Software staging clusters only grow.

As production accrues more services, staging’s costs ramp up.

And maintaining a single, massive, production-like staging may no longer be the right answer.

But several, small staging clusters—each fit for their purpose—offers a more maintainable, cheaper alternative.

🍝 There is no perfect staging; there are only perfect stagings

Each reason for having a staging cluster requires a different level of “production-likeness” to fit its use.

You could put Apache and PHP on raspberry pi and call it Wikipedia’s “staging.”

And that’d be a fine place to demo a MediaWiki patch. But to be confident deploying that patch into Wikipedia’s production: (to paraphrase “Jaws” 🦈): you’re gonna need a bigger staging .

In the 1970s, the pasta sauce brand Ragu tasked the psychophysicist Howard Moskowitz with finding the perfect pasta sauce.1

Moskowitz concluded the perfect pasta sauce doesn’t exist—it depends on each individual’s wants and needs. There is no perfect pasta sauce; there are only perfect pasta sauces.

Howard’s work is why you’ll find Ragu Old World Style® next to Ragu Chunky Garden Vegetable next to 10s of other Ragus.

And much like pasta sauce2: there can be no perfect staging; only perfect stagings.

💱 Staging trades cost vs. production-likeness

The requirements for a staging cluster depend on its use.

  • Demos – Demoing new code requires the same software, maybe a few microservices, and (possibly) a subset of production data.
    • 🟢 Low cost
    • 🟢 Low production-likeness
  • Exploratory testing – QA requires the same software, services, and a subset of production data. It’d be nice to run it on the same type of infrastructure, too.
    • 🟡 Medium cost
    • 🟡 Medium production-likeness
  • Deployment confidence – 100% confidence requires a parallel universe you destroy whenever a deployment goes wrong.
    • 🔴 High cost
    • 🔴 High production-likeness
Staging trades costs for nearness to production

Staging is a trade-off: resources (money, people, time) against asymptotically approaching actual production.

The closer you get to production, the higher the costs and complexity.

🔁 Production should be reproducable

Setting up a staging server should be easy. If it is not easy, you already have a problem in your infrastructure, you just don’t know it yet

– Patrick McKenzie 🐉, Staging Servers, Source Control & Deploy Workflows, And Other Stuff Nobody Teaches You

Configuration management should make it easy to rebuild production from scratch. Otherwise, you’ve got a disaster in the offing.

This creates an environment suitable for demos and end-to-end test automation.

But organizations are evolving away from using pre-production staging to build deployment confidence.

They’ve replaced their high-cost staging with a mix of canary deployments and advanced feature flagging.

Small environments with narrow scope—like testing or demoing—seem like a reasonable trade-off of cost vs. benefit.

But using pre-production staging as insurance for your deployments—requiring snapshots of production data and maybe even replayed traffic—seems too. darn. expensive.

📚 Further reading

  1. This is an anecdote related by Malcolm Gladwell in a 2007 Ted Talk, "Choice, Happiness, and Spaghetti Sauce↩︎

  2. There’s a joke here about “spaghetti code” that I’m too lazy to find.↩︎