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Sending print jobs to a printer from your app

Register Printer Button

If you want to learn about the capabilities of Spark’s APIs, you’ll definitely want to experiment with our open source 3D printing sample apps. These interactive live samples guide you through typical API workflows. You can freely copy the sample app code and build your own apps with it.

This blog introduces you to the Printer management sample app. The workflow demonstrates:

  1. Authenticating the user. Get an access token and a user ID to use for future API calls.
  2. Checking which printers the user is registered to. To use a printer, a user needs to be registered to it.
  3. Registering the user with a new printer.
  4. Submitting a print job to the printer.
  5. Following the print job’s progress.

The sample app includes a UI that takes you through the API calls. It’s built using the Spark JavaScript SDK.

Printing UI

You can follow each stage of the workflow on a console located below the UI, which displays the API calls as you follow the instructions. If you want to view an APIs request and response, simply select the API in the console.

Console

This sample app is incredibly easy to use. All you need to do is to create a username and password in the Spark sandbox environment and log in to the app. Then simply follow the instructions in the UI, such as giving your printer a name, and sit back and watch the API magic.

You can see the API calls in a variety of languages:

Sample Languages

Another feature of the Printer management sample app is Spark’s print simulator; created so that you can test Spark’s APIs without the need for a physical printer. It is a virtual printer that mimics the behavior of a regular printer. For example, it has an LCD screen that mimics a printer display. For more info, see https://github.com/spark3dp/print-simulator. Of course, you can also connect your own printer to the sample app instead of the print simulator.

To view a list of APIs and other exciting sample apps on our 3D printing platform, see Spark’s documentation.