pg8000 VS opteryx

As others have said you can use a custom compiled version of the lib, lambda layer or use lambda images, however, if you're not committed to psycogp2 I've found pg8000 a much easier library to work with in Lambda. You can just install it as any other library without any problems.

When people say "pure X", to me, it normally means they didn't involve an FFI or external compiler. This is an often beneficial thing since it simplifies your build process.

For example, here [0] is a "pure Python postgres driver" and the implication is that it doesn't use libpg.

Or see also this discussion [1].

[0] https://github.com/tlocke/pg8000

[1] https://www.reddit.com/r/learnpython/comments/nktut1/eli5_th...

I agree that information shouldn't be duplicated, but in one of my projects I've taken the opposite approach and made the FAQ the only place that certain information is presented. It's for the library https://github.com/tlocke/pg8000 and I've called them 'Examples' rather than a FAQ, but each time I get a question that isn't covered by the examples I add in a new example. I'd be interested to hear what people think of this approach.

Thanks for sharing.

I have a SQL Engine in Python too (https://github.com/mabel-dev/opteryx). I focused my initial effort on supporting SQL statements and making the usage feel like a database - that probably reflects the problem I had in front of me when I set out - only handling handfuls of gigabytes in a batch environment for ETLs with a group of new-to-data-engineering engineers. Have recently started looking more at real-time performance, such as distributing work. Am interesting in how you've approached.