--- title: Get SPA recommendations v2 description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > API Reference > Spa --- > For the complete documentation index, see [llms.txt](https://docs.datadoghq.com/llms.txt). # Get SPA recommendations v2{% #get-spa-recommendations-v2 %} {% tab title="v2" %} **Note**: This endpoint is in preview and may change in the future. It is not yet recommended for production use. | Datadog site | API endpoint | | ----------------- | -------------------------------------------------------------------------- | | ap1.datadoghq.com | POST https://api.ap1.datadoghq.com/api/v2/spa/recommendations-v2/{service} | | ap2.datadoghq.com | POST https://api.ap2.datadoghq.com/api/v2/spa/recommendations-v2/{service} | | app.datadoghq.eu | POST https://api.datadoghq.eu/api/v2/spa/recommendations-v2/{service} | | app.ddog-gov.com | POST https://api.ddog-gov.com/api/v2/spa/recommendations-v2/{service} | | us2.ddog-gov.com | POST https://api.us2.ddog-gov.com/api/v2/spa/recommendations-v2/{service} | | uk1.datadoghq.com | POST https://api.uk1.datadoghq.com/api/v2/spa/recommendations-v2/{service} | | app.datadoghq.com | POST https://api.datadoghq.com/api/v2/spa/recommendations-v2/{service} | | us3.datadoghq.com | POST https://api.us3.datadoghq.com/api/v2/spa/recommendations-v2/{service} | | us5.datadoghq.com | POST https://api.us5.datadoghq.com/api/v2/spa/recommendations-v2/{service} | ### Overview This endpoint is experimental and restricted to Datadog internal use only. Retrieve resource recommendations for a Spark job. The caller (Spark Gateway) provides a service name and the job's raw arguments. SPA determines which arguments are relevant for the service and returns structured recommendations for driver and executor resources. ### Arguments #### Path Parameters | Name | Type | Description | | ------------------------- | ------ | -------------------------------- | | service [*required*] | string | The service name for a Spark job | ### Request #### Body Data (required) {% tab title="Model" %} | Parent field | Field | Type | Description | | ------------ | ---------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | | data [*required*] | object | JSON:API resource object for the SPA v2 recommendation request. | | data | attributes [*required*] | object | Attributes for requesting SPA recommendations by forwarding a Spark job's raw arguments instead of a pre-computed shard. | | attributes | arguments [*required*] | [string] | Raw, unfiltered Spark job arguments as submitted (for example, `--org_id=2`). SPA determines which arguments are relevant for the given service. | | data | type [*required*] | enum | JSON:API resource type for the SPA v2 recommendation request. Allowed enum values: `recommendation_v2_request` | {% /tab %} {% tab title="Example" %} ```json { "data": { "attributes": { "arguments": [ "" ] }, "type": "recommendation_v2_request" } } ``` {% /tab %} ### Response {% tab title="200" %} OK {% tab title="Model" %} | Parent field | Field | Type | Description | | ------------ | ---------------------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data [*required*] | object | JSON:API resource object for SPA Recommendation. Includes type, optional ID, and resource attributes with structured recommendations. | | data | attributes [*required*] | object | Attributes of the SPA Recommendation resource. Contains recommendations for both driver and executor components. | | attributes | confidence_level | double | The confidence level of the recommendation, expressed as a value between 0.0 (low confidence) and 1.0 (high confidence). | | attributes | driver [*required*] | object | Resource recommendation for a single Spark component (driver or executor). Contains estimation data used to patch Spark job specs. | | driver | estimation [*required*] | object | Recommended resource values for a Spark driver or executor, derived from recent real usage metrics. Used by SPA to propose more efficient pod sizing. | | estimation | cpu | object | CPU usage statistics derived from historical Spark job metrics. Provides multiple estimates so users can choose between conservative and cost-saving risk profiles. | | cpu | max | int64 | Maximum CPU usage observed for the job, expressed in millicores. This represents the upper bound of usage. | | cpu | p75 | int64 | 75th percentile of CPU usage (millicores). Represents a cost-saving configuration while covering most workloads. | | cpu | p95 | int64 | 95th percentile of CPU usage (millicores). Balances performance and cost, providing a safer margin than