Flow creation and management#

Programmatically building a Flow#

The flow, including datasets, recipes, … can be fully managed and created programmatically.

Datasets can be created and managed using the methods detailed in Datasets (other operations).

Recipes can be created using the dataikuapi.dss.project.DSSProject.new_recipe() method. This follows a builder pattern: new_recipe returns you a recipe creator object, on which you add settings, and then call the create() method to actually create the recipe object.

The builder objects reproduce the functionality available in the recipe creation modals in the UI, so for more control on the recipe’s setup, it is often necessary to get its settings after creation, modify it, and save it again.

Creating a Python recipe#

builder = project.new_recipe("python")

# Set the input
# Create a new managed dataset for the output in the filesystem_managed connection
builder.with_new_output_dataset("grouped_dataset", "filesystem_managed")

# Set the code - builder is a PythonRecipeCreator, and has a ``with_script`` method
import dataiku
from dataiku import recipe
input_dataset = recipe.get_inputs_as_datasets()[0]
output_dataset = recipe.get_outputs_as_datasets()[0]

df = input_dataset.get_dataframe()
df = df.groupby("something").count()

recipe = builder.create()

# recipe is now a ``DSSRecipe`` representing the new recipe, and we can now run it
job = recipe.run()

Creating a Sync recipe#

builder = project.new_recipe("sync")
builder = builder.with_input("input_dataset_name")
builder = builder.with_new_output("output_dataset_name", "hdfs_managed", format_option_id="PARQUET_HIVE")

recipe = builder.create()
job = recipe.run()

Creating and modifying a grouping recipe#

The recipe creation mostly handles setting up the inputs and outputs of the recipes, so most of the setup of the recipe has to be done by retrieving its settings, altering and saving them, then applying schema changes to the output

builder = project.new_recipe("grouping")
# Create a new managed dataset for the output in the "filesystem_managed" connection
builder.with_new_output("grouped_dataset", "filesystem_managed")
recipe = builder.build()

# After the recipe is created, you can edit its settings
recipe_settings = recipe.get_settings()
recipe_settings.set_column_aggregations("myvaluecolumn", sum=True)

# And you may need to apply new schemas to the outputs
# This will add the myvaluecolumn_sum to the "grouped_dataset" dataset

# It should be noted that running a recipe is equivalent to building its output(s)
job = recipe.run()

A complete example#

This examples shows a complete chain:

  • Creating an external dataset

  • Automatically detecting the settings of the dataset (see Datasets (other operations) for details)

  • Creating a prepare recipe to cleanup the dataset

  • Then chaining a grouping recipe, setting an aggregation on it

  • Running the entire chain

dataset = project.create_sql_table_dataset("mydataset", "PostgreSQL", "my_sql_connection", "mytable", "myschema")

dataset_settings = dataset.autodetect_settings()

# As a shortcut, we can call new_recipe on the DSSDataset object. This way, we don't need to call "with_input"

prepare_builder = dataset.new_recipe("prepare")
prepare_builder.with_new_output("mydataset_cleaned", "filesystem_managed")

prepare_recipe = prepare_builder.create()

# Add a step to clean values in "doublecol" that are not valid doubles
prepare_settings = prepare_recipe.get_settings()
prepare_settings.add_filter_on_bad_meaning("DoubleMeaning", "doublecol")


# Grouping recipe

grouping_builder = project.new_recipe("grouping")
grouping_builder.with_new_output("mydataset_cleaned_grouped", "filesystem_managed")
grouping_recipe = grouping_builder.build()

grouping_recipe_settings = grouping_recipe.get_settings()
grouping_recipe_settings.set_column_aggregations("myvaluecolumn", sum=True)


Working with flow zones#

Creating a zone and adding items in it#

flow = project.get_flow()
zone = flow.create_zone("zone1")

# First way of adding an item to a zone
dataset = project.get_dataset("mydataset")

# Second way of adding an item to a zone
dataset = project.get_dataset("mydataset")

# Third way of adding an item to a zone
dataset = project.get_dataset("mydataset")

Listing and getting zones#

# List zones

for zone in flow.list_zones():
    print("Zone id=%s name=%s" % (zone.id, zone.name))

    print("Zone has the following items:")
    for item in zone.items:
        print("Zone item: %s" % item)

# Get a zone by id - beware, id not name
zone = flow.get_zone("21344ZsQZ")

# Get the "Default" zone
zone = flow.get_default_zone()

Changing the settings of a zone#

flow = project.get_flow()
zone = flow.get_zone("21344ZsQZ")

settings = zone.get_settings()
settings.name = "New name"


Getting the zone of a dataset#

dataset = project.get_dataset("mydataset")

zone = dataset.get_zone()
print("Dataset is in zone %s" % zone.id)

Schema propagation#

When the schema of an input dataset is modified, or when the settings of a recipe are modified, you need to propagate this schema change across the flow.

This can be done from the UI, but can also be automated through the API

flow = project.get_flow()

# A propagation always starts from a source dataset and will move from left to right till the end of the Flow

propagation = flow.new_schema_propagation("sourcedataset")

future = propagation.start()

There are many options for propagation, see dataikuapi.dss.flow.DSSSchemaPropagationRunBuilder

Exporting a flow documentation#

This sample shows how to generate and download a flow documentation from a template.

See Flow Document Generator for more information.

# project is a DSSProject object

flow = project.get_flow()

# Launch the flow document generation by either
# using the default template by calling without arguments
# or specifying a managed folder id and the path to the template to use in that folder
future = flow.generate_documentation(FOLDER_ID, "path/my_template.docx")

# Alternatively, use a custom uploaded template file
with open("my_template.docx", "rb") as f:
    future = flow.generate_documentation_from_custom_template(f)

# Wait for the generation to finish, retrieve the result and download the generated
# flow documentation to the specified file
result = future.wait_for_result()
export_id = result["exportId"]

flow.download_documentation_to_file(export_id, "path/my_flow_documentation.docx")

Detailed examples#

This section contains more advanced examples on Flow-based operations.

Delete orphaned Datasets#

It can happen that after some operations on a Flow one or more Datasets end up not being linked to any Recipe and thus become disconnected from the Flow branches. In order to programmatically remove those Datasets from the Flow, you can list nodes that have neither predecessor nor successor in the graph using the following function:

def delete_orphaned_datasets(project, drop_data=False, dry_run=True):
    """Delete datasets that are not linked to any recipe.

    flow = project.get_flow()
    graph = flow.get_graph()
    cpt = 0
    for name, props in graph.nodes.items():
        if not props["predecessors"] and not props["successors"]:
            print(f"- Deleting {name}...")
            ds = project.get_dataset(name)
            if not dry_run:
                cpt +=1 
                print("Dry run: nothing was deleted.")
    print(f"{cpt} datasets deleted.")


Note that the function has additional flags with default values set up to prevent accidental data deletion. Even so, we recommend you to remain extra cautious when clearing/deleting Datasets.

Reference documentation#