Services and dependencies

You’ll want to use external systems within your transformations, including databases, HTTP APIs, other web services, filesystems, etc.

Hardcoding those services is a good first step, but as your codebase grows, this approach will show its limits rather quickly.

  • Hardcoded and tightly linked dependencies make your transformations hard to test, and hard to reuse.

  • Processing data on your laptop is great, but being able to do it on different target systems (or stages), in different environments is more realistic. You’ll want to configure a different database on a staging environment, pre-production environment, or production system. Maybe you have similar systems for different clients and want to select the system at runtime, etc.


This document is currently reviewed to check for correctness.

Definition of service dependencies

To solve this problem, we introduce a lightweight dependency injection system. It allows to define named dependencies in your transformations and provide an implementation at runtime.

For function-based transformations, you can use the bonobo.config.use() decorator to mark the dependencies. You’ll still be able to call it manually, providing the implementation yourself, but in a bonobo execution context, it will be resolve and injected automatically, as long as you provided an implementation to the executor (more on that below).

from bonobo.config import use

def select_all(database):
    yield from database.query('SELECT * FROM foo;')

For class based transformations, you can use bonobo.config.Service, a special descriptor (and subclass of bonobo.config.Option) that will hold the service names and act as a marker for runtime resolution of service instances.

from bonobo.config import Configurable, Service

class JoinDatabaseCategories(Configurable):
    database = Service('orders_database')

    def __call__(self, database, row):
        return {
            'category': database.get_category_name_for_sku(row['sku'])

Both of the above code samples tell bonobo that your transformation expects a service called “orders_database”, which will be injected to your calls under the parameter name “database”.

Providing implementations at run-time

Bonobo expects you to provide a dictionary of all service implementations required by your graph.

import bonobo

graph = bonobo.graph(...)

def get_services():
    return {
        'orders_database': my_database_service,

if __name__ == '__main__':, services=get_services())


A dictionary, or dictionary-like, “services” named argument can be passed to the API method. The “dictionary-like” part is the real keyword here. Bonobo is not a DIC library, and won’t become one. So the implementation provided is pretty basic and feature-less. You can use much more involved libraries instead of the provided stub and, as long as it implements a dictionary-like interface, the system will use it.

The command line interface will look for services in two different places:

  • A get_services() function present at the same level of your graph definition.

  • A get_services() function in a file in the same directory as your graph’s file, allowing to reuse the same service implementations for more than one graph.

Solving concurrency problems

If a service cannot be used by more than one thread at a time, either because it’s just not threadsafe, or because it requires to carefully order the calls made (apis that includes nonces, or work on results returned by previous calls are usually good candidates), you can use the bonobo.config.Exclusive context processor to lock the use of a dependency for the time of the context manager (with statement)

from bonobo.config import Exclusive

def t1(api):
    with Exclusive(api):
        # ... etc

Where to jump next?

We suggest that you go through the tutorial first.

Then, you can read the guides, either using the order suggested or by picking the chapter that interest you the most at one given moment: