The Key – back-fitting responsive design (with exciting graphs!)

As an industry we talk a lot about the importance of responsive design. There are a lot of oft-repeated facts about the huge rise in mobile usage, alongside tales of woe about the 70,000 different Android screen resolutions. Customers often say the word ‘responsive’ to us with a terrified, hunted expression. There’s a general impression that it’s a) incredibly vital but b) incredibly hard.

As to the former, it’s certainly becoming hard to justify sites not being responsive from the very beginning. 18 months ago, we’d often find ourselves reluctantly filing ‘responsive design’ along with all the other things that get shunted into ‘phase 2′ early in the project. Nowadays, not so much: Mailchimp reported recently that 60% of mail they send is opened on a mobile phone.

For the latter, there’s this blog post. We hope it demonstrates that retro-fitting responsive design can be simple to achieve and deliver measurable results immediately.

And, because there are graphs and graphs are super boring, we’ve had our Gwilym illustrate them with farm animals and mountaineers. Shut up; they’re great.

What were the principles behind the design?

We’re not really fans of change for change’s sake, and generally, when redesigning a site, we try to stick to the principle of not changing something unless it’s solving a problem, or a clear improvement.

In this redesign project we were working under certain constraints. We weren’t going to change how the sites worked or their information architecture. We were even planning on leaving the underlying HTML alone as much as possible. We ‘just’ had to bring the customer’s three websites clearly into the same family and provide a consistent experience for mobile.

In many ways, this was a dream project. How often does anyone get to revisit old work and fix the problems that have niggled at you since the project was completed? The fact that these changes would immediately benefit the thousands of school leaders and governors who use the sites every day was just the icing on the cake.

And, to heighten the stakes a little more, one of the sites in the redesign was The Key – a site that we built 7 years ago and which has been continually developed since it first went live. Its criticality to the customer cannot be overstated and the build was based on web standards that are almost as old as it’s possible to be on the internet.

What did we actually do?

The changes we made were actually very conservative.

Firstly, text sizes were increased across the board. In the 7 years since the site was first designed, monitor sizes and screen resolutions have increased, making text appear smaller as a result. You probably needed to lean in closer to the screen than was comfortable. We wanted the site to be easy to read from a natural viewing distance.

We retained the site’s ability to adapt automatically to whatever size screen you are using, without anything being cut off. But this now includes any device, from a palm-sized smartphone, to a notebook-sized tablet, up to desktop monitors. (And if your screen is gigantic, we prevent lines from getting too long.) The reading experience should be equally comfortable on any device.

On article pages, the article text used to compete for attention with the menu along the left. While seeing the other articles in the section is often useful, we wanted them to recede to the background when you’re not looking at them.

We wanted to retain the colourfulness that was a hallmark of the previous design. This is not only to be pleasing to the eye – colours are really helpful in guiding the eye around the page, making the different sections more distinct, and helping the most important elements stand out.

Finally, we removed some clutter. These sites have been in production for many years and any CMS used in anger over that kind of period will generate some extraneous bits and bobs. Our principle here was that if you don’t notice anything missing once we’ve removed it, then we’ve removed the right things.

What was the result?

The striking thing about the changes we made was not just the extent of the effect, but also the speed with which it was demonstrable. The following metrics were all taken in the first 4 weeks of the changes being live in production in August 2014.

The most significant change is the improvement in mobile usage on The Key for School Leaders. Page views went up – fast (and have stayed there.)

total page views

Secondly, the bounce rate for mobile dropped significantly in the three months following the additions:

bounce rateMost interestingly for us, this sudden bounce in mobile numbers wasn’t from a new, unheard of group of users that The Key had never heard from before. The proportion of mobile users didn’t increase significantly in the month after the site was relaunched. The bump came almost exclusively from registered users who could suddenly now use the site the way they wanted to.


A note about hardness

What we did here wasn’t actually hard or complicated – it amounted to a few weeks work for Francois. I’ve probably spent longer on this blog post, to be honest. And so our take-away point is this: Agencies you work with should be delivering this by default for new work; should be proposing simple steps you can take to add it for legacy work or explaining why they can’t or won’t.

About usIsotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

SSL broken in gevent on Python 2.7.9 – a debugging tale of woe

I thought it would be worth writing this up quickly as a blog post, just so it’s documented, though I’m guessing the bug is common knowledge by now. The process of finding out the issue was (eventually) enlightening for me though, especially how far the initial problem was from the bug.

I was having problems last week deploying changes to one of our projects hosted on Heroku. I’d done a full run-down of dependencies trying to bring in security and bug fixes, making everything Python 2.7 and Ubuntu Trusty compatible (or, for Heroku, cedar-14 stack compatible). Everything worked fine locally, even using foreman (which is the Heroku tool that runs your code as if it was deployed on Heroku—in this case running through gunicorn with gevent). However, on deploying to a clean app and database on Heroku, the Persona Single-Sign-On authentication wasn’t working. The project’s settings are slightly involved, but the fact the admin site was working and I was getting a login page at all indicated that things were probably okay on the Django side of things. Persona itself worked fine locally, as well as on stage and production deployments on Heroku. I suspected DNS issues, but this turned out not to be the case—site domains and urls were being resolved correctly.

The only difference I could see between my new app and the stage deployment (that was working) was the database version (I’d deliberately matched the PostgreSQL version on my new app to that used in production, while staging was a point release ahead, for some reason) and the build stack that Heroku was using (part of what I was doing was testing the cedar-14 stack, which is based on trusty and supports Python 2.7.9). The customer was keen on having a test instance, so I decided to deploy on staging instead, but with an updated stack. I provisioned a new database to match production (and with production data), and used the cedar-14 stack, and everything worked fine (well apart from the bugs in my pre-Christmas development changes, but that was why I needed a test deployment for them to look at).

So there was something iffy in the test Heroku deployment I had. Off I set trying to debug a live Heroku deployment. Now after this experience, I must say I am in the market for decent logging/debugging tools for Heroku, so any suggestions are welcome. Papertrail, I found to be almost useless in this scenario, so was reduced to using django logging and running “heroku logs --app <app_name>” to see the output. So basically, print statement debugging, urgh. This was thwarted temporarily by a Heroku outage which they put apps into maintenance mode, preventing build updates. Eventually I worked out that while the Persona authentication was working ok, the authentication on the Django side was failing (using an old version of django-browserid). However, nothing was blowing up or throwing errors, the authentication was just silently failing.

Being unable to directly inject debug logging into a 3rd party library on Heroku, I decided to call the deeper API directly from my own code, and immediately I got a 500 error, and a stack trace – the definition of some internal SSL calls had changed in Python 2.7.9, and gevent was relying on them: reported here So technically not a Python bug, but the app blew up querying SSL URLs when running in Gunicorn + gevent. This was the cause of my authentication issue, as even though this project doesn’t currently run over SSL, Persona verification does. This was not initially causing 500 errors, as although it raised a TypeError in an authentication backend, Django was then just falling back to the default authentication, failing and returning no valid user.

So, bug found, and my current work-around is to stick to a Python 2.7.8 runtime (technically unsupported by Heroku), until either gevent or Python is updated. But why did I not see the issue on staging or locally using foreman? The Heroku stack on staging turned out to be using Python 2.7.4, and locally my virtualenv was running Python 2.7.3.

A few lessons to take away, I suppose:

  • Make sure you include the Python runtime in your list of dependencies to check and versions to match on Development vs. Staging vs. Production.
  • Sometimes underlying components silently riding over errors can mask the true source of odd behaviour (I really should have twigged about the authentication backends, but also custom backends should deal with TypeErrors from internal calls properly).
  • Logging is super-useful, I should probably use it more, and in a smarter way.
  • I would love a decent debugging tool for Heroku.
  • Don’t rely on underscore methods in Python internal libraries (stares daggers at the gevent developers).

If you weren’t aware of this bug, watch out for it!

Update: According to comments on the gevent issue on github, this might be an issue on Amazon even with Python 2.7.8 as they have backported the SSL code from 2.7.9 to their 2.7 runtimes on AWS. This emphasises the need to be aware of what Python runtimes are being deployed on cloud services, where debugging may be more difficult.

About usIsotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

Hacking together a standing desk

We’ve had a few people in the office transition to standing desks recently and the process by which everyone achieved their goal has been quite interesting. Or at least interesting to me.

doug's deskDoug was the first to try it and ended up, as a lot of people do, taking the old ‘big pile of books’ approach. It’s cheap, quick and,so long as you’re using a solid and non-tottering pile of books, probably pretty safe. Luckily the Isotoma office has a pretty extensive library of books–which in Doug’s case have mostly been memorised.

(I wouldn’t let him tidy his desk before I took this photo. He wanted me to tell you.)

A different approach that David introduced (as far as I know) and which I’ve adopted is the music stand approach. It’s a wee bit more expensive and depends on you having the kind of job that doesn’t really involve paper or double monitors – but I love it. The bonus of this approach is you get to feel a little bit like a member of an nineties synth-rock band. Always a bonus.

But the canonical Isotoma standing desk was “Ikea-hacked” together by Tom. He went to town on it to such an extent that we made him do an intranet post about it:

I’ve had a couple of enquiries about the parts for my standing desk, so rather than send sporadic emails, I thought I’d just put it here.


I built my standing desk based on this article, which is great, and has assembly instructions and everything (although it’s pretty trivial to put together). The guy bought all the bits from IKEA for $22. But obviously we don’t live in America, and also I needed to double up on some of the parts due to my dual monitor setup, so here is the full parts list, with links to the UK IKEA website:

Also, you will need to find 4 screws to screw the brackets into the table legs because annoyingly, the brackets have holes drilled, but no screws to go in said holes.

Grand total: £29 (including £10 postage)

Oh and don’t forget, you’ll probably want some kind of chair to give your legs a rest every now and then (yes it’s hilarious that Tom still has a chair etc, but it does come in handy, even if it’s just for 2×30 minute spells during the day). I got a bar stool with a back from Barnitts :)

This concludes my blog post about standing desks. Please add photos of your sweet, customised desks below in the comments.

Just kidding.

About usIsotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.


Why mobile last?

In his post Mobile Last? Jonathan Stark recounts his experiences at a recent hackathon, where teams were given 48 hours to build an innovative web application. While he ensured as usual that the site he built looked and worked great on any device, he was dismayed that “only one of the top ten winning entries was even a little bit mobile friendly.” He concludes that “the vast majority of web professionals have not truly embraced mobile.”

Jonathan points out, correctly, that mobile devices are already the default connected device for most people, and that poor mobile experiences are driving users towards native apps. After all, he says, working mobile-first is not that hard, and asks:

Are you working mobile-first? If not, why not? If you are working mobile-first, how do you like it? What were the biggest challenges you faced in making the transition from desktop? What platforms are you targeting on mobile? Web? Native iOS/Android? Something else?

I thought I’d write a quick response.

I’d guess the top reason why front-end web developers are not working mobile-first is that they are usually not the visual designers for the sites they’re building, and the designs they receive from the visual designers will usually be desktop designs. Visual designers:

  • are likely to be more set in their ways of designing for the desktop, and unaware of the “mobile first” philosophy
  • are working in tools that are not responsive (such as Photoshop), and creating mobile mockups as well means a doubling of effort

Clients are also slow to adapt. Unless they are conceiving of their project as primarily a mobile site/app, they’ll expect to be sold the design on the strength of a desktop mockup. This is what most visual designers are used to delivering.

Finally, the front-end developer and everyone else on the team will be working on desktop systems, where it’s far easier to view the work in progress in desktop browsers. It takes extra effort to view the site in a mobile device or emulation thereof.

I’m usually the front-end developer in this equation. I am given a clear visual spec for the desktop, and it’s left up to me to make it responsive. It’s usually not that difficult, but it means starting by implementing the visual spec I have, i.e. the desktop design, and adapting it via media queries afterwards, resulting in a “mobile last” process.

Mobile last is somewhat inefficient, as it means redefining many rules. (It’s “subtractive”, where mobile first is “additive”.) But in my experience it’s not that bad. In my 2 most recent projects the responsive.less include file (containing all the styles dependent on media queries) accounted for only 179 of 1492 lines of CSS (11%) and 641 of 5047 (12%), and adds at most 3 or 4 extra days. It’s also a conceptually simple way of working. The “layering” of a mobile first stylesheet can make the stylesheet a lot harder to interpret and maintain.

I’m currently implementing quite a complex visual design, provided to me, as usual, as desktop mockups. I have been attempting to follow a mobile first approach, but have found it extremely difficult, given that I don’t know at the outset what a polished look will be at mobile sizes (since I don’t have mockups for it). I’ve ended up following a mobile-first approach in some areas of the CSS, and mobile-last in others. Rather than a single responsive.less include right at the end, every one of the 20-odd Less files is riddled with media queries. The whole thing is, I have to admit, a lot more complex and confusing.

If I were starting a new project where I am also the visual designer, or a project where the visual design is relatively simple, then I’d probably work mobile-first. (Photoshop plays quite a small role in such cases, as my ideal process is to do much of the design in the browser.) But that, alas, wouldn’t be a typical project.

I hope this provides some insights into why so many developers are currently not yet working mobile first. What are your experiences? Let me know in the comments below or @fjordaan

On December 11 Jonathan will be redesigning his personal site in front of a live audience – you can sign up for the webcast here. I’m sure it will be a very interesting demonstration of mobile-first development.

My experiences are with websites and web apps, rather than native iOS or Android. I test on iPhone and Android smartphones, 7″ tablets and 10″ tablets (both iOS and Android). I also use the “Responsive Design View” on Firefox and Chrome’s mobile device emulation.

About usIsotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.


There is a new version of gunicorn, 19.0 which has a couple of significant changes, including some interesting workers (gthread and gaiohttp) and actually responding to signals properly, which will make it work with Heroku.

The HTTP RFC, 2616, is now officially obsolete. It has been replaced by a bunch of RFCs from 7230 to 7235, covering different parts of the specification. The new RFCs look loads better, and it’s worth having a look through them to get familiar with them.

Some kind person has produced a recommended set of SSL directives for common webservers, which provide an A+ on the SSL Labs test, while still supporting older IEs. We’ve struggled to find a decent config for SSL that provides broad browser support, whilst also having the best levels of encryption, so this is very useful.

A few people are still struggling with Git.  There are lots of git tutorials around the Internet, but this one from Git Tower looks like it might be the best for the complete beginner. You know it’s for noobs, of course, because they make a client for the Mac :)

I haven’t seen a lot of noise about this, but the EU has outlawed pre-ticked checkboxes.  We have always recommended that these are not used, since they are evil UX, but now there’s an argument that might persuade everyone.

Here is a really nice post about splitting user stories. I think we are pretty good at this anyhow, but this is a nice way of describing the approach.

@monkchips gave a talk at IBM Impact about the effect of Mobile First. I think we’re on the right page with most of these things, but it’s interesting to see mobile called-out as one of the key drivers for these changes.

I’d not come across the REST Cookbook before, but here is a decent summary of how to treat PUT vs POST when designing RESTful APIs.

Fastly have produced a spectacularly detailed article about how to get tracking cookies working with Varnish.  This is very relevant to consumer facing projects.

This post from Thought Works is absolutely spot on, and I think accurately describes an important aspect of testing The Software Testing Cupcake.

As an example for how to make unit tests less fragile, this is a decent description of how to isolate tests, which is a key technique.

The examples are Ruby, but the principle is valid everywhere. Still on unit testing, Facebook have open sourced a Javascript unit testing framework called Jest. It looks really very good.

A nice implementation of “sudo mode” for Django. This ensures the user has recently entered their password, and is suitable for protecting particularly valuable assets in a web application like profile views or stored card payments.

If you are using Redis directly from Python, rather than through Django’s cache wrappers, then HOT Redis looks useful. This provides atomic operations for compound Python types stored within Redis.

Selecting off-the-shelf software (or, how to avoid buying a lemon)

Here at Isotoma we often get asked what the differences are between off-the-shelf and bespoke software, and how to decide which is the right approach for a given situation.

Generally software that has been written specifically for you (by companies like us) is referred to as “custom” or “bespoke”, while software that is customised, configured and deployed to many customers is referred to as “boxed”, “off-the-shelf” or “COTS” (Commercial Off-The-Shelf).

Isotoma is a bespoke software development company. This means that we take generic frameworks and build applications tailored to our customers’ needs on top of those frameworks, rather than having a single product of our own that meets a particular need that we resell to our customers (for example a content management system or an ecommerce system).

We’re very upfront in our answer to the “bespoke or off-the-shelf” question; if there’s off-the-shelf software that meets your needs we strongly recommend you go that route. While bespoke software brings huge benefits in the right situation it also has limitations, complexities and costs. You need to be sure that it’s right for you before going down the bespoke road.

Equally you need to be sure that you’re assessing the right things (not just features!) when considering off-the-shelf software for your project. All software investments bring risks to an organisation, and the risks of off-the-shelf can often be subtler and harder to spot, yet equally as impactful, as those of bespoke software.

At its best buying off-the-shelf software can be an extremely cost effective way of getting the features you needs and of future-proofing your software investment. But at its worst it can be a way of tying yourself in to a closed environment that is expensive to customise and hard to migrate away from.

Any off-the-shelf product works best when the requirements it serves are shared by lots of organisations. There the economy of scale means that every customer gets a great product without having to pay for the development of all the features. We wouldn’t ever suggest, for example, building a blogging platform from scratch, nor a word processor or an accounting package. These are all problems shared by countless individuals and organisations around the globe where product companies are easily able to serve everyone’s needs.

However, whenever an organisation’s requirements for the product diverge substantially from the needs of the primary customers problems very rapidly occur, meaning either increased cost, compromised experience or, more often than not, both.

When selecting a product consider how much customisation you will need to make the product really sing for your organisation. And consider how you would be affected if you simply couldn’t have half of those customisations you’re imagining (regardless of whether this was through technical limitations or lack of budget for the necessary changes).

Next it’s important to think about the existing customer base of the product. If the product has thousands of customers, many like you, all successfully using it then you can probably feel comfortable that a) the product really is likely to meet your needs and b) the team behind it are likely to be around and supporting the product for the lifetime of your use of it.

Taking on very niche off-the-shelf products or off-the-shelf products from small suppliers can be extremely risky. The worst case is that under the hood each installation is in fact an alteration of the underlying base product, the “licence fee” being used to cover the cost of the developers implementing the features that the salesman told you already existed. Here you risk expensive maintenance costs, delays in implementation and a high rate of bugs, as you were sold something off-the-shelf (and had assumed the time scales that go with that) yet in fact custom software is being developed on your behalf at cut rates and at double quick time.

You should definitely carefully assess products with very small numbers of installed customers. Unless you are extremely confident that it meets your needs entirely unchanged or you are happy to invest in the development of the product in the long term they should probably be avoided. These are often only products in the sense of “hey, we’ve made this thing for so and so, I bet others like them will need it too.”

Potentially as troublesome is the risk of delays or exorbitant fees if the resources of the team behind the product become stretched. If you need to call upon consultancy or professional services will the team be able to support you in a timely and cost effective manner?

Finally it’s worth considering the impact on your business processes. In general products work well for organisations that are just setting out, as any limitations imposed by a boxed product do not conflict directly with the way the organisation wants to do business. In addition, very young organisations do not entirely understand their requirements and so having a set of predefined features given to them allows them to develop their own business processes alongside those features.

Older organisations tend to struggle with products, particularly at the cheaper (and less customisable) end of the spectrum. They have a clear understanding of their requirements and are looking to create efficiency within their organisation. It is no longer acceptable to have predefined solutions forced upon the business, as it is exactly those predefined solutions that are causing the inefficiencies that the company is trying to drive out. In this case bespoke software has huge advantages, being tailored exactly to the organisation’s understanding of its own needs.

So you must consider the scope and scale of changes to your business processes that the new software will bring. How substantial are they? Are you ready for them? Will your users support you in making them? And can you drive them through at the same time as running a new software project?

With all the above in mind, how do you make sure that an off-the-shelf product will meet your needs and that you’re not investing in a lemon?

Here are some questions worth asking during the selection process. There are no right answers, but by having the conversation with potential vendors at this stage you may flush out some problems that you would otherwise have uncovered much later in the project:

  • How many paying customers do you already have for the version you are selling to me?
  • How many paying customers of our installation size and type do you have?
  • Can I talk to at least 2 of your customers about their experiences of the product and your services?
  • What is the average customisation cost of an implementation?
  • How much do you charge per day/per hour for professional services?
  • What is the average turnaround time for professional services requests?
  • Is there a VAR (Value Added Reseller) or partner network around the product that I can tap into if I need extra help with customisation/integration?
  • Is there an API that I can build on and use to integrate with our other systems? (if yes, can we see the documentation before we sign up?)
  • How easy is it for me to export my data from your product in a way that’s possible to import into an alternative?
  • Can I host the product myself? If so, what are the minimum hardware specifications for an installation of our size and type, and who is responsible for performance issues should they occur?
  • Do you offer a support agreement of any sort? What SLA options do you offer and how much might a support agreement cost?
  • When was the last major version released, and how many customers upgraded (and how many do you still have running the old version)?
  • What was the average upgrade cost when customers moved to this major version?
  • When do you plan to release the next major version?
  • Is there a user group for the product that I can talk to or join?
  • How are new features for future versions decided?
  • Do features I request and pay for get rolled out to other customers in future versions?
  • And perhaps most importantly: what is the roadmap for the product over the next 18 to 24 months?

Of course, there are equally detailed lists of questions you should be asking when selecting a bespoke development partner or when selecting an open source project, but both of those are for another day. Hopefully this list at least helps a little with the decision making process.

About usIsotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

The problem with Backing Stores, or what is NoSQL and why would you use it anyway

Durability is something that you normally want somewhere in a system: where the data will survive reboots, crashes, and other sorts of things that routinely happen to real world systems.

Over the many years that I have worked in system design, there has been a recurring thorny problem of how to handle this durable data.  What this means in practice when building a new system is the question “what should we use as our backing store?”.  Backing stores are often called “databases”, but everyone has a different view of what database means, so I’ll try and avoid it for now.

In a perfect world a backing store would be:

  • Correct
  • Quick
  • Always available
  • Geographically distributed
  • Highly scalable

While we can do these things quite easily these days with the stateless parts of an application, doing them with durable data is non-trivial. In fact, in the general case, it’s impossible to do all of these things at once (The CAP theorem describes this quite well).

This has always been a challenge, but as applications move onto the Internet, and as businesses become more geographically distributed, the problem has become more acute.

Relational databases (RDBMSes) have been around a very long time, but they’re not the only kind of database you can use. There have always been other kinds of store around, but the so-called NoSQL Movement has had particular prominence recently. This champions the use of new backing stores not based on the relational design, and not using SQL as a language. Many of these have radically different designs from the sort of RDBMS system that has been widely used for the last 30 years.

When and how to use NoSQL systems is a fascinating question, and I put forward our thinking on this. As always, it’s kind of complicated.  It certainly isn’t the case that throwing out an RDBMS and sticking in Mongo will make your application awesome.

Although they are lumped together as “NoSQL”, this is not actually a useful definition, because there is very little that all of these have in common. Instead I suggest that there are these types of NoSQL backing store available to us right now:

  • Document stores – MongoDB, XML databases, ZODB
  • Graph databases – Neo4j
  • Key/value stores – Dynamo, BigTable, Cassandra, Redis, Riak, Couch

These are so different from each other that lumping them in to the same category together is really quite unhelpful.

Graph databases

Graph databases have some very specific use cases, for which they are excellent, and probably a lot of utility elsewhere. However, for our purposes they’re not something we’d consider generally, and I’ll not say any more about them here.

Document stores

I am pretty firmly in the camp that Document stores, such as MongoDB, should never be used generally either (for which I will undoubtedly catch some flak). I have a lot of experience with document databases, particularly ZODB and dbxml, and I know whereof I speak.

These databases store “documents” as schema-less objects. What we mean by a “document” here is something that is:

  • self-contained
  • always required in it’s entirety
  • more valuable than the links between documents or it’s metadata.

My experience is that although often you may think you have documents in your system, in practice this is rarely the case, and it certainly won’t continue to be the case. Often you start with documents, but over time you gain more and more references between documents, and then you gain records and and all sorts of other things.

Document stores are poor at handling references, and because of the requirement to retrieve things in their entirety you denormalise a lot. The end result of this is loss of consistency, and eventually doom with no way of recovering consistency.

We do not recommend document stores in the general case.

Key/value stores

These are the really interesting kind of NoSQL database, and I think these have a real general potential when held up against the RDBMS options.  However, there is no magic bullet and you need to choose when to use them carefully.

You have to be careful when deciding to build something without an RDBMS. An RDBMS delivers a huge amount of value in a number of areas, and for all sorts of reasons. Many of the reasons are not because the RDBMS architecture is necessarily better but because they are old, well-supported and well-understood.

For example, PostgreSQL (our RDBMS of choice):

  • has mature software libraries for all platforms
  • has well-understood semantics for backup and restore, which work reliably
  • has mature online backup options
  • has had decades of performance engineering
  • has well understood load and performance characteristics
  • has good operational tooling
  • is well understood by many developers

These are significant advantages over newer stores, even if they might technically be better in specific use cases.

All that said, there are some definite reasons you might consider using a key/value store instead of an RDBMS.

Reason 1: Performance

Key/value stores often naively appear more performant than RDBMS products, and you can see some spectacular performance figures in direct comparisons. However, none of them really provide magic performance increases over RDBMS systems, what they do is provide different tradeoffs. You need to decide where your performance tradeoffs lie for your particular system.

