This repository includes code samples that use the DBSQL REST API.

To setup the Spreadsheet application follow these steps:

1. Create a new Google Spreadsheet file called Orders.

2. Inside, create a sheet called Orders.

3. Navigate to Extensions -> Apps Script and rename the script to Orders.

4. In the Apps Script editor, create four files with the names and content of the files located in the spreadsheet folder: Interface.gs, DBSQL.gs, sidebar.html, and info.html.

5. Inside the DBSQL.gs file, provide the connectivity parameters for the Databricks server: HOST, WAREHOUSE_ID, and AUTH_TOKEN.

6. Save the files.

7. Go back to your Spreadsheet and refresh it.

8. You will now get a Managed Orders menu next to the Extensions menu.

9. Select Managed Orders -> Show Monthly Orders and accept the authorization request.

10. To configure the permissions in your sheet, select Project Settings -> Show appscript.json manifest file in editor.

11. This will show a new file in the App Script workspace where you can edit the grants. See more details.

12. You are now all set. Run the predefined queries using the entries in the Managed Orders menu.

The Postman collection consists of two json files Databricks Environment.postman_environment.json and Databricks SQL Execution API.postman_collection.json. The former can be used to define environment variables needed to establish connection with the Databricks server: HOST, WAREHOUSE_ID, and AUTH_TOKEN. The latter includes several API requests using the sync and async flows.

To use the Postman collection follow the steps below:

1. Open Postman, go to My Workspace and click Import to add the two files.

2. Go to Environments and select Databricks Environment to update the connection parameters. Make sure to save the changes.

3. Select the Databricks Enviroment from the environment selector.

4. Run one of the API requests for executing statements.

5. Use the GET requests to poll for status and fetch chunks.

The Python script executes a statement in asynchronous mode with disposition and format set to EXTERNAL_LINKS and ARROW_STREAM, respectively. Then, it retrieves each chunk using the presigned URL, deserializes the Arrow bytes and converts the result into a pandas DataFrame.