Hi Mich,
Thank you for your comments! I've left some comments on the SPIP, but let's
continue the discussion here.
You've highlighted the potential advantages of Python stored procedures,
and I'd like to emphasize two important aspects:
1. *Versatility*: Integrating Python into SQL provides
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imer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
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> The author will in no case be liable for any monetary damages arising from
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Hi all,
I would like to start a discussion on “Python Stored Procedures".
This proposal aims to extend Spark SQL by introducing support for stored
procedures, starting with Python as the procedural language. This will
enable users to run complex logic using Python within their SQL workflows
and
The vote passes with 12 +1s (8 binding +1s) and one +0 (binding).
(* = binding)
+1:
- Hyukjin Kwon *
- Xiao Li *
- Denny Lee
- Martin Grund
- Mich Talebzadeh
- Huaxin Gao *
- Holden Karau *
- Reynold Xin *
- Jungtaek Lim
- Ruifeng Zheng *
- Takuya Ueshin *
- Matei Zaharia *
+0: Maciej
Hi all,
I'd like to start the vote for SPIP: Python Data Source API.
The high-level summary for the SPIP is that it aims to introduce a simple
API in Python for Data Sources. The idea is to enable Python developers to
create data sources without learning Scala or dealing with the complexities
of
Hi everyone,
I would like to start a discussion on “Python Data Source API”.
This proposal aims to introduce a simple API in Python for Data Sources.
The idea is to enable Python developers to create data sources without
having to learn Scala or deal with the complexities of the current data
Thanks everyone for voting! With 11 +1s and no -1s, this vote passes.
+1s:
Mridul Muralidharan
Angers Zhu
Chandni Singh
Eve Liao
Matei Zaharia
Kalyan
Wenchen Fan
Gengliang Wang
Xiao Li
Takeshi Yamamuro
Herman van Hovell
Thanks,
Allison
--
Sent from:
Hi Mridul,
Thanks for reviewing the SPIP. As you correctly pointed out, this proposal
is an internal, Spark engine developer-facing improvement to make error
messages more consistent and user-friendly across the Spark codebase, as
opposed to establishing a public contract with end-users.