I’m pretty sure that Catalyst was built before Calcite, or at least in
parallel. Calcite 1.0 was only released in 2015. From a technical standpoint,
building Catalyst in Scala also made it more concise and easier to extend than
an optimizer written in Java (you can find various presentations
It's fairly common for adapters (Calcite's abstraction of a data
source) to push down predicates. However, the API certainly looks a
lot different than Catalyst's.
--
Michael Mior
mm...@apache.org
Le lun. 13 janv. 2020 à 09:45, Jason Nerothin
a écrit :
>
> The implementation they chose supports
The implementation they chose supports push down predicates, Datasets and
other features that are not available in Calcite:
https://databricks.com/glossary/catalyst-optimizer
On Mon, Jan 13, 2020 at 8:24 AM newroyker wrote:
> Was there a qualitative or quantitative benchmark done before a
Was there a qualitative or quantitative benchmark done before a design
decision was made not to use Calcite?
Are there limitations (for heuristic based, cost based, * aware optimizer)
in Calcite, and frameworks built on top of Calcite? In the context of big
data / TCPH benchmarks.
I was unable
Hi,
Is it possible to read 7z compressed file in spark?
Kind Regards
Harsh Takkar
