This method publishes the provided custom model (target LDM) to the project and returns the link to a polling resource that returns the project LDM after publishing.</br> You provide the target LDM as a JSON structure in the request body.</br> </br> While running, this method performs the following operations:</br> 1. Obtains the current model of the project (if any) </br> 2. Merges the current model with the target model </br> 3. Generates and executes MAQL DDL scripts to update the current model to make it equal to the merged model</br> </br> By default, the target LDM is published only if the API will not delete any data or dependencies(metrics, reports, dashboards, etc.) from the project datasets. If the API detects that publishing will delete any data or dependencies, the publishing task will fail, and the existing LDM will not change. In this case, you can have the following options:</br>

• Review and update the target LDM so that the API does not delete any data. Then execute the API again.
• Force-publish the LDM even if that deletes some data.</br> To do so, add the preserveData parameter under publishOptions in the request body, set it to false, and execute the API again.</br> NOTE: If not explicitly set to false, this parameter defaults to true.</br> This option is not available for some update combinations (for example, if preserveData=false exists in all possible MAQL DDL scripts). In case the API detects any deleted data sets or date dimensions using the dependencies(metrics, reports, dashboards, etc.), you can use cascadeDrops parameter under publishOptions in the request body and set it to true to delete the data sets or date dimensions and the dependencies.

{
  "asyncTask": {
    "links": {
      "poll": "/gdc/uri/poll"
    }
  }
}