#Fews python library how to#

# And now I moved into this directory since we'll be running our commands here: cd /d/src/tmp/pythonxml # I created mine within WSL at this location: mkdir /d/src/tmp/pythonxml It doesn’t matter where you create this directory but we will be creating a few Python files, an XML file and optionally a Dockerfile. Creating a directory to store a few files: This article will have a few code snippets and if you plan to follow along you will need to install the xmltodict library as well as the lxml library so we can compare both libraries.

#Fews python library how to#

This was the main bottleneck of the service.Īfter I spent a few hours researching how to improve the parsing speed, I landed on using the lxml library and I was able to bring the parse time down from 80 seconds to 4 seconds which is a 20x improvement. Suddenly it was taking 80 seconds to convert an XML string into a proper data structure that I could iterate over and access fields on. Most requests were less than 20MB in which case the first solution I used (which was the xmltodict Python library) was fine and dandy but once I had to deal with 400mb of data things got quite slow. Not too long ago I was writing a Flask service for a client that had to interact with a SOAP API (gross, I know), and one of the goals of this service was to take a bunch of XML data and then compare -> manipulate -> save it to a database. Quick Jump: Following Along? Getting Set Up | xmltodict vs Python's Standard Library vs lxml Updated on August 20th, 2019 in #docker, #flask How I Used the lxml Library to Parse XML 20x Faster in Python I had to parse 400mb of XML for some client work and I tried a few different strategies.