7 min read

In March 2013, Solomon Hykes presented Docker, which democratized access to Linux containers. The underlying technology, control groups, was already incubating for a few years at Google. However, Docker abstracts away the complexity of containers’ lifecycle and adoption skyrocketed among developers. In June 2016, Datadog published some compelling statistics about Docker adoption: the industry as a whole increasingly adopted containers for production.

Since everybody is talking about how to containarize everything, I would like to take a step back and study how it is influencing the development of our most fundamental medium: programming languages. The rise of Golang, the Java8 release, Python 3.6 improvements–how do language development and containerization marketsplay together in 2017?

Scope of Container Technologies

Let’s define the scope of what we call container technologies. Way back in 2006, two Google engineers started to work on a new technology for the partition hierarchical group of tasks. They called it cgroups and submitted the code to the Linux Kernel. This lightweight approach of virtualization (sorry Mike) was an opportunity for infrastructure-heavy companies and Heroku and Google, among others, took advantage of it to orchestrate so-called containers. Put simply, they were now able to think of application deployment as the dynamic manipulation of theses determinist runtimes. Whatever the code or the business logic, it was encapsulated into a uniform execution format.

Cgroups are very low level though, and tooling around the original primitives quickly emerged, like LXC backed by Canonical. Then, Solomon Hykes came in and made the technology widely accessible with Docker. The possibilities were endless and, indeed, developers and startups alike rushed in all directions. Lately, however, the hype seems to have cooled down. Docker market share is being questioned while the company sorts its business strategy. At the end of the day, developers forgetabout vendors/technology and just want simple tooling for more efficient coding. Docker-compose, Red Hat Container Development Kit, GC Container Builder, or local Kubernetes are very sophisticated pieces of technologies that hide the details of the underlying container mechanics. What they give to engineers are powerful primitives for advanced development practices: development/production environment parity, transparent services replication, and predictable runtime configuration.

However,this is not just about development correctness or convenience, considering how containers are eating the IaaS landscape. It is also about deployment optimizations and resilience. Tech giants who operate crazy large infrastructures developed incredible frameworks, often in the open, to push how fast they could deploy auto-scalable, self-healing, zero-downtime fleets. Apache Mesos backed by Microsoft, or Kubernetes by Google, make at least two promises:

  • Safe and agile deployments at the (micro-)service level
  • Reliable orchestration with elegant service discovery, load-balancing, and failure management (because you have to accept that production always goes wrong at some point)

Containers enabled us to manage complexity with infrastructure design patterns like micro-services or serverless. Behind the hype of these buzzwords, engineers try to improve team collaboration, safe and agile deployments, large project maintenance, and monitoring. However,we quickly came to realize it was sold with a DevOps tax. Fortunately, the software industry has a hard-won experience of such balance, and we start to see it converging toward the most robust approaches.

This container landscape overview hopefully provides the requirements to now study how it has impacted the development of programming languages. We will take a look first at their ecosystems, and then we will dive into language designs themselves.

Language Ecosystems and Usages

Most developers are now aware of how invasive container technologies can be. It makes its way into your development toolbox or how your company manages its servers. Some will argue that the fundamentals of coding did not evolve much, but the trend is hard to ignore anyway.

While we are free, of course, to stay away from Silicon Valley’s latest fashions, I think containers tackle a problem most languages struggle with: dependencies and packaging. Go, for example, got packaging right, but it’s still trying to figure out how to handle dependencies versioning and vendoring. JavaScript, on the other hand, has npm to manage fine-grained third-party code, but build tools are scattered all over Github. Containers won’t spare you the pain of setting things up (they target runtimes, not build steps), but it can lower the bar of language adoption. Official images can run most standard language projects and one can both give a try and deploy a basic hello world in no time. When you realize that Go1.5+ needs Go1.4 to be compiled, it can be a relief to just docker run your five-lines-long main.go.

Growing a language community is a sure way to develop its tooling and libraries, but containers also influence how we design those components. They are the cloud counterparts of the current functional trend. We tend to embrace a world where both functions and servers are immutable and single-purpose. We want predictable, pure primitives (in the mathematical sense). All of that to match increasingly distributed and intensive workloads.

I hope those approaches come from a product’s need but, obviously, having the right technology at hand drives the innovation. As software engineers in 2017, we also design libraries and tools with containers in mind: high performance networking, distributed process management, Data pipelines, and so on.

Language Design

What about languages? To get things straight, I don’t think containers influence how Guido Van Rossum designs Python. And that is the point of containers. They abstract the runtime to let you focus on your code™  (it is literally on every Docker-based PaaS landing page). You should be able to design whatever logic implementation you need, and containers will come in handy to help you run it when needed.

I do believe, however, that both languages last evolutions and the rise of containers serve the same maturation of ideas in the tech community.

There is something elegant about having language features, programmatical design patterns, and infrastructure operations going hand in hand. While I don’t think one of them influences the other, I certainly believe that their development smoothen other’s innovations.


Container technologies and the fame around them are finally starting to converge toward fewer and more robust usages. At the same time, infrastructure designs, new languages, and evolutions of existing ones seem to promote the same underlying patterns: simple, functional, decoupled components. I think this coincidence comes from industry maturity and openness, more than, as I said, one technology influencing the other.

Containers, however, are shaking how we collaborate and design tools for the languages we love. It changes the way we on-board developers learning a new language. It changes how we setup local development environments with micro-replicates of production topology. It changes the way we package and deploy code. And, most importantly, it enables architectures like micro-services or lambdas that influence how we design our programs.

In my opinion, programming language design should continue to evolve decoupled from containers. They serve different purposes, and given the pace of the tech industry, major languages should never depend on new shining tools. That being said, the evolution of languages now comes with the activity of its community—what they build, how they use it, and how they spread it in companies. Coping with containers is an opportunity to bring new developers, improve production robustness, and accelerate both technical and human growth.

About the author

Xavier Bruhiere is a lead developer at AppTurbo in Paris, where he develops innovative prototypes to support company growth. He is addicted to learning, hacking on intriguing hot techs (both soft and hard), and practicing high-intensity sports.


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