Programming

Recommendation: use explicit type names

I attended an excellent talk recently at Lambdale about teaching children to code (not just Scratch but actual computer languages) and one of the points of feedback was that the children found the use of names in type declarations and their corresponding implementations in languages such as Haskell confusing because frequently names are reused despite the two things being completely different categories of things.

The suggestion was that instead of f[A] -> B it was simpler to say f[AType] -> BType.

Since I am in the process of introducing Python type checking at work this immediately sparked some interest in me and when I returned to work I ran a quick survey with the team that revealed that they too preferred the explicit type name with the suffix Type rather than package qualifier for types that I had been using.

Instead of kitchen_types.Kettle the preference was for KettleType.

The corresponding function type annotations would therefore read approximately:

def boil(water: WaterType, kettle: KettleType) -> WaterType

So that’s the format we’re going to be adopting.

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Programming

PyPika

PyPika is a DSL for creating SQL queries. It works by generating SQL from definitions that you create inline rather than by interpreting models or data classes or structures.

It makes it easy to define minimum projections and is also straight-forward to bind data to as you simply provide the values into the query that is generated rather than binding values to a query definition.

Once you have the query object constructed you call str on the object to obtain the actual SQL which you then need to pass to some execution context.

This also means that it is easy to test what SQL you are generating (although really using a DSL like this means you should really be trusting the library).

I have only had one real problem with the statements I have used to date and that is around the Postgres `RETURNING` statement that allows a generated key to be returned to the caller of an INSERT.

Apart from this using Pypika has been better than using a Python template or writing raw SQL.

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Programming, Python

403 Forbidden errors with Flask and Zappa

One thing that tripped me up when creating applications with Zappa was an error I encountered with form posting that seems to have also caught out several other developers.

The tldr is that if you are getting a 403 Forbidden error but your application is working locally then you probably have a URL error due to the stage segment that Zappa adds to the URL of the deployed application. You need to make sure you are using url_for and not trying to write an absolute path.

The stage segment

Zappa’s url structure is surprisingly complicated because it allows you to have different versions of the code deployed under different aliases such as dev, staging and production.

When running locally your code doesn’t have the stage prefix so it is natural to use a bare path, something like flask.redirect(‘/’) for example.

If you’re using the standard form sequence of GET – POST – Redirect then everything works fine locally and remotely until the raw redirect occurs remotely and instead of getting a 404 error (which might tip you off to the real problem more quickly) you get a 403 forbidden because you are outside the deployed URL space.

If you bind a DNS name to a particular stage (e.g. app-dev.myapp.com) then the bare path will work again because the stage is hidden behind the CloudFront origin binding.

Always use url_for

The only safe way to handle URLs is for you to delegate all the path management and prefixing to Zappa. Fortunately Flask’s in-built url_for function, in conjunction with the Zappa wrapper can take care of all the grunt work for you. As long as all your urls (both in the template and the handlers) use url_for then the resulting URLs will work locally, on the API Gateway stages and if you bind a DNS name to the stage.

If this is already your development habit then great, this post is irrelevant to you but as I’ve mostly been using Heroku and App Engine for my hobby projects I’d found myself to be in the habit of writing the URLs as strings, as you do when you write the route bindings.

Then when the error occurred I was checking the URL against my code, seeing that they matched and then getting confused about the error because mentally I’d glossed over the stage.

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Programming

Using Google App Engine with Pyenv

I recently started using PyEnv to control my Python installations and make it easier to try to move more of my code to Python 3.

Google App Engine though is unapologetically Python 2.7. Google wants people to move away from the platform in favour of Google Compute custom environments and therefore has little incentive to upgrade the App Engine SDK and environments to support Python 3.

When I set my default Python to be Python 3 with PyEnv I found that despite setting a local version of Python 2.7 my App Engine instance was failing to run with an execfile is not defined exception.

