Hooks

Introduction

Hooks are a mechanism to add extra behaviour to Kedro’s main execution in an easy and consistent manner. Some examples might include:

  • Adding a log statement after the data catalog is loaded

  • Adding data validation to the inputs before a node runs, and to the outputs after a node has run. This makes it possible to integrate with other tools like Great-Expectations

  • Adding machine learning metrics tracking, e.g. using MLflow, throughout a pipeline run

Concepts

A Hook consists of a Hook specification, and Hook implementation. To add Hooks to your project, you must:

  • Create or modify the file <your_project>/src/<package_name>/hooks.py to define a Hook implementation for an existing Kedro-defined Hook specification

  • Register your Hook implementation in the src/<your_project>/settings.py file under the HOOKS key

Hook specification

Kedro defines Hook specifications for particular execution points where users can inject additional behaviour. Currently, the following Hook specifications are provided in kedro.framework.hooks:

  • after_catalog_created

  • before_node_run

  • after_node_run

  • on_node_error

  • before_pipeline_run

  • after_pipeline_run

  • on_pipeline_error

  • before_dataset_loaded

  • after_dataset_loaded

  • before_dataset_saved

  • after_dataset_saved

  • after_context_created

The naming convention for non-error Hooks is <before/after>_<noun>_<past_participle>, in which:

  • <before/after> and <past_participle> refers to when the Hook executed, e.g. before <something> was run or after <something> was created.

  • <noun> refers to the relevant component in the Kedro execution timeline for which this Hook adds extra behaviour, e.g. catalog, node and pipeline.

The naming convention for error hooks is on_<noun>_error, in which:

  • <noun> refers to the relevant component in the Kedro execution timeline that throws the error.

kedro.framework.hooks lists the full specifications for which you can inject additional behaviours by providing an implementation.

CLI hooks

Lastly, Kedro defines a small set of CLI hooks that inject additional behaviour around execution of a Kedro CLI command:

  • before_command_run

  • after_command_run

This is what the kedro-telemetry plugin relies on under the hood in order to be able to collect CLI usage statistics.

Hook implementation

You should provide an implementation for the specification that describes the point at which you want to inject additional behaviour. The Hook implementation should have the same name as the specification. The Hook must provide a concrete implementation with a subset of the corresponding specification’s parameters (you do not need to use them all).

To declare a Hook implementation, use the @hook_impl decorator.

For example, the full signature of the after_data_catalog_created Hook specification is:

@hook_spec
def after_catalog_created(
    self,
    catalog: DataCatalog,
    conf_catalog: Dict[str, Any],
    conf_creds: Dict[str, Any],
    save_version: str,
    load_versions: Dict[str, str],
) -> None:
    pass

However, if you just want to use this Hook to list the contents of a data catalog after it is created, your Hook implementation can be as simple as:

# <your_project>/src/<your_project>/hooks.py
import logging

from kedro.framework.hooks import hook_impl
from kedro.io import DataCatalog


class DataCatalogHooks:
    @property
    def _logger(self):
        return logging.getLogger(self.__class__.__name__)

    @hook_impl
    def after_catalog_created(self, catalog: DataCatalog) -> None:
        self._logger.info(catalog.list())

Note

The name of a module that contains Hooks implementation is arbitrary and is not restricted to hooks.py.

We recommend that you group related Hook implementations under a namespace, preferably a class, within a hooks.py file that you create in your project.

Registering your Hook implementations with Kedro

Hook implementations should be registered with Kedro using the <your_project>/src/<package_name>/settings.py file under the HOOKS key.

You can register more than one implementation for the same specification. They will be called in LIFO (last-in, first-out) order.

The following example sets up a Hook so that the after_data_catalog_created implementation is called every time after a data catalog is created.

# <your_project>/src/<your_project>/settings.py
from <your_project>.hooks import ProjectHooks, DataCatalogHooks

HOOKS = (ProjectHooks(), DataCatalogHooks())

Kedro also has auto-discovery enabled by default. This means that any installed plugins that declare a Hooks entry-point will be registered. To learn more about how to enable this for your custom plugin, see our plugin development guide.

Note

Auto-discovered Hooks will run first, followed by the ones specified in settings.py.

Disable auto-registered plugins’ Hooks

Auto-registered plugins’ Hooks can be disabled via settings.py as follows:

# <your_project>/src/<your_project>/settings.py

DISABLE_HOOKS_FOR_PLUGINS = ("<plugin_name>",)

where <plugin_name> is the name of an installed plugin for which the auto-registered Hooks must be disabled.

Under the hood

Under the hood, we use pytest’s pluggy to implement Kedro’s Hook mechanism. We recommend reading their documentation if you have more questions about the underlying implementation.