] | string | The environment to query. | | Option 5 | is_upstream | boolean | Determines whether stats for upstream or downstream dependencies should be queried. | | Option 5 | name [*required*] | string | The variable name for use in formulas. | | Option 5 | operation_name [*required*] | string | The APM operation name. | | Option 5 | primary_tag_name | string | The name of the second primary tag used within APM; required when `primary_tag_value` is specified. See [https://docs.datadoghq.com/tracing/guide/setting_primary_tags_to_scope/#add-a-second-primary-tag-in-datadog](https://docs.datadoghq.com/tracing/guide/setting_primary_tags_to_scope.md#add-a-second-primary-tag-in-datadog). | | Option 5 | primary_tag_value | string | Filter APM data by the second primary tag. `primary_tag_name` must also be specified. | | Option 5 | resource_name [*required*] | string | The resource name to filter by. | | Option 5 | service [*required*] | string | The service name to filter by. | | Option 5 | stat [*required*] | enum | The APM dependency statistic to query. Allowed enum values: `avg_duration,avg_root_duration,avg_spans_per_trace,error_rate,pct_exec_time,pct_of_traces,total_traces_count` | | queries | Option 6 | object | A query for SLO status, error budget, and burn rate metrics. | | Option 6 | additional_query_filters | string | Additional filters applied to the SLO query. | | Option 6 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 6 | data_source [*required*] | enum | A data source for SLO queries. Allowed enum values: `slo` | | Option 6 | group_mode | enum | How SLO results are grouped in the response. Allowed enum values: `overall,components` | | Option 6 | measure [*required*] | enum | The SLO measurement to retrieve. Allowed enum values: `good_events,bad_events,slo_status,error_budget_remaining,error_budget_remaining_history,error_budget_burndown,burn_rate,slo_status_history,good_minutes,bad_minutes` | | Option 6 | name | string | The variable name for use in formulas. | | Option 6 | slo_id [*required*] | string | The unique identifier of the SLO to query. | | Option 6 | slo_query_type | enum | The type of SLO definition being queried. Allowed enum values: `metric,time_slice,monitor` | | queries | Option 7 | object | A query for host-level process metrics such as CPU and memory usage. | | Option 7 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 7 | data_source [*required*] | enum | A data source for process-level infrastructure metrics. Allowed enum values: `process` | | Option 7 | is_normalized_cpu | boolean | Whether CPU metrics should be normalized by core count. | | Option 7 | limit | int64 | Maximum number of results to return. | | Option 7 | metric [*required*] | string | The process metric to query. | | Option 7 | name [*required*] | string | The variable name for use in formulas. | | Option 7 | sort | enum | Direction of sort. Allowed enum values: `asc,desc` | | Option 7 | tag_filters | [string] | Tag filters to narrow down processes. | | Option 7 | text_filter | string | A full-text search filter to match process names or commands. | | queries | Option 8 | object | A query for container-level metrics such as CPU and memory usage. | | Option 8 | cross_org_uuids | [string] | Organization UUIDs to query when using [cross-organization visibility](https://docs.datadoghq.com/account_management/org_settings/cross_org_visibility.md). Limited to one organization UUID. | | Option 8 | data_source [*required*] | enum | A data source for container-level infrastructure metrics. Allowed enum values: `container` | | Option 8 | is_normalized_cpu | boolean | Whether CPU metrics should be normalized by core count. | | Option 8 | limit | int64 | Maximum number of results to return. | | Option 8 | metric [*required*] | string | The container metric to query. | | Option 8 | name [*required*] | string | The variable name for use in formulas. | | Option 8 | sort | enum | Direction of sort. Allowed enum values: `asc,desc` | | Option 8 | tag_filters | [string] | Tag filters to narrow down containers. | | Option 8 | text_filter | string | A full-text search filter to match container names. | | attributes | to [*required*] | int64 | End date (exclusive) of the query in milliseconds since the Unix epoch. | | data | type [*required*] | enum | The type of the resource. The value should always be timeseries_request. Allowed enum values: `timeseries_request` | {% /tab %} {% tab title="Example" %} ##### ```json { "data": { "attributes": { "formulas": [ { "formula": "a", "limit": { "count": 10, "order": "desc" } } ], "from": 1636625471000, "interval": 5000, "queries": [ { "data_source": "metrics", "query": "avg:datadog.estimated_usage.metrics.custom{*}", "name": "a" } ], "to": 1636629071000 }, "type": "timeseries_request" } } ``` ##### ```json { "data": { "attributes": { "formulas": [ { "formula": "a", "limit": { "count": 10, "order": "desc" } } ], "from": 1636625471000, "interval": 5000, "queries": [ { "data_source": "rum", "name": "a", "compute": { "aggregation": "count" }, "search": { "query": "*" }, "indexes": [ "*" ] } ], "to": 1636629071000 }, "type": "timeseries_request" } } ``` ##### ```json { "data": { "attributes": { "formulas": [ { "formula": "a", "limit": { "count": 10, "order": "desc" } } ], "from": 1636625471000, "interval": 5000, "queries": [ { "data_source": "apm_dependency_stats", "name": "a", "env": "ci", "service": "cassandra", "stat": "avg_duration", "operation_name": "cassandra.query", "resource_name": "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?", "primary_tag_name": "datacenter", "primary_tag_value": "edge-eu1.prod.dog" } ], "to": 1636629071000 }, "type": "timeseries_request" } } ``` {% /tab %} ### Response {% tab title="200" %} OK {% tab title="Model" %} A message containing one response to a timeseries query made with timeseries formula query request. | Parent field | Field | Type | Description | | ------------ | ------------ | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data | object | A message containing the response to a timeseries query. | | data | attributes | object | The object describing a timeseries response. | | attributes | series | [object] | Array of response series. The index here corresponds to the index in the `formulas` or `queries` array from the request. | | series | group_tags | [string] | List of tags that apply to a single response value. | | series | query_index | int32 | The index of the query in the "formulas" array (or "queries" array if no "formulas" was specified). | | series | unit | [object] | Detailed information about the unit. The first element describes the "primary unit" (for example, `bytes` in `bytes per second`). The second element describes the "per unit" (for example, `second` in `bytes per second`). If the second element is not present, the API returns null. | | unit | family | string | Unit family, allows for conversion between units of the same family, for scaling. | | unit | name | string | Unit name | | unit | plural | string | Plural form of the unit name. | | unit | scale_factor | double | Factor for scaling between units of the same family. | | unit | short_name | string | Abbreviation of the unit. | | attributes | times | [integer] | Array of times, 1-1 match with individual values arrays. | | attributes | values | [array] | Array of value-arrays. The index here corresponds to the index in the `formulas` or `queries` array from the request. | | data | type | enum | The type of the resource. The value should always be timeseries_response. Allowed enum values: `timeseries_response` | | | errors | string | The error generated by the request. | {% /tab %} {% tab title="Example" %} ```json { "data": { "attributes": { "series": [ { "group_tags": [ "env:production" ], "query_index": 0, "unit": [ { "family": "time", "name": "minute", "plural": "minutes", "scale_factor": 60, "short_name": "min" } ] } ], "times": [], "values": [ 1575317847, 0.5 ] }, "type": "timeseries_response" }, "errors": "string" } ``` {% /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="401" %} Unauthorized {% 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" %} Forbidden {% 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/query/timeseries" \ -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": { "formulas": [ { "formula": "a+b", "limit": { "count": 10 } } ], "from": 1568899800000, "interval": 5000, "queries": [ { "data_source": "metrics", "query": "avg:system.cpu.user{*} by {env}" } ], "to": 1568923200000 }, "type": "timeseries_request" } } EOF ##### \## default # \# Curl command curl -X POST "https://api.datadoghq.com/api/v2/query/timeseries" \ -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": { "formulas": [ { "formula": "a+b", "limit": { "count": 10 } } ], "from": 1568899800000, "interval": 5000, "queries": [ { "data_source": "metrics", "query": "avg:system.cpu.user{*} by {env}" } ], "to": 1568923200000 }, "type": "timeseries_request" } } EOF ##### \## default # \# Curl command curl -X POST "https://api.datadoghq.com/api/v2/query/timeseries" \ -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": { "formulas": [ { "formula": "a+b", "limit": { "count": 10 } } ], "from": 1568899800000, "interval": 5000, "queries": [ { "data_source": "metrics", "query": "avg:system.cpu.user{*} by {env}" } ], "to": 1568923200000 }, "type": "timeseries_request" } } EOF ##### ```go // Timeseries cross product query 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.TimeseriesFormulaQueryRequest{ Data: datadogV2.TimeseriesFormulaRequest{ Attributes: datadogV2.TimeseriesFormulaRequestAttributes{ Formulas: []datadogV2.QueryFormula{ { Formula: "a", Limit: &datadogV2.FormulaLimit{ Count: datadog.PtrInt32(10), Order: datadogV2.QUERYSORTORDER_DESC.Ptr(), }, }, }, From: 1636625471000, Interval: datadog.PtrInt64(5000), Queries: []datadogV2.TimeseriesQuery{ datadogV2.TimeseriesQuery{ MetricsTimeseriesQuery: &datadogV2.MetricsTimeseriesQuery{ DataSource: datadogV2.METRICSDATASOURCE_METRICS, Query: "avg:datadog.estimated_usage.metrics.custom{*}", Name: datadog.PtrString("a"), }}, }, To: 1636629071000, }, Type: datadogV2.TIMESERIESFORMULAREQUESTTYPE_TIMESERIES_REQUEST, }, } ctx := datadog.NewDefaultContext(context.Background()) configuration := datadog.NewConfiguration() apiClient := datadog.NewAPIClient(configuration) api := datadogV2.NewMetricsApi(apiClient) resp, r, err := api.QueryTimeseriesData(ctx, body) if err != nil { fmt.Fprintf(os.Stderr, "Error when calling `MetricsApi.QueryTimeseriesData`: %v\n", err) fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r) } responseContent, _ := json.MarshalIndent(resp, "", " ") fmt.Fprintf(os.Stdout, "Response from `MetricsApi.QueryTimeseriesData`:\n%s\n", responseContent) } ``` ##### ```go // Timeseries cross product query with apm_dependency_stats data source 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.TimeseriesFormulaQueryRequest{ Data: datadogV2.TimeseriesFormulaRequest{ Attributes: datadogV2.TimeseriesFormulaRequestAttributes{ Formulas: []datadogV2.QueryFormula{ { Formula: "a", Limit: &datadogV2.FormulaLimit{ Count: datadog.PtrInt32(10), Order: datadogV2.QUERYSORTORDER_DESC.Ptr(), }, }, }, From: 1636625471000, Interval: datadog.PtrInt64(5000), Queries: []datadogV2.TimeseriesQuery{ datadogV2.TimeseriesQuery{ ApmDependencyStatsQuery: &datadogV2.ApmDependencyStatsQuery{ DataSource: datadogV2.APMDEPENDENCYSTATSDATASOURCE_APM_DEPENDENCY_STATS, Name: "a", Env: "ci", Service: "cassandra", Stat: datadogV2.APMDEPENDENCYSTATNAME_AVG_DURATION, OperationName: "cassandra.query", ResourceName: "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?", PrimaryTagName: datadog.PtrString("datacenter"), PrimaryTagValue: datadog.PtrString("edge-eu1.prod.dog"), }}, }, To: 1636629071000, }, Type: datadogV2.TIMESERIESFORMULAREQUESTTYPE_TIMESERIES_REQUEST, }, } ctx := datadog.NewDefaultContext(context.Background()) configuration := datadog.NewConfiguration() apiClient := datadog.NewAPIClient(configuration) api := datadogV2.NewMetricsApi(apiClient) resp, r, err := api.QueryTimeseriesData(ctx, body) if err != nil { fmt.Fprintf(os.Stderr, "Error when calling `MetricsApi.QueryTimeseriesData`: %v\n", err) fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r) } responseContent, _ := json.MarshalIndent(resp, "", " ") fmt.Fprintf(os.Stdout, "Response from `MetricsApi.QueryTimeseriesData`:\n%s\n", responseContent) } ``` ##### ```go // Timeseries cross product query with apm_metrics data source and span_kind 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.TimeseriesFormulaQueryRequest{ Data: datadogV2.TimeseriesFormulaRequest{ Attributes: datadogV2.TimeseriesFormulaRequestAttributes{ Formulas: []datadogV2.QueryFormula{ { Formula: "a", Limit: &datadogV2.FormulaLimit{ Count: datadog.PtrInt32(10), Order: datadogV2.QUERYSORTORDER_DESC.Ptr(), }, }, }, From: 1636625471000, Interval: datadog.PtrInt64(5000), Queries: []datadogV2.TimeseriesQuery{ datadogV2.TimeseriesQuery{ ApmMetricsQuery: &datadogV2.ApmMetricsQuery{ DataSource: datadogV2.APMMETRICSDATASOURCE_APM_METRICS, Name: "a", Stat: datadogV2.APMMETRICSSTAT_HITS, Service: datadog.PtrString("web-store"), QueryFilter: datadog.PtrString("env:prod"), SpanKind: datadogV2.APMMETRICSSPANKIND_SERVER.Ptr(), GroupBy: []string{ "resource_name", }, }}, }, To: 1636629071000, }, Type: datadogV2.TIMESERIESFORMULAREQUESTTYPE_TIMESERIES_REQUEST, }, } ctx := datadog.NewDefaultContext(context.Background()) configuration := datadog.NewConfiguration() apiClient := datadog.NewAPIClient(configuration) api := datadogV2.NewMetricsApi(apiClient) resp, r, err := api.QueryTimeseriesData(ctx, body) if err != nil { fmt.Fprintf(os.Stderr, "Error when calling `MetricsApi.QueryTimeseriesData`: %v\n", err) fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r) } responseContent, _ := json.MarshalIndent(resp, "", " ") fmt.Fprintf(os.Stdout, "Response from `MetricsApi.QueryTimeseriesData`:\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 // Timeseries cross product query returns "OK" response import com.datadog.api.client.ApiClient; import com.datadog.api.client.ApiException; import com.datadog.api.client.v2.api.MetricsApi; import com.datadog.api.client.v2.model.FormulaLimit; import com.datadog.api.client.v2.model.MetricsDataSource; import com.datadog.api.client.v2.model.MetricsTimeseriesQuery; import