Waylon Walkerkedro hooks are an exciting upcoming feature of kedro 0.16.0. They allow you to hook into catalog_created,pipeline_run, and node_run(nouns). With a before, or after (adjective). This really reminds me of reacts lifecycle hooks, that let you hook into various state of react web components. This is going to make kedro so extendable by the community. I am super pumped to see what the community is able to do with this ability.
What is Kedro 🤔
If you are completely unsure what kedro is be sure to check out my what is kedro post
Docs
a work in progress
As this is a part of an upcoming release you will need to look in the latest docs, not stable and you will find a 15_hoooks page. As these docs are still in development they are not very complete at this point and do require a bit more existing kedro knowledge to understand. I am sure they will get much better as we approach the realease of hooks.
This doesn't mean that we can't still install the latest/unstable version and have some fun learning!
Installation
Straight from GitHub
As this is part of an upcoming release you will need to get the library straight from github. Since this is not a stable release of kedro I cannot express the importance of using a virtual environment enough. Trying to install this version in the same place that you are trying to develop a pipeline potentially break your existing working development environment.
conda create -n kedro0160 -y
conda activate kedro0160 # may also be source activate kedro0160 or activate kedro0160
pip install git+https://github.com/quantumblacklabs/kedro.git
pip install colorama
note the version is still somewhere between
0.15.9and0,16.0.kedro. __version__will still be0.15.9and wiill not roll until the official release.
Create a sample project
Kedro new
For more details check out my full post on kedro new
For this post I really just want a working pipeline as fast as possible. For this I am going to use iris pipeline that is generated from the kedro new command in the cli. It's important that you answer y to create an example pipeline.
Hold On ✋
Did you create a separate environment for this? Please do.
kedro new
After you run the kedro new command it will ask a series of questions.
👇 Here is how I answered them.
Project Name:
=============
Please enter a human readable name for your new project.
Spaces and punctuation are allowed.
[New Kedro Project]: Kedro Hooks
Repository Name:
================
Please enter a directory name for your new project repository.
Alphanumeric characters, hyphens and underscores are allowed.
Lowercase is recommended.
[kedro-hooks]:
Python Package Name:
====================
Please enter a valid Python package name for your project package.
Alphanumeric characters and underscores are allowed.
Lowercase is recommended. Package name must start with a letter or underscore.
[kedro_hooks]:
Generate Example Pipeline:
==========================
Do you want to generate an example pipeline in your project?
Good for first-time users. (default=N)
[y/N]: y
Change directory to the project generated in /mnt/c/temp/kedro-hooks/
A best-practice setup includes initialising git and creating a virtual environment before running `kedro install` to install project-specific dependencies. Refer to the Kedro documentation: https://kedro.readthedocs.io/
Install the Project
Next install the project itself and all of its dependencies with the kedro install command.
cd kedro-hooks
kedro install
🏃♀️ Run the pipeline
Before we start developing any hooks lets make sure everything is setup correctly by running the pipeline with kedro run.
kedro run
Let's make a hook
getting to the meat of the post
Now that we have a project scaffolded up and running we can develop a hook for it. As far as I can tell hooks can be implemented one of two ways. As a function inside of a module, then import that module and pass it into the hooks list, or implemented as a method on a class , then the class is passed into the hooks list. Either method must follow the naming convention with the @hook_impl decorator. Each module/class can implement more than one hook, but not more than one of the same type. One of each kind will be created below.
Full list of hooks available
before_catalog_created
after_catalog_created
before_pipeline_run
after_pipeline_run
before_node_run
after_node_run
debug_hook (class)
quick and dirty
I highly recommend this as your first hook. It's super easy to make and lets you explore the arguments passed into the hook. For this one I am going to pop the following class right into kedro-hooks/src/kedro-hooks/run.py, remember that I chose kedro-hooks as my project name. Your path might be slightly different. If you wanted to make a real hook it might make sense to put it in its own module, but for simplicity of your first hook you can put it directly in the same module that it gets implemented.
class debug_hook:
@hook_impl
def before_pipeline_run(run_params, pipeline, catalog):
"pops into a debugger before pipeline run"
print('I hooked in right before the pipeline run')
breakpoint()
It is really that easy to create a kedro hook! Now lets apply it to our project. All we need to do is add one line (hooks = [debug_hook]) to the existing ProjectContext class within kedro-hooks/src/kedro-hooks/run.py. Once we do that our ProjectContext will look like this.
class ProjectContext(KedroContext):
"""Users can override the remaining methods from the parent class here,
or create new ones (e.g. as required by plugins)
"""
project_name = "kedro-hooks"
# `project_version` is the version of kedro used to generate the project
project_version = "0.15.9"
package_name = "kedro-hooks"
hooks = [debug_hook] # 👈 This is where you implement the hook
def _get_pipelines(self) -> Dict[str, Pipeline]:
return create_pipelines()
Run it! While you are in the debugger, explore what the run_params, pipeine, and catalog arguments give you. This will give you some insight to what to expect when creating your next hook.
preflight hook (module)
giving it a bit more flair
Create a new file kedro-hooks/src/kedro-hooks/preflight.py and place the following content into the file. This will raise a DataSetNotFoundError before wasting time running any of the pipeline. This could be useful to save some developer time for long running pipelines by warning them that they don't have all of the raw data they need before running.
# kedro-hooks/src/kedro-hooks/preflight.py
from kedro.hooks import hook_impl
from kedro.io.core import DataSetNotFoundError
from colorama import Fore
import textwrap
@hook_impl
def before_pipeline_run(run_params, pipeline, catalog):
missing_input = [i for i in pipeline.inputs() if not getattr(catalog.datasets, i)._exists()]
if len(missing_input) != 0:
raise DataSetNotFoundError(textwrap.dedent(f'''
{Fore.LIGHTBLACK_EX}―――――――― {Fore.RED}PREFLIGHT ERROR {Fore.LIGHTBLACK_EX}―――――――――
{Fore.RESET} preflight of pipeline failed due to {Fore.YELLOW}missing datasets
{Fore.BLUE} {missing_input}{Fore.RESET}
'''))
Once we are happy with this hook, it can live anywhere. It can be a module inside our project. It can be a separate libarary that gets handed out as a back ally wheel, or we can even publish it as its own package to pypi so that anyone can easily pip install it.
One Step Back
a bit of explanation of preflight
If you are not familiar, pipeline.inputs() gives us all of the edge inputs into the pipeline. kedro does also have a pipeline.all_inputs() that tells us all of the edge and internal pipeline inputs that will be called throughout the pipeline run. For this hook we are just concerned with the edge inputs as internal inputs will be generated during the run.
Also each one of the kedro datasets have an _exists() method attached to them to check if the dataset exists or not. For a more robust implementation of preflight it would probably be best to ignore AttributeErrors, i.e the dataset type does not have an implementation of _exists. It would probably also be a good idea to filter for types such as SQLQueryDataSets that assume _exists is False by default.
Ideas
Now that the juices are flowing what ideas do you have for kedro hooks?
