Python | How to use delayed functions for aggregating in Python

Aggregating with delayed Functions

Using delayed functions in Dask allows us to perform complex operations on large datasets by breaking them into smaller, more manageable chunks. We can also use delayed functions to perform aggregations on large datasets.

Here's an example of using delayed functions to perform an aggregation on a large dataset:

import dask

import dask.bag as db

def count_words(filename):

with open(filename, 'r') as f:

text = f.read()

words = text.split()

return len(words)

filenames = ['file1.txt', 'file2.txt', 'file3.txt']

b = db.from_sequence(filenames)

counts = b.map(dask.delayed(count_words)).compute()

total_count = sum(counts)

print(total_count)

In this example, we define a function count_words that takes a filename as input and returns the number of words in the file. We then create a list of filenames filenames.

We use Dask's from_sequence method to create a Dask bag b from the list of filenames. We then use the map method to apply the count_words function to each filename in the bag. However, we wrap the function in the dask.delayed function, which tells Dask to defer the computation until it is needed.

We then call the compute method on the bag, which triggers the computations for each delayed function. The results are returned as a list of counts, which we then sum to get the total count of words in all the files.

Using delayed functions allows us to perform the aggregation on each file separately and in parallel, which can significantly speed up the computation for large datasets.

We can also use delayed functions to perform more complex aggregations. Here's an example of computing the average length of words in a collection of files:

import dask

import dask.bag as db

def word_lengths(filename):

with open(filename, 'r') as f:

text = f.read()

words = text.split()

lengths = [len(word) for word in words]

return lengths

filenames = ['file1.txt', 'file2.txt', 'file3.txt']

b = db.from_sequence(filenames)

lengths = b.map(dask.delayed(word_lengths)).flatten()

total_length = lengths.sum().compute()

total_count = lengths.count().compute()

average_length = total_length / total_count

print(average_length)

In this example, we define a function word_lengths that takes a filename as input and returns a list of word lengths in the file. We then create a list of filenames filenames.

We use Dask's from_sequence method to create a Dask bag b from the list of filenames. We then use the map method to apply the word_lengths function to each filename in the bag. Again, we wrap the function in the dask.delayed function to defer the computation.

We then use the flatten method to combine the lists of word lengths into a single bag. We compute the sum and count of the lengths using the sum and count methods, respectively. We then divide the sum by the count to get the average length of words in the files.

Using delayed functions in this way allows us to perform complex aggregations on large datasets in parallel, which can be much faster than performing the computations sequentially.