com.datadog.api.client.v2.model.QueryFormula; import com.datadog.api.client.v2.model.QuerySortOrder; import com.datadog.api.client.v2.model.TimeseriesFormulaQueryRequest; import com.datadog.api.client.v2.model.TimeseriesFormulaQueryResponse; import com.datadog.api.client.v2.model.TimeseriesFormulaRequest; import com.datadog.api.client.v2.model.TimeseriesFormulaRequestAttributes; import com.datadog.api.client.v2.model.TimeseriesFormulaRequestType; import com.datadog.api.client.v2.model.TimeseriesQuery; import java.util.Collections; public class Example { public static void main(String[] args) { ApiClient defaultClient = ApiClient.getDefaultApiClient(); MetricsApi apiInstance = new MetricsApi(defaultClient); TimeseriesFormulaQueryRequest body = new TimeseriesFormulaQueryRequest() .data( new TimeseriesFormulaRequest() .attributes( new TimeseriesFormulaRequestAttributes() .formulas( Collections.singletonList( new QueryFormula() .formula("a") .limit( new FormulaLimit() .count(10) .order(QuerySortOrder.DESC)))) .from(1636625471000L) .interval(5000L) .queries( Collections.singletonList( new TimeseriesQuery( new MetricsTimeseriesQuery() .dataSource(MetricsDataSource.METRICS) .query("avg:datadog.estimated_usage.metrics.custom{*}") .name("a")))) .to(1636629071000L)) .type(TimeseriesFormulaRequestType.TIMESERIES_REQUEST)); try { TimeseriesFormulaQueryResponse result = apiInstance.queryTimeseriesData(body); System.out.println(result); } catch (ApiException e) { System.err.println("Exception when calling MetricsApi#queryTimeseriesData"); System.err.println("Status code: " + e.getCode()); System.err.println("Reason: " + e.getResponseBody()); System.err.println("Response headers: " + e.getResponseHeaders()); e.printStackTrace(); } } } ``` ##### ```java // Timeseries cross product query with apm_dependency_stats data source returns "OK" response import com.datadog.api.client.ApiClient; import com.datadog.api.client.ApiException; import com.datadog.api.client.v2.api.MetricsApi; import com.datadog.api.client.v2.model.ApmDependencyStatName; import com.datadog.api.client.v2.model.ApmDependencyStatsDataSource; import com.datadog.api.client.v2.model.ApmDependencyStatsQuery; import com.datadog.api.client.v2.model.FormulaLimit; import com.datadog.api.client.v2.model.QueryFormula; import com.datadog.api.client.v2.model.QuerySortOrder; import com.datadog.api.client.v2.model.TimeseriesFormulaQueryRequest; import com.datadog.api.client.v2.model.TimeseriesFormulaQueryResponse; import com.datadog.api.client.v2.model.TimeseriesFormulaRequest; import com.datadog.api.client.v2.model.TimeseriesFormulaRequestAttributes; import com.datadog.api.client.v2.model.TimeseriesFormulaRequestType; import com.datadog.api.client.v2.model.TimeseriesQuery; import java.util.Collections; public class Example { public static void main(String[] args) { ApiClient defaultClient = ApiClient.getDefaultApiClient(); MetricsApi apiInstance = new MetricsApi(defaultClient); TimeseriesFormulaQueryRequest body = new TimeseriesFormulaQueryRequest() .data( new TimeseriesFormulaRequest() .attributes( new TimeseriesFormulaRequestAttributes() .formulas( Collections.singletonList( new QueryFormula() .formula("a") .limit( new FormulaLimit() .count(10) .order(QuerySortOrder.DESC)))) .from(1636625471000L) .interval(5000L) .queries( Collections.singletonList( new TimeseriesQuery( new ApmDependencyStatsQuery() .dataSource( ApmDependencyStatsDataSource.APM_DEPENDENCY_STATS) .name("a") .env("ci") .service("cassandra") .stat(ApmDependencyStatName.AVG_DURATION) .operationName("cassandra.query") .resourceName( "DELETE FROM" + " monitor_history.monitor_state_change_history" + " WHERE org_id = ? AND monitor_id IN ? AND" + " group = ?") .primaryTagName("datacenter") .primaryTagValue("edge-eu1.prod.dog")))) .to(1636629071000L)) .type(TimeseriesFormulaRequestType.TIMESERIES_REQUEST)); try { TimeseriesFormulaQueryResponse result = apiInstance.queryTimeseriesData(body); System.out.println(result); } catch (ApiException e) { System.err.println("Exception when calling MetricsApi#queryTimeseriesData"); System.err.println("Status code: " + e.getCode()); System.err.println("Reason: " + e.getResponseBody()); System.err.println("Response headers: " + e.getResponseHeaders()); e.printStackTrace(); } } } ``` ##### ```java // Timeseries cross product query with apm_metrics data source and span_kind returns "OK" response import com.datadog.api.client.ApiClient; import com.datadog.api.client.ApiException; import com.datadog.api.client.v2.api.MetricsApi; import com.datadog.api.client.v2.model.ApmMetricsDataSource; import com.datadog.api.client.v2.model.ApmMetricsQuery; import com.datadog.api.client.v2.model.ApmMetricsSpanKind; import com.datadog.api.client.v2.model.ApmMetricsStat; import