viztracer VS Apache Arrow

* https://github.com/gaogaotiantian/viztracer get a timeline of execution vs call-stack (great to discover what's happening deep inside pandas)

Do not use bad instrumenting profilers. A good modern tracing-based instrumenting profiler provides so much more actionable information and insights into where problems are than a sampling profiler it is ridiculous.

As a example consider viztracer [1] for Python. By using a aggregate visualizer such as a flame graph you can figure out what is taking the most time then you can use a tracing visualizer to figure out the exact call stacks and system execution and state that caused it. Not only that, a tracing visualizer lets you diagnose whole system performance and makes it trivial to identify 1 in 1000 anomalous execution patterns (with a 4k screen a anomalous execution pattern stands out like a 4 pixel dead spot). In addition you also get vastly less biased information for parallel execution and get easy insights into parallel execution slowdowns, interference, contention, and blocking behaviors.

The only advantages highlighted in your video that still apply to a good instrumenting profiler are:

1. Multi-language support.

2. Performance counters (though that is solved by doing manual tracking after you know the hotspots and causes).

3. Overhead (if you are using low sampling frequency). Even then a good tracing instrumentation implementation should only incur low double-digit percent overhead and maybe 100% overhead in truly pathological cases involving only small functions where the majority of the execution time is literally spent in function call overhead.

4. No need for recompilation, but you are already looking to make performance changes and test so you already intend to rebuild frequently to test those experiments. In addition, the relative difference in information is so humongous that this is not even worth contemplating unless it is a hard requirement like evaluating something in the field.

[1] https://github.com/gaogaotiantian/viztracer

Actually it has explicit support for async task based reporting:

https://github.com/gaogaotiantian/viztracer#async-support

Let us run perf on this, similarly to what we did to example0.py. However, we add the argument -k CLOCK_MONOTONIC so that we use the same clock as VizTracer and ask VizTracer to generate a JSON, instead of an HTML file:

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?