In practice what key/value stores mostly do is provide some form of precomputed cache of your data, by making it easy (or even mandatory) to denormalize your data, and by providing the performance characteristics to make pre-computation reasonable.

If you have a key/value store that has high write throughput characteristics, and you write denormalized data into it in a read-friendly manner then what you are actually doing is precomputing values. This is basically Just A Cache. Although it’s a pattern that is often facilitated by various NoSQL solutions, it doesn’t depend on them.

RDBMS products are optimised for correctness and query performance and  write performance takes second place to these.  This means they are often not a good place to implement a pre-computed cache (where you often write values you never read).

It’s not insane to combine an RDBMS as your master source of data with something like Redis as an intermediate cache.  This can give you most of the advantages of a completely NoSQL solution, without throwing out all of the advantages of the RDBMS backing store, and it’s something we do a lot.

Reason 2: Distributed datastores

If you need your data to be highly available and distributed (particularly geographically) then an RDBMS is probably a poor choice. It’s just very difficult to do this reliably and you often have to make some very painful and hard-to-predict tradeoffs in application design, user interface and operational procedures.

Some of these key/value stores (particularly Riak) can really deliver in this environment, but there are a few things you need to consider before throwing out the RDBMS completely.

Availability is often a tradeoff one can sensibly make.  When you understand quite what this means in terms of cost, both in design and operational support (all of these vary depending on the choices you make), it is often the right tradeoff to tolerate some downtime occasionally.  In practice a system that works brilliantly almost all of the time, but goes down in exceptional circumstances, is generally better than one that is in some ways worse all of the time.

If you really do need high availability though, it is still worth considering a single RDBMS in one physical location with distributed caches (just as with the performance option above).  Distribute your caches geographically, offload work to them and use queue-based fanout on write. This gives you eventual consistency, whilst still having an RDBMS at the core.

This can make sense if your application has relatively low write throughput, because all writes can be sent to the single location RDBMS, but be prepared for read-after-write race conditions. Solutions to this tend to be pretty crufty.

Reason 3: Application semantics vs SQL

NoSQL databases tend not to have an abstraction like SQL. SQL is decent in its core areas, but it is often really hard to encapsulate some important application semantics in SQL.

A good example of this is asynchronous access to data as parts of calculations. It’s not uncommon to need to query external services, but SQL really isn’t set up for this. Although there are some hacky workarounds if you have a microservice architecture you may find SQL really doesn’t do what you need.

Another example is staleness policies.  These are particularly problematic when you have distributed systems with parts implemented in other languages such as Javascript, for example if your client is a browser or a mobile application and it encapsulates some business logic.

Endpoint caches in browsers and mobile apps need to represent the same staleness policies you might have in your backing store and you end up implementing the same staleness policies in Javascript and then again in SQL, and maintaining them. These are hard to maintain and test at the best of times. If you can implement them in fewer places, or fewer languages, that is a significant advantage.

In addition, it is a practical case that we’re not all SQL gurus. Having something that is suboptimal in some cases but where we are practically able to exploit it more cheaply is a rational economic tradeoff.  It may make sense to use a key/value store just because of the different semantics it provides – but be aware of how much you are losing without including an RDBMS, and don’t be surprised if you end up reintroducing one later as a platform for analysis of your key/value data.

Reason 4: Load patterns

NoSQL systems can exhibit very different performance characteristics from SQL systems under real loads. Having some choice in where load falls in a system is sometimes useful.

For example, if you have something that scales front-end webservers horizontally easily, but you only have one datastore, it can be really useful to have the load occur on the application servers rather than the datastore – because then you can distribute load much more easily.

Although this is potentially less efficient, it’s very easy and often cheap to spin up more application servers at times of high load than it is to scale a database server on the fly.

Also, SQL databases tend to have far better read performance than write performance, so fan-out on write (where you might have 90% writes to 10% reads as a typical load pattern) is probably better implemented using a different backing store that has different read/write performance characteristics.

Which backing store to use, and how to use it, is the kind of decision that can have huge ramifications for every part of a system.  This post has only had an opportunity to scratch the surface of this subject and I know I’ve not given some parts of it the justice they deserve – but hopefully it’s clear that every decision has tradeoffs and there is no right answer for every system.

About us: Isotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

A Different View (part 2)

In the previous post, we saw how we could use a single, raw query in Django to combat ORMs’ tendency to generate query explosions within loops. We used raw() with joins and some column renaming to ensure all the data we needed came back in one go. We had to modify the property names in the template slightly (e.g. book.jacket.image became book.jacket_image) and the result was a RawQuerySet which had a fair number of limitations but, we avoided the typical order-of-magnitude increase in query count.

Super models

supermodel - by Jamie Beck and Kevin Burg

There is a way we can use a raw query, to get all the benefits described in the previous post, and return a real QuerySet. We need a Django model and so need an underlying table. A much underused feature of SQL databases is the view. Views are virtual tables derived from queries. Most are currently implemented as read-only, but they provide many benefits – again another blog post would be needed to explain them all – but they include:

  • adding a level of abstraction, effectively an API (which is also available to other applications)
  • protecting the application from underlying table and column changes
  • giving fine-grained control of permissions, especially row-level
  • providing a way to efficiently add calculated columns such as line_total = quantity * price
  • avoiding de-normalising things which should remain derived from other data
  • avoiding the not-insubstantial cost of repeatedly sending the same complicated queries over the network
  • enabling the database to pre-compile and cache the privilege-checks and query plans

So we could define a database view (with a few extra foreign-key columns for use later):

SELECT, AS book_id,
  book.title, AS jacket_id,
  jacket.image AS jacket_image, AS author_id AS author_name,

and then map a Django model to it, using Meta.db_table='vbook', and set the Meta.managed = False to tell Django not to maintain the table definition.

(note again the use of natural join for simplicity (and short table names) and that they don’t actually work as-is with Django’s table/column naming)

We’ve done this in the past for some of our projects, and shoe-horning the view-creation script into Django/buildout was always the fiddly bit.

Recently, we used django-postgres which makes the model-view mapping a bit simpler. You subclass pg.View instead of models.Model and set the sql property to your query, e.g.

class vBook(pg.View):    title = models.CharField(max_length=100)
    jacket_image = models.ImageField(null=True)
    author_name = models.CharField(max_length=100)
    shelf_position = models.IntegerField()
    sql = BOOK_DETAILS sync_pgviews then creates the views for the models in the specified application. It’s quite a lightweight tool, and simply builds and issues the SQL CREATE VIEW statements needed to wrap each view-based model’s sql query.

