Welcome to Streams’s documentation!¶
Streams is an easy to use library to allow you to interpret your information as a data flow and process it in this way. It allows you parallel processing of a data flow and you can control it.
Actually Streams is dramatically inspired by Java 8 Stream API. Of course it is not a new beast in the zoo, I used the same approach in several projects before but this pattern goes to mainstream now and it is good to have it in Python too.
Just several examples to help you to feel what is it:
from requests import get from operator import itemgetter average_price = Stream(urls) \ # make a stream from the list of urls .map(requests.get, parallel=4) \ # do url fetching in parallel. 4 threads / greenlets .map(lambda response: response.json()["model"]) \ # extract required field from JSON. .exclude(lambda model: model["deleted_at"] is None) \ # we need only active accounts so filter out deleted ones .map(itemgetter("price")) \ # get a price from the model .decimals() \ # convert prices into decimals .average() \ # calulate average from the list
And not let’s check the piece of code which does almost the same.
from concurrent.futures import ThreadPoolExecutor from requests import get with ThreadPoolExecutor(4) as pool: average_price = Decimal("0.00") fetched_items = pool.map(requests.get, urls) for response in fetched_items: model = response.json()["model] if model["deleted_at"] is None: continue sum_of += Decimal(model["price"]) average_price /= len(urls)
So this is Stream approach. Streams are lazy library and won’t do anything if it is not needed. Let’s say you have urls as iterator and it contains several billions of URLs you can’t fit into the memory (ThreadPoolExecutor creates a list in the memory) or you want to build a pipeline of your data management and manipulate it according to some conditions, checkout Streams, maybe it will help you to create more accurate and maintainable code.
Just suppose Streams as a pipes from your *nix environment but migrated into Python. It also has some cool features you need to know about:
- Small memory footprint even for massive data sets,
- Automatic and configurable parallelization,
- Smart concurrent pool management.