p75. | | estimation | ephemeral_storage | int64 | Recommended ephemeral storage allocation (in MiB). Derived from job temporary storage patterns. | | estimation | heap | int64 | Recommended JVM heap size (in MiB). | | estimation | memory | int64 | Recommended total memory allocation (in MiB). Includes both heap and overhead. | | estimation | overhead | int64 | Recommended JVM overhead (in MiB). Computed as total memory - heap. | | attributes | executor [*required*] | object | Resource recommendation for a single Spark component (driver or executor). Contains estimation data used to patch Spark job specs. | | executor | estimation [*required*] | object | Recommended resource values for a Spark driver or executor, derived from recent real usage metrics. Used by SPA to propose more efficient pod sizing. | | estimation | cpu | object | CPU usage statistics derived from historical Spark job metrics. Provides multiple estimates so users can choose between conservative and cost-saving risk profiles. | | cpu | max | int64 | Maximum CPU usage observed for the job, expressed in millicores. This represents the upper bound of usage. | | cpu | p75 | int64 | 75th percentile of CPU usage (millicores). Represents a cost-saving configuration while covering most workloads. | | cpu | p95 | int64 | 95th percentile of CPU usage (millicores). Balances performance and cost, providing a safer margin than p75. | | estimation | ephemeral_storage | int64 | Recommended ephemeral storage allocation (in MiB). Derived from job temporary storage patterns. | | estimation | heap | int64 | Recommended JVM heap size (in MiB). | | estimation | memory | int64 | Recommended total memory allocation (in MiB). Includes both heap and overhead. | | estimation | overhead | int64 | Recommended JVM overhead (in MiB). Computed as total memory - heap. | | attributes | matched_params | string | Only returned by the v2 endpoint. The job parameters whose values the recommendation was matched on, as `parameter=value` pairs joined by `|`. An empty string means the service-wide (coarse) recommendation was used. | | data | id | string | Resource identifier for the recommendation. Optional in responses. | | data | type [*required*] | enum | JSON:API resource type for Spark Pod Autosizing recommendations. Identifies the Recommendation resource returned by SPA. Allowed enum values: `recommendation` | {% /tab %} {% tab title="Example" %} ```json { "data": { "attributes": { "confidence_level": "number", "driver": { "estimation": { "cpu": { "max": "integer", "p75": "integer", "p95": "integer" }, "ephemeral_storage": "integer", "heap": "integer", "memory": "integer", "overhead": "integer" } }, "executor": { "estimation": { "cpu": { "max": "integer", "p75": "integer", "p95": "integer" }, "ephemeral_storage": "integer", "heap": "integer", "memory": "integer", "overhead": "integer" } }, "matched_params": "detailed_metrics=false|Region=US1" }, "id": "string", "type": "recommendation" } } ``` {% /tab %} {% tab title="Model" %} JSON:API document containing a single Recommendation resource. Returned by SPA when the Spark Gateway requests recommendations. | Parent field | Field | Type | Description | | ------------ | ---------------------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | data [*required*] | object | JSON:API resource object for SPA Recommendation. Includes type, optional ID, and resource attributes with structured recommendations. | | data | attributes [*required*] | object | Attributes of the SPA Recommendation resource. Contains recommendations for both driver and executor components. | | attributes | confidence_level | double | The confidence level of the recommendation, expressed as a value between 0.0 (low confidence) and 1.0 (high confidence). | | attributes | driver [*required*] | object | Resource recommendation for a single Spark component (driver or executor). Contains estimation data used to patch Spark job specs. | | driver | estimation [*required*] | object | Recommended resource values for a Spark driver or executor, derived from recent real usage metrics. Used by SPA to propose more efficient pod sizing. | | estimation | cpu | object | CPU usage statistics derived from historical Spark job metrics. Provides multiple estimates so users can choose between conservative and cost-saving risk profiles. | | cpu | max | int64 | Maximum CPU usage observed for the job, expressed in millicores. This represents the upper bound of usage. | | cpu | p75 | int64 | 75th percentile of CPU usage (millicores). Represents a cost-saving configuration while covering most