Apache Arrow VS Cap'n Proto

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?

Yeah I pretty much only use my own alternate container implementations (from KJ[0]), which avoid these footguns, but the result is everyone complains our project is written in Kenton-Language rather than C++ and there's no Stack Overflow for it and we can't hire engineers who know how to write it... oops.

[0] https://github.com/capnproto/capnproto/blob/v2/kjdoc/tour.md

- may massively reduce the latency involved.

Those sharing Cap'n Proto-encoded data may have particular interest. Cap'n Proto (https://capnproto.org) is fantastic at its core task - in-place serialization with zero-copy - and we wanted to make the IPC (inter-process communication) involving capnp-serialized messages be zero-copy, end-to-end.

That said, we paid equal attention to other varieties of payload; it's not limited to capnp-encoded messages. For example there is painless (<-- I hope!) zero-copy transmission of arbitrary combinations of STL-compliant native C++ data structures.

To help determine whether Flow-IPC is relevant to you we wrote an intro blog post. It works through an example, summarizes the available features, and has some performance results. https://www.linode.com/blog/open-source/flow-ipc-introductio...

Of course there's nothing wrong with going straight to the GitHub link and getting into the README and docs.

Currently Flow-IPC is for Linux. (macOS/ARM64 and Windows support could follow soon, depending on demand/contributions.)

FWIW, my C++ toolkit library, KJ, does the same thing.[0]

But presumably you could still write a condition predicate which looks at things which aren't actually part of the mutex-wrapped structure? Or does is the Rust type system able to enforce that the callback can only consider the mutex-wrapped value and values that are constant over the lifetime of the wait? (You need the latter e.g. if you are waiting for the mutex-wrapped value to compare equal to some local variable...)

[0] https://github.com/capnproto/capnproto/blob/e6ad6f919aeb381b...

The second one is to encode data in such a way that you can read it and operate on it directly from the buffer. You write data in a layout that is the same, or easily transformed as types in memory. To do that you usually need to encode with a known schema, only Sized types to efficiently compute fields locations as offsets in the buffer, and you usually represent pointers as offset into the encode. You can look at capnproto protocol for instance https://capnproto.org/

Worked well for Cap'n Proto (the cerealization protocol)! https://capnproto.org/

With all due respect, you read completely wrong.

* The very first use case for which Cap'n Proto was designed was to be the protocol that Sandstorm.io used to talk between sandbox and supervisor -- an explicitly adversarial security scenario.

* The documentation explicitly calls out how implementations should manage resource exhaustion problems like deep recursion depth (stack overflow risk).

* The implementation has been fuzz-tested multiple ways, including as part of Google's oss-fuzz.

* When there are security bugs, I issue advisories like this:

https://github.com/capnproto/capnproto/tree/v2/security-advi...

* The primary aim of the entire project is to be a Capability-Based Security RPC protocol.

I like how they use capability-based security [0] and use Cap'n Proto protocol. This is another technology that is slow to get broad adoption, but has many things going for when compared to e.g. Protocol Buffers (Cap'n Proto is created by the primary author of Protobuf v2, Kenton Varda).

[0] https://sandstorm.io/how-it-works#capabilities

[1] https://capnproto.org

Related but not Rust-specific: FlatBuffers, Cap'n Proto.