arrow-datafusion VS db-benchmark

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.

Real-world performance is complicated since data science covers a lot of use cases.

If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.

Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/

I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.

[1] https://h2oai.github.io/db-benchmark/

https://news.ycombinator.com/item?id=33270638 :

> Apache Ballista and Polars do Apache Arrow and SIMD.

> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *

LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database

/? inurl:awesome site:github.com "vector database"

I agree with your conclusion but want to add that switching from Julia may not make sense either.

According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.

For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.

Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks

The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.

If interested in benchmarks comparing different dataframe implementations, here is one:

https://h2oai.github.io/db-benchmark/

Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.