TIL: Bind arguments to dynamically generated lambdas in python


This past week I had a really weird bug in my kedro pipeline. For some reason data running through my pipeline was coming out completely made no sense, but if I manually request raw data outside of the pipeline it matched expectations.

NOTE While this story is about a kedro pipeline, it can be applied anywhere closures are put into an iterable.

Debugger to the rescue

After a few days of looking at it off and on, I pinpointed that it was all the way down in the raw layer. Right as data is coming off of the database. For this I already had existing sql files stored and a read_sql function to get the data so I opted to just set up the pipeline to utilize the existing code as much as possible, leaning on the kedro framework a bit less.

I have dynamically created lists of pipeline nodes many times in the past, but typically I take data from kedro input and use it in the lambda. I prefer the simplicity of using lambdas over functools.partial. It typically looks something like this.

# 👍  I do this all the time
from kedro.pipeline import node
from my_generic_project_lib import clean

datasets_to_clean = ['sales', 'production', 'inventory']
nodes = []
for dataset in datasets_to_clean:
   nodes.append(
      node(
         func=lambda x: clean(x)
         inputs = f'raw_{dataset}'
         outputs=f'int_{dataset}'
         tags=['int', dataset]
         name=f'create_int_{dataset}'
      )
   )

What was different this time is that I needed to pass in the name of the dataset to my read_sql function, not the data loaded in the framework.

# ❌ This does not work
from kedro.pipeline import node
from my_generic_project_lib import read_sql

datasets_to_read = ['sales', 'production', 'inventory']
nodes = []
for dataset in datasets_to_clean:
   nodes.append(
      node(
         func=lambda: read_sql(dataset) # 💥 The major issue
         inputs = f'dummy'
         outputs=f'int_{dataset}'
         tags=['int', dataset]
         name=f'create_int_{dataset}'
      )
   )

Seriously

As I am still oblivious to what has happened I pop in a breakpoint() and quickly see that during the first run the dataset passed into read_sql was 'inventory', in fact, every single one was 'inventory'. The lambda is just using the latest value of dataset from outside and has no local dataset attached to it.

The simple fix

# 👍 Much Better
from kedro.pipeline import node
from my_generic_project_lib import read_sql

datasets_to_read = ['sales', 'production', 'inventory']
nodes = []
for dataset in datasets_to_clean:
   nodes.append(
      node(
         func=lambda dataset=dataset: read_sql(dataset) # dataset is now bound to the lambda ✨
         inputs = f'dummy'
         outputs=f'int_{dataset}'
         tags=['int', dataset]
         name=f'create_int_{dataset}'
      )
   )

Try it yourself

I made a slightly more simple example so that you can try it and play with it yourself, edit it, share it with your friends, laugh at my mistake, whatever you like.



👀 see an issue, edit this post on GitHub

If you found value in this post
and want to send a tip.

Buy Me A Coffee

If you want to show your support, non-monetarily

🙌 Share this post on

Check out my otherblogs

.