pg8000
datafusion-python
| pg8000 | datafusion-python | |
|---|---|---|
| 5 | 2 | |
| 467 | 296 | |
| - | 4.8% | |
| 7.4 | 8.4 | |
| 7 days ago | 2 days ago | |
| Python | Rust | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pg8000
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How to run psycogp2 in Aws lambda?
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.
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Pure Python Distributed SQL Engine
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...
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FAQs: Why we don’t have them (2013)
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.
- Pg8000 – Pure-Python PostgreSQL driver
datafusion-python
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Pure Python Distributed SQL Engine
hmm I wasn't aware of https://github.com/apache/arrow-datafusion-python... thanks for the pointer.
time series target release by April this year. main challenge is supporting them in the SQL API -- execution engine support is already done
What are some alternatives?
Django - The Web framework for perfectionists with deadlines.
sqlglot - Python SQL Parser and Transpiler
opteryx - 🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
datafusion-ballista - Apache Arrow Ballista Distributed Query Engine
TheAlgorithms - All Algorithms implemented in Python
sqlparser-rs - Extensible SQL Lexer and Parser for Rust
quokka - Making data lake work for time series
system-design-primer - Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.