arrow-datafusion VS databend

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.

Databend vs. Snowflake: https://github.com/datafuselabs/databend/issues/13059

Indeed, under a pay-as-you-go model, if there's a lack of precise control over the warehouse, such as a 10-minute suspension, it could lead to significant waste. This is because most queries might only take a few seconds, and the rest of the time is wasted. If you find Snowflake expensive, consider Databend. It's an open-source, cost-efficient alternative to Snowflake, and it maintains a consistent product experience with Snowflake.

Open-source: https://github.com/datafuselabs/databend

They're advertising as an open source direct competitor with Snowflake, with the ability to store data in parquet files. Github repo (5.6k stars) here.

Databend has recently introduced an SQL function that generates SQL statements from natural language. This feature can significantly reduce the time required for writing and debugging SQL statements.

Hi, we used to use sqlparser in [Databend](https://github.com/datafuselabs/databend). But at last we decide to write our own sqlparser using nom-rule.

Link to Github: https://github.com/datafuselabs/databend