(There is a Kickstarter project under way which should also add view handling to Django)

book_listingWe now have an almost fully-fledged Django model. We can query the objects against an effectively de-normalised table, while still retaining an essentially normalised schema. We can chain refinement methods to the resulting QuerySet to further filter and order the objects, including select_related() if necessary (although it would always be preferable, given time, to put the extra join into the view). We can have custom object managers, Meta options, introspection and all the other features of a Django model, even some read-only admin which would be good for reporting.

Note that we removed the

WHERE shelf.position <= 10

from the view definition. We can define the view without such filters and leave it to the Django application to apply them, e.g.

vbooks = vBook.objects.filter(shelf__position__lte = 10)

This gives a more flexible model, more akin to the original Book model, although you could keep the WHERE clauses in if you wanted to logically partition a table into multiple views/models.


Another great reason for using such view-based models is to postpone decisions about optimising bits of the system before we know anything about it. As an example, let’s say that a book needs to have an average rating that should be calculated from the entries in a ratings table. This could accurately be calculated using SQL’s AVG function, but that might be expensive and so we might need to pre-calculate it and possibly store it outside of the database. That would require us to design, build, test, document, and maintain:

  • an external data source capable of handling the unknown load and volume
  • a way to link a book to the external data source, possibly using new drivers
  • a way of updating the external data source for a book, duplicating the rating table entries
  • a way to synchronise the two data storage systems, possibly involving cron jobs and queues
  • a way to build and sustain the data source and links in whatever production environment we use, e.g. AWS

All of this would add complexity, up-front R&D and ongoing maintenance. The results would not be as fresh or reliable as the database results, and not necessarily any faster. Using AVG in a view leaves us scope to replace how it is calculated at a later date, once we know more about the performance.

Often, caching the book results using Django’s cache is enough and we can stick with the basic approach. Premature optimisation could well be a huge waste of effort, and the view-based models let us defer those decisions and get started very quickly.


Picasso - Skull and Pitcher

Defining views early in a project could also be a way to postpone building complex parts of the system. Let’s say a book has a complicated ‘cost’ attribute and we’re not sure yet how it will be calculated or where all the data will come from but we need to start to display it during the initial iterations. We could add the column to the book view on day one and worry about where it comes from later. e.g.

SELECT, AS book_id,
  6.283185 AS cost, --todo: use a cost function here instead  ...

And then vbook.cost can be used in the knowledge that the reference won’t change. Also, if the cost calculation is defined within the view and it needs to change post-production, the view can be recreated while the application is running with no migration or down-time.

More modelling

We can further enhance the view-based model and add relationships to give us back the all-too-convenient dot-notation – still useful if we’re careful to use it outside of large loops. We should make sure any relationships don’t give Django the impression that it needs to try to maintain integrity – it doesn’t need to since the underlying table only has virtual rows. We can do this using on_delete=models.DO_NOTHING, e.g.

class vBook(pg.View):
    title = models.CharField(max_length=100)
    jacket_image = models.ImageField(null=True)
    author_name = models.CharField(max_length=100)
    shelf_position = models.IntegerField()
    author = models.ForeignKey(Author,                               on_delete=models.DO_NOTHING)             #needs author_id to be returned by the sql 
    sql = BOOK_DETAILS

These are, of course, complete Django relationships and so we can access them from any direction, e.g.


would return an author’s books as vBook objects using a single extra query. You could even go further and use select_related('vbook') when getting the author. Django treats these models just like any other.

We can’t save or create using such view-based models – the database will reject that for anything but the simplest views, so we still use the underlying table-based models to do that. But we can link the models together, with a one-to-one relationship, to make things easier, e.g.

class vBook(pg.View):
    book = models.OneToOneField(Book,                                on_delete=models.DO_NOTHING)           #needs book_id to be returned by the sql    title = models.CharField(max_length=100)
    jacket_image = models.ImageField(null=True)
    author_name = models.CharField(max_length=100)
    shelf_position = models.IntegerField()
    author = models.ForeignKey(Author,
             #needs author_id to be returned by the sql
    sql = BOOK_DETAILS

Now, given a vbook instance, we can update the underlying book, for example:'A Tale of Two Cities'

And also, given a book instance, we can ask for all the pre-joined book information:


There are still some questions about how best to use these models, e.g. how to share methods with underlying table-based models and how best to name fields from other models.

Deferred Optimisation

Once we’ve defined view-based models to bring together the core concepts of the system, we can build the application to use them for reading and the base-table models for writing. We then have a variety of options to greatly optimise the system at a later date.

Here are some ways we can optimise database views. The important points are:

  • just by using views to join tables, we’ve already made great savings in the number of database queries needed
  • the ideas below can be applied later, once we know if and where further optimisation is needed
  • none of these would require any changes to the application code using the data: they’re true implementation changes, and database views give us such a clear separation of interface and implementation that the application could stay running!

Pull from other tables

For example, at a later point, we could modify the view to join to a table containing pre-calculated ratings and return that instead of the AVG sub-select.

Pull from external systems

We could use PostgreSQL’s foreign data wrappers to join to external data sources. It might be useful in itself to map a Django model to such a foreign table without an intermediate view, if all the data is foreign.

Use database functions

Database functions can be written in a number of languages including PL/pgSQL, Python, Ruby and Javascript. They are pre-compiled and are very efficient not least because they can access the data without it needing to be returned first and are in situ so can filter out unwanted results.

Load-balance across read-only replicas

PostgreSQL has the concept of standby/slave servers that can be kept synchronised with a master server. These can be made available for read-only queries to spread the load. Since we can be sure that view-based models are only used for reading, we could use Django’s database router to route all queries against those models to the slave servers.


Views are virtual, except when they’re not. PostgreSQL 9.3 added materialised views.


These are still declared as derived tables but the data in them is physically stored for much faster access: the results are effectively cached in the database. These are useful if the application can handle some staleness, and the only change necessary is in the way the view is declared – the application needn’t know or care. Such views can be refreshed periodically (although before 9.4 the refresh takes out a table lock). Indexes can be added to materialised views, potentially giving massive performance increases, especially for calculated columns.

Although I think materialisation is a last resort, it’s a very powerful one. I’ve added a MaterializedView class to isotoma/django-postgres which should help create them, though it could do with some more testing and options to control refreshing.

