DictDataBase VS quokka

Nice! Quite similar to a database I am working on: https://github.com/mkrd/DictDataBase

So in the end, this is an attempt to but a truly document oriented equivalent to SQLite, and I think there is potential. I would love to do the same benchmark with the SQLite solution you proposed and compare the two, but my time is limiting me right now… Here is the github link if you are interested: https://github.com/mkrd/DictDataBase

Hi guys! I'm working on DictDataBae, and just wanted to share some performance numbers which might be interesting to you.

Someone recently posted their project, I'd imagine you're after something like this? Haven't tried it but I have a drop in use case I might fool around with (giant json flat file store). Duct database

The project is available on [Github](https://github.com/mkrd/DictDataBase) and [PyPi](https://pypi.org/project/dictdatabase) if you wanna take a look!

DictDataBase – A python NoSQL database that uses dicts, and provided thread and process safety

If a thread holds a lock (so is in a with DDBSession(): block) for longer than 30 seconds, its hold is automatically revoked, but DDBSession.write doesn't detect this case. I would definitely recommend using operating system functions designed for file locking instead.

Here is the Github Link and the PyPi Page if you wanna take a look!

An awesome read!

Something related that I found out about from HN a few months back is another engine called quokka. It's particularly interesting and applicable how quokka schedules distributed queries to outperform Spark https://github.com/marsupialtail/quokka/blob/master/blog/why...

Hi Justin, you might be interested in my blog: https://github.com/marsupialtail/quokka/blob/master/blog/bac... advocating a cloud based approach.

You don't have to use the system I am building, but it's worth thinking about that design.

SQL support is very challenging.

I work on Quokka (https://github.com/marsupialtail/quokka). I support Iceberg reads. Recently we are adding SQL support from just parsing the DuckDB logical plan, though that is very challenging as well.

The Python world lacks a standard for a plug and play SQL query optimizer. Apache Calcite is good for the JVM world, but not great if you are trying to cut out the JVM.

In case people are interested, I wrote a post about fault tolerance strategies of data systems like Spark and Flink: https://github.com/marsupialtail/quokka/blob/master/blog/fau...

The key difference here is that these systems don't store data, so fault tolerance means recovering within a query instead of not losing data.

would love to collaborate on an integration with pyquokka (https://github.com/marsupialtail/quokka) once I put out a stable release end of this month :-)

Then you should keep an eye on quokka. This may become the "Spark" for Polars/DuckDB. It seems to be under active development though I'm not sure how stable it is.