Yes, I’ve been on a Rego kick lately. I posted Kickstart Your Journey With Rego as a way for others to get onboard as well. As with any language, it’s always good to know the impact of your logic. After gaining an understanding of the language and flow, it only makes sense to begin benchmarking your Rego policies to understand the impact of awesome policies.
Introduction to Benchmarking Your Rego Policies
In the delightful world of policy and security, Rego is the belle of the ball. It’s the language of choice for crafting policy in Open Policy Agent (OPA). Like a skilled magician pulling rabbits out of hats, OPA uses Rego to create abracadabra moments, transforming complex policy language into straightforward, executable rules.
But let’s get to the meat of the matter: benchmarking. It’s like a superpower that enables you to evaluate the performance of your Rego queries. And who doesn’t want to be a superhero in the realm of policy enforcement, right?
The Benchmark Command: Your Superpower
The benchmark command is like your secret weapon in the quest for ultimate policy performance. It behaves pretty much like the ‘eval’ command, except it doesn’t just evaluate your Rego query—it also measures how long it takes.
Here’s a quick run-through of how you can wield this power:
opa bench -b ./policy-bundle -i input.json 'data.authz.allow'
Now, if you’re the kind who loves to delve deeper, the –metrics and –benchmem flags will get your heart racing. They offer a more detailed analysis of your Rego performance.
The Wonders of Rego
Alright, let’s take a step back and appreciate the grandeur of Rego. It’s a DSL (domain-specific language) that’s not restricted to one kind of policy. Rego is like a chameleon, adapting to different scenarios and making it possible to share policies across various services.
Rego is based on Datalog, a declarative language where the programmer specifies the results they want, not how to compute them. It’s like ordering a pizza—you don’t need to tell them how to make it, just what toppings you want!
The Magic of Bottom-Up and Top-Down Evaluation
Rego has two remarkable strategies up its sleeve: bottom-up and top-down evaluation. It’s like choosing between two amazing flavors of ice cream, except in this case, the choice depends on your policy needs.
If you’re wondering what these strategies are, think of them as two different ways of eating a sandwich. The bottom-up strategy starts from the bottom (the first slice of bread) and works its way to the top (the second slice of bread). It’s simple, but may sometimes take a few more bites (computations) than necessary.
On the other hand, the top-down strategy begins at the top and works its way down, munching on the fillings as needed. It’s more efficient, especially when you’re not that hungry (i.e., when you want to limit your computations).
A Juicy Example
To illustrate these strategies, let’s imagine a situation where we need to ensure that write permissions are set granularly. We have some roles with “dangerous” write permissions, and we want to print an error for each user with a dangerous role assigned. We define two rules:
dangerous_roles: a set that contains all policies withwrite: *deny: a set with all the error messages
The full policy looks something like this:
package main
dangerous_roles[role_name]
{
role := input.roles[_]
role_name := role.name
role.write == "*"
}
deny[msg]
{
user := input.users[_]
role_name := user.roles[_]
dangerous_roles[role_name]
msg := sprintf(
"Please remove role %s from user %s", [role_name, user.name]
)
}
You can then use opa eval to check if the output is as expected.
The Pros and Cons of Bottom-Up and Top-Down Evaluation
Bottom-up evaluation is straightforward, but it might compute more than necessary. It’s like making a whole pot of coffee when you only want one cup.
Top-down evaluation offers a solution to this problem. It evaluates only what’s necessary, starting from the top. However, beware! This approach can sometimes lead to more computation time, especially with larger inputs.
Top-Down, Meet Bottom-Up
Don’t worry, we’re not stuck with slow queries! By using a set comprehension, we can evaluate a rule “all at once,” improving our performance times. It’s like taking a shortcut on your daily commute—same destination, but faster!
The Importance of Declarative Language
A declarative language lets you focus on what you want to compute, not how to do it. But, like a dog chasing its tail, we can’t completely ignore the actual execution model. If we’re not careful, it could increase runtime and lead to timeouts. Thankfully, the fix is simple and quick—like applying a band-aid on a paper cut.
High Performance Policy Decisions: Turbocharge Your Policies
For those superhero moments when you need high-performance policy decisions, there’s a subset of the policy language that’s built for speed. It’s like the fast lane on the highway, designed to get you to your destination quickly, regardless of how much traffic there is.
Concluding Our Adventure
Benchmarking your Rego policies might not be as exciting as a rollercoaster ride, but it’s an essential part of policy enforcement. With the right tools and strategies, you can ensure your policies are running as efficiently as possible.
Remember, even in the world of Rego, it’s not always about the destination—it’s about the journey. So buckle up, enjoy the ride, and don’t forget to laugh along the way!