FlatBuffers
Apache Arrow
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FlatBuffers | Apache Arrow | |
---|---|---|
48 | 75 | |
22,005 | 13,480 | |
0.9% | 2.2% | |
8.7 | 10.0 | |
6 days ago | 4 days ago | |
C++ | C++ | |
Apache License 2.0 | Apache License 2.0 |
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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.
FlatBuffers
- FlatBuffers – an efficient cross platform serialization library for many langs
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Cap'n Proto 1.0
I don't work at Cloudflare but follow their work and occasionally work on performance sensitive projects.
If I had to guess, they looked at the landscape a bit like I do and regarded Cap'n Proto, flatbuffers, SBE, etc. as being in one category apart from other data formats like Avro, protobuf, and the like.
So once you're committed to record'ish shaped (rather than columnar like Parquet) data that has an upfront parse time of zero (nominally, there could be marshalling if you transmogrify the field values on read), the list gets pretty short.
https://capnproto.org/news/2014-06-17-capnproto-flatbuffers-... goes into some of the trade-offs here.
Cap'n Proto was originally made for https://sandstorm.io/. That work (which Kenton has presumably done at Cloudflare since he's been employed there) eventually turned into Cloudflare workers.
Another consideration: https://github.com/google/flatbuffers/issues/2#issuecomment-...
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Anyone has experience with reverse engineering flatbuffers?
Much more in the discussion of this particular issue onGitHub: flatbuffers:Reverse engineering #4258
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Flatty - flat message buffers with direct mapping to Rust types without packing/unpacking
Related but not Rust-specific: FlatBuffers, Cap'n Proto.
- flatbuffers - FlatBuffers: Memory Efficient Serialization Library
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How do AAA studios make update-compatible save systems?
If json files are a concern because of space, you can always look into something like protobuffers or flatbuffers. But whatever you use, you should try to find a solution where you don't have to think about the actual serialization/deserialization of your objects, and can just concentrate on the data.
- QuickBuffers 1.1 released
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Choosing a protocol for communication between multiple microcontrollers
Or, as an alternative to protobuffers, there's also flatbuffers, which is lighter weight and needs less memory: https://google.github.io/flatbuffers/
- FlatBuffers: FlatBuffers
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Is using Flatbuffers to parse sensor data a bad application of Flatbuffers?
As the title suggests, I am considering using Flatbuffers as a way to parse sensor data that has been stored in local datafiles. The project language is python.
Apache Arrow
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How moving from Pandas to Polars made me write better code without writing better code
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++.
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From slow to SIMD: A Go optimization story
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.
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Time Series Analysis with Polars
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.
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TXR Lisp
IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/
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3D desktop Game Engine scriptable in Python
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:
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
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.
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Cap'n Proto 1.0
Worker should really adopt Apache Arrow, which has a much bigger ecosystem.
https://github.com/apache/arrow
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C++ Jobs - Q3 2023
Apache Arrow
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Wheel fails for pyarrow installation
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?
What are some alternatives?
Protobuf - Protocol Buffers - Google's data interchange format
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
MessagePack - MessagePack implementation for C and C++ / msgpack.org[C/C++]
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
MessagePack - MessagePack serializer implementation for Java / msgpack.org[Java]
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Cap'n Proto - Cap'n Proto serialization/RPC system - core tools and C++ library
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
cereal - A C++11 library for serialization
ClickHouse - ClickHouse® is a free analytics DBMS for big data
Kryo - Java binary serialization and cloning: fast, efficient, automatic
beam - Apache Beam is a unified programming model for Batch and Streaming data processing.