arrow-rs
polars
arrow-rs | polars | |
---|---|---|
16 | 144 | |
2,198 | 26,378 | |
3.0% | 3.4% | |
9.8 | 10.0 | |
1 day ago | 3 days ago | |
Rust | 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.
arrow-rs
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Rkyv: Rkyv zero-copy deserialization framework for rust
https://github.com/djkoloski/rust_serialization_benchmark
Apache/arrow-rs: https://github.com/apache/arrow-rs
From https://arrow.apache.org/faq/ :
> How does Arrow relate to Flatbuffers?
> Flatbuffers is a low-level building block for binary data serialization. It is not adapted to the representation of large, structured, homogenous data, and does not sit at the right abstraction layer for data analysis tasks.
> Arrow is a data layer aimed directly at the needs of data analysis, providing a comprehensive collection of data types required to analytics, built-in support for “null” values (representing missing data), and an expanding toolbox of I/O and computing facilities.
> The Arrow file format does use Flatbuffers under the hood to serialize schemas and other metadata needed to implement the Arrow binary IPC protocol, but the Arrow data format uses its own representation for optimal access and computation
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Polars: Company Formation Announcement
One of the interesting components of Polars that I've been watching is the use of the Apache Arrow memory format, which is a standard layout for data in memory that enables processing (querying, iterating, calculating, etc) in a language agnostic way, in particular without having to copy/convert it into the local object format first. This enables cross-language data access by mmaping or transferring a single buffer, with zero [de]serialization overhead.
For some history, there's has been a bit of contention between the official arrow-rs implementation and the arrow2 implementation created by the polars team which includes some extra features that they find important. I think the current status is that everyone agrees that having two crates that implement the same standard is not ideal, and they are working to port any necessary features to the arrow-rs crate and plan on eventually switching to it and deprecating arrow2. But that's not easy.
https://github.com/apache/arrow-rs/issues/1176
https://github.com/jorgecarleitao/arrow2/pull/1476
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InfluxDB 3.0 System Architecture
It's built around the arrow-rs library, which we've contributed to significantly: https://github.com/apache/arrow-rs
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best cache type for 5gb size tables
For loading Parquet in memory, probably worth a look at arrow-rs.
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The state of Apache Avro in Rust
From what I've seen, most of the Rust community seems to be adopting Apache Arrow as the go-to for data processing. It has strong community support and good interoperability with many cross-language tools. It is natively a columnar format. If row-oriented is a must for your use case, consider looking into alternatives like gRPC that might better suit your needs.
- Arrow-Rs - Official Rust implementation of Apache Arrow
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Apache Arrow Feature Parity Timeline?
That matrix doesn't seem up to date. For example looking at the rust crate it does seem to support things like map, float16, and IPC. The changelog shows an impressive development pace.
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Apache Arrow Flight SQL: Accelerating Database Access
Oh, and for anyone interested in pitching in on the Rust implementation, there's an issue logged here along with some discussion: https://github.com/apache/arrow-rs/issues/1323
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February 2022 Rust Apache Arrow and Parquet Highlights
There is more discussion about the decision here: https://github.com/apache/arrow-rs/issues/1120
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Arrow2 0.9 has been released
I'm still not sure how this differs from https://github.com/apache/arrow-rs. What does transmute even mean?
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?
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
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 🚀
arrow2 - Transmute-free Rust library to work with the Arrow format
modin - Modin: Scale your Pandas workflows by changing a single line of code
datafusion - Apache DataFusion SQL Query Engine
byo-sql - An in-memory SQL database in Rust.
DataFrames.jl - In-memory tabular data in Julia
db-benchmark - reproducible benchmark of database-like ops
datatable - A Python package for manipulating 2-dimensional tabular data structures
parquet2 - Fastest and safest Rust implementation of parquet. `unsafe` free. Integration-tested against pyarrow