DevOps System Calls
One thing I’ve found when looking at DevOps is the adherance to specific tools. For example, if an organization uses chef, then it is expected that chef be responsible for all tasks. It is understandable to reuse knowledge gained in a system, but at the same time, all systems have pros and cons.
More importantly, each tool adheres to its own philosophies for how a system should be defined. Some are declarative while others are iterative and almost all systems define their own (clever at times) verbage for what the different elements of a system should be.
What the DevOps ecosystem really needs is a low level suite of common primitives we can build off of. A set of DevOps System Calls, if you will, we can use to build higher order systems. The reason is to gain the ability to have some gaurantees we can start to assume will work.
For example, in Python, when I write tests, I assume the standard library functions such as open or the socket module work as expected. You don’t see tests such as:
def test_open(): with open('test_file.txt') as fh: fh.write('foo') assert open('test_file.txt').read() = 'foo'
We have similar expectations regarding much of the TCP/IP stack. We assume the bits are read correctly on the network hardware and passed to the OS, eventually landing in our program correctly. We take it for granted that the HTTP request becomes something like request.headers[‘Content-Type’] in our language of choice.
These assumptions let us consider our program in higher level terms that are portable across languages and systems. Every programmer understands what it means to open file, connect to a database or make a HTTP request within our programs because our level of abstraction is reasonably high.
DevOps could use a similar standard and the implementation doesn’t matter. A machine might be created with Ansible, but configured via Chef. That part doesn’t matter. What matters is we can write simple code that manages our operations.
For example, lets say I want to spin up a machine to run an app and a DB. Here is some psuedo code that might get the job done.
machine = cloud.create(flavor=provider.FLAVOR_COMPUTE) machine.bootstrap() app = packages.find('my-app') machine.deploy(app)
This would compile to a suite of commands that trigger some DevOps tools do the work necessary to build the machines. The configuration of what provider, available flavors, and repository locations would all live in OS level config like you see for your OS networking, auth and everything else in /etc.
The key is that we can assume the calls will work or throw an error. The process is ecapsulated in such a way that we don’t need to think about the provider, setting API keys in an environment, bootstrapping the node for our configuration managment and every other tiny detail that needs to be performed and validated in order to consider the “recipe” or “playbook” as done.
Obviously, this is not trivial. But, if we consider where our tools excel and begin the process of encapsulating the tools behind some higher order concepts, we can begin to create a glossary and shared expectations. The result is a true Cloud OS.