Ok thanks.

What is your experience of VