--- title: Aggregate spans description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > API Reference > Spans --- # Aggregate spans{% #aggregate-spans %} {% tab title="v2" %} | Datadog site | API endpoint | | ----------------- | ------------------------------------------------------------------- | | ap1.datadoghq.com | POST https://api.ap1.datadoghq.com/api/v2/spans/analytics/aggregate | | ap2.datadoghq.com | POST https://api.ap2.datadoghq.com/api/v2/spans/analytics/aggregate | | app.datadoghq.eu | POST https://api.datadoghq.eu/api/v2/spans/analytics/aggregate | | app.ddog-gov.com | POST https://api.ddog-gov.com/api/v2/spans/analytics/aggregate | | us2.ddog-gov.com | POST https://api.us2.ddog-gov.com/api/v2/spans/analytics/aggregate | | app.datadoghq.com | POST https://api.datadoghq.com/api/v2/spans/analytics/aggregate | | us3.datadoghq.com | POST https://api.us3.datadoghq.com/api/v2/spans/analytics/aggregate | | us5.datadoghq.com | POST https://api.us5.datadoghq.com/api/v2/spans/analytics/aggregate | ### Overview The API endpoint to aggregate spans into buckets and compute metrics and timeseries. This endpoint is rate limited to `300` requests per hour. This endpoint requires the `apm_read` permission. OAuth apps require the `apm_read` authorization [scope](https://docs.datadoghq.com/api/latest/scopes.md#spans) to access this endpoint. ### Request #### Body Data (required) {% tab title="Model" %} | Parent field | Field | Type | Description | | ------------ | ----------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data | object | The object containing the query content. | | data | attributes | object | The object containing all the query parameters. | | attributes | compute | [object] | The list of metrics or timeseries to compute for the retrieved buckets. | | compute | aggregation [*required*] | enum | An aggregation function. Allowed enum values: `count,cardinality,pc75,pc90,pc95,pc98,pc99,sum,min,max` | | compute | interval | string | The time buckets' size (only used for type=timeseries) Defaults to a resolution of 150 points. | | compute | metric | string | The metric to use. | | compute | type | enum | The type of compute. Allowed enum values: `timeseries,total` | | attributes | filter | object | The search and filter query settings. | | filter | from | string | The minimum time for the requested spans, supports date-time ISO8601, date math, and regular timestamps (milliseconds). | | filter | query | string | The search query - following the span search syntax. | | filter | to | string | The maximum time for the requested spans, supports date-time ISO8601, date math, and regular timestamps (milliseconds). | | attributes | group_by | [object] | The rules for the group by. | | group_by | facet [*required*] | string | The name of the facet to use (required). | | group_by | histogram | object | Used to perform a histogram computation (only for measure facets). Note: At most 100 buckets are allowed, the number of buckets is (max - min)/interval. | | histogram | interval [*required*] | double | The bin size of the histogram buckets. | | histogram | max [*required*] | double | The maximum value for the measure used in the histogram (values greater than this one are filtered out). | | histogram | min [*required*] | double | The minimum value for the measure used in the histogram (values smaller than this one are filtered out). | | group_by | limit | int64 | The maximum buckets to return for this group by. | | group_by | missing | | The value to use for spans that don't have the facet used to group by. | | missing | Option 1 | string | The missing value to use if there is string valued facet. | | missing | Option 2 | double | The missing value to use if there is a number valued facet. | | group_by | sort | object | A sort rule. | | sort | aggregation | enum | An aggregation function. Allowed enum values: `count,cardinality,pc75,pc90,pc95,pc98,pc99,sum,min,max` | | sort | metric | string | The metric to sort by (only used for `type=measure`). | | sort | order | enum | The order to use, ascending or descending. Allowed enum values: `asc,desc` | | sort | type | enum | The type of sorting algorithm. Allowed enum values: `alphabetical,measure` | | group_by | total | | A resulting object to put the given computes in over all the matching records. | | total | Option 1 | boolean | If set to true, creates an additional bucket labeled "$facet_total". | | total | Option 2 | string | A string to use as the key value for the total bucket. | | total | Option 3 | double | A number to use as the key value for the total bucket. | | attributes | options | object | Global query options that are used during the query. Note: You should only supply timezone or time offset but not both otherwise the query will fail. | | options | timeOffset | int64 | The time offset (in seconds) to apply to the query. | | options | timezone | string | The timezone can be specified as GMT, UTC, an offset from UTC (like UTC+1), or as a Timezone Database identifier (like America/New_York). | | data | type | enum | The type of resource. The value should always be aggregate_request. Allowed enum values: `aggregate_request` | {% /tab %} {% tab title="Example" %} ```json { "data": { "attributes": { "compute": [ { "aggregation": "count", "interval": "5m", "type": "timeseries" } ], "filter": { "from": "now-15m", "query": "*", "to": "now" } }, "type": "aggregate_request" } } ``` {% /tab %} ### Response {% tab title="200" %} OK {% tab title="Model" %} The response object for the spans aggregate API endpoint. | Parent field | Field | Type | Description | | -------------------- | ---------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------- | | | data | [object] | The list of matching buckets, one item per bucket. | | data | attributes | object | A bucket values. | | attributes | by | object | The key, value pairs for each group by. | | additionalProperties | | | The values for each group by. | | attributes | compute | object | The compute data. | | attributes | computes | object | A map of the metric name -> value for regular compute or list of values for a timeseries. | | additionalProperties | | | A bucket value, can be either a timeseries or a single value. | | | Option 1 | string | A single string value. | | | Option 2 | double | A single number value. | | | Option 3 | [object] | A timeseries array. | | Option 3 | time | string | The time value for this point. | | Option 3 | value | double | The value for this point. | | data | id | string | ID of the spans aggregate. | | data | type | enum | The spans aggregate bucket type. Allowed enum values: `bucket` | | | meta | object | The metadata associated with a request. | | meta | elapsed | int64 | The time elapsed in milliseconds. | | meta | request_id | string | The identifier of the request. | | meta | status | enum | The status of the response. Allowed enum values: `done,timeout` | | meta | warnings | [object] | A list of warnings (non fatal errors) encountered, partial results might be returned if warnings are present in the response. | | warnings | code | string | A unique code for this type of warning. | | warnings | detail | string | A detailed explanation of this specific warning. | | warnings | title | string | A short human-readable summary of the warning. | {% /tab %} {% tab title="Example" %} ```json { "data": [ { "attributes": { "by": { "": "undefined" }, "compute": {}, "computes": { "": { "description": "undefined", "type": "undefined" } } }, "id": "string", "type": "bucket" } ], "meta": { "elapsed": 132, "request_id": "MWlFUjVaWGZTTTZPYzM0VXp1OXU2d3xLSVpEMjZKQ0VKUTI0dEYtM3RSOFVR", "status": "done", "warnings": [ { "code": "unknown_index", "detail": "indexes: foo, bar", "title": "One or several indexes are missing or invalid, results hold data from the other indexes" } ] } } ``` {% /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" %} 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/spans/analytics/aggregate" \ -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": { "compute": [ { "aggregation": "pc90", "interval": "5m", "metric": "@duration", "type": "timeseries" } ], "filter": { "from": "now-15m", "query": "service:web* AND @http.status_code:[200 TO 299]", "to": "now" }, "group_by": [ { "facet": "host", "histogram": { "interval": 10, "max": 100, "min": 50 }, "sort": { "aggregation": "count", "order": "asc" } } ], "options": { "timezone": "GMT" } }, "type": "aggregate_request" } } EOF ##### ```go // Aggregate spans 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.SpansAggregateRequest{ Data: &datadogV2.SpansAggregateData{ Attributes: &datadogV2.SpansAggregateRequestAttributes{ Compute: []datadogV2.SpansCompute{ { Aggregation: datadogV2.SPANSAGGREGATIONFUNCTION_COUNT, Interval: datadog.PtrString("5m"), Type: datadogV2.SPANSCOMPUTETYPE_TIMESERIES.Ptr(), }, }, Filter: &datadogV2.SpansQueryFilter{ From: datadog.PtrString("now-15m"), Query: datadog.PtrString("*"), To: datadog.PtrString("now"), }, }, Type: datadogV2.SPANSAGGREGATEREQUESTTYPE_AGGREGATE_REQUEST.Ptr(), }, } ctx := datadog.NewDefaultContext(context.Background()) configuration := datadog.NewConfiguration() apiClient := datadog.NewAPIClient(configuration) api := datadogV2.NewSpansApi(apiClient) resp, r, err := api.AggregateSpans(ctx, body) if err != nil { fmt.Fprintf(os.Stderr, "Error when calling `SpansApi.AggregateSpans`: %v\n", err) fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r) } responseContent, _ := json.MarshalIndent(resp, "", " ") fmt.Fprintf(os.Stdout, "Response from `SpansApi.AggregateSpans`:\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 // Aggregate spans returns "OK" response import com.datadog.api.client.ApiClient; import com.datadog.api.client.ApiException; import com.datadog.api.client.v2.api.SpansApi; import com.datadog.api.client.v2.model.SpansAggregateData; import com.datadog.api.client.v2.model.SpansAggregateRequest; import com.datadog.api.client.v2.model.SpansAggregateRequestAttributes; import com.datadog.api.client.v2.model.SpansAggregateRequestType; import com.datadog.api.client.v2.model.SpansAggregateResponse; import com.datadog.api.client.v2.model.SpansAggregationFunction; import com.datadog.api.client.v2.model.SpansCompute; import com.datadog.api.client.v2.model.SpansComputeType; import com.datadog.api.client.v2.model.SpansQueryFilter; import java.util.Collections; public class Example { public static void main(String[] args) { ApiClient defaultClient = ApiClient.getDefaultApiClient(); SpansApi apiInstance = new SpansApi(defaultClient); SpansAggregateRequest body = new SpansAggregateRequest() .data( new SpansAggregateData() .attributes( new SpansAggregateRequestAttributes() .compute( Collections.singletonList( new SpansCompute() .aggregation(SpansAggregationFunction.COUNT) .interval("5m") .type(SpansComputeType.TIMESERIES))) .filter(new SpansQueryFilter().from("now-15m").query("*").to("now"))) .type(SpansAggregateRequestType.AGGREGATE_REQUEST)); try { SpansAggregateResponse result = apiInstance.aggregateSpans(body); System.out.println(result); } catch (ApiException e) { System.err.println("Exception when calling SpansApi#aggregateSpans"); 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 """ Aggregate spans returns "OK" response """ from datadog_api_client import ApiClient, Configuration from datadog_api_client.v2.api.spans_api import SpansApi from datadog_api_client.v2.model.spans_aggregate_data import SpansAggregateData from datadog_api_client.v2.model.spans_aggregate_request import SpansAggregateRequest from datadog_api_client.v2.model.spans_aggregate_request_attributes import SpansAggregateRequestAttributes from datadog_api_client.v2.model.spans_aggregate_request_type import SpansAggregateRequestType from datadog_api_client.v2.model.spans_aggregation_function import SpansAggregationFunction from datadog_api_client.v2.model.spans_compute import SpansCompute from datadog_api_client.v2.model.spans_compute_type import SpansComputeType from datadog_api_client.v2.model.spans_query_filter import SpansQueryFilter body = SpansAggregateRequest( data=SpansAggregateData( attributes=SpansAggregateRequestAttributes( compute=[ SpansCompute( aggregation=SpansAggregationFunction.COUNT, interval="5m", type=SpansComputeType.TIMESERIES, ), ], filter=SpansQueryFilter( _from="now-15m", query="*", to="now", ), ), type=SpansAggregateRequestType.AGGREGATE_REQUEST, ), ) configuration = Configuration() with ApiClient(configuration) as api_client: api_instance = SpansApi(api_client) response = api_instance.aggregate_spans(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 # Aggregate spans returns "OK" response require "datadog_api_client" api_instance = DatadogAPIClient::V2::SpansAPI.new body = DatadogAPIClient::V2::SpansAggregateRequest.new({ data: DatadogAPIClient::V2::SpansAggregateData.new({ attributes: DatadogAPIClient::V2::SpansAggregateRequestAttributes.new({ compute: [ DatadogAPIClient::V2::SpansCompute.new({ aggregation: DatadogAPIClient::V2::SpansAggregationFunction::COUNT, interval: "5m", type: DatadogAPIClient::V2::SpansComputeType::TIMESERIES, }), ], filter: DatadogAPIClient::V2::SpansQueryFilter.new({ from: "now-15m", query: "*", to: "now", }), }), type: DatadogAPIClient::V2::SpansAggregateRequestType::AGGREGATE_REQUEST, }), }) p api_instance.aggregate_spans(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 // Aggregate spans returns "OK" response use datadog_api_client::datadog; use datadog_api_client::datadogV2::api_spans::SpansAPI; use datadog_api_client::datadogV2::model::SpansAggregateData; use datadog_api_client::datadogV2::model::SpansAggregateRequest; use datadog_api_client::datadogV2::model::SpansAggregateRequestAttributes; use datadog_api_client::datadogV2::model::SpansAggregateRequestType; use datadog_api_client::datadogV2::model::SpansAggregationFunction; use datadog_api_client::datadogV2::model::SpansCompute; use datadog_api_client::datadogV2::model::SpansComputeType; use datadog_api_client::datadogV2::model::SpansQueryFilter; #[tokio::main] async fn main() { let body = SpansAggregateRequest::new().data( SpansAggregateData::new() .attributes( SpansAggregateRequestAttributes::new() .compute(vec![SpansCompute::new(SpansAggregationFunction::COUNT) .interval("5m".to_string()) .type_(SpansComputeType::TIMESERIES)]) .filter( SpansQueryFilter::new() .from("now-15m".to_string()) .query("*".to_string()) .to("now".to_string()), ), ) .type_(SpansAggregateRequestType::AGGREGATE_REQUEST), ); let configuration = datadog::Configuration::new(); let api = SpansAPI::with_config(configuration); let resp = api.aggregate_spans(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 /** * Aggregate spans returns "OK" response */ import { client, v2 } from "@datadog/datadog-api-client"; const configuration = client.createConfiguration(); const apiInstance = new v2.SpansApi(configuration); const params: v2.SpansApiAggregateSpansRequest = { body: { data: { attributes: { compute: [ { aggregation: "count", interval: "5m", type: "timeseries", }, ], filter: { from: "now-15m", query: "*", to: "now", }, }, type: "aggregate_request", }, }, }; apiInstance .aggregateSpans(params) .then((data: v2.SpansAggregateResponse) => { 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 %}