workloads. | | cpu | p95 | int64 | 95th percentile of CPU usage (millicores). Balances performance and cost, providing a safer margin than p75. | | estimation | ephemeral_storage | int64 | Recommended ephemeral storage allocation (in MiB). Derived from job temporary storage patterns. | | estimation | heap | int64 | Recommended JVM heap size (in MiB). | | estimation | memory | int64 | Recommended total memory allocation (in MiB). Includes both heap and overhead. | | estimation | overhead | int64 | Recommended JVM overhead (in MiB). Computed as total memory - heap. | | attributes | executor [*required*] | object | Resource recommendation for a single Spark component (driver or executor). Contains estimation data used to patch Spark job specs. | | executor | estimation [*required*] | object | Recommended resource values for a Spark driver or executor, derived from recent real usage metrics. Used by SPA to propose more efficient pod sizing. | | estimation | cpu | object | CPU usage statistics derived from historical Spark job metrics. Provides multiple estimates so users can choose between conservative and cost-saving risk profiles. | | cpu | max | int64 | Maximum CPU usage observed for the job, expressed in millicores. This represents the upper bound of usage. | | cpu | p75 | int64 | 75th percentile of CPU usage (millicores). Represents a cost-saving configuration while covering most workloads. | | cpu | p95 | int64 | 95th percentile of CPU usage (millicores). Balances performance and cost, providing a safer margin than p75. | | estimation | ephemeral_storage | int64 | Recommended ephemeral storage allocation (in MiB). Derived from job temporary storage patterns. | | estimation | heap | int64 | Recommended JVM heap size (in MiB). | | estimation | memory | int64 | Recommended total memory allocation (in MiB). Includes both heap and overhead. | | estimation | overhead | int64 | Recommended JVM overhead (in MiB). Computed as total memory - heap. | | attributes | matched_params | string | Only returned by the v2 endpoint. The job parameters whose values the recommendation was matched on, as `parameter=value` pairs joined by `|`. An empty string means the service-wide (coarse) recommendation was used. | | data | id | string | Resource identifier for the recommendation. Optional in responses. | | data | type [*required*] | enum | JSON:API resource type for Spark Pod Autosizing recommendations. Identifies the Recommendation resource returned by SPA. Allowed enum values: `recommendation` | {% /tab %} {% tab title="Example" %} ```json { "data": { "attributes": { "confidence_level": "number", "driver": { "estimation": { "cpu": { "max": "integer", "p75": "integer", "p95": "integer" }, "ephemeral_storage": "integer", "heap": "integer", "memory": "integer", "overhead": "integer" } }, "executor": { "estimation": { "cpu": { "max": "integer", "p75": "integer", "p95": "integer" }, "ephemeral_storage": "integer", "heap": "integer", "memory": "integer", "overhead": "integer" } }, "matched_params": "detailed_metrics=false|Region=US1" }, "id": "string", "type": "recommendation" } } ``` {% /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="404" %} Not Found {% 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 ##### \# Path parameters export service="CHANGE_ME" \# Use a Personal Access Token or Service Access Token export DD_BEARER_TOKEN="" \# Curl command curl -X POST "https://api.datadoghq.com/api/v2/spa/recommendations-v2/${service}" \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer ${DD_BEARER_TOKEN}" \ -d @- << EOF { "data": { "attributes": { "arguments": [ "" ] }, "type": "recommendation_v2_request" } } EOF ##### ```python """ Get SPA recommendations v2 returns "OK" response """ from os import environ from datadog_api_client import ApiClient, Configuration from datadog_api_client.v2.api.spa_api import SpaApi from datadog_api_client.v2.model.recommendation_v2_request_attributes import RecommendationV2RequestAttributes from datadog_api_client.v2.model.recommendation_v2_request_body import RecommendationV2RequestBody from datadog_api_client.v2.model.recommendation_v2_request_data import RecommendationV2RequestData from datadog_api_client.v2.model.recommendation_v2_request_type import RecommendationV2RequestType body = RecommendationV2RequestBody( data=RecommendationV2RequestData( attributes=RecommendationV2RequestAttributes( arguments=[ "", ], ), type=RecommendationV2RequestType.RECOMMENDATION_V2_REQUEST, ), ) configuration = Configuration() configuration.access_token = environ["DD_BEARER_TOKEN"] configuration.unstable_operations["get_spa_recommendations_v2"] = True