Flow Management Patterns

Reference patterns for building and orchestrating Dataiku flows via the Python API.

When to Use This Skill

• Building datasets that depend on upstream datasets

• Running multiple recipes in the correct order

• Creating multi-step pipelines (e.g., aggregate then join then train)

• Checking job status and handling failures mid-pipeline

Build a Single Dataset

recipe = project.get_recipe("my_recipe") job = recipe.run(job_type='NON_RECURSIVE_FORCED_BUILD', partitions=None, wait=True, no_fail=False) state = job.get_status()["baseStatus"]["state"] # "DONE" or "FAILED"

recipe.run() already waits for completion. Use no_fail=True to prevent exceptions on failure.

Note: job_type options are NON_RECURSIVE_FORCED_BUILD (default), RECURSIVE_BUILD , RECURSIVE_FORCED_BUILD .

Build Multiple Datasets in Dependency Order

When downstream datasets depend on upstream ones, build them sequentially:

def build_recipe(project, recipe_name): """Build a recipe and return success status.""" print(f"Building {recipe_name}...") recipe = project.get_recipe(recipe_name) job = recipe.run(no_fail=True) status = job.get_status() state = status.get("baseStatus", {}).get("state")

if state == "DONE":
    print(f"  {recipe_name}: success")
    return True
else:
    # Extract error details
    activities = status.get("baseStatus", {}).get("activities", {})
    for name, info in activities.items():
        if info.get("firstFailure"):
            print(f"  {recipe_name} error: {info['firstFailure'].get('message')}")
    return False

Build in dependency order: upstream first, then downstream

pipeline = [ "group_LAB_RESULTS_AGG", # Step 1: aggregate "group_CLINICAL_NOTES_AGG", # Step 2: aggregate (independent of step 1) "join_ML_TRAINING_DATA", # Step 3: join (depends on steps 1 & 2) ]

for recipe_name in pipeline: success = build_recipe(project, recipe_name) if not success: print(f"Pipeline failed at {recipe_name}. Fix and retry.") break

Build Independent Recipes in Parallel

For recipes with no dependency between them, you can build the output datasets directly:

These two aggregations are independent — build them before the join

ds1 = project.get_dataset("LAB_RESULTS_AGG") ds2 = project.get_dataset("CLINICAL_NOTES_AGG")

Build both (sequentially via API, but could overlap in Dataiku)

job1 = project.get_recipe("group_LAB_RESULTS_AGG").run(no_fail=True) job2 = project.get_recipe("group_CLINICAL_NOTES_AGG").run(no_fail=True)

Then build the dependent join

job3 = project.get_recipe("join_ML_TRAINING_DATA").run(no_fail=True)

Check What Exists Before Creating

Before creating recipes or datasets, check if they already exist to make scripts idempotent:

existing_datasets = [d.get("name") for d in project.list_datasets()] existing_recipes = [r.get("name") for r in project.list_recipes()]

if "MY_OUTPUT" not in existing_datasets: # Create the dataset... pass

if "my_recipe" not in existing_recipes: # Create the recipe... pass

Verify Pipeline Results

After building a pipeline, verify the final output:

ds = project.get_dataset("ML_TRAINING_DATA") schema = ds.get_settings().get_raw().get("schema", {}).get("columns", [])

print(f"Output has {len(schema)} columns:") for col in schema: print(f" - {col['name']} ({col.get('type', 'unknown')})")

Common Pipeline Patterns

Pattern Steps Key Concern

Aggregate + Join Group inputs → Join aggregated outputs Build aggregations before the join

Clean + Transform Prepare recipe → Group/Join Schema updates after each step

ETL to Warehouse Prepare → Sync to SQL connection Set SQL schema before sync

ML Pipeline Prep → Aggregate → Join → Train Full dependency chain, verify schema at each step

Handling Failures Mid-Pipeline

for recipe_name in pipeline: success = build_recipe(project, recipe_name) if not success: # Get detailed error info jobs = project.list_jobs() job = project.get_job(jobs[0]['def']['id']) print(job.get_log()[-2000:]) # Last 2000 chars of log break

See skills/troubleshooting/ for detailed error diagnosis patterns.

Schema Propagation

When an upstream schema changes, propagate it through the flow:

flow = project.get_flow() propagation = flow.new_schema_propagation("source_dataset") future = propagation.start() future.wait_for_result()

For more control, use the builder options:

propagation.set_auto_rebuild(True) propagation.stop_at("recipe_name") propagation.mark_recipe_as_ok("recipe_name")

Build Datasets via Project API

For more control than recipe.run() , use the project-level job builder:

job = project.new_job('RECURSIVE_FORCED_BUILD') job.with_output('dataset_name', object_type='DATASET') result = job.start_and_wait(no_fail=True)

This supports building multiple outputs in one job:

job = project.new_job('NON_RECURSIVE_FORCED_BUILD') job.with_output('dataset_1', object_type='DATASET') job.with_output('dataset_2', object_type='DATASET') job.start_and_wait()

Detailed References

• references/build-strategies.md — Dependency ordering, idempotent builds, dataset status checks

Related Skills

• skills/recipe-patterns/ — How to create and configure individual recipes

• skills/dataset-management/ — How to create and manage datasets

• skills/troubleshooting/ — How to debug failed builds