Application-as-recipe¶

Introduction¶

You can design and package a Dataiku flow into a reusable recipe for other projects.

Using an Application-as-recipe¶

To create a recipe from an existing Application-as-recipe, click on the New recipe button from the Flow. Application-as-recipes are grouped by category in this menu.

Application-as-recipes can only be run by users that:

are allowed to instantiate the corresponding Application-as-recipe (see Using a Dataiku application)
have the Write project content permission on the project using the Application-as-recipe

Developing an Application-as-recipe¶

Only users that are administrator of the project can contribute to the development of an Application-as-recipe. But only users with the Develop plugins permission are allowed to configure project variables through the recipe settings with custom code.

To convert a project into an Application-as-recipe, click on Application designer from the project menu. A project can be converted either into a Dataiku application or into an Application-as-recipe. Once the project is converted, the project menu will open the Application designer directly.

Application header¶

The Application header panel allows to configure:

the recipe name and description;
which user can instantiate the application.

Included content¶

The Included content panel allows to configure the additional data — from the original project containing the application — to include into the application instances.

Recipe definition¶

Icon¶

The icon defines the icon for the Application-as-recipe. Available icons can be found in Font Awesome v3.2.1.

Category¶

Application-as-recipes with the same category are grouped under the same section in the New recipe menu.

Inputs/Outputs¶

This panel allows to define the inputs and outputs of the recipe that is to say a mapping between elements of the project using the Application-as-recipe and the corresponding elements in the Application-as-recipe flow. Each element is made of:

a label: this label is displayed in the recipe editor to identify the element
a type: an element can be a Dataset, a Managed folder or a Saved model
the corresponding element in the Application-as-recipe flow

Scenario¶

It is mandatory to specify the Scenario to build the outputs of the recipe. This scenario will be executed when running the Application-as-recipe.

Settings¶

This panel allows to configure the form displayed in the recipe settings. See the section Edit project variables > Runtime form from the Dataiku application tiles for more details.

Code recipes in Application-as-recipe¶

When DSS runs the Application-as-recipe, it makes a copy of the project where it is defined and swaps the datasets/managed folders/saved models that where defined as inputs in the Application designer for the ones selected by the user in the instance of the Application-as-recipe, inside the copy project. For visual recipes like Prepare or Join, this is transparent, and DSS will automatically adjust to the changes in the copy project. But code recipes like Python or SQL are run as is, without any change to their code.

In the case of Python recipes to run, it is advised to have them refer to their input by index in the Input/Output tab (see Python recipes):

from dataiku import recipe
inputA = recipe.get_input(0, object_type="DATASET")
# even simpler for a recipe with a single dataset as input:
inputA = recipe.get_input()

Another option is to adjust the code of these recipes using a “Execute python code” step in the scenario of the Application-as-recipe using the public API.

client = dataiku.api_client()
current_project = client.get_project(dataiku.default_project_key())
current_recipe = current_project.get_recipe("the_recipe_name")
recipe_settings = current_recipe.get_settings()
# get dataset currently used as input
first_input_dataset_name = recipe_settings.get_recipe_inputs()['main']['items'][0]['ref']
# adjust code
code = recipe_settings.get_code()
# ... modify code
recipe_settings.set_code(...modified code)
recipe_settings.save()

Limitations¶

Partitioned inputs and outputs are not supported.
Outputs must be writable by DSS (e.g. should not be a BigQuery or Redshift dataset)