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.
Logical isolation vs. cryptographic isolation
Consider logical isolation vs. cryptographic isolation. 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.
What isolation guarantees
What isolation guarantees deserves its own treatment. 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. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.
- 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
What isolation does not guarantee
Consider what isolation does not guarantee. 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.
Why common approaches fall short
That brings us to why common approaches fall short. 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.
Make the boundary explicit.
One key for everything
One key for everything 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.
One key per service or data type
One key per service or data type deserves its own treatment. 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. A p50 that looks fine while the p99 is unusable is a reporting failure, not a performance one.
Per-tenant keys managed manually
Consider per-tenant keys managed manually. 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.
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.
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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