com.datadog.api.client.v2.model.FormulaLimit; import com.datadog.api.client.v2.model.QueryFormula; import com.datadog.api.client.v2.model.QuerySortOrder; import com.datadog.api.client.v2.model.TimeseriesFormulaQueryRequest; import com.datadog.api.client.v2.model.TimeseriesFormulaQueryResponse; import com.datadog.api.client.v2.model.TimeseriesFormulaRequest; import com.datadog.api.client.v2.model.TimeseriesFormulaRequestAttributes; import com.datadog.api.client.v2.model.TimeseriesFormulaRequestType; import com.datadog.api.client.v2.model.TimeseriesQuery; import java.util.Collections; public class Example { public static void main(String[] args) { ApiClient defaultClient = ApiClient.getDefaultApiClient(); MetricsApi apiInstance = new MetricsApi(defaultClient); TimeseriesFormulaQueryRequest body = new TimeseriesFormulaQueryRequest() .data( new TimeseriesFormulaRequest() .attributes( new TimeseriesFormulaRequestAttributes() .formulas( Collections.singletonList( new QueryFormula() .formula("a") .limit( new FormulaLimit() .count(10) .order(QuerySortOrder.DESC)))) .from(1636625471000L) .interval(5000L) .queries( Collections.singletonList( new TimeseriesQuery( new ApmMetricsQuery() .dataSource(ApmMetricsDataSource.APM_METRICS) .name("a") .stat(ApmMetricsStat.HITS) .service("web-store") .queryFilter("env:prod") .spanKind(ApmMetricsSpanKind.SERVER) .groupBy(Collections.singletonList("resource_name"))))) .to(1636629071000L)) .type(TimeseriesFormulaRequestType.TIMESERIES_REQUEST)); try { TimeseriesFormulaQueryResponse result = apiInstance.queryTimeseriesData(body); System.out.println(result); } catch (ApiException e) { System.err.println("Exception when calling MetricsApi#queryTimeseriesData"); 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" ##### ```python """ Timeseries cross product query returns "OK" response """ from datadog_api_client import ApiClient, Configuration from datadog_api_client.v2.api.metrics_api import MetricsApi from datadog_api_client.v2.model.formula_limit import FormulaLimit from datadog_api_client.v2.model.metrics_data_source import MetricsDataSource from datadog_api_client.v2.model.metrics_timeseries_query import MetricsTimeseriesQuery from datadog_api_client.v2.model.query_formula import QueryFormula from datadog_api_client.v2.model.query_sort_order import QuerySortOrder from datadog_api_client.v2.model.timeseries_formula_query_request import TimeseriesFormulaQueryRequest from datadog_api_client.v2.model.timeseries_formula_request import TimeseriesFormulaRequest from datadog_api_client.v2.model.timeseries_formula_request_attributes import TimeseriesFormulaRequestAttributes from datadog_api_client.v2.model.timeseries_formula_request_queries import TimeseriesFormulaRequestQueries from datadog_api_client.v2.model.timeseries_formula_request_type import TimeseriesFormulaRequestType body = TimeseriesFormulaQueryRequest( data=TimeseriesFormulaRequest( attributes=TimeseriesFormulaRequestAttributes( formulas=[ QueryFormula( formula="a", limit=FormulaLimit( count=10, order=QuerySortOrder.DESC, ), ), ], _from=1636625471000, interval=5000, queries=TimeseriesFormulaRequestQueries( [ MetricsTimeseriesQuery( data_source=MetricsDataSource.METRICS, query="avg:datadog.estimated_usage.metrics.custom{*}", name="a", ), ] ), to=1636629071000, ), type=TimeseriesFormulaRequestType.TIMESERIES_REQUEST, ), ) configuration = Configuration() with ApiClient(configuration) as api_client: api_instance = MetricsApi(api_client) response = api_instance.query_timeseries_data(body=body) print(response) ``` ##### ```python """ Timeseries cross product query with apm_dependency_stats data source returns "OK" response """ from datadog_api_client import ApiClient, Configuration from datadog_api_client.v2.api.metrics_api import MetricsApi from datadog_api_client.v2.model.apm_dependency_stat_name import ApmDependencyStatName from datadog_api_client.v2.model.apm_dependency_stats_data_source import ApmDependencyStatsDataSource from datadog_api_client.v2.model.apm_dependency_stats_query import ApmDependencyStatsQuery from datadog_api_client.v2.model.formula_limit import FormulaLimit from datadog_api_client.v2.model.query_formula import QueryFormula from datadog_api_client.v2.model.query_sort_order import QuerySortOrder from datadog_api_client.v2.model.timeseries_formula_query_request import TimeseriesFormulaQueryRequest from datadog_api_client.v2.model.timeseries_formula_request import TimeseriesFormulaRequest from datadog_api_client.v2.model.timeseries_formula_request_attributes import TimeseriesFormulaRequestAttributes from datadog_api_client.v2.model.timeseries_formula_request_queries import TimeseriesFormulaRequestQueries from datadog_api_client.v2.model.timeseries_formula_request_type import TimeseriesFormulaRequestType body = TimeseriesFormulaQueryRequest( data=TimeseriesFormulaRequest( attributes=TimeseriesFormulaRequestAttributes( formulas=[ QueryFormula( formula="a", limit=FormulaLimit( count=10, order=QuerySortOrder.DESC, ), ), ], _from=1636625471000, interval=5000, queries=TimeseriesFormulaRequestQueries( [ ApmDependencyStatsQuery( data_source=ApmDependencyStatsDataSource.APM_DEPENDENCY_STATS, name="a", env="ci", service="cassandra", stat=ApmDependencyStatName.AVG_DURATION, operation_name="cassandra.query", resource_name="DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?", primary_tag_name="datacenter", primary_tag_value="edge-eu1.prod.dog", ), ] ), to=1636629071000, ), type=TimeseriesFormulaRequestType.TIMESERIES_REQUEST, ), ) configuration = Configuration() with ApiClient(configuration) as api_client: api_instance = MetricsApi(api_client) response = api_instance.query_timeseries_data(body=body) print(response) ``` ##### ```python """ Timeseries cross product query with apm_metrics data source and span_kind returns "OK" response """ from datadog_api_client import ApiClient, Configuration from datadog_api_client.v2.api.metrics_api import MetricsApi from datadog_api_client.v2.model.apm_metrics_data_source import ApmMetricsDataSource from datadog_api_client.v2.model.apm_metrics_query import ApmMetricsQuery from datadog_api_client.v2.model.apm_metrics_span_kind import ApmMetricsSpanKind from datadog_api_client.v2.model.apm_metrics_stat import ApmMetricsStat from datadog_api_client.v2.model.formula_limit import FormulaLimit from datadog_api_client.v2.model.query_formula import QueryFormula from datadog_api_client.v2.model.query_sort_order import QuerySortOrder from datadog_api_client.v2.model.timeseries_formula_query_request import TimeseriesFormulaQueryRequest from datadog_api_client.v2.model.timeseries_formula_request import TimeseriesFormulaRequest from datadog_api_client.v2.model.timeseries_formula_request_attributes import TimeseriesFormulaRequestAttributes from datadog_api_client.v2.model.timeseries_formula_request_queries import TimeseriesFormulaRequestQueries from datadog_api_client.v2.model.timeseries_formula_request_type import TimeseriesFormulaRequestType body = TimeseriesFormulaQueryRequest( data=TimeseriesFormulaRequest( attributes=TimeseriesFormulaRequestAttributes( formulas=[ QueryFormula( formula="a", limit=FormulaLimit( count=10, order=QuerySortOrder.DESC, ), ), ], _from=1636625471000, interval=5000, queries=TimeseriesFormulaRequestQueries( [ ApmMetricsQuery( data_source=ApmMetricsDataSource.APM_METRICS, name="a", stat=ApmMetricsStat.HITS, service="web-store", query_filter="env:prod", span_kind=ApmMetricsSpanKind.SERVER, group_by=[ "resource_name", ], ), ] ), to=1636629071000, ), type=TimeseriesFormulaRequestType.TIMESERIES_REQUEST, ), ) configuration = Configuration() with ApiClient(configuration) as api_client: api_instance = MetricsApi(api_client) response = api_instance.query_timeseries_data(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 # Timeseries cross product query returns "OK" response require "datadog_api_client" api_instance = DatadogAPIClient::V2::MetricsAPI.new body = DatadogAPIClient::V2::TimeseriesFormulaQueryRequest.new({ data: DatadogAPIClient::V2::TimeseriesFormulaRequest.new({ attributes: DatadogAPIClient::V2::TimeseriesFormulaRequestAttributes.new({ formulas: [ DatadogAPIClient::V2::QueryFormula.new({ formula: "a", limit: DatadogAPIClient::V2::FormulaLimit.new({ count: 10, order: DatadogAPIClient::V2::QuerySortOrder::DESC, }), }), ], from: 1636625471000, interval: 5000, queries: [ DatadogAPIClient::V2::MetricsTimeseriesQuery.new({ data_source: DatadogAPIClient::V2::MetricsDataSource::METRICS, query: "avg:datadog.estimated_usage.metrics.custom{*}", name: "a", }), ], to: 1636629071000, }), type: DatadogAPIClient::V2::TimeseriesFormulaRequestType::TIMESERIES_REQUEST, }), }) p api_instance.query_timeseries_data(body) ``` ##### ```ruby # Timeseries cross product query with apm_dependency_stats data source returns "OK" response require "datadog_api_client" api_instance = DatadogAPIClient::V2::MetricsAPI.new body = DatadogAPIClient::V2::TimeseriesFormulaQueryRequest.new({ data: DatadogAPIClient::V2::TimeseriesFormulaRequest.new({ attributes: DatadogAPIClient::V2::TimeseriesFormulaRequestAttributes.new({ formulas: [ DatadogAPIClient::V2::QueryFormula.new({ formula: "a", limit: DatadogAPIClient::V2::FormulaLimit.new({ count: 10, order: DatadogAPIClient::V2::QuerySortOrder::DESC, }), }), ], from: 1636625471000, interval: 5000, queries: [ DatadogAPIClient::V2::ApmDependencyStatsQuery.new({ data_source: DatadogAPIClient::V2::ApmDependencyStatsDataSource::APM_DEPENDENCY_STATS, name: "a", env: "ci", service: "cassandra", stat: DatadogAPIClient::V2::ApmDependencyStatName::AVG_DURATION, operation_name: "cassandra.query", resource_name: "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?", primary_tag_name: "datacenter", primary_tag_value: "edge-eu1.prod.dog", }), ], to: 1636629071000, }), type: DatadogAPIClient::V2::TimeseriesFormulaRequestType::TIMESERIES_REQUEST, }), }) p api_instance.query_timeseries_data(body) ``` ##### ```ruby # Timeseries cross product query with apm_metrics data source and span_kind returns "OK" response require "datadog_api_client" api_instance = DatadogAPIClient::V2::MetricsAPI.new body = DatadogAPIClient::V2::TimeseriesFormulaQueryRequest.new({ data: DatadogAPIClient::V2::TimeseriesFormulaRequest.new({ attributes: DatadogAPIClient::V2::TimeseriesFormulaRequestAttributes.new({ formulas: [ DatadogAPIClient::V2::QueryFormula.new({ formula: "a", limit: DatadogAPIClient::V2::FormulaLimit.new({ count: 10, order: DatadogAPIClient::V2::QuerySortOrder::DESC, }), }), ], from: 1636625471000, interval: 5000, queries: [ DatadogAPIClient::V2::ApmMetricsQuery.new({ data_source: DatadogAPIClient::V2::ApmMetricsDataSource::APM_METRICS, name: "a", stat: DatadogAPIClient::V2::ApmMetricsStat::HITS, service: "web-store", query_filter: "env:prod", span_kind: DatadogAPIClient::V2::ApmMetricsSpanKind::SERVER, group_by: [ "resource_name", ], }), ], to: 1636629071000, }), type: DatadogAPIClient::V2::TimeseriesFormulaRequestType::TIMESERIES_REQUEST, }), }) p api_instance.query_timeseries_data(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" ##### ```rust // Timeseries cross product query returns "OK" response use datadog_api_client::datadog; use datadog_api_client::datadogV2::api_metrics::MetricsAPI; use datadog_api_client::datadogV2::model::FormulaLimit; use datadog_api_client::datadogV2::model::MetricsDataSource; use datadog_api_client::datadogV2::model::MetricsTimeseriesQuery; use datadog_api_client::datadogV2::model::QueryFormula; use datadog_api_client::datadogV2::model::QuerySortOrder; use datadog_api_client::datadogV2::model::TimeseriesFormulaQueryRequest; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequest; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequestAttributes; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequestType; use datadog_api_client::datadogV2::model::TimeseriesQuery; #[tokio::main] async fn main() { let body = TimeseriesFormulaQueryRequest::new(TimeseriesFormulaRequest::new( TimeseriesFormulaRequestAttributes::new( 1636625471000, vec![TimeseriesQuery::MetricsTimeseriesQuery(Box::new( MetricsTimeseriesQuery::new( MetricsDataSource::METRICS, "avg:datadog.estimated_usage.metrics.custom{*}".to_string(), ) .name("a".to_string()), ))], 1636629071000, ) .formulas(vec![QueryFormula::new("a".to_string()) .limit(FormulaLimit::new().count(10).order(QuerySortOrder::DESC))]) .interval(5000), TimeseriesFormulaRequestType::TIMESERIES_REQUEST, )); let configuration = datadog::Configuration::new(); let api = MetricsAPI::with_config(configuration); let resp = api.query_timeseries_data(body).await; if let Ok(value) = resp { println!("{:#?}", value); } else { println!("{:#?}", resp.unwrap_err()); } } ``` ##### ```rust // Timeseries cross product query with apm_dependency_stats data source returns // "OK" response use datadog_api_client::datadog; use datadog_api_client::datadogV2::api_metrics::MetricsAPI; use datadog_api_client::datadogV2::model::ApmDependencyStatName; use datadog_api_client::datadogV2::model::ApmDependencyStatsDataSource; use datadog_api_client::datadogV2::model::ApmDependencyStatsQuery; use datadog_api_client::datadogV2::model::FormulaLimit; use datadog_api_client::datadogV2::model::QueryFormula; use datadog_api_client::datadogV2::model::QuerySortOrder; use datadog_api_client::datadogV2::model::TimeseriesFormulaQueryRequest; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequest; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequestAttributes; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequestType; use datadog_api_client::datadogV2::model::TimeseriesQuery; #[tokio::main] async fn main() { let body = TimeseriesFormulaQueryRequest::new( TimeseriesFormulaRequest::new( TimeseriesFormulaRequestAttributes::new( 1636625471000, vec![ TimeseriesQuery::ApmDependencyStatsQuery( Box::new( ApmDependencyStatsQuery::new( ApmDependencyStatsDataSource::APM_DEPENDENCY_STATS, "ci".to_string(), "a".to_string(), "cassandra.query".to_string(), "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?".to_string(), "cassandra".to_string(), ApmDependencyStatName::AVG_DURATION, ) .primary_tag_name("datacenter".to_string()) .primary_tag_value("edge-eu1.prod.dog".to_string()), ), ) ], 1636629071000, ) .formulas( vec![ QueryFormula::new( "a".to_string(), ).limit(FormulaLimit::new().count(10).order(QuerySortOrder::DESC)) ], ) .interval(5000), TimeseriesFormulaRequestType::TIMESERIES_REQUEST, ), ); let configuration = datadog::Configuration::new(); let api = MetricsAPI::with_config(configuration); let resp = api.query_timeseries_data(body).await; if let Ok(value) = resp { println!