ploomber.tasks.PythonCallable

class ploomber.tasks.PythonCallable(source, product, dag, name=None, params=None, unserializer=None, serializer=None, debug_mode=None)

Execute a Python function

Parameters
  • source (callable) – The callable to execute

  • product (ploomber.products.Product) – Product generated upon successful execution

  • dag (ploomber.DAG) – A DAG to add this task to

  • name (str) – A str to indentify this task. Should not already exist in the dag

  • params (dict) – Parameters to pass to the callable, by default, the callable will be executed with a “product” (which will contain the product object). It will also include a “upstream” parameter if the task has upstream dependencies along with any parameters declared here

  • unserializer (callable, optional) – A callable to unserialize upstream products, the product object is passed as unique argument. If None, the source function receives the product object directly. If the task has no upstream dependencies, this argument has no effect

  • serializer (callable, optional) – A callable to serialize this task’s product, must take two arguments, the first argument passed is the value returned by the task’s source, the second argument is the product oject. If None, the task’s source is responsible for serializing its own product. If used, the source function must not have a “product” parameter but return its result instead

  • debug_mode (None, 'now' or 'later', default=None) – If ‘now’, runs notebook in debug mode, this will start debugger if an error is thrown. If ‘later’, it will serialize the traceback for later debugging. (Added in 0.20)

Examples

Spec API:

tasks:
  - source: my_functions.my_task
    product: data.csv
# content of my_functions.py
from pathlib import Path

def my_task(product):
    Path(product).touch()

Spec API (multiple outputs):

tasks:
  - source: my_functions.another_task
    product:
        one: one.csv
        another: another.csv
# content of my_functions.py
from pathlib import Path

def another_task(product):
    Path(product['one']).touch()
    Path(product['another']).touch()

Python API:

>>> from pathlib import Path
>>> from ploomber import DAG
>>> from ploomber.tasks import PythonCallable
>>> from ploomber.products import File
>>> from ploomber.executors import Serial
>>> dag = DAG(executor=Serial(build_in_subprocess=False))
>>> def my_function(product):
...     # create data.csv
...     Path(product).touch()
>>> PythonCallable(my_function, File('data.csv'), dag=dag)
PythonCallable: my_function -> File('data.csv')
>>> summary = dag.build()

Python API (multiple products):

>>> from pathlib import Path
>>> from ploomber import DAG
>>> from ploomber.tasks import PythonCallable
>>> from ploomber.products import File
>>> from ploomber.executors import Serial
>>> dag = DAG(executor=Serial(build_in_subprocess=False))
>>> def my_function(product):
...     Path(product['first']).touch()
...     Path(product['second']).touch()
>>> product = {'first': File('first.csv'),
...            'second': File('second.csv')}
>>> task = PythonCallable(my_function, product, dag=dag)
>>> summary = dag.build()

Notes

changelog

New in version 0.20: debug constructor flag renamed to debug_mode to avoid conflicts with the debug method.

More examples using the Python API. # noqa

The executor=Serial(build_in_subprocess=False) argument is only required if copy-pasting the example in a Python session. If you store the code in a script, you may delete it and call dag.build like this:

if __name__ == '__main__':
    dag.build()

Then call your script:

python script.py

Methods

build([force, catch_exceptions])

Build a single task

debug([kind])

Run callable in debug mode.

develop([app, args])

Edit function interactively using Jupyter

load([key])

Loads the product.

render([force, outdated_by_code, remote])

Renders code and product, all upstream tasks must have been rendered first, for that reason, this method will usually not be called directly but via DAG.render(), which renders in the right order.

run()

This is the only required method Task subclasses must implement

set_upstream(other[, group_name])

status([return_code_diff, sections])

Prints the current task status

build(force=False, catch_exceptions=True)

Build a single task

Although Tasks are primarily designed to execute via DAG.build(), it is possible to do so in isolation. However, this only works if the task does not have any unrendered upstream dependencies, if that’s the case, you should call DAG.render() before calling Task.build()

Returns

A dictionary with keys ‘run’ and ‘elapsed’

Return type

dict

Raises
  • TaskBuildError – If the error failed to build because it has upstream dependencies, the build itself failed or build succeded but on_finish hook failed

  • DAGBuildEarlyStop – If any task or on_finish hook raises a DAGBuildEarlyStop error

debug(kind='ipdb')

Run callable in debug mode.

Parameters

kind (str ('ipdb' or 'pdb')) – Which debugger to use ‘ipdb’ for IPython debugger or ‘pdb’ for debugger from the standard library

Notes

Be careful when debugging tasks. If the task has run successfully, you overwrite products but don’t save the updated source code, your DAG will enter an inconsistent state where the metadata won’t match the overwritten product.

develop(app='notebook', args=None)

Edit function interactively using Jupyter

Parameters
  • app (str, {'notebook', 'lab'}, default='notebook') – Which jupyter application to use

  • args (str) – Extra args passed to the selected jupyter application

Notes

Cells whose first line is an empty commenf (“#”), will be removed when exporting back to the function, you can use this for temporary, exploratory work

Be careful when developing tasks interacively. If the task has run successfully, you overwrite products but don’t save the updated source code, your DAG will enter an inconsistent state where the metadata won’t match the overwritten product.

load(key=None, **kwargs)

Loads the product. It uses the unserializer function if any, otherwise it tries to load it based on the file extension

Parameters
  • key – Key to load, if this task generates more than one product

  • **kwargs – Arguments passed to the unserializer function

render(force=False, outdated_by_code=True, remote=False)

Renders code and product, all upstream tasks must have been rendered first, for that reason, this method will usually not be called directly but via DAG.render(), which renders in the right order.

Render fully determines whether a task should run or not.

Parameters
  • force (bool, default=False) – If True, mark status as WaitingExecution/WaitingUpstream even if the task is up-to-date (if there are any File(s) with clients, this also ignores the status of the remote copy), otherwise, the normal process follows and only up-to-date tasks are marked as Skipped.

  • outdated_by_code (bool, default=True) – Factors to determine if Task.product is marked outdated when source code changes. Otherwise just the upstream timestamps are used.

  • remote (bool, default=False) – Use remote metadata to determine status

Notes

This method tries to avoid calls to check for product status whenever possible, since checking product’s metadata can be a slow operation (e.g. if metadata is stored in a remote database)

When passing force=True, product’s status checking is skipped altogether, this can be useful when we only want to quickly get a rendered DAG object to interact with it

run()

This is the only required method Task subclasses must implement

set_upstream(other, group_name=None)
status(return_code_diff=False, sections=None)

Prints the current task status

Parameters

sections (list, optional) – Sections to include. Defaults to “name”, “last_run”, “oudated”, “product”, “doc”, “location”

Attributes

PRODUCT_CLASSES_ALLOWED

client

debug_mode

exec_status

name

A str that represents the name of the task, you can access tasks in a dag using dag[‘some_name’]

on_failure

Callable to be executed if task fails (passes Task as first parameter and the exception as second parameter)

on_finish

Callable to be executed after this task is built successfully (passes Task as first parameter)

on_render

params

dict that holds the parameter that will be passed to the task upon execution.

product

The product this task will create upon execution

source

Source is used by the task to compute its output, for most cases this is source code, for example PythonCallable takes a function as source and SQLScript takes a string with SQL code as source.

upstream

A mapping for upstream dependencies {task name} -> [task object]