zetasql VS sqlparse

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.

Suggest checking out the sqlparse library for a way to do the different flavours without needing to address each case directly: https://github.com/andialbrecht/sqlparse

Gotcha, since we haven't actually written all of this yet I don't have any useful code snippets to share but we've discussed tackling the problem internally using something like sqlparse. You'd need to identify the relevant sql chunks, parse them for table dependency information and then create the relevant entities in whichever data lineage tool you were using.

sqlparse – A non-validating SQL parser module for Python

Regular expressions is a popular approach to extract information from SQL statements. However, regular expressions quickly become too complex to handle common features like WITH, sub-queries, windows clauses, aliases and quotes. sqlparse is a popular python package that uses regular expressions to parse SQL.

This one is not bad: https://github.com/andialbrecht/sqlparse.

We used this repo for this: https://github.com/andialbrecht/sqlparse. I may have miscommunicated. We didn't write the parser from scratch, we created a way for the parser to detect downstream and upstream dependencies of the resource.