simdjson-go
Apache Arrow
simdjson-go | Apache Arrow | |
---|---|---|
6 | 75 | |
1,761 | 13,562 | |
0.7% | 1.4% | |
4.0 | 10.0 | |
6 months ago | 4 days ago | |
Go | C++ | |
Apache License 2.0 | Apache License 2.0 |
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simdjson-go
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Speaking of Go, there's a simdjson implementation for golang too:
> Performance wise, simdjson-go runs on average at about 40% to 60% of the speed of simdjson. Compared to Golang's standard package encoding/json, simdjson-go is about 10x faster.
I haven't tried it yet but I don't really need that speed.
https://github.com/minio/simdjson-go
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How to Use AVX512 in Golang
I agree. For performance-sensitive situations, C/C++ or Rust is the only choice. However, many developers choose Go or other languages for engineering efficiency. A typical use case of SIMD in Go is simdjson-go. Besides, there are plenty of bindings and ports of simdjson. "Other languages" developers also need performance improvement from native instructions such as SIMD.
- Sonic: A fast JSON serializing and deserializing library
- Whats the fastest JSON unmarshaling package as of right now?
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What is the best solution to unique data in golang
I suggest to use a streaming library to parse your file. Like jstream or simdjson-go
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I wrote yet another json parser. It may be a contender for fastest.
You can also try comparing with https://github.com/minio/simdjson-go. It does use a different API, however, would be good to compare nevertheless.
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?
easyjson - Fast JSON serializer for golang.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
jstream - Streaming JSON parser for Go
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
jsonparser - One of the fastest alternative JSON parser for Go that does not require schema
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
sonic - A blazingly fast JSON serializing & deserializing library
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
jsonlite - A simple, self-contained, serverless, zero-configuration, json document store.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
rjson - A fast json parser for go
ClickHouse - ClickHouse® is a free analytics DBMS for big data