Pragmatic Testing with GraphQL

We’ve been using GraphQL at OpenTable for a little over half a year now. I won’t go into detail as to why we started using it, but suffice to say, we really enjoyed creating our very first GraphQL endpoint. It eased a lot of the inconsistencies that we were experiencing with some of our REST-ful services.

This post assumes you have some experience building a GraphQL endpoint. For those of who you aren’t familiar with it, it feels a lot like having a querying language via an HTTP endpoint. If you want to try it for yourself, I recommend GitHub’s endpoint. To get started with GraphQL, the official documentation is a perfect place.

The Problem

Now, let’s get back to writing tests for an endpoint. When we were creating our first endpoint, we started facing regression testing problems while expanding our schema. It seemed our existing testing methods were ill-equipped to handle it.

Finding a good solution to this is important because I’ve had such a love–hate affair with testing. All tests are not created equal and a naive developer would say, “write a test in case something changes”. This certainly isn’t a good measure. I’ve also found that just attempting to write tests immediately without forethought can be a costly distraction if you grow your codebase.

The Approach

So we took a step back and looked at the code we were writing. Most of the connectors we wrote were fairly anemic. We didn’t have much logic for our API nor did we want any. We could have written unit tests for each parts of the schema but mocking portions of the system seemed more work than it was worth.

So where did we start? I personally enjoy the outside-in approach and started writing a few acceptance tests. So, we wrote a test for each query in the schema that we had. It would fire an HTTP request and expect a 200 status code and have no errors in the resulting JSON body. Thanks to GraphQL most errors are handily reported using this mechanism so we would be able to make a decent impact using little work.

As you can see, we found that this would be good enough at the time but eventually found that it would not scale very well. If you forget to update one of these queries, you could leave out a major section out accidentally. Being a big fan of automation, we wondered how we could scale this out.

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OpenTable's Global Hackathon 2017

Last week we were excited to kick-off our first OpenTable Global Hackathon, underway simultaneously in San Francisco, Los Angeles, Mumbai, Melbourne, and right here in London. Having personally never attended a hackathon before let alone helped organise one I was initially daunted, but with some careful planning, good suggestions from the team and a fair amount of making it up as we went along, the end result was quite a success.

This post discusses what format our hackathon took, what challenges we faced in coordinating across countries, the experience in the London office and what we learned.

The basic format

The hackathon was conceived in our San Francisco headquarters and could easily have been confined to that one office, but I was delighted to learn is was intended to be a global event from the outset. The basic format, described below, was however optimised for our SF office.

In the weeks leading up to the hackathon, individuals were asked to submit their hack proposals. Idea prompts were circulated such as “engages and delights diners” or “socially connected”, as well as the judging criteria; Originality, Feasibility, Likely to adopt, Fidelity of prototype, Business impact and Captivating presentation.

One week before the hackathon, our San Francisco office held a ‘happy-hour’ in which everyone taking part mingled and discussed ideas. This social event encouraged the developers, designers and product owners to self-organise into teams and submit their proposals in advance.

The hackathon itself was devised as a 2½ day event, running from Tuesday morning to Thursday lunchtime during normal working hours, with OpenTable providing breakfast and lunch. Live incidents or outages still had to be fixed, but otherwise the teams would be uninterrupted for the duration.

The hackathon concluded with each team having up to five minutes to present their hack to their colleagues and the judges. The awards were for the top three hacks, special CEO and CTO’s prizes, and an open vote - and generous cash prizes and gifts were up for grabs.

Tweaks for the London version and building enthusiasm

The big headache in London that we always have to live with is the eight hour time difference between London and San Francisco - and the same constraint obviously existed for the hackathon. Each team concluded that it wouldn’t be feasible to form teams across the offices so all our build-up and actual hacking remained independent. However we submitted our recorded presentations for the final judging along with the rest of the company.

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The goal driven organisation

Quarterly team goals are an effective way to establish organisational purpose, direction and alignment while supporting team agility. But be vigilant - they can be used inappropriately.


Due to factors such as growth, acquisition, changing markets amongst others, organisations can find themselves in new environments to which they struggle to adapt.