The App Engine Python scripts use #!/usr/bin/env python to invoke the interpreter and for some reason PyEnv doesn’t seem to override the global setting for this despite it being correct when you check it in your shell.

After a lot of frustration and googling for an answer I haven’t found anything elegant. Instead I found this Stack Overflow answer which helpful explained that you can use #!/usr/bin/env/python2 to invoke a specific language interpreter.

Manually changing the shebang line in appcfg.py and dev_appserver.py solved the problem for me and got me running locally.

Obviously this is a pain if I upgrade and I feel it might be better for Google to change the scripts since they don’t have a plan to move to Python3.

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Programming

Python: Preferring Named Tuples over Classes

One of the views that I decided to take in my recent Python teaching is that named tuples and functions are preferable to class-based data structures.

Python's object-orientated (OO) code is slightly strange anyway since it is retrospectively applied to the original language and most programmers find things like the self reference confusing compared to OO idioms in languages like Ruby or Java.

On top of this Python's dynamic nature means that objects are actually "open" (i.e. can take new attributes at runtime) and have few strong encapsulation guarantees. Most of which is going to be surprising to most OO-programmers who would expect the type to be binding.

Named-tuples on the other hand are immutable so their values cannot be changed and they cannot be expanded or reduced by adding or removing attributes. Their behaviour is much more defined while retaining syntax-sugar access to the attributes themselves.

Functions that operate on tuples and return tuples have some nice properties in terms of working with code. Firstly you know that there are no sequencing issues. A function that takes a tuple as an argument cannot change it so any other function is free to consume it again as an argument.

In addition you know that you are free to consume the tuple value generated by a function. As the value cannot be changed it is safe to pass it around the codebase.

I think the question should be: where are classes appropriate in ways that tuples are not?

The most common valid use of classes and inheritance is to provide a structure in a library where you expect other programmers to supply appropriate behaviour. Using classes you can simply allow the relevant methods to be implemented in the inheriting implementation. A number of Python web frameworks use this Template pattern to allow the behaviour of handlers to be defined.

Even then this is not the definitive solution. Frameworks such as Flask, use decorators instead which fits with the functional approach.

So in general I think it is simpler and easier to maintain programs that consists of functions taking and generating immutable data structures like tuples. Using Python's object-orientation features should be considered advanced techniques and used only when necessary.

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Gadgets, Programming, Python

Creating the Guardian’s Glassware

For the last two months on and off I’ve been developing the Guardian’s Glassware in conjunction with my colleague Lindsey Dew.

Dealing with secret-pre-alpha hardware has at times being interesting but the actual process of writing services using the Mirror API is actually pretty straight-forward.

Glass applications are divided into native, using the Glass SDK, and service-based Glassware using Mirror. Mirror is built on web-friendly technologies such as HTTP, JSON and OAuth2. It also follows the Google patterns for APIs so things like authentication, discovery and the client libraries are all as you would expect if you’ve used a modern Google API before.

For our project, which was focussed on trying to create a sensible, useful newsfeed to Glass, we went with Mirror. If you want to do things like geolocation or picture and video upload then you’ll want to go native.

For various reasons we had a very narrow initial window for development. Essentially we had to start and finish in May. Our prototyping was done with a sample app from Google (you can use Mirror without an actual device), the Mirror playground and a lot of imagination.

When we actually got our Glass devices it took about a week to get my head round what the usecase was. I was thinking that it was like a very lightweight mobile phone but it is much more pervasive with lots of light contact points. I realised that we could be more aggressive about pushing information out and could go for larger sets of stories (although that was dialled back a bit in the final app to emphasise editorial curated content).

Given the tight, fixed deadline for an unknown product the rest of the application was build using lots of known elements. We used a lot of the standard Glass card templates. We used Python on Google App Engine to simplify the integration service and because we had been building a number of apps on that same stack. The application has a few concerns:

  • performing Google Authentication flow
  • polling the Guardian’s Content API and our internal Notification platform
  • writing content to Mirror
  • handling webhook callbacks from Mirror
  • storing a user’s saved stories

We use Content API all the time and normally we are rendering it into widgets or pages but here we are just transforming JSON into JSON.