This really gives the best of both worlds: a single query can provide Django objects with the speed expected from de-normalised storage, but derived by the database from a normalised schema.


Django’s ORM makes accessing related objects simple and convenient. However, when using an ORM, accessing related objects in loops often leads to an explosion of supporting queries, which can go unnoticed during development but which can lead to poor performance. Django’s ORM has some methods that try to alleviate the problem but they have limitations. We can use raw SQL to efficiently join related information in the database to avoid these query explosions, as well as giving us more powerful ways to group and summarise data. I think these SQL queries should be given more prominence in our projects.

We can go further and push the raw SQL into the database by declaring virtual tables (database views). We can then map Django models onto these virtual tables to also give an extra layer of abstraction on top of the base models. This lets us defer implementation decisions and provides lots of ways to optimise the system at a later stage, once we know more about its performance, without affecting the application code.

A Different View (materialised)

About us: Isotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

A Different View

A Different View

One of the consequences of the mismatches between databases and ORMs is that iterating over objects can generate explosions of database queries. Often, these explosions go unnoticed because the individual queries are fast and the number of concurrent users is small, especially during development. The explosions are sometimes hard to detect, either because developers don’t look for them or the tools that can expose the explosions don’t report everything (e.g. the Django debug toolbar doesn’t report the queries generated by ajax calls).


debug_toolbarDjango does a good job of lazily evaluating what it can, but the simple act of using the convenient dot-notation inside a loop can turn a single database query into a cascade of supporting queries.

For example, given this book model:

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.ForeignKey(Author)
    jacket = models.ForeignKey(Jacket)
    shelf = models.ForeignKey(Shelf)

and this books query:

books = Book.objects.filter(shelf__position__lte = 10)

which returns 10 rows. Then this template:

{% for book in books %}
  {{ book.jacket.image }} {{ book.title }} 
  by {{ }}
{% endfor %}

book_listing_simplewould issue a single query to get all 10 rows, but would then generate 2 additional queries per iteration to get the jacket and the author for each book. So a total of 21 queries. You really need to use the Django debug toolbar to realise this – install it now and keep your eye on it until the very end of the project: it only takes a late-breaking, well-intentioned dot to cause an explosion.

A good way to detect when explosions happen is to check the number of queries in your unit tests.

self.assertNumQueries(self.BOOKS_QUERY_COUNT, self.client.get, reverse('books'))

Add these at the start of your project. It means you have to keep increasing the counts while developing, but it does make it obvious when something has exploded.

Remember, every one of those queries involves the following steps:

  • create a connection to the database (tcp/ip – handshake – username, password) (set CONN_MAX_AGE in Django 1.6 to help reduce this overhead)
  • send the SQL query over the network
  • parse the SQL query
  • check the table and column read/write permissions
  • optimise the query plan based on the latest statistics
  • start a transaction
  • perform the query (checking for other users’ locks)
  • return the resulting metadata and rows over the network
  • commit the transaction
  • close the connection


A far better approach is to get the database to do the work. It can join together the jacket and author information much more efficiently (orders of magnitude more efficiently – but that’s another topic).

Django added select_related() to try to address this problem. Given a field name, it passes the required table to the database to be joined with the original queryset, e.g.

books = Book.objects.filter(shelf__position__lte = 10

It is limited though, since it only works with some relationships in some directions, and it pulls in all the columns which is usually very wasteful, especially if large text fields are present. Adding defer() or only() to work around this can help, but isn’t pretty, e.g.

books = Book.objects.filter(shelf__position__lte = 10
                           ).select_related('jacket', 'author'

prefetch_related() was added later to help with more complex relationships but still issues multiple queries and does the ‘joining’ in Python. Besides that, it also has a number of caveats and special interactions with other methods. Trying to combine these with annotations for aggregate counts and such would lead to some fragile, hard-to-construct and, I think, hard-to-read syntax that can fail even for slightly complicated queries.

So instead, we could issue the following query:

books = Book.objects.raw("""
  SELECT, book.title,
    jacket.image AS jacket_image, AS author_name
  FROM book
  WHERE shelf.position <= 10

(note: I’ll use natural join throughout for simplicity, and short table names for that matter, but they don’t actually work as-is with Django’s table/column naming)

And then, a slightly modified template:

{% for book in books %}
  {{ book.jacket_image }} {{ book.title }} 
  by {{ book.author_name }}
{% endfor %}

would only issue 1 query, regardless of the number of books.

Adding further related information to the original template will dramatically increase the number of queries, whereas joining another table to the raw SQL version will add none. Similarly, returning 20 books instead of 10 would just about double the number of queries for the original version (so 41 for our example), whereas the raw one would still only issue a single query.

As well as avoiding query explosions, there are other benefits to using raw SQL. A full explanation needs another blog post, but some of the major ones are:

  • it’s the only way to do anything but the simplest of aggregation queries.
  • it only returns what is required (assuming you avoid SELECT *). Pulling every column, including large blobs, over the network when they’re not referenced wastes CPU, temporary sort/cache space and network bandwidth.
  • you can use easy-to-read comparisons (e.g. position <= 10 instead of shelf__position__lte = 10).
  • if you need/want, you can use database-specific features, such as function calls, range queries, NULL ordering, etc.
  • checking the query plan is easier – there’s no need to set a breakpoint and then print the queryset.query.
  • it returns consistent results, whereas multiple queries, even in the same transaction, can see different data depending on what’s been committed by concurrent users (unless we used the serializable isolation level, which we don’t).


I’ve tried a number of ways to embed SQL queries into Django modules:

FROM ...

is jarring.

...raw("""SELECT ...
          FROM ...
          WHERE ...

is better, but adds too much whitespace and makes it harder to edit.

...raw("""SELECT ..."""
       """FROM ..."""
       """WHERE ...""")

avoids the extra whitespace but doesn’t ensure enough whitespace unless you’re careful to add a space at the end of each line.

None are great and none of them provide an easy way to copy and paste the query, which is necessary to tune it and check its plan in a database client.


For a better approach, I suggest creating an sql directory (alongside – it is that important) and create python modules in there to hold the named query declarations, e.g. sql/

  jacket.image AS jacket_image, AS author_name
  shelf.position <= 10

then it can be called like this:

from sql.books import BOOK_DETAILS
books = Book.objects.raw(BOOK_DETAILS)

Clicking the BOOK_DETAILS reference in any decent IDE will take you to the query. Now that the query has a bit more respect it can more easily be copied and pasted into other tools, and formatted as you’d like, and re-used without repetition.

