arrow-datafusion VS arrow-rs

Python's Substrait seems like the biggest/most-used competitor-ish out there. I'd love some compare & contrast; my sense is that Substrait has a smaller ambition, and more wants to be a language for talking about execution rather than a full on execution engine. https://github.com/substrait-io/substrait

We can also see from the DataFusion discussion that they too see themselves as a bit of a Velox competitor. https://github.com/apache/arrow-datafusion/discussions/6441

Agree, substrait is a really cool project! Related: if you like substrait you might want to check out datafusion too. The project is a query execution engine built on top of Apache Arrow (with SQL parser, query planner & optimizer, execution engine, extensible user defined functions, among others) and it implements a substrait provider and consumer: https://github.com/apache/arrow-datafusion/tree/main/datafus...

The draft contains some preliminary benchmark results, comparing it to DuckDB.

https://github.com/apache/arrow-datafusion/issues/6782

Apache Arrow is a pretty common memory structure these days. Datafusion is an open query engine built in Rust started by Andy Grove.

DuckDB is a great piece of software if you are

If you are looking for a query engine implemented in a safe language (Rust) I definitely suggest checking out DataFusion. It is comparable to DuckDB in performance, has all the standard built in SQL functionality, and is extensible in pretty much all areas (query language, data formats, catalogs, user defined functions, etc)

https://arrow.apache.org/datafusion/

Disclaimer I am a maintainer of DataFusion

https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb

Problem comes when you want to do this inside an async context since we couldn't block an async task. https://users.rust-lang.org/t/sync-function-invoking-async/43364/6 You might need to do it in another runtime/thread. It is not recommended to do this, but sometimes it is unavoidable while implementing a third-party trait. https://github.com/apache/arrow-datafusion/issues/3777 However, I believe this isn't a problem particular to tokio, or any specific runtime.

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

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

It's built around the arrow-rs library, which we've contributed to significantly: https://github.com/apache/arrow-rs

For loading Parquet in memory, probably worth a look at arrow-rs.

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.

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.

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

There is more discussion about the decision here: https://github.com/apache/arrow-rs/issues/1120

I'm still not sure how this differs from https://github.com/apache/arrow-rs. What does transmute even mean?