mangle VS zetasql

Mangle https://github.com/google/mangle is an open-source implementation in golang, it was an explicit goal to make it easy to learn. Meaning: it is easy to recognize the pure datalog part, the syntax is following the good old course material.

It was discussed here: https://news.ycombinator.com/item?id=33756800

Logic programming offers a good foundation for anything that people call "rule engines." Within logic programming, there is some variation on the degree of declarativeness.

Datalog is arguably the minimal core logic programming, similar to what the lambda calculus achieves for functional programming. Unfortunately, it has been forgotten outside of database and query processing realm. A resurgence has happened in recent years, as PL researchers and also industry have discovered the virtues of datalog (e.g. Flix, DataFun). My own attempt at making this more widely known is here https://github.com/google/mangle, a language from the datalog family and its implementation as a go library.

As the example shows: plain "rules" (or: plain datalog) is rarely enough to capture everything that one wants to express: the question then is, how to combine a pure declarative "kernel" with more general purpose programming (e.g. mapping a list).

PROLOG offered one answer, already in the 1980s, but I fully reject it: the fact that the writing a program in the wrong order with negation and recursion makes it non-terminating is not something we'd want everyone to deal with. Datalog with stratified recursion is somewhat better, as "layers of rules" is a concept that is easy to understand.

In mainstream programming languages, the possibility of writing non-terminating programs also exists, but is rarely an issue. That is why I believe a good combination of declarative and general-purpose has to make it really easy to recognize which parts of a program are in the declarative, terminating, safe kernel and which parts require more attention from the programmer.

There are even table-valued functions.

These things are not widespread, and differ by implementation, and the way these are used by clients are copy-and-paste. Something as thoughtful as ZetaSQL https://github.com/google/zetasql does not have mechanisms for structuring (modules, packages, interfaces). SQL will not, cannot evolve into such a direction (or, anything that evolves, will not be recognizable as SQL).

Hi, I develop a BigQuery emulator ( https://github.com/goccy/bigquery-emulator ) from early 2022. It is written in Go, but can be used from bq command line tool and other language's (e.g. Python ) client SDK after installing docker image or released binary. It currently supports over 200 of the nearly 330 standard functions in BigQuery and all data types except GEOGRAPHY ( see https://github.com/goccy/go-zetasqlite#status for details ). ZetaSQL ( https://github.com/google/zetasql ) is used to parse and analyze queries.

We are using a Python client binding for ZetaSQL GRPC local service in our application to analyze statements and extract referenced tables and output columns.

If you don't want to do it yourself, there's this:

https://github.com/google/zetasql

Parsing is huge but it's amazing how small a part of the job it is. This library isn't even the half of it.

I mean the postgres parser (and semantic changes) for ZetaSQL. The zetasql parser is in a file called zetasql/parser/bison_parser.y, I strongly suspect they now have a file called something like zetasql/pgparser/bison_parser.y as well (and much more pervasive changes to support the deeper differences in the dialects).

This is the lexical structure and syntax docs for the new postgres inteface to cloud spanner:

https://cloud.google.com/spanner/docs/postgresql/lexical

And this is the zetasql lexical structure and syntax docs:

https://github.com/google/zetasql/blob/master/docs/lexical.m...

Notice that the new PG docs are an edit of the Zeta ones - evidence that my hypothesis is correct.

zetasql implements BigQuery, Spanner, and Dataflow dialects.