In the upcoming Spark 3.0, we introduced a new framework for Adaptive Query Execution in Catalyst. This can adjust the plans based on the runtime statistics. This is missing in Calcite based on my understanding. 

Catalyst is also very easy to enhance. We also use the dynamic programming approach in our cost-based join reordering. If needed, in the future, we also can improve the existing CBO and make it more general. The paper of Spark SQL was published 5 years ago. A lot of great contributions were made in the past 5 years. 

Cheers,

Xiao

Debajyoti Roy <newroyker@gmail.com> 于2020年1月15日周三 上午9:23写道:
Thanks all, and Matei.

TL;DR of the conclusion for my particular case:
Qualitatively, while Catalyst[1] tries to mitigate learning curve and maintenance burden, it lacks the dynamic programming approach used by Calcite[2] and risks falling into local minima.
Quantitatively, there is no reproducible benchmark, that fairly compares Optimizer frameworks, apples to apples (excluding execution).

References:
[1] - https://amplab.cs.berkeley.edu/wp-content/uploads/2015/03/SparkSQLSigmod2015.pdf
[2] - https://arxiv.org/pdf/1802.10233.pdf

On Mon, Jan 13, 2020 at 5:37 PM Matei Zaharia <matei.zaharia@gmail.com> wrote:
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 <mmior@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
> mmior@apache.org
>
> Le lun. 13 janv. 2020 à 09:45, Jason Nerothin
> <jasonnerothin@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 <newroyker@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/
>>>
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>>>
>>
>>
>> --
>> Thanks,
>> Jason
>
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