One of my favorite scenes from Disney’s film Fantasia is that of the “Sorcerer’s Apprentice” (originally, a poem by Goethe). Mickey Mouse, as the apprentice, is being trampled by an army of enchanted, cloned brooms. The brooms multiply automatically – in an endless march to “get the job done”: fetching water in buckets to fill up the room – threatening to drown Mickey.
For Release Managers responsible for software delivery in large organizations – managing the multitude of software releases the enterprise needs to support can feel a lot like drowning/being trampled by an army of brooms release pipelines.
Just like Mickey, sometimes you may feel you’ve found the perfect ‘Magic’: this one, simple, automated pipeline to command the release of your one application and daisy-chain your tools of the trade along the pipeline. And that feels great – as you configure this one ‘broom’ to automate your chores, and release this software to Production.
And so, different groups in the organization may repeat this process across hundredths (if not thousands) of individual teams and applications, each focusing on their one ‘broom’, ignoring the implications on the bigger picture.
One application or a simple pipeline is a pretty easy – let’s get that broom! But some of our enterprise customers need to support 20,000 concurrent applications- all in various stages of being built, tested, deployed and released – what do you do then?
High volume, high complexity, high stakes.
If all you had to worry about was one single track – things would be simple. But the realities of software delivery for enterprises are rarely that simple – and the scale of software production and releases that large organizations need to support can be daunting, primarily for two reasons:
- Sheer Volume: As enterprises digitize more aspect of their business, and applications become more critical to their operations and market traction –software-production is going into hyper-drive, putting strains on the organization to release an ever-growing number of applications, at a faster and faster pace. And for some enterprises- we’re talking thousands of applications and inter-dependent components.
This volume of applications and velocity of releases are also – naturally – mirrored in the volume of large numbers of geographically distributed teams and infrastructure that develop and run these apps that need to be supported as well.
- Diversification and Complexity: DevOps implementation usually starts with one small team, and a pretty simple pipeline. But as you want to scale DevOps and optimize your release processes across other teams – pretty soon, another tool – or 50 – gets introduced into the mix; another complex process-branching is required; Team B needs a different pipeline all together than Team A; your Security officer needs to approve code promotions for teams A-through-n and review the output of tests; Team C can’t have access to certain environments; Team G has a unique snow-flake configuration mandating the use of 10 other similarly one-off tools and processes; you need a process to manage priorities when writing tests to a specific environment configuration that’s too costly to replicate across all locations, and lock-in artifacts so that teams competing for the same resource pool do not override each other; then Team X needs… – You get the point.
Keep in mind, too, that while we all know the basic 4-stages path of the pipeline – from CI build, through Testing, Deployment and Release – for some organizations, each of these stages can be comprised of hundreds of different processes, encompassing thousands of tasks, and sometimes millions of jobs being executed. Sure, we want to keep it simple, but real-world complexities having to do with legacy code, regulatory requirements, and others, often make enterprise processes to be not as simple as “fetch water in bucket from here, and dump it there”. Furthermore, software delivery pipelines become more complex as organizations find they need to support both the applications of ‘yesterday’ and those of tomorrow.
As pipelines multiply, exponentially – (gaining up on you) – release managers are left struggling to stay afloat and bring some order, visibility and predictability to the multitudes of tracks that they need to coordinate and make sense of (in a room soon to be overflowing.)
Reining it in
While IT struggles to keep up, the evidence of the ROI of DevOps – along with the continued advancements in DevOps adoption in the enterprise – are leading organizations to look for ways to scale DevOps practices throughout the organization. As a next phase to this evolution, enterprises are looking to address this “Sorcerer’s apprentice” challenge of software releases:
How can you gain shared visibility, centralized management and governance over all your “brooms” across your entire software delivery processes? How can you have a system that ensures you don’t end up battling to take back control over an ever growing number of separate automation tracks running amok – that are not “aware” of each other, and are not coordinated as part of a larger effort.
As in Fantasia, as DevOps matures – in comes the “Sorcerer” to rein-in your sprawl of “automation-gone-wild”. Enterprises realize the need for a seasoned “Conductor”, to command and orchestrate all their disparate DevOps tools, processes, pipelines, and multitudes of “islands of automation” – to bring order, predictability, and scale.
It’s not Magic
But it does take a lot of work — and planning.
To improve developer productivity, product quality and resource utilization – as well as to enable enterprise-scale and cross-project visibility and management – organizations need to automate and orchestrate their entire end-to-end software delivery pipeline. “Automate All the Things” is a key tenant to any DevOps or Continuous Delivery initiative, and is a requirement to achieve quality, compliance, speed and efficiency – at scale.
This end-to-end orchestration enables standardization and consolidation of all tools and processes under a centralized, shared, platform. This allows for re-use across teams, shortening of cycle times, cost reduction, and more. Mainly, it reduces the risk of software releases by having predictable processes, security checks, consistent monitoring and one pane of glass for the entire organization.
End-to-end orchestration and standardization are required for scaling DevOps effectively for today’s large enterprises. To get it right, you need to map all of your pipelines across all teams and applications and design your DevOps solution from the get-go in a way that will allow you to scale while avoiding the “Sorcerer’s Apprentice” trap.
Make sure your end-to-end solution enables you to:
- Model: Pipeline Models allow you to define your end-to-end software delivery process – encompassing all the teams, tools, stages, tasks, approval gates, artifacts and environments involved in this process. Modeling your application, environments and pipeline enables reuse and consistency.
- Automate: Automate and orchestrate your entire toolchain and workflows to eliminate manual handoffs and silos of automation to accelerate your pipeline and improve quality.
- Integrate: Be able to support off-the-shelf plugins, robust DSL and APIs allow for extensibility and flexibility, making it easy to tie-in any tool chain, technology stack or cloud resources to gain shared control and visibility.
- Govern: Role-based access control, approval gateways and automatic logging ensure security, visibility and compliance for governance and auditability.
- Scale: Ensure HA and scale jobs and workloads predictably and efficiently across pools of on-premises or cloud resources.
DevOps provides enterprises with an effective approach to eliminate risk from software releases, and support the ever-growing demand for more frequent application updates. By automating your entire end-to-end pipeline and managing all release processes in one centralized platform, organizations gain visibility into the progress of all releases and DevOps processes, and ensure they can control, govern and optimize their delivery pipelines as they grow exponentially – and not get run over by an army of automated ‘brooms’.
This article was originally published on InfoWorld
*Image: Disney’s Fantasia
Latest posts by Anders Wallgren (see all)
- Changing the Game! - April 18, 2019
- Should you use AI to make decisions about your software team? - February 28, 2019
- Continuous Discussions (#c9d9) Podcast, Episode 90: Gene Kim and DOES’18 Speakers - August 21, 2018