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Comment by chatmasta | Hacker News Reader

They’re similar, but DuckDb is more of a batteries-included database whereas DataFusion is an embeddable query engine. You can use DuckDb in embedded-ish scenarios, but it’s not primarily targeting that use case. To put it another way, DataFusion is sometimes described as “the LLVM of databases.”

Another difference is that DuckDb is written in C++ whereas DataFusion is in Rust, so all the usual memory-safety and performance arguments apply. In fact DataFusion has recently overtaken DuckDb in Clickbench results after a community push last year to optimize its performance.

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We tried both about 8 months ago, at the time DuckDB’s Node driver leaked memory and segfaulted, DataFusion was missing some features we wanted. But they are both improving rapidly.

> DataFusion has recently overtaken DuckDb in Clickbench results after a community push last year

Really? I don't see it near the top.

[CH benchmarks](https://benchmark.clickhouse.com/#eyjzexn0zw0ionsiqwxsb3leqi...)

You might need to adjust filters to do an apple to apple comparison.

https://benchmark.clickhouse.com/#eyJzeXN0ZW0iOnsiQWxsb3lEQi...

Not clear why someone need to give up on native duckdb format if it is much faster.

Because it means you need to keep another copy of your data in a special format just for DuckDb. The point of Parquet is that it’s an open format queryable by multiple tools. You don’t need to wait to load every table into a new format, you don’t need to retain multiple copies, and you don’t need to keep them in sync.

If DuckDb is the only query engine in your analytics stack, then it makes sense to use its specialized format. But that’s not the typical Lakehouse use case.

> But that’s not the typical Lakehouse use case.

that benchmark is also not typical lakehouse use case, since all data is hosted locally, so they don't test significant component of the stack.

Yeah, that’s one of many issues with Clickbench. It’s also one table so it can’t test joins.

TPC-H is okay but not Lakehouse specific. I’m not aware of any benchmarks that specifically test performance of engines under common setups like external storage or scalable compute. It would be hard to design one that’s easily reproducible. (And in fairness to Clickbench, it’s intentionally simple for that exact reason - to generate a baseline score for any query engine that can query tabular data).

Specifically, DataFusion is faster when querying parquet directly.

Most of the leaderboard of ClickBench is for database specific file formats (that you first have to load the data into)

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