Say “halting problem” without saying “halting problem” ;)

There is a practical solution to it called “metering”, like gas mechanism in Ethereum’s EVM or cost calculation for complex GraphQL queries.

It's not fully applicable here, but industry standard DSLs also stick around because non-programmers find learning it a good investment.

I have a business analytics friend that knows SQL because it's part of his workflows.

But Excel, Notion, Power BI, and other low/no-code tools all have their own data filtering and transformation languages (or dialects). He'd rather spend his time learning more about his line of business, than an aspect of yet another cloud tool that gets forced on him.

Ease/ability to embed in other language safely. Predictability of memory, execution. Known constraints like guaranteed to terminate is useful.

no Doom running on cel.

I recently wanted to expose some basic user auto tagging/labeling based on the json data.

I chose cel, over python, SQL because I could just import the runtime in C++, or any language that implements it (python, js etc..)

Safely running a sandboxed python execution engine is significantly more effort and lower performance.

At this cel excels.

Where it didn't was user familiarity and when the json data itself was complex.

No, they're equating _Turing completeness_ with _might not terminate_. CEL, Expr, Rego, and other languages like them are intended to guarantee to complete. You can't do that cleanly with a Turing complete language.

What you really want is "can be completed after a certain amount of time", not "can be cancelled". You don't want iam policy rules to be skipped because they took too long.