Friday, August 30, 2013

Provisions to last the journey

In the last post, I talked about Vagrant as half of the most important tool IT organizations have gained in the past decade. This post talks about the other half - the provisioner.

The problem we need to solve is replicating the build of a server exactly over and over. In the past (some 10 years ago), I would use Norton Ghost (a backup utility) to clone a server once I had it setup perfectly, then restore that clone to the other servers. And that worked great, so long as I never needed to change what was on that server. For example, a web-server might have had Apache, various modules (mod_perl, mod_proxy, mod_rewrite, etc), and the MySQL client software. Then, we would install the language dependencies (at the time, I was writing Perl apps) from CPAN. We would take a Ghost at that point, replicate that out, then deploy the application using SVN. If we needed a new module or a new version of a module, that required a new Ghost. If we needed a new Apache module or an upgrade, that required a new Ghost. It only took an hour or two, but it was very manual.

This worked great, for production. All of our production servers would be exactly the same, because they were clones of the same Ghost. But, since the production configuration would be on the Ghost, we couldn't use that in QA or in development.

The other problem was that we had no record of what we were doing. Nothing was in source control, largely because there was nothing to put in source control. SVN (and now Git) are only really useful with text files. (Yes, they take binary files, but only as undifferentiable blobs. Not useful.) This meant no code reviews, no history, and no controls. Everyone had to be a sysadmin.

I've heard of other places using a master package (rpm or deb) that does nothing but require all the other packages necessary for the server to be setup properly. And, this works great . . . until it doesn't. The syntax for building packages can be inscrutable. And, while you can do anything in a package (because packages are just tarballs of scripts with metadata), it's very dangerous to allow anyone the ability to do anything. Even if there are no bad actors, everyone is still a human. Humans make mistakes and making mistakes as root is a good way to lose weekends rebuilding from tape.

Luckily, there is a better way.

Unlike the virtualization manager (Vagrant), there are several good choices for a provisioner. Puppet and Chef are the two big ones right now, but several others are nipping at their heels. They differ in various ways, but all of them provide the same primary function - describing how a server should be set up in a parseable format. If you are underwhelmed, just wait a few minutes. (I'll use Puppet in my examples because it's the one I'm using right now. All these examples could be written just as easily in Chef, SaltStack, or Ansible. Juju is a little different.)

The basic unit of work is the manifest (in Puppet) or cookbook (in Chef). This is what contains the parseable description of what needs to be accomplished. In both, you describe what you want to exist, after execution is complete. (Unlike a script, you do not describe how to do it or in what order - it's the provisioner's job to figure that out.) So, you might have something like:

$name = "apache"
package { "apache2":
  require => User[$name],
group { $name:
  ensure => "present",
user { $name:
  ensure => "present",
  gid => $name,
  require => Group[$name],

This would install the apache2 package (found in Ubuntu), create an 'apache' group and an 'apache' user. You'll notice that the apache2 package requires the apache user. So, creating the user would run before installing the package, even though it's defined afterwards. So, define things in the order that makes sense and the provisioner will figure things out. This means, however, that when you watch it run, things won't run in the same order from time to time, and that's okay.

Provisioners are designed to run again and again. They are idempotent, meaning that they will only do something if it hasn't been done already. This property is extremely powerful because we can make a change to a manifest (or cookbook) and, when we run it, only the change (and anything dependent on that change) will execute. This solves the problem of the upgrades with Ghost.

Now, we have a executable description of what a given server should look like. The best part? It's in plaintext. We're going to check this description into our source control so that we can track the changes necessary for each request. We can now treat this as any other code - with changesets, pair programming, and code reviews. Changes to servers can be deployed like every other piece of code in our application. Best of all, they can be tied to the application changes that spawned the need for them (if appropriate). So, our structural changes go through the exact same QA process as the application changes, increasing our confidence in them.

These days, it's really hard to argue against using a provisioner. We can argue which provisioner to use, but it's kinda like using source control. We can argue Git vs. Subversion vs. Mercurial vs. Darcs vs. Bazaar. But, no-one is arguing for the position of "Don't want it." The same should go for provisioners.

Tuesday, August 27, 2013

Use Vagrant for a Great Good

Vagrant is the one half of the best tool for IT organizations in the past decade. Hands down. And I'm going to tell you exactly why you are going to believe me.

No-one focuses on it and no-one cares about it, but environment mismatches are one of the biggest problems IT organizations face. It's a silent threat that doesn't take down whole sites. It's more insidious., only biting you every few months. Things that pass QA sometimes mostly work in production. It's really hard to replicate that bug in production. So, you write it off as a heisenbug. Or maybe the test suite passes on the developer's machine and the QA's machine, but sometimes fails in Jenkins. So, you disable that test from running in Jenkins because you've already wasted three days trying to figure it out.

Everyone kinda knows what the root problem is - you bitch about it over lunch every so often. But, it seems like such a high-class problem, so you don't fix it. Yeah, sure, Github and Etsy do it, but those are huge teams with tons of operations resources to put towards making everything perfect, right?

Not really. Both of them are actually small teams, relatively speaking. And, they don't devote huge amounts of time to it. They just do things right from the get-go. There's a bunch of tools these and similar teams use. The first and most foundational tool is Vagrant.

Vagrant is virtualization made easy. Vagrant creates and manages a semi-anonymous virtual machine (VM) using a simple configuration file (called a Vagrantfile). There are three basic commands:

  • vagrant up
  • vagrant halt
  • vagrant ssh
(There's more to it - a total of 15 commands as of this writing, but those are the three big ones.) And they do exactly what they say on the tin - bring the VM up, bring it down, and login to it. It works with Virtualbox, VMWare, and several other virtualization providers.

