AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Python typo generator1/11/2024 ![]() ![]() And the nice part about them is: you can write them as if they were writing regular sequential Python code. ![]() You can combine them, you can wrap them, you can even send them values. Generators are like a swiss-army knife of iterables. More flexible solution with less boilerplateįortunately Python has a very nice language feature, named generators. To my delight many of my desired patterns were already implemented, present in the recipe section of the docs, or could be coded up quickly without much boilerplate. ![]() This was the point where I decided to look at the itertools ((Python 2 documentation: itertools - Functions creating iterators for efficient looping)) module again. It just doesn’t feel right to code at this level in Python. Another objection I gave myself was that these patterns look so common they ought to be solved already. Testing it thorougly will also take some time. Grouping by some arbitrary key would also be nice, wouldn’t it? Although it’s actually quite simple to extend this solution, the base-class will just grow and grow with every feature. Regardless, our next requirement would be: Buffer 500 entries per step to enable a grouped database flush. We would add a before_loop() and probably a after_loop() hook method to every step. So to achieve that we’ll have to change either the ProcessingStep or Pipeline class. Examples are caching, database access, etc. Sometimes we want code to run only once in the process though, be it before the items get passed or afterwards. When implementing the feed parser we identified our basic steps: parsing the feed, processing each article, downloading images, storing the articles in our database. It has a reasonable small line count and no magic. ![]() Nice! This solution looks very clean and simple. # Feed the item into one step, get the result, feed the # We need some management class which will control our pipeline We just have to subclass ProcessingStep, override process() and we’re done.Ĭlass IdentityStep(ProcessingStep): # if it should be preserved, an empty list ifĬreating new steps is trivially easy now. # (self.process() has to return the item again # can either drop or add items to the results. # This is built that way so that self.process() We just start with simple class with some hook-methods. We want to separate out processing steps (to stay maintainable) so we need to agree on an interface to pass information from one step to the next. □ A naive solution with lots of boilerplate The implementation of the concrete parts is left as an excercise to the reader. One especially brilliant example of this type of architecture is UNIX’ pipe system.ĭisclaimer: For this post I will show you the outline of a simple yet extensible feed (RSS or other) parsing system. While this may lead to an increase in perceived complexity, it actually reduces the complexity you have to handle by confining it to some other level. They allow you to separate out responsibilities in distinct processing units. Pipe and filters architectures are a natural fit to this problem. Apprentice programmers would just write it all down sequentially, but – as many fellow programmers can testify – this approach leads to maintenance problems due to a mixing of responsibilities in one place. Be it products, feed-items, images, you name it. Most developers have to write programs that read and process items of stuff. ![]()
0 Comments
Read More
Leave a Reply. |