go-mysql-server VS zetasql

We implemented a query optimizer with a flexible intermediate representation in pure Go:

https://github.com/dolthub/go-mysql-server

Getting the IR correct so that it's both easy to use and flexible enough to be useful is a really interesting design challenge. Our primary abstraction in the query plan is called a Node, and is way more general than the IR type described in the article from OP. This has probably hurt us: we only recently separated the responsibility to fetch rows into its own part of the runtime, out of the IR -- originally row fetching was coupled to the Node type directly.

This is also the query engine that Dolt uses:

https://github.com/dolthub/dolt

But it has a plug-in architecture, so you can use the engine on any data source that implements a handful of Go interface.

Might be interested in https://github.com/dolthub/go-mysql-server, which also does this

https://github.com/dolthub/go-mysql-server

One item under "Scope of this project":

Provide a runnable server speaking the MySQL wire protocol, connected to data sources of your choice.

I just wanted to say thanks for https://github.com/dolthub/go-mysql-server

This is incredibly useful for anyone who wants to build their own DB or wrap another datasource so it's queryable via MySQL protocol.

a very cool project they also maintain is a MySQL server framework for arbitrary backends (in Go): https://github.com/dolthub/go-mysql-server

You can define a "virtual" table (schema, how to retrieve rows/columns) and then a MySQL client can connect and execute arbitrary queries on your table (which could just be an API or other source)

Thanks for this write up! I've been really interested in postgres compatibility in the context of a tool I maintain (https://github.com/mergestat/mergestat) that uses SQLite. I've been looking for a way to expose the SQLite capabilities over a more commonly used wire-protocol like postgres (or mysql) so that existing BI and visualization tools can access the data.

This project is an interesting one: https://github.com/dolthub/go-mysql-server that provides a MySQL interface (wire and SQL) to arbitrary "backends" implemented in go.

It's really interesting how compatibility with existing protocols has become an important feature of new databases - there's so much existing tooling that already speaks postgres (or mysql), being able to leverage that is a huge advantage IMO

Open-source GoogleSQL implementation, ZetaSQL

It is, using table-valued functions (TVFs).

There's an example at the bottom of this file:

https://github.com/google/zetasql/blob/master/zetasql/exampl...

ZetaSQL’s docs for the ‘OVER’ keyword you mention: https://github.com/google/zetasql/blob/master/docs/window-fu...

I disagree that the paper not mentioning ‘OVER’ implies that the paper authors secretly think pipe syntax is a bad idea. They probably just wanted to keep the paper concise, or forgot about that one less-used bit of syntax.

Do you think that ‘OVER’ keyword implies something fundamentally wrong about pipe syntax?

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.