Python: Demystifying AWS’ Boto3

As the GitHub page says, “Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python, which allows Python developers to write software that makes use of services like Amazon S3 and Amazon EC2.”

The good news is that Boto 3 is extremely well documented. However, the bad news is that it is quite difficult to follow. The documentation starts with a Quickstart guide, followed by a Sample Tutorial followed then by Code Examples. This is all good stuff, though it doesn’t give you much of an understanding of how to actually use Boto 3. For example,  we see things such as:

and,

But we haven’t yet learned what a client and a resource is, nor do we see sessions mentioned until much later in the documentation. But I digress. Let’s go ahead and get started!

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Git: Merging & Rebasing basics

In the Git: Keeping in sync post we learned how to merge the orgin/master commits into our local master branch. Then in Git: Effective branching using workflows we learned about how to use branches effectively. What we haven’t yet touched on yet though is rebasing and its affect on merging.

Commit log

Before we get started on merging and rebasing, let’s first see how we can view our git log as we will need to do it throughout this post:

In a nutshell, the above command shows us the last three commits which were made in this repo. If we want to get a little fancier, we can have git draw a graph for us:

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Git: Effective branching using workflows

In the Getting started with git we learned about local and remote branches ( master and origin/master respectively), and in Git: Keeping in sync we learned how to keep the two branches in sync. This is all great stuff, but if you’re working in a team and/or on a serious project, using only the master branch is not a good idea. The reason being that if master is having development code pushed to it continuously, it will never be stable.

What you should do instead, at a minimum, is have two branches. For example, dev and master, where  dev is used for features which are being developed and master is used for production code. Once the in-development features have been tested and are ready for production, they can then be merged into master. By employing this method master will always be in a stable state.

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Git: Keeping in sync

In the Getting start with git post we covered a number of things, one of which was using push to send our commits to a remote git repo. This is works fine when both of these conditions are met:

  1. You’re the only person working on the project
  2. You’re doing all of your development from the same machine

If one of these points is not true, you’ll soon find the push command fails to work. The reason for this is because you must first retrieve all of the commits from the remote branch before you can merge your own. In other words, you must first be in sync before you can make modifications.

Therefore if someone does a push before you, and/or you do a push from a different machine, the machine you’re currently using will be out of sync with the remote branch. As a result you will be unable to do a push until you first re-sync with the remote branch.

Note: The reason why this issue is not encountered when you meet the two criterion listed above is because your machine will always be in sync with the remote branch given that it’s the only one doing any commits.

Let’s now run through an example to see this in action.

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Getting started with git

What is git?

Wikipedia has a great answer for this question:

Git is a version control system for tracking changes in computer files and coordinating work on those files among multiple people. It is primarily used for source code management in software development, but it can be used to keep track of changes in any set of files. As a distributed revision control system it is aimed at speed, data integrity, and support for distributed, non-linear workflows.

OK cool, now that we know what is git let’s now take a look at git repositories.

What is a Repository (repo)?

A repository is receptacle for files which are part of a project. Each project should be stored in a separate repository so that their files are kept separate, access to them can be administered separately, etc.

Creating a new repo

When you create a new repo using a git server provided by the likes of GitHub and GitLab, you will be given a few options to help you get started. One of these options is as follows:

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Allure2: A GUI for your code tests

In my previous post I touched on the basics of how you can use pytest to test your code. In this post I’ll be covering how you can use Allure2 to prettify your pytest results.

Allure2 Adapter for pytest

The first thing we need to is install the Allure adapter for Pytest. As the documentation states, this repository contains a plugin for py.test which automatically prepares input data used to generate Allure Report.

Issue the following command to install the adapter:

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Testing your code with pytest

If you’re fairly new to coding chances are you’ve run into an issue where you make a minor change in one place, and then end up breaking your script in another place. In order to find out what went wrong you start adding print statements all over the place to debug your code.

While it sound like a good idea, what you’re actually doing is relying on Python to tell you when you’ve made a syntactical error. However, what if your syntax is find, but your code is incorrect?

For example, say you accidentally changed your addition function to a multiplication function by replacing the + with a *:

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Python: Shadowing

In my previous post, Python: Scope, I touched on the topic of Shadowing. In this post I’ll be delving deeper into it.

As Wikipedia saysvariable shadowing occurs when a variable declared within a certain scope (decision block, method, or inner class) has the same name as a variable declared in an outer scope.

There are some interesting debates on whether shadowing is a bad thing or not in this StackOverflow Q&A as well as this one. In a nutshell, there are three trains of thought:

  1. It’s fine to use shadowing.
  2. You should avoid shadowing by ensuring all names are unique.
  3. You should avoid shadowing by using functions.

Let’s now run through each of these options to see how they work.

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Python: Scope

Scope is the term used to define the location(s) in which Python searches for a name to object mapping (e.g a variable).

As described in this StackOverflow post, Python uses the LEGB Rule to locate a definition. LEGB stands for:

  • L, Local — Names assigned in any way within a function ( def or lambda)), and not declared global in that function.
  • E, Enclosing-function locals — Name in the local scope of any and all statically enclosing functions ( def or lambda), from inner to outer.
  • G, Global (module) — Names assigned at the top-level of a module file, or by executing a global statement in a def within the file.
  • B, Built-in (Python) — Names preassigned in the built-in names module open,range,SyntaxError,...

In a nutshell, Python will first look at the local scope for a name to object mapping (e.g people = 5). If it cannot find one, it will continue going up the hierarchy until it finds one. If it doesn’t find a mapping, it will raise an exception.

To shed some more light on this let’s take a step back and analyse each of the points listed above. I’ll do so in reverse order because that is the way we write Python code, as you’ll see in a moment.

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