Runtime invariant to rule count in a single-pass boundary execution model(targetedwebresults.com)
targetedwebresults.com
Runtime invariant to rule count in a single-pass boundary execution model
https://targetedwebresults.com/pounce-demo-final.gif
5 comments
This is a small experiment showing per-record runtime independent of the number of compiled obligations in a single-pass boundary execution model.
The demo focuses on behavior, not throughput tuning. Startup cost scales; runtime does not.
Details available under NDA. I’m reachable at: michaelallenkuykendall [at] gmail [dot] com
The demo focuses on behavior, not throughput tuning. Startup cost scales; runtime does not.
Details available under NDA. I’m reachable at: michaelallenkuykendall [at] gmail [dot] com
What’s a record, obligation, and boundary execution model?
I did not expect a reply sorry!
A record is one unit of structured input handled by the system, such as a JSON document, an NDJSON entry, or an API request body. An obligation is a single requirement evaluated against that record, for example a field must be present, a value must satisfy a constraint, or a record must be accepted or rejected based on its contents.
A boundary execution model refers to evaluating those obligations at the system boundary where data enters, rather than deep inside downstream application logic. The intent is to make decisions as part of intake handling, before the data is handed off to other components.
:)
A record is one unit of structured input handled by the system, such as a JSON document, an NDJSON entry, or an API request body. An obligation is a single requirement evaluated against that record, for example a field must be present, a value must satisfy a constraint, or a record must be accepted or rejected based on its contents.
A boundary execution model refers to evaluating those obligations at the system boundary where data enters, rather than deep inside downstream application logic. The intent is to make decisions as part of intake handling, before the data is handed off to other components.
:)
Requests that would normally be fully parsed, tokenized, embedded, and sent to a model are often decided early and dropped… before any of that expensive work happens.
That’s fewer tokens generated, fewer CPU cycles burned, and fewer dollars spent at scale.