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Engineering23 August 2024·3 min read

8 Role-Based Access Control (RBAC) examples in action

Most systems do not fail because someone chose the wrong database.

Paycux engineering

Most systems do not fail because someone chose the wrong database. They fail because a reasonable decision kept being reasonable long after the conditions that justified it had changed.

This piece walks through how we think about it at Paycux, what we have changed our minds about, and where the sharp edges are.

What is Role-Based Access Control (RBAC)?

Consider what is role-based access control (rbac)?. Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

RBAC examples

RBAC examples is where this gets concrete. Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

  • Name the boundary before you cross it
  • Measure what users feel, not what is easy to instrument
  • Make every retried operation idempotent
  • Write down the assumption that would invalidate the design

Example 1: Corporate IT system

Example 1: Corporate IT system is where this gets concrete. Make the boundary explicit. When one part of the system can only talk to another through a named interface, you can change either side without a meeting; when it cannot, every change becomes a negotiation.

Measure before you optimise, then measure the thing users feel rather than the thing that is easy to instrument. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.

Example 2: Healthcare management system

Example 2: Healthcare management system is where this gets concrete. Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

Measure before you optimise, then measure the thing users feel rather than the thing that is easy to instrument. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.

Measure before you optimise, then measure the thing users feel rather than the thing that is easy to instrument.

Example 3: Educational institution system

Consider example 3: educational institution system. Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

Measure before you optimise, then measure the thing users feel rather than the thing that is easy to instrument. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.

Example 4: Financial services application

Consider example 4: financial services application. Measure before you optimise, then measure the thing users feel rather than the thing that is easy to instrument. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.

Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

Example 5: E-commerce platform

Example 5: E-commerce platform deserves its own treatment. Idempotency is not a nice-to-have in any system with retries. Key on an identifier the caller supplies, store the outcome, and return the same answer to the same key rather than doing the work twice.

Measure before you optimise, then measure the thing users feel rather than the thing that is easy to instrument. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.

Where this leaves us

The pattern repeats across every system we have looked at: the hard part is not the mechanism, it is keeping the mechanism honest as the surrounding assumptions change.

If you are working through the same problem and want to compare notes, the docs cover the mechanics and the console shows the behaviour on your own data.

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