fastjson2
polars
| fastjson2 | polars | |
|---|---|---|
| 2 | 144 | |
| 3,442 | 26,514 | |
| 2.1% | 3.9% | |
| 9.8 | 10.0 | |
| 7 days ago | 4 days ago | |
| Java | Rust | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
fastjson2
- FLaNK Stack Weekly for 20 June 2023
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Serverless Speed: Rust vs. Go, Java, and Python in AWS Lambda Functions
https://github.com/alibaba/fastjson2/wiki/fastjson_benchmark Alibaba has a fastjson2 that claims to be a lot faster.
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
jackson-databind - General data-binding package for Jackson (2.x): works on streaming API (core) implementation(s)
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
java-json-benchmark - Performance testing of serialization and deserialization of Java JSON libraries
modin - Modin: Scale your Pandas workflows by changing a single line of code
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
datafusion - Apache DataFusion SQL Query Engine
StanfordQuadruped
DataFrames.jl - In-memory tabular data in Julia
json-data-storage - 🚀 The Shared Preferences API for Java
datatable - A Python package for manipulating 2-dimensional tabular data structures
simdjson-java - A Java version of simdjson, a high-performance JSON parser utilizing SIMD instructions
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing