What Happened Last Week
I've been working on my Python unit tests involving AWS Lambda functions. Those unit tests then became particularly handy, as I changed from using Python 2.7 to Python 3.6. Finally, I also started work on a class for creating log files but that is unfinished.
The source code can be found here https://gitlab.com/neilspink/aws-lambda-price-grabber
Why Change from Python 2.7 to Python 3.6
It hasn't bothered me so far, but I got the hint that going to Python 3 is a must after receiving a newsletter from DigitalOcean with their book 'how to code in python'.
Apparently in 2020 Python 2 is to lose support andgenerally when creating a solution or program you want it to have a little longer life than a year.
Up to this point, I was not aware that in Python 2 any number that you type without decimals is treated as an integer and it does floor division. So, a division like 5/2 = 2 instead of 2.5. My progress has already slowed a little and I don't need any additional programming in the next modules of my project, where there will definitely be division going on.
The final additional benefit of going to Python 3 is that it uses Unicode by default and because web pages usually are containing Unicode this could also save extra development time. I remember in my first wee seeing output like u'90.00' for the price, the u' being Python 2 syntax for Unicode.
What I did to upgrade to Python3
I started by creating a branch in the source code, because I wasn't sure if I'd complete the job.
git checkout -b python3
The next thing I had to do was setting changing the Python interpreter in PyCharm my Python editor, that's found under-> file -> settings-> project -> project interpreter.
I installed PIP3 and boto3 which I am using for accessing AWSresources like S3 buckets and Lambda functions.
sudo apt install python3-pip pip3 install boto3
Appart from having to change print statements, e.g. Print 'Hello' v2 style to v3 style Print('Hello'). I found the importing references to other classes had slightly changed.
After all the changes I was exceptionally happy to have unit tests which proved everything was still working. The final changes were in my AWS CloudFormation template making the "Runtime": "python3.6" and in my .gitlab-ci.yml for GitLab to take image: python:3.6
Time to merge the changes back into the master.
git checkout master git merge python3 git push
Unit Test Code Coverage
Knowing what % of the source code gets tested can help you identify parts of a system you forgot to unit test and happened to me while developing AWS Lambda functions that were calling other Lambda functions. I started searching how to get the code coverage on my project and found there is a --with-coverage command line switch for nose2 utility I've been using.
I increased the code coverage from 26% to 65% the invoker class which was calling other AWS Lambda functions. The increase is good enough for me. I'm not a believer in having high percentage coverages, because all too often the tests are less functional. I prefer if I can learn something about a program from a unit test, i.e. a unit test setting and reading string parameters is not valuable, doing an action or calculation is.
Unit Test Mocking
I'm not sure if you know what mocking is, but in case you haven't heard this term, it is just a way to replace parts of your system under test with mock ones, faking part of the system to simulate the behaviour of the real ones. For me, I needed to mock AWS Lambda functions.
I lost a lot of time trying a library called Moto which I found on GitHub, it looked very promising, but I gave up on it. I spent several hours trying to get it to work, my final test before I gave up was cloning the library and running nose2, and none of the unit tests worked on my computer.
Luckily I found the documentation on Python.org :D https://docs.python.org/3/library/unittest.mock.html, and I got my unit tests working with the patch. I said this at the beginning 5-weeks ago; I don't think you need to pay for any online courses, the documentation is all there. Although I don't find it easy to read sometimes, it was worth the effort.
Mocking an AWS Lambda Function
A little background on what I was testing. In my web scraper project, I want to get the prices from multiple websites. I have a list of jobs and invoke the grabber to get the prices.
My first step, I moved all the code from my Lambda function to a class named Invoker.
from invoker import Invoker def lambda_handler(event, context): if 'source' not in event: raise Exception("The 'source' key is missing from the event dictionary.") job = Invoker() result = job.grab(event) print(result) return result
def grab(self, event): ... for site in website_list['sites']: ... response = self._invoke_lambda(payload)
@staticmethod def _invoke_lambda(payload): client = boto3.client('lambda') return client.invoke(FunctionName='grab-price', InvocationType='RequestResponse', Payload=payload)
In just wanted it to return an arbitrary text message "none". For that I added at the top
from mock import patch
and used the patch function decorator
lambda_result = "none" with patch.object(Invoker, '_invoke_lambda', return_value=lambda_result): result = Invoker().grab(events)
I want to finish saving prices that get scrapped from websites, schedule my crawler and watch it run for a couple of days.