One more note about formatting. Although you could put Python comments before the declaration, I suggest keeping the query comments inside the query because they may be useful on the server, e.g. when viewing the database logs. So instead of:

#Get top 10 book summaries
#(including author and jacket details)


Get top 10 book summaries
(including author and jacket details)


There are some downsides with raw(), however.

  • You need some SQL knowledge of course, and an understanding of how Django links tables together, but I think you should have that anyway.
  • If your SQL does introduce some non-standard syntax (which shouldn’t be the case for straightforward joins) it could make your application less portable.  Although I’d argue that some features are worth that sacrifice, and would hope the likelihood of switching to a different database would be low if you’re already using a good one.
  • Even though raw() ensures only 1 query is used, that query must still be sent over the network and parsed every time it’s run.
  • The real problem is that raw() returns a RawQuerySet, not a QuerySet, so no further refinement methods can be applied (e.g. filter, order_by) – though arguably they’re better off being added to the SQL.

We can do better, as I’ll explain in part 2

About us: Isotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

Using mock.patch in automated unit testing

Mocking is a critical technique for automated testing. It allows you to isolate the code you are testing, which means you test what you think are testing. It also makes tests less fragile because it removes unexpected dependencies.

However, creating your own mocks by hand is fiddly, and some things are quite difficult to mock unless you are a metaprogramming wizard. Thankfully Michael Foord has written a mock module, which automates a lot of this work for you, and it’s awesome. It’s included in Python 3, and is easily installable in Python 2.

Since I’ve just written a test case using mock.patch, I thought I could walk through the process of how I approached writing the test case and it might be useful for anyone who hasn’t come across this.

It is important to decide when you approach writing an automated test what level of the system you intend to test. If you think it would be more useful to test an orchestration of several components then that is an integration test of some form and not a unit test. I’d suggest you should still write unit tests where it makes sense for this too, but then add in a sensible sprinkling of integration tests that ensure your moving parts are correctly connected.

Mocks can be useful for integration tests too, however the bigger the subsystem you are mocking the more likely it is that you want to build your own “fake” for the entire subsystem.

You should design fake implementations like this as part of your architecture, and consider them when factoring and refactoring. Often the faking requirements can drive out some real underlying architectural requirements that are not clear otherwise.

Whereas unit tests should test very limited functionality, I think integration tests should be much more like smoke tests and exercise a lot of functionality at once. You aren’t interested in isolating specific behaviour, you want to make it break. If an integration test fails, and no unit tests fail, you have a potential hotspot for adding additional unit tests.

Anway, my example here is a Unit Test. What that means is we only want to test the code inside the single function being tested. We don’t want to actually call any other functions outside the unit under test. Hence mocking: we want to replace all function calls and external objects inside the unit under test with mocks, and then ensure they were called with the expected arguments.

Here is the code I need to test, specifically the ‘fetch’ method of this class:

class CloudImage(object):
    __metaclass__ = abc.ABCMeta
    blocksize = 81920
    def __init__(self, pathname, release, arch):
        self.pathname = pathname
        self.release = release
        self.arch = arch
        self.remote_hash = None
        self.local_hash = None
    def remote_image_url(self):
        """ Return a complete url of the remote virtual machine image """
    def fetch(self):
        remote_url = self.remote_image_url()"Retrieving {0} to {1}".format(remote_url, self.pathname))
            response = urllib2.urlopen(remote_url)
        except urllib2.HTTPError:
            raise error.FetchFailedException("Unable to fetch {0}".format(remote_url))
        local = open(self.pathname, "w")
        while True:
            data =
            if not data:

I want to write a test case for the ‘fetch’ method. I have elided everything in the class that is not relevant to this example.

Looking at this function, I want to test that:

  1. The correct URL is opened
  2. If an HTTPError is raised, the correct exception is raised
  3. Open is called with the correct pathname, and is opened for writing
  4. Read is called successive times, and that everything returned is passed to local.write, until a False value is returned

I need to mock the following:

  1. self.remote_image_url()
  2. urllib2.urlopen()
  3. open()
  5. local.write()

This is an abstract base class, so we’re going to need a concrete implementation to test. In my test module therefore I have a concrete implementation to use. I’ve implemented the other abstract methods, but they’re not shown.

class MockCloudImage(base.CloudImage):
    def remote_image_url(self):
        return "remote_image_url"

Because there are other methods on this class I will also be testing, I create an instance of it in setUp as a property of my test case:

class TestCloudImage(unittest2.TestCase):

    def setUp(self):
        self.cloud_image = MockCloudImage("pathname", "release", "arch")

Now I can write my test methods.

I’ve mocked self.remote_image_url, now i need to mock urllib2.urlopen() and open(). The other things to mock are things returned from these mocks, so they’ll be automatically mocked.

Here’s the first test:

    def test_fetch(self, m_open, m_urlopen):
        m_urlopen().read.side_effect = ["foo", "bar", ""]
        self.assertEqual(m_open.call_args,'pathname', 'w'))
        self.assertEqual(m_open().write.call_args_list, ['foo'),'bar')])

The mock.patch decorators replace the specified functions with mock objects within the context of this function, and then unmock them afterwards. The mock objects are passed into your test, in the order in which the decorators are applied (bottom to top).

Now we need to make sure our read calls return something useful. Retrieving any property or method from a mock returns a new mock, and the new returned mock is consistently returned for that method. That means we can write:


To get the read call that will be made inside the function. We can then set its “side_effect” – what it does when called. In this case, we pass it an iterator and it will return each of those values on each call.

Now we call call our fetch method, which will terminate because read eventually returns an empty string.

Now we just need to check each of our methods was called with the appropriate arguments, and hopefully that’s pretty clear how that works from the code above. It’s important to understand the difference between:




The first is the arguments passed to “open(…)”. The second are the arguments passed to:

local = open(); local.write(...)

Another test method, testing the exception is now very similar:

    def test_fetch_httperror(self, m_open, m_urlopen):
        m_urlopen.side_effect = urllib2.HTTPError(*[None] * 5)
        self.assertRaises(error.FetchFailedException, self.cloud_image.fetch)

You can see I’ve created an instance of the HTTPError exception class (with dummy arguments), and this is the side_effect of calling urlopen().

Now we can assert our method raises the correct exception.

Hopefully you can see how the mock.patch decorator saved me a spectacular amount of grief.

If you need to, it can be used as a context manager as well, with “with”, giving similar behaviour. This is useful in setUp functions particularly, where you can use the with context manager to create a mocked closure used by the system under test only, and not applied globally.

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