This page explores the performance of Oso across three main axes:
1. In practice. How does Oso perform under typical workloads?
2. Internals and Micro-benchmarks. How is Oso built? What are the micro-benchmarks?
3. Scaling. What is the theoretical complexity of a query?
There are two main areas to consider when measuring the performance of Oso queries: the time to evaluate a query relative to a policy, and the time needed to fetch application data.
In a complex policy, the time it takes to run a single query depends on the complexity of the answer. For example, a simple rule that says anyone can “GET” the path “/” will execute in less than 1 ms. On the other hand, rules that use HTTP path mapping, resource lookups, roles, inheritance, etc. can take approximately 1-20 ms. (These numbers are based on queries executing against a local SQLite instance to isolate Oso’s performance from the time to perform database queries.)
The time needed to fetch application data is, of course, dependent on your specific environment and independent of Oso. Aggressive caching can be used to reduce some of the effect of such latencies.
Oso does not currently have built-in profiling tools, but this is a high-priority item on our near-term roadmap. Our benchmark suite uses Rust’s statistical profiling package, but is currently better suited to optimizing the implementation than to optimizing a specific policy.
Oso has a default maximum query execution time of 30s. If you hit this maximum, it likely means that you have created an infinite loop in your policy. You can use the Polar debugger to help track down such bugs.
For performance issues caused by slow database queries or too many database queries, we recommend that you address these issues at the data access layer, i.e., in the application. See, for example, our guidance on The “N+1 Problem”.
Internals and Micro-benchmarks
The core of Oso is the Polar virtual machine, which is written in Rust.
(For more on the architecture and implementation, see Internals.)
A single step of the virtual machine takes approximately 1-2 us, depending
on the instruction or goal. Simple operations like comparisons and assignment
typically take just a few instructions, whereas more complex operations like
pattern matching against an application type or looking up application data
need a few more. The debugger can show you the VM instructions remaining to
be executed during a query using the
The current implementation of Oso has not yet been aggressively optimized for performance, but several low-hanging opportunities for optimizations (namely, caches and indices) are on our near-term roadmap. We do ensure that all memory allocated during a query is reclaimed by its end, and our use of Rust ensures that the implementation is not vulnerable to many common classes of memory errors and leaks.
You can check out our current benchmark suite in the repository, along with instructions on how to run it. We would be delighted to accept any example queries that you would like to see profiled; please feel free to email us at firstname.lastname@example.org.
At its core, answering queries against a declarative policy is a depth-first search problem: nodes correspond to rules, and nodes are connected if a rule references another rule in its body.
As a result, the algorithmic complexity of a policy is in theory very large — exponential in the number of rules. However, in practice there shouldn’t be that many distinct paths that need to be taken to make a policy decision. Oso filters out rules that cannot be applied to the inputs early on in the execution. What this means is that if you are hitting a scaling issue, you can make your policies perform better by either by splitting up your rules to limit the number of possibilities, or by adding more specializers to your rule heads.
For example, suppose you have 20 different resources,
…, and each has 10 or so
allow(actor, action, resource: ResourceA) rules.
The performance of evaluating a rule with input of type
ResourceA will primarily
depend on those 10 specific rules, and not the other 190 rules. In addition,
you might consider refactoring this rule to
allow(actor, action, resource: ResourceA) if allowResourceA(actor, action, resource). This would mean there
are only 20
allow rules to sort through, and for a given resource only one
of them will ever need to be evaluated.
The performance of evaluating policies is usually independent of the number
of users or resources in the application when fetching data is handled by your
application. However, as in any programming system, you need to be on the
lookout for linear and super-linear searches. For example, if you have a method
user.expenses() that returns a list of the user’s expenses, the check
expense in user.expenses() will require O(n) VM instructions, where n
is the length of the list. It would be better to replace the linear search
with a single comparison, e.g.
expense.user_id = user.id. Be especially
careful when nesting such rules.
Oso typically answers simple authorization queries in less than 1 ms, but may take (much) longer depending on the complexity of your rules, the latency of application data access, and algorithmic choices. Some simple solutions such as caching and refactoring may be used to improve performance where needed.