When the Python codebase for a project (let's call the project LasagnaFest) starts getting big, and when you feel the urge to re-use a chunk of code (let's call that chunk foodutils) in multiple places, there are a variety of steps at your disposal. The most obvious step is to move that foodutils code into its own file (thus making it a Python module), and to then import that module wherever else you want in the codebase.
Most of the time, doing that is enough. The Python module importing system is powerful, yet simple and elegant.
But… what happens a few months down the track, when you're working on two new codebases (let's call them TortelliniFest and GnocchiFest – perhaps they're for new clients too), that could also benefit from re-using foodutils from your old project? What happens when you make some changes to foodutils, for the new projects, but those changes would break compatibility with the old LasagnaFest codebase?
What happens when you want to give a super-charged boost to your open source karma, by contributing foodutils to the public domain, but separated from the cruft that ties it to LasagnaFest and Co? And what do you do with secretfoodutils, which for licensing reasons (it contains super-yummy but super-secret sauce) can't be made public, but which should ideally also be separated from the LasagnaFest codebase for easier re-use?
Or – not to be forgotten – what happens when, on one abysmally rainy day, you take a step back and audit the LasagnaFest codebase, and realise that it's got no less than 38 different *utils chunks of code strewn around the place, and you ponder whether surely keeping all those utils within the LasagnaFest codebase is really the best way forward?
Moving foodutils to its own module file was a great first step; but it's clear that in this case, a more drastic measure is needed. In this case, it's time to split off foodutils into a separate, independent codebase, and to make it an external dependency of the LasagnaFest project, rather than an internal component of it.
This article is an introduction to the how and the why of cutting up parts of a Python codebase into dependencies. I've just explained a fair bit of the why. As for the how: in a nutshell, pip (for installing dependencies), the public PyPI repo (for hosting open-sourced dependencies), and a private PyPI repo (for hosting proprietary dependencies). Read on for more details.
PostgreSQL is my favourite RDBMS, and it's the fave of many others too. And rightly so: it's a good database! Nevertheless, nobody's perfect.
When it comes to exporting Postgres data (as SQL INSERT statements, at least), the tool of choice is the standard pg_dump utility. Good ol' pg_dump is rock solid but, unfortunately, it doesn't allow for any row-level filtering. Turns out that, for a recent project of mine, a filtered SQL dump is exactly what the client ordered.
On account of this shortcoming, I spent some time whipping up a lil' Python script to take care of this functionality. I've converted the original code (written for a client-specific data set) to a more generic example script, which I've put up on GitHub under the name "PG Dump Filtered". If you're just after the code, then feel free to head over to the repo without further ado. If you'd like to stick around for the tour, then read on.
Flask is still a relative newcomer in the world of Python frameworks (it recently celebrated its fifth birthday); and because of this, it's still sometimes trailing behind its rivals in terms of plugins to scratch a given itch. I recently discovered that this was the case, with storing and retrieving user-uploaded files on Amazon S3.
For static files (i.e. an app's seldom-changing CSS, JS, and images), Flask-Assets and Flask-S3 work together like a charm. For more dynamic files, there exist numerous snippets of solutions, but I couldn't find anything to fill in all the gaps and tie it together nicely.
Due to a pressing itch in one of my projects, I decided to rectify this situation somewhat. Over the past few weeks, I've whipped up a bunch of Python / Flask tidbits, to handle the features that I needed:
I've also published an example app, that demonstrates how all these tools can be used together. Feel free to dive straight into the example code on GitHub; or read on for a step-by-step guide of how this Flask S3 tool suite works.
Over the past century or so, much has been achieved in combating the famous Tyranny of Distance that naturally afflicts this land. High-quality road, rail, and air links now traverse the length and breadth of Oz, making journeys between most of her far-flung corners relatively easy.
Nevertheless, there remain a few key missing pieces, in the grand puzzle of a modern, well-connected Australian infrastructure system. This article presents five such missing pieces, that I personally would like to see built in my lifetime. Some of these are already in their early stages of development, while others are pure fantasies that may not even be possible with today's technology and engineering. All of them, however, would provide a new long-distance connection between regions of Australia, where there is presently only an inferior connection in place, or none at all.
For a Flask-based project that I'm currently working on, I just added some front-end functionality that depends on Font Awesome. Getting Font Awesome to load properly (in well-behaved modern browsers) shouldn't be much of a chore. However, my app spans multiple subdomains (achieved with the help of Flask's Blueprints per-subdomain feature), and my static assets (CSS, JS, etc) are only served from one of those subdomains. And as it turns out (and unlike cross-domain CSS / JS / image requests), cross-domain font requests are forbidden unless the font files are served with an appropriate Access-Control-Allow-Origin HTTP response header. For example, this is the error message that's shown in Google Chrome for such a request:
Font from origin 'http://foo.local' has been blocked from loading by Cross-Origin Resource Sharing policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'http://bar.foo.local' is therefore not allowed access.