Scaling Agile in these new environments is hard. The practices and tools that are frequently used to solve this problem can give the appearance of acting against team autonomy and agility. Teams and individuals naturally try to protect the engineering culture that they have worked hard to establish but by allowing new organisational needs to go unmet they put their autonomy and agility at risk.

In this post I will show how quarterly goal setting, using Agile principles and with one eye on the dysfunctions that can arise, is an effective way to meet organisational needs while protecting team agility and engineering culture.

What needs are we trying to meet?

Senior leaders in an organisation need confidence that the creativity, intelligence, skills and knowledge of their employees are directed most effectively towards delivering long term business objectives. Ensuring that teams are aligned around those business objectives is traditionally called governance.

The top-down command-and-control connotations of that term do not sit comfortably in modern software development and rightly so. The responsibility for organisational alignment should rest with teams and individuals.

A bottom-up approach to organisational alignment relies on transparency. Everyone in the business needs to know the business objectives and what all teams are doing to meet the objectives. Arguably the primary output of all product development and engineering teams is a statement of intent and a narrative of their progress.

Put another way, the first output of all knowledge work is shared, actionable knowledge - we can then decide how best to utilise that knowledge. Teams need to know that the decisions that they are making right now are the right decisions for the business. Without full transparency this is not possible.

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testing-node-apps-with-docker-compose (and some Soul)

Contents of this post

  • Purpose: the reason for this blog post.
  • Scenario: what this example of using docker-compose can be useful for.
  • Prerequisites: basic setup to be able to run the code contained in this post.
  • Code example: an actual step-by-step guide on how you can setup your test environment to run with docker-compose.
  • Improvements: a couple of ideas on how to expand this technique.


As I am sure the audience of this post knows to some extent, Docker is a technology that has grown to become popular over the last few years, allowing developers to deploy pieces of software by packaging them into standardized containers, in a number of various ecosystems (Apache Mesos, Amazon Web Services and many more).

So we can use Docker for our deployment needs, awesome. But let’s pay attention to a key word I used above. Docker grants isolation. And what do we like to perform on our application in isolation? Yeah, you guessed right – testing!

Specifically, with this post, I aim to dig deeper into how to use docker-compose (a specific Docker-based tool that enables creation of multi-container Docker applications) to build and run a Node.js application connected to MongoDB, to test their interaction and the interaction of the app with the external world, all inside containers running on your machine. All isolated and testable thanks to the usage of containers that we can spin up, hit with tests, and clean up with little effort.

Interested? Let’s go!


In this scenario we will use Docker and one of its functionalities, docker-compose, to build a container and spin up our app. Then we build another container with a copy of the database where we can freely create and manipulate data, and finally we perform all the integration testing we want against those self-contained entities, which we can clean up after the tests ran. Total isolation and, very importantly, no need to pollute our development or pre-production environment with superfluous test data.

Let’s imagine an app that we can build and test, for example a directory of soul music artists.

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At OpenTable, we have an engineering culture that empowers us to research, experiment and learn.

In an effort to foster innovation and to try new ideas, Chris Cartlidge, Nick Balestra, Tom Martin and myself started to work on a side project nicknamed big-mars. Our project is a mobile-first, responsive web application that uses Falcor by Netflix.

Falcor, a JavaScript library for efficient data fetching, is an implementation of the Backend for Frontend (BFF) or the API Gateway pattern.

One powerful concept that Falcor has is its query model, in which you access your data as if it was a single JSON model in memory. You can navigate your data structure the way you would navigate a JSON object; either with a “dot notation” (e.g. or with an “array notation” (e.g. restaurant[‘address’][‘city’]).

Really easy and intuitive.

However, while experimenting and learning how to properly use its query model, we noticed that it was quite hard to “visualise” the expected result from the API calls without using, for instance, our beloved in-browser Developer Tools console.


The Falcor project released many additional packages in order to facilitate developers who are willing to use this technology (e.g. falcor-express) but in this ecosystem we noticed a lack of tools with a GUI on top.

We also noticed that the GraphQL, another project that shares with Falcor the same core concepts, has tools with visual interfaces (e.g. express-graphql and GraphiQL).

So as a spin-off of our big-mars project, Nick and I decided to build a tool with a nice and intuitive GUI responsible for exercising the Falcor endpoint in order to help us to validate our queries, and we named this tool falcor-postman.

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