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Lambda web scraper project (week 6)

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

I was amazed it all worked in just 2 commits. You can compare my before and after source code here; Commit af9a61a3 and Commit 5711a10e

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.

nose2 --with-coverage

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. 

Showing code coverage using the nose2 utility with the command line switch --with-coverage

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)

    return result
In the Invoker class, I moved the moved the AWS Lambda call into a private method called _invoke_lambda. The underscore prefix is just meant as a hint to another programmer that it is intended for internal use.
   def grab(self, event):
        for site in website_list['sites']:  
            response = self._invoke_lambda(payload)
The AWS Lambda call being
def _invoke_lambda(payload):

    client = boto3.client('lambda')

    return client.invoke(FunctionName='grab-price',

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)

Coming Next

I want to finish saving prices that get scrapped from websites, schedule my crawler and watch it run for a couple of days.