That's secret sauce #1 - Vagrant is just sugar around virtualization providers. It does all the heavy lifting of setting up the VM, managing it, and making sure it doesn't conflict with other VMs. (Yes, we're going to talk about multi-VM setups!)

So, now you have create a VM. So what? Because the setup of the VM is automated and everything is checked into your source control, every user of this repository has the exact same VM setup on their machine. As the setup of the server changes, a quick vagrant reload and everyone is in sync again.

Setting up multiple VMs is also very simple. You might want to do this for all kinds of reasons.
  1. An application server and its database.
    1. If they're both in the same repository, the same Vagrantfile can define both VMs.
    2. If they're not, each repository has its own Vagrant file. In this case, defining your own subnet works wonders. (I like 33.33.33.xx - it's a private DoD subnet that's not routable.)
    3. Remember - coworkers shouldn't share cups, plates, or databases. It's just not sanitary.
  2. Front-end developers working with services.
    1. The services can run on their own VMs and be deployed to as if they were in the QA environment. Your designers can now work on their code without having to know how the services are managed AND not have conflicts.
So, when do you want to set up a VM? I strongly believe that every source code repository should have its own VM. This includes backend code, like Python or Ruby applications as well as front-end code, like Backbone or Ember applications.

"Rob, really?! Front-end code? Doesn't it run in the browser already? Why go through all the hassle of setting up a VM?"

Yes, really, for several reasons:
  1. Front-end applications may run in the browser, but they aren't built in the browser. Compass/SASS, Less - these are all tools that are versioned and depend on a toolchain of specific versions.
  2. No-one ever works on a single project these days. Each project has its own toolchain, but many of these tools expect to be installed globally.
  3. Most front-end applications depend on some REST API to be available. If it's not, you may choose to build a stub application instead of hard-coding the responses in text files. Now you have a back-end application that needs to be managed.
  4. Test tools often want to run in a server. This is especially true for PhantomJS and ZombieJS. It really sucks when your testing frameworks aren't in sync between developers.
And, finally, Vagrant provides the foundation for the other half of the most important tool of the past decade - the provisioner.

Tuesday, August 6, 2013

Designing for testability

I'm going to assume you agree that writing tests is good and that 100% code coverage (or as close to it as possible) is a great ideal to strive for.

Testing stuff is hard. Any stuff. By anyone. (QA teams don't have it any easier.) This is true if you don't have tests and if you have tests. And, sometimes, the tests you have make it harder to write more tests.

The root problem is testability. I define testability as "the ease by which a system is verifiable." (This is different from "How well can someone describe a testcase." The latter is a skill of the person, the former an attribute of the system.) The easier a system is to test, the greater its testability.

Testability affects and is affected by everything. Every decision made by anyone on the project can reduce the project's testability. Often in ways that aren't obvious until months later. For example, the ops team adds a new service and it needs a configuration file. The person in charge of doing it is focused on getting this service up and running, so they hard-code the file's path into a module that's included in the application. They didn't know the dev team's process for adding a new configuration file - they're ops, not dev. But, that's now a block to testability. Instead of creating a new configuration file with appropriate values for testing and pointing the code at it, the tester has to put the file in that spot. The spot might be in a directory that requires privileges to write in, meaning tests now have to run with elevated privileges. It's also a spot which might change later, intermittently breaking the test suite in hard-to-diagnose ways.

A lot of ink (digital and not) has been spent on discussing ways of improving the application code within a system to make it easier to write unit-tests. An incomplete list would include:
  • Decoupling
  • Interfaces
  • Mock objects
A nearly equal amount has described how to write integration tests, though with less prescription for making a system more testable (we'll see why in a later post). And, still further, people have talked about other ways of distinguishing this test from that test.

At the heart, testing any system is just this:

  1. Hook up an input stream with testing data
  2. Hook up monitors on an output stream
  3. Run the test
This process works for everything, so we'll look at it in the light of a car. When I take my car into the local oil change place, they test a whole bunch of components in my car, not just the oil. For example, to test the transmission fluid, they:
  1. (input) Extract a small amount of fluid from my transmission and put it on a card.
  2. (output) The card has a reference color on it.
  3. (run test) Compare the fluid color against the reference color using a Mark-1 eyeball.
That's a highly repeatable and very strong test. It's cheap to execute (for time, materials, and training) and it works. (Happily for me, they are able to do this - the transmission fluid in one of my older cars was filthy and would have caused the transmission to fail if it hadn't been changed. I wouldn't have known to do it otherwise.) They test the air filter, the transmission fluid, the lights, the wipers - pretty much every component in my car. 

Well, not quite. They test every highly-testable component in my car. They don't test the integrity of the engine mounts, the safety of the seat-belts, or if the airbags are charged. Why not? What's different about those components that makes tests for them much harder?

Unlike the various fluids and filters, the airbags (for example), aren't designed to be tested. There may be very good reasons for doing so, but that's not the question. If there was a car that designed the airbags in such a way that my oil changing place could cheaply test their charge, they would jump all over it. Running several dozen cheap tests make clueless drivers (like me!) want to use them and the more they can test, the more they will find that (legitimately) needs replaced. (Likely, by them, because why go somewhere else?)

The oil change experience also gives us another crucial point - unit tests and integration tests are the same thing. The mechanics use different inputs, outputs, and tests when examining different components. But, the point of input, the point of output, and the expectation are all well-defined. There's no distinction between someone who is capable of judging the transmission fluid vs. the performance of the car as a whole. Nor is there a distinction between the types of tests (or inspections, as they call them).

More on this in part 2.