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python Task


The python task allows you to run python scripts. Therefore, those tasks can do anything Python can do. They are extremely useful for data extraction or data science models.

There are two models for specifying python tasks in SAYN: a simple way through using decorators and a more advanced way which is class based.

Simple Definition of python Tasks

You can define python tasks in SAYN very simply by using decorators. This will let you write a Python function and turn that function into a task. First, you need to add a group in project.yaml pointing to the .py file where the task code lives:


    type: python
    module: decorator_tasks
      param1: some_value

Now all tasks defined in python/ will be added to the DAG. The module property expects a python path from the python folder in a similar way as you would import a module in python. For example, if our task definition exists in python/example_mod/ the value in module would be example_mod.decorator_tasks.


from sayn import task

@task(outputs='logs.api_table', sources='logs.another_table')
def example_task(context, warehouse, param1):
    src_table = context.src('logs.another_table')
    out_table = context.out('logs.api_table')
    warehouse.execute(f'CREATE OR REPLACE TABLE {out_table} AS SELECT * from {src_table}')

The above example showcases the key elements to a python task:

  • task: we import SAYN's task decorator which is used to turn functions into SAYN tasks added to the DAG.
  • parameters to task: we can pass parameters sources, outputs and parents which are either lists of table names or a single table name. This allows SAYN define the task dependencies.
  • function name: the name of the function (example_task here) will be the name of the task. We can use this name with -t to execute this task only for example.
  • function parameters: arguments to the function have special meaning and so the names need to be respected:
    • context: is an object granting access to some functionality like project parameters, connections and other functions as seen further down.
    • warehouse: connection names (required_credentials in project.yaml) will automatically provide the object of that connection. You can specify any number of connections here.
    • param1: the rest of the function arguments are matched against task parameters, these are values defined in the parameter property in the group.

Python decorators

Decorators in python are used to modify the behaviour of a function. It can be a bit daunting to understand when we first encounter them but for the purpose of SAYN all you need to know is that @task turns a standard python function into a SAYN task which can assess useful properties via arguments. There are many resources online describing how decorators work, for example this.

Given the code above, this task will:

  • Depend on (execute after) the tasks that produce logs.another_table since we added the sources argument to the decorator.
  • Be the parent of (execute before) any task reading from logs.api_table since we added the outputs argument to the decorator.
  • Get the compiled value of logs.another_table and logs.api_table and keep in 2 variables. For details on database objects compilation make sure you check the database objects page.
  • Execute a create table statement using the tables above on the database called warehouse in the project.

Advanced python Task Definition With Classes

The second model for defining python tasks is through classes. When using this model we get an opportunity to:

  • do validation before the task is executed by overloading the setup method, which is useful as a way to alert early during the execution that something is incorrectly defined rather than waiting for the task to fail.
  • define more complex dependencies than sources and outputs by overloading the config method.
  • implement code for the compile stage allowing for more early stage indication of problems.

A python task using classes is defined as follows:


  type: python
  class: file_name.ClassName

Where class is a python path to the Python class implementing the task. This code should be stored in the python folder of your project, which in itself is a python module that's dynamically loaded, so it needs an empty file in the folder. The class then needs to be defined as follows:


from sayn import PythonTask

class ClassName(PythonTask):
    def config(self):

    def setup(self):
        # Do some validation of the parameters
        return self.success()

    def run(self):
        # Do something useful

        return self.success()

In this example:

  • We create a new class inheriting from SAYN's PythonTask.
  • We set some dependencies by calling self.src and self.out.
  • We define a setup method to do some sanity checks. This method can be skipped, but it's useful to check the validity of project parameters or so some initial setup.
  • We define the actual process to execute during sayn run with the run method.
  • Both setup and run return the task status as successful return self.success(), however we can indicate a task failure to sayn with return Failing a python task forces child tasks to be skipped.

Python tasks can return self.success() or to indicate the result of the execution, but it's not mandatory. If the code throws a python exception, the task will be considered as failed.

Using the SAYN API

When defining our python task, you would want to access parts of the SAYN infrastructure like parameters and connections. When using the decorator model, to access this functionality we need to include the context argument in the function, when using the class model the more standard self is used, and both give access to the same functionality. The list of available properties through self and context is:

  • parameters: accesses project and task parameters. For more details on parameters, see the Parameters section.
  • run_arguments: provides access to the arguments passed to the sayn run command like the incremental values (full_load, start_dt and end_dt).
  • connections: dictionary containing the databases and other custom API credentials. API connections appear as simple python dictionaries, while databases are SAYN's Database objects.
  • default_db: provides access to the default_db database object specified in the project.yaml file.
  • src: the src macro that translates database object names as described in database objects. Bear in mind that using this function also adds dependencies to the task, but only when called from the config method of a python task defined with the class model.
  • out: the out macro that translates database object names as described in database objects. Bear in mind that using this function also creates dependencies between tasks, but only when called from the config method of a python task defined with the class model.


You can use self.default_db to easily perform some operations on the default database such as reading or loading data or if using decorators simply include an argument with the name of the connection. See the methods available on the Database API.


We all love pandas! If you want to load a pandas dataframe you can use one of these options:

  • with the pandas.DataFrame.to_sql method: df.to_sql(self.default_db.engine, 'table').
  • with the self.default_db.load_data method: self.default_db.load_data('table', df.to_dict('records')).

Logging For python Tasks With The SAYN API

The unit of process within a task in SAYN is the step. Using steps is useful to indicate current progress of execution but also for debugging purposes. The tutorial is a good example of usage, as we define the load_data task as having 5 steps:


           "Generate Data",
           "Load fighters",
           "Load arenas",
           "Load tournaments",
           "Load battles",

This code defines which steps form the task. Then we can define the start and end of that step with:


with context.step('Generate Data'):
    data_to_load = get_data(tournament_battles)

Which will output the following on the screen:

CLI output

[1/7] load_data (started at 15:25): Step [1/5] Generate Data

The default cli presentation will show only the current step being executed, which in the case of the tutorial project goes very quickly. However we can persist these messages using the debug flag to the cli sayn run -d giving you this:

CLI ouput

[1/7] load_data (started at 15:29)
  Run Steps: Generate Data, Load fighters, Load arenas, Load tournaments, Load battles
  ℹ [1/5] [15:29] Executing Generate Data
  ✔ [1/5] [15:29] Generate Data (19.5ms)[2/5] [15:29] Executing Load fighters
  ✔ [2/5] [15:29] Load fighters (16.9ms)[3/5] [15:29] Executing Load arenas
  ✔ [3/5] [15:29] Load arenas (12.3ms)[4/5] [15:29] Executing Load tournaments
  ✔ [4/5] [15:29] Load tournaments (10.9ms)[5/5] [15:29] Executing Load battles
  ✔ [5/5] [15:29] Load battles (210.3ms)
✔ Took (273ms)

So you can see the time it takes to perform each step.

Sometimes it's useful to output some extra text beyond steps. In those cases, the API provides some methods for a more adhoc logging model:

  • self.debug(text): debug log to console and file. Not printed unless -d is used.
  • info log to console and file. Not persisted to the screen if -d is not specified.
  • self.warning(text): warning log to console and file. Remains on the screen after the task finishes (look for yellow lines).
  • self.error(text): error log to console and file. Remains on the screen after the task finishes (look for red lines).


self.error doesn't abort the execution of the task, nor it sets the final status to being failed. To indicate a python task has failed, use this construct: return where text is an optional message string that will be showed on the screen.

For more details on the SAYN API, check the API reference page.