Dask provides a way to build complex workflows using a technique called "delayed evaluation". This technique allows you to define a sequence of computations as a graph of "delayed" objects that represent the computations to be performed. The actual computation is deferred until the result is needed, and Dask automatically parallelizes the computation across multiple cores or nodes.
Here's an example of how to build a delayed pipeline in Dask:
import daskfrom dask import delayed@delayeddef read_csv(file): df = pd.read_csv(file) return df@delayeddef preprocess(df): # do some preprocessing here return df@delayeddef analyze(df): # do some analysis here return resultfiles = ['file1.csv', 'file2.csv', 'file3.csv']dfs = [read_csv(file) for file in files]preprocessed = [preprocess(df) for df in dfs]results = [analyze(df) for df in preprocessed]final_result = dask.compute(*results) |
In this example, we define three delayed functions: read_csv(), preprocess(), and analyze(). These functions represent the steps of our pipeline.
We then create a list of delayed objects using a list comprehension. Each delayed object represents a call to the read_csv() function with a file name from the files list.
We then create another list of delayed objects by applying the preprocess() function to each of the delayed objects in the dfs list.
Finally, we create a third list of delayed objects by applying the analyze() function to each of the delayed objects in the preprocessed list.
We can then trigger the computation of the final result by calling dask.compute() on the list of delayed objects in the results list. This will parallelize the computation of the entire pipeline across multiple cores or nodes, depending on your Dask setup.
Note that the @delayed decorator is used to indicate that a function should be treated as a delayed object. This allows Dask to build a graph of computations that can be parallelized.