As a result of this, I had to quickly learn how to conditionally add custom HTTP response headers based on the URL being requested, both for Flask (when running locally with Flask's built-in development server), and for Apache (when running in staging and production). In a typical production Flask setup, it's impossible to do anything at the Python level when serving static files, because these are served directly by the web server (e.g. Apache, Nginx), without ever hitting WSGI. Conversely, in a typical development setup, there is no web server running separately to the WSGI app, and so playing around with static files must be done at the Python level.
In classical antiquity, a number of advanced civilisations flourished in the area that today comprises parts of Turkmenistan, Uzbekistan, Tajikistan, and Afghanistan. Through this area runs a river most commonly known by its Persian name, as the Amu Darya. However, in antiquity it was known by its Greek name, as the Oxus (and in the interests of avoiding anachronism, I will be referring to it as the Oxus in this article).
The Oxus region is home to archaeological relics of grand civilisations, most notably of ancient Bactria, but also of Chorasmia, Sogdiana, Margiana, and Hyrcania. However, most of these ruined sites enjoy far less fame, and are far less well-studied, than comparable relics in other parts of the world.
I recently watched an excellent documentary series called Alexander's Lost World, which investigates the history of the Oxus region in-depth, focusing particularly on the areas that Alexander the Great conquered as part of his legendary military campaign. I was blown away by the gorgeous scenery, the vibrant cultural legacy, and the once-majestic ruins that the series featured. But, more than anything, I was surprised and dismayed at the extent to which most of the ruins have been neglected by the modern world – largely due to the region's turbulent history of late.
This article has essentially the same aim as that of the documentary: to shed more light on the ancient cities and fortresses along the Oxus and nearby rivers; to get an impression of the cultures that thrived there in a bygone era; and to explore the climate change and the other forces that have dramatically affected the region between then and now.
For the past few months, my main dev project has been a custom tool that imports metric data from a variety of sources (via APIs), and that generates reports showing that data in numerous graphical and tabular formats. The app is private (and is still in alpha), so I'm afraid I can't go into more detail than that at this time.
I decided (and I was encouraged by stakeholders) to build the tool as a single-page application, i.e. as a web app where almost all of the front-end is powered by JavaScript, and where the page is redrawn via AJAX calls and client-side templates. This was my first experience developing such an app; as such, I'd like to reflect on the choices I made, and on my understanding of the technology as it stands now.
For those of you who have some experience working with Google's APIs, you may be aware of the fact that they fall into two categories: the Google Data APIs, which is mainly for older services; and the discovery-based APIs, which is mainly for newer services.
There has been considerable confusion regarding the difference between the two APIs. I'm no expert, and I admit that I too have fallen victim to the confusion at times. Both systems now require the use of OAuth2 for authentication (it's no longer possible to access any Google APIs without Oauth2). However, each of Google's APIs only falls into one of the two camps; and once authentication is complete, you must use the correct library (either GData or Discovery, for your chosen programming language) in order to actually perform API requests. So, all that really matters, is that for each API that you plan to use, you're crystal clear on which type of API it is, and you use the correct corresponding library.
The GData Python library has a very handy mechanism for exporting an authorised access token as a blob (i.e. a serialised string), and for later re-importing the blob back as a programmatic access token. I made extensive use of this when I recently worked with the Google Analytics API, which is GData-based. I couldn't find any similar functionality in the Discovery API Python library; and I wanted to interact similarly with the YouTube Data API, which is discovery-based. What to do?
For a recent project, I needed to know the LGAs (Local Government Areas) of all postcodes in Australia, and vice versa. As it turns out, there is no definitive Australia-wide list containing this data anywhere. People have been discussing the issue for some time, with no clear outcome. So, I decided to get creative.
If you want the full story: I imported both the LGA boundaries data and the Postal Area boundaries data from the ABS, into PostGIS, and I did an "Intersects" query on the two datasets. I exported the results of this query to CSV. Done! And all perfectly reproducible, using freely available public data sets, and using free and open-source software tools.
Tagging data (e.g. in a blog) is many-to-many data. Each content item can have multiple tags. And each tag can be assigned to multiple content items. Many-to-many data needs to be stored in a database. Preferably a relational database (e.g. MySQL, PostgreSQL), otherwise an alternative data store (e.g. something document-oriented like MongoDB / CouchDB). Right?
If you're not insane, then yes, that's right! However, for a recent little personal project of mine, I decided to go nuts and experiment. Check it out, this is my "mapping data" store:
Just a list of files in a directory.
And check it out, this is me querying the data store:
Show me all posts with the tag 'fun-stuff'.
And again:
Show me all tags for the post 'rant-about-seashells'.
And that's all there is to it. Many-to-many tagging data stored in a list of files, with content item identifiers and tag identifiers embedded in each filename. Querying is by simple directory listing shell commands with wildcards (also known as "globbing").
Is it user-friendly to add new content? No! Does it allow the rich querying of SQL and friends? No! Is it scalable? No!
But… Is the basic querying it allows enough for my needs? Yes! Is it fast (for a store of up to several thousand records)? Yes! And do I have the luxury of not caring about user-friendliness or scalability in this instance? Yes!
