boto3 VS Apache Arrow

Former AWS here.

My literal job for the last part of my time at AWS was "help triage bugs in the AWS SDK." This is by far the best repro I've ever seen for such an in-depth event.

Most of the tickets you get in open ticket trackers are incomplete [ https://github.com/boto/boto3/issues/4011 ] nonsensical [ https://github.com/boto/boto3/issues/4018 ] or weird [ https://github.com/boto/boto3/issues/358 ].

Recently, my colleague brought up the difficulty of using the AWS SDK for Python (Boto3) while working with DynamoDB, especially the cumbersome mapping of AttributeValue objects on the Table operations. One of the easiest ways to get around this difficulty is to switch from the clients interface to the resources interface.

A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example I've decided to expand our math functionality with NumPy. pdm add is what's used to add dependencies like this to our project:

The updates could be parsed from the github repo's CHANGELOG files (ex: javascript, java, python). I'm picturing an RSS feed generated for a specific language and module (ex: python s3, javascript s3, java sqs)

This can be quite annoying because it makes you wonder why the high-level API isn't able to deal with these common data types. Part of the reason for this is most likely that floats in Python can be counter-intuitive, so Decimal is a better data type if you want numbers to behave as non-computer-scientists expect it. To learn more about these complexities, check out this discussion on GitHub about implementing float support in boto3 and the Python documentation on the subject. Additionally, DynamoDB has no native DateTime data type, so there is no straightforward mapping.

AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.

Alternatively, you could create a Python script using either Boto3 or her asynchronous sister, aioBoto3 that will spin through the contents of the origin bucket and move it over to the destination.

Once my site was stood up, I needed to build out the user count API. Through the console, I set up a DynamoDB table and created a user count item. Getting my lambda to interface with AWS resources was a breeze with the Boto3 SDK. You can see my Python code that increments the user count whenever someone visits the site here. The key is the usage of the update_item method that comes from Boto3.

If you want to get a feel for what kind of logging and how much logging is done in projects, boto3 is a very widely used SDK created by Amazon: https://github.com/boto/boto3

In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++.

I learned yesterday about GoLang's assembler https://go.dev/doc/asm - after browsing how arrow is implemented for different languages (my experience is mainly C/C++) - https://github.com/apache/arrow/tree/main/go/arrow/math - there are bunch of .S ("asm" files) and I'm still not able to comprehend how these work exactly (I guess it'll take more reading) - it seems very peculiar.

The last time I've used inlined assembly was back in Turbo/Borland Pascal, then bit in Visual Studio (32-bit), until they got disabled. Then did very little gcc with their more strict specification (while the former you had to know how the ABI worked, the latter too - but it was specced out).

Anyway - I wasn't expecting to find this in "Go" :) But I guess you can always start with .go code then produce assembly (-S) then optimize it, or find/hire someone to do it.

One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on par with its NumPy-based features. Polars was built around Arrow from the get go. This makes it very powerful when it comes to exchanging data with other languages and reducing the number of in-memory copying operations, thus leading to better performance.

IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/

https://www.reddit.com/r/O3DE/comments/rdvxhx/why_python/ :

> Python is used for scripting the editor only, not in-game behaviors.

> For implementing entity behaviors the only out of box ways are C++, ScriptCanvas (visual scripting) or Lua. Python is currently not available for implementing game logic.

C++, Lua, and Python all implement CFFI (C Foreign Function Interface) for remote function and method calls.

"Using CFFI for embedding" https://cffi.readthedocs.io/en/latest/embedding.html :

> You can use CFFI to generate C code which exports the API of your choice to any C application that wants to link with this C code. This API, which you define yourself, ends up as the API of a .so/.dll/.dylib library—or you can statically link it within a larger application.

Apache Arrow already supports C, C++, Python, Rust, Go and has C GLib support Lua:

https://github.com/apache/arrow/tree/main/c_glib/example/lua :

> Arrow Lua example: All example codes use LGI to use Arrow GLib based bindings

pyarrow.from_numpy_dtype:

AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.

Worker should really adopt Apache Arrow, which has a much bigger ecosystem.

https://github.com/apache/arrow

Apache Arrow

I am aware of the fact that there are other posts about this issue but none of the ideas to solve it worked for me or sometimes none were found. The issue was discussed in the wheel git hub last December and seems to be solved but then it seems like I'm installing the wrong version? I simply used pip3 install pyarrow, is that wrong?