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 about how Catalyst works).
Matei > On Jan 13, 2020, at 8:41 AM, Michael Mior <mm...@apache.org> wrote: > > 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 > <jasonnerot...@gmail.com> a écrit : >> >> 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 <newroy...@gmail.com> wrote: >>> >>> 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 to dig up anything concrete from user group / Jira. Appreciate >>> if any Catalyst veteran here can give me pointers. Trying to defend >>> Spark/Catalyst. >>> >>> >>> >>> >>> >>> -- >>> Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >> >> >> -- >> Thanks, >> Jason > > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