The cards are actually rendered by our application and the rendered content is packaged into the JSON along with a text representation. However rendering according to the public Glass stylesheet and the actual device differed, and therefore checking the actual output was important.

The webhooks are probably best handled using deferred tasks so that you are handing off the processing quickly and limiting the concern to just processing the webhook’s payload.

For the most part the application is a mix of Google stock API code and some cron tasks that reads a web API and writes to one.

Keeping the core simple meant it was possible to iterate on things like the content mix and user interactions. The need to verify everything in device served as a limiting factor.

Glass is a super divisive technology, people are very agitated when they see you wearing it. No-one seems to have an indifferent opinion about them.

Google have done a number of really interesting things with Glass that are worth considering from a technology point of view even if you feel concerned about privacy and privilege.

Firstly the miniaturisation is amazing. The Glass hardware is about the size of a highlighter and packs a camera, memory, voice synth, wifi and bluetooth. The screen is amazingly vivid and records and plays video well. It has a web browser that seems really capable of standard HTML rendering.

The vocal recognition and command menus are really interesting and you feel a little bit space age when you fire off a Google query and get the information you’re looking for read back to you in seconds.

Developing with the Mirror API is really interesting because it solves the Android fragmentation issue. My application talks to Mirror, not to the native device. If Google want to change the firmware, wire protocol or security they can without worrying about how it will affect the apps. If they do have to make breaking changes then can use the standard webapi versioning they already use.

Unlike most of the Guardian projects this one has been embargoed before the UK launch but it is great to see it out in the open. Glass might not be the ultimate wearable tech answer; just as the brick phones didn’t directly point to the iPhone. Glass is a bold device and making the Guardian’s journalism available on a new platform has been an interesting test of our development processes and an interesting challenge to the idea of what web-capable devices are (just as the Pixel exposed some flaky thinking about what a touch device is).

What will be interesting from here is how journalists will use Glass. Our project didn’t touch on how you can use Glass to share content from the scene, but the Glass has powerful capabilities to capture pictures and video hands-free and deliver it back to desk editors. There’s already a few trials planned in less stressful feature pieces and it will be interesting to see if people find the interface intuitive and more convenient that firing up their phone.

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Programming

Why I’m excited about Julia

Julia is a new language that promises (or threatens) to take the best of all existing languages and put them into one fantastic package.

As a Python fan I found the syntax immediately appealing and familiar. However there are a number of Python niggles that appear to be improved in Julia.

Don’t like significant whitespace? Julia is using a end keyword.

function add(x, y) x + y end

Find Python’s keyword initialising rule counter-intuitive? Julia re-initialises on invocation.

function test_init(a, b = [1])
println(a, b)
push!(b, a)
println(b)
end

test_init(5) # Prints 5, 1, 1, 5

test_init(7) # Prints 7, 1, 1, 7

Wish that sequence operations were more consistent?

a = [1, 2, 3] # 3-element Array{Int64,1}: (via type inference)
shift!(a) # 1
pop!(a) # 3

Julia also comes with a shell which has all the niceties of ipython.

Julia is typed but so far the inferencing for primitives has been good enough. For collections you only seem to need a type declaration if the collection is initially empty.

Julia is also trying to do a lot with parallel processing and performance. I haven’t looked into that so much because that isn’t my usecase but so far the requirements for efficient typing aren’t causing any ceremony in the code. There is also some innate immutability in things like tuples but apart from that it is mutable by default with an immutable keyword applied to data structures that allows compiler and GC optimisation.

So Julia is a language that is immediately accessible to all Pythonistas, currently has less surprises than Python and is typed but enough inference to avoid them getting in the way. Pretty exciting!

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