("{:#?}", value); } else { println!("{:#?}", resp.unwrap_err()); } } ``` ##### ```rust // Timeseries cross product query with apm_metrics data source and span_kind // returns "OK" response use datadog_api_client::datadog; use datadog_api_client::datadogV2::api_metrics::MetricsAPI; use datadog_api_client::datadogV2::model::ApmMetricsDataSource; use datadog_api_client::datadogV2::model::ApmMetricsQuery; use datadog_api_client::datadogV2::model::ApmMetricsSpanKind; use datadog_api_client::datadogV2::model::ApmMetricsStat; use datadog_api_client::datadogV2::model::FormulaLimit; use datadog_api_client::datadogV2::model::QueryFormula; use datadog_api_client::datadogV2::model::QuerySortOrder; use datadog_api_client::datadogV2::model::TimeseriesFormulaQueryRequest; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequest; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequestAttributes; use datadog_api_client::datadogV2::model::TimeseriesFormulaRequestType; use datadog_api_client::datadogV2::model::TimeseriesQuery; #[tokio::main] async fn main() { let body = TimeseriesFormulaQueryRequest::new(TimeseriesFormulaRequest::new( TimeseriesFormulaRequestAttributes::new( 1636625471000, vec![TimeseriesQuery::ApmMetricsQuery(Box::new( ApmMetricsQuery::new( ApmMetricsDataSource::APM_METRICS, "a".to_string(), ApmMetricsStat::HITS, ) .group_by(vec!["resource_name".to_string()]) .query_filter("env:prod".to_string()) .service("web-store".to_string()) .span_kind(ApmMetricsSpanKind::SERVER), ))], 1636629071000, ) .formulas(vec![QueryFormula::new("a".to_string()) .limit(FormulaLimit::new().count(10).order(QuerySortOrder::DESC))]) .interval(5000), TimeseriesFormulaRequestType::TIMESERIES_REQUEST, )); let configuration = datadog::Configuration::new(); let api = MetricsAPI::with_config(configuration); let resp = api.query_timeseries_data(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 /** * Timeseries cross product query returns "OK" response */ import { client, v2 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration(); const apiInstance = new v2.MetricsApi(configuration); const params: v2.MetricsApiQueryTimeseriesDataRequest = { body: { data: { attributes: { formulas: [ { formula: "a", limit: { count: 10, order: "desc", }, }, ], from: 1636625471000, interval: 5000, queries: [ { dataSource: "metrics", query: "avg:datadog.estimated_usage.metrics.custom{*}", name: "a", }, ], to: 1636629071000, }, type: "timeseries_request", }, }, }; apiInstance .queryTimeseriesData(params) .then((data: v2.TimeseriesFormulaQueryResponse) => { console.log( "API called successfully. Returned data: " + JSON.stringify(data) ); }) .catch((error: any) => console.error(error)); ``` ##### ```typescript /** * Timeseries cross product query with apm_dependency_stats data source returns "OK" response */ import { client, v2 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration(); const apiInstance = new v2.MetricsApi(configuration); const params: v2.MetricsApiQueryTimeseriesDataRequest = { body: { data: { attributes: { formulas: [ { formula: "a", limit: { count: 10, order: "desc", }, }, ], from: 1636625471000, interval: 5000, queries: [ { dataSource: "apm_dependency_stats", name: "a", env: "ci", service: "cassandra", stat: "avg_duration", operationName: "cassandra.query", resourceName: "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?", primaryTagName: "datacenter", primaryTagValue: "edge-eu1.prod.dog", }, ], to: 1636629071000, }, type: "timeseries_request", }, }, }; apiInstance .queryTimeseriesData(params) .then((data: v2.TimeseriesFormulaQueryResponse) => { console.log( "API called successfully. Returned data: " + JSON.stringify(data) ); }) .catch((error: any) => console.error(error)); ``` ##### ```typescript /** * Timeseries cross product query with apm_metrics data source and span_kind returns "OK" response */ import { client, v2 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration(); const apiInstance = new v2.MetricsApi(configuration); const params: v2.MetricsApiQueryTimeseriesDataRequest = { body: { data: { attributes: { formulas: [ { formula: "a", limit: { count: 10, order: "desc", }, }, ], from: 1636625471000, interval: 5000, queries: [ { dataSource: "apm_metrics", name: "a", stat: "hits", service: "web-store", queryFilter: "env:prod", spanKind: "server", groupBy: ["resource_name"], }, ], to: 1636629071000, }, type: "timeseries_request", }, }, }; apiInstance .queryTimeseriesData(params) .then((data: v2.TimeseriesFormulaQueryResponse) => { 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 %}