Understanding State

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Let’s talk about state, shall we? State is the collective properties of the system from one point in time. Think of it effectively as a snapshot of a system. State in computer science is actually a lot like state in physics, so let’s start with something that’s a bit easier to understand.

We’re going to examine a physical system: A ball dropping from my hand to the ground one meter (1m) below. The ball starts out at one point in time where it is at rest in my hand. It has no velocity, no motion. It has properties like color, texture, etc. that do not and will not change. The state of the ball can be thought of as a position of 1m off the ground, with a color, texture, etc., and no velocity. Each of these variables has a specific value at that point in time.

Now I open my hand. At the instant the ball leaves my hand, the ball has moved some distance to the ground, and its velocity has increased. Its state, therefore, has changed. If we imagine capturing the ball’s motion with a slow-motion camera, we see a single frame for each position of the ball. Each frame is a single state of the system, and the difference between the frames is a change in state. When our state changes, one or more variables change. In this case, the variables of speed and direction (combined as velocity) and distance from the ground all change in each snapshot of time, or each state. We can use this knowledge to predict how the ball’s state will change, allowing us to identify patterns.

When we think about our infrastructure that we manage with systems like Pulumi, we’re thinking about states of the infrastructure system. How we move from one state to another, which variables change from state to state, and what the starting state and ending state are would all be considered and tracked. Most infrastructure-as-code systems track state in some fashion, though most rely on you, the user, to manage that state tracking with state files or other systems that you have to manage and choose. For this deep dive, though, I’m going to focus on how the hosted Pulumi service manages state.

When considering the state of your infrastructure over time, we need to think about the transition of the infrastructure’s state between one point in time and another. Our program for any infrastructure-as-code platform defines the ideal, final state of the system. As the code executes, the infrastructure goes through a sequence of states, which we call the behavior of the system. For each tick of processing of the code, there is a defined state. Therefore, during the execution of the code, we see transitions in state. That state change needs to be tracked so that, at any point in time, we know how the behavior of the system changed. That’s important for having multiple programs trying to execute at once, debugging system changes, and other important considerations for working in teams across a remote, cloud-based environment. In short, it’s good to know what changed! When you use Pulumi, you have access to that change information through audit logging and can use webhooks to feed those changes into other systems for observation, like a shared monitoring system with your security team or a distributed team that can’t look over your shoulder as something deploys.

Now, code execution doesn’t always happen exactly as we want it to due to all kinds of environmental factors from different chipsets to varying network connectivity and more. If you really want to go down the rabbit hole here, I’m going to point you to formal methods, especially TLA+. Formal methods are a great way to model state for distributed, concurrent systems to identify race conditions, poor assumptions, and other common flaws in temporal logic. For now, though, we’re going to keep talking about state in the more abstract sense.

Putting all of the states together along with the transitions they can have so that we have pathways from initial states to next states in a clean pattern, we get what’s called a state machine. When working with concurrent distributed systems, or systems that can have multiple things happening simultaneously that are spread out over many machines—basically, any cloud system created ever,—knowing the various states, changes, and combinations thereof is extremely important to ensuring that the one path we want the system to take to a final desired state is the one that is taken.

When using Pulumi, you don’t have to worry about the state machine. The Pulumi Service tracks all of those states for you once the infrastructure’s initial state is declared by importing the infrastructure to or creating it with Pulumi. You declare the desired state in code in the language of your choosing, and then that code tells the Pulumi CLI what you want. The CLI does all of the state computation, requesting and defining the pathway to the infrastructure final state defined in the program, and the Pulumi Service stores the state at each moment in time. The Pulumi dashboard, by extension, is your window into the Pulumi Service where you can see current state, desired state, and the behaviors of the system.

I hope this short introduction to how state works, especially with infrastructure-as-code platforms, helps get you on your way! If you want to read more about state with Pulumi (and get some nifty diagrams), head to State and Backends. Until next time!

Leslie Lamport has some fantastic, free resources and videos about the formal specifications in TLA+, which he created, at his site on TLA+. I’m a huge fan.

Also, if you want to watch a short video on state to get a better sense of the physics example, head on over to PulumiTV for episode 3 of our Quick Bites of Cloud Engineering series all about state.

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