with ApiClient(configuration) as api_client: api_instance = SpaApi(api_client) response = api_instance.get_spa_recommendations_v2(service="service", 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_BEARER_TOKEN="" python3 "example.py" ##### ```ruby # Get SPA recommendations v2 returns "OK" response require "datadog_api_client" DatadogAPIClient.configure do |config| config.access_token = ENV["DD_BEARER_TOKEN"] config.unstable_operations["v2.get_spa_recommendations_v2".to_sym] = true end api_instance = DatadogAPIClient::V2::SpaAPI.new body = DatadogAPIClient::V2::RecommendationV2RequestBody.new({ data: DatadogAPIClient::V2::RecommendationV2RequestData.new({ attributes: DatadogAPIClient::V2::RecommendationV2RequestAttributes.new({ arguments: [ "", ], }), type: DatadogAPIClient::V2::RecommendationV2RequestType::RECOMMENDATION_V2_REQUEST, }), }) p api_instance.get_spa_recommendations_v2("service", 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_BEARER_TOKEN="" rb "example.rb" ##### ```go // Get SPA recommendations v2 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.RecommendationV2RequestBody{ Data: datadogV2.RecommendationV2RequestData{ Attributes: datadogV2.RecommendationV2RequestAttributes{ Arguments: []string{ "", }, }, Type: datadogV2.RECOMMENDATIONV2REQUESTTYPE_RECOMMENDATION_V2_REQUEST, }, } ctx := datadog.NewDefaultContext(context.Background()) ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN")) configuration := datadog.NewConfiguration() configuration.SetUnstableOperationEnabled("v2.GetSPARecommendationsV2", true) apiClient := datadog.NewAPIClient(configuration) api := datadogV2.NewSpaApi(apiClient) resp, r, err := api.GetSPARecommendationsV2(ctx, "service", body) if err != nil { fmt.Fprintf(os.Stderr, "Error when calling `SpaApi.GetSPARecommendationsV2`: %v\n", err) fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r) } responseContent, _ := json.MarshalIndent(resp, "", " ") fmt.Fprintf(os.Stdout, "Response from `SpaApi.GetSPARecommendationsV2`:\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_BEARER_TOKEN="" go run "main.go" ##### ```java // Get SPA recommendations v2 returns "OK" response import com.datadog.api.client.ApiClient; import com.datadog.api.client.ApiException; import com.datadog.api.client.v2.api.SpaApi; import com.datadog.api.client.v2.model.RecommendationDocument; import com.datadog.api.client.v2.model.RecommendationV2RequestAttributes; import com.datadog.api.client.v2.model.RecommendationV2RequestBody; import com.datadog.api.client.v2.model.RecommendationV2RequestData; import com.datadog.api.client.v2.model.RecommendationV2RequestType; import java.util.Collections; public class Example { public static void main(String[] args) { ApiClient defaultClient = ApiClient.getDefaultApiClient(); defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN")); defaultClient.setUnstableOperationEnabled("v2.getSPARecommendationsV2", true); SpaApi apiInstance = new SpaApi(defaultClient); RecommendationV2RequestBody body = new RecommendationV2RequestBody() .data( new RecommendationV2RequestData() .attributes( new RecommendationV2RequestAttributes() .arguments(Collections.singletonList(""))) .type(RecommendationV2RequestType.RECOMMENDATION_V2_REQUEST)); try { RecommendationDocument result = apiInstance.getSPARecommendationsV2("service", body); System.out.println(result); } catch (ApiException e) { System.err.println("Exception when calling SpaApi#getSPARecommendationsV2"); 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_BEARER_TOKEN="" java "Example.java" ##### ```rust // Get SPA recommendations v2 returns "OK" response use datadog_api_client::datadog; use datadog_api_client::datadogV2::api_spa::SpaAPI; use datadog_api_client::datadogV2::model::RecommendationV2RequestAttributes; use datadog_api_client::datadogV2::model::RecommendationV2RequestBody; use datadog_api_client::datadogV2::model::RecommendationV2RequestData; use datadog_api_client::datadogV2::model::RecommendationV2RequestType; #[tokio::main] async fn main() { let body = RecommendationV2RequestBody::new(RecommendationV2RequestData::new( RecommendationV2RequestAttributes::new(vec!["".to_string()]), RecommendationV2RequestType::RECOMMENDATION_V2_REQUEST, )); let mut configuration = datadog::Configuration::new(); configuration.set_unstable_operation_enabled("v2.get_spa_recommendations_v2", true); let api = SpaAPI::with_config(configuration); let resp = api .get_spa_recommendations_v2("service".to_string(), 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" cargo run {% /tab %}