> For the complete documentation index, see [llms.txt](https://docs.coherent.global/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coherent.global/spark-apis/service-apis/how-to-upload-spark-service-files-via-api.md).
# How to: Upload Spark service files via API
To upload and publish a service to a new environment, we recommend using the following 3 step process:
1. [Upload Excel file to Spark](/spark-apis/service-apis/upload-spark-service.md)
2. [Check compilation status](/spark-apis/service-apis/check-compilation-status.md)
3. [Publish service](/spark-apis/service-apis/publish-service.md)
This guide walks through how to use the `3` APIs together. All `3` APIs are necessary to create a new service. This guide uses [Postman](/integrations/postman.md) to interact with the APIs, but you can use any tool you like.
## Authorization
* `Bearer {token}` accessible from [Authorization - Bearer token](/spark-apis/authorization-bearer-token.md) or systematically via [Client credentials](/identity-and-access-management/client-credentials.md).
* The request headers should include a key for `Authorization` with the value `Bearer {token}`.
* API key created from [Authorization - API keys](/spark-apis/authorization-api-keys.md). It must contain [Manage users](/tenant-administration/manage-users.md#user-groups) that are also assigned to [Permissions - Features permissions](/spark-apis/authorization-api-keys/permissions-features-permissions.md) `Spark.ServiceUpload.json`, `Spark.ServiceCompilationProgress.json`, and `Spark.ServicePublish.json` or just `Spark.AllEncompassingProxy.json`.
* The request headers should include the keys `x-synthetic-key` and `x-tenant-name` with the API key and tenant name respectively.
## Upload Spark service files
### Request
1. Setup a `POST` request to `https://excel.{environment}.coherent.global/{tenant}/api`/`v3/folders/{folder}/services/{service}/upload`.
* `{folder}` and `{service}` should represent the target destination for the uploaded service.
2. Setup the request headers for the API call.
* Include the necessary [#authorization](#authorization "mention").
* Note that the `Content-Type` should be `multpart/form-data`.
* Postman may automatically create some headers for you.
3. For the request body:
1. Create the `serviceFile` key and change the key type to `File`. Then you can select the Excel file you want to upload.
2. Enter the values for `engineUploadRequestEntity` as described below. For information about customizing the values, see [Upload Excel file to Spark](/spark-apis/service-apis/upload-spark-service.md). These values are placeholders as they will be updated in [#publish-service](#publish-service "mention").
{
"request_data":
{
"version_difference":"minor",
"effective_start_date":"2010-01-01",
"effective_end_date":"2120-01-01"
}
}
4. **Send** the API call. Spark will inform you if the service name already exists.
### Response
`HTTP 200 OK` `Content-Type: application/json`
The success response should look similar to the output below. Take note of the `nodegen_compilation_jobid`, `original_file_documentid`, and `engine_file_documentid` as this information is needed for the subsequent steps.
```json
{
"status": "Success",
"response_data": {
"lines_of_code": 11.0,
"hours_saved": 0.02107043788185858,
"nodegen_compilation_jobid": "19bd64d6-f567-0bd4-c73f-190ed4bc49c3",
"original_file_documentid": "265ddaee-89b9-42ff-96af-3cf08ceaf344",
"engine_file_documentid": "f15269d5-0104-4269-8c46-ba5996b5b1c4",
"hash_signature_chain": false,
"warnings": [],
"infos": [],
"current_statistics": null,
"no_of_sheets": 1,
"no_of_inputs": 2,
"no_of_outputs": 1,
"no_of_formulas": 1,
"no_of_cellswithdata": 15
},
"response_meta": {
"service_id": null,
"version_id": "b4555085-1535-4058-8335-212216c89044",
"version": "0.1.0",
"process_time": 1138,
"call_id": null,
"compiler_type": "Neuron",
"compiler_version": null,
"source_hash": null,
"engine_id": null,
"correlation_id": null,
"parameterset_version_id": null,
"system": "SPARK",
"request_timestamp": "2026-01-19T12:49:16.092Z"
},
"error": null
}
```
## Check compilation status
After the Excel file is uploaded, the file is converted from Excel to code using the [Neuron](/build-spark-services/neuron.md) compiler. It's important to check the status of the compilation to ensure that the job completed successfully before the service can be published. We'll use the following API to do so.
### Request
1. Setup a `GET` request to `https://excel.{environment}.coherent.global/{tenant}/api/v3/folders/{folder}/services/{service}/getcompilationprogess/{nodegen_compilation_job_id}`.
* `{folder}` and `{service}` should represent the target destination for the uploaded service. This should be the same as the previous API call.
2. `{nodegen_compilation_job_id}` can be found from [#upload-spark-service-files](#upload-spark-service-files "mention") [#api-response](#api-response "mention").
3. Setup the request headers for the API call.
* Include the necessary [#authorization](#authorization "mention").
* Postman may automatically create some headers for you.
4. **Send** the API call.
### Response
`HTTP 200 OK` `Content-Type: application/json`
If the `progress` is `100`, the compilation has completed.
```json
{
"status": "Success",
"response_data": {
"status": "Success",
"last_error_message": "null",
"progress": 100
},
"response_meta": {
"system": "SPARK",
"request_timestamp": "2023-05-19T01:39:42.074Z"
},
"error": null
}
```
## Publish service
Once the compilation was successful, we can publish the uploaded service.
### Request
The success response should look similar to the output below. Take note of the `nodegen_compilation_jobid`, `original_file_documentid`, and `engine_file_documentid` as this information is needed for the subsequent steps.
1. Setup a `POST` request to `https://excel.{environment}.coherent.global/{tenant}/api`/`v3/folders/{folder}/services/{service}/publish`.
* `{folder}` and `{service}` should represent the target destination for the uploaded service. This should be the same as the previous API calls.
2. Setup the request headers for the API call.
* Include the necessary [#authorization](#authorization "mention").
* Note that the `Content-Type` should be `application/json`.
* Postman may automatically create some headers for you.
3. For the request body, use the JSON below and complete the following parameters:
1. Replace `{service}` with the name used and previous steps.
2. Replace `{original_file_documentid}` and `{engine_file_document_id}` with the values returned from [#upload-spark-service-files](#upload-spark-service-files "mention") [#api-response](#api-response "mention").
3. For information about customizing the values, see [Upload Excel file to Spark](/spark-apis/service-apis/upload-spark-service.md).{
```json
{
"request_data": {
"draft_service_name": "my service name",
"version_label": "my service label",
"effective_start_date": "2024-01-19T12:12:00.000Z",
"effective_end_date": "2126-01-19T12:12:00.000Z",
"original_file_documentid": "265ddaee-89b9-42ff-96af-3cf08ceaf344",
"engine_file_documentid": "f15269d5-0104-4269-8c46-ba5996b5b1c4",
"version_difference": "minor",
"release_note": "my release note",
"description": "my description",
"should_trck_user_action": "true"
}
}
```
4. **Send** the API call.
### Response
`HTTP 200 OK` `Content-Type: application/json`
The service is now published!
```json
{
"status": "Success",
"response_data": {
"version_id": "{version_id}"
},
"response_meta": {
"system": "SPARK",
"request_timestamp": "2023-05-18T21:20:42.498Z"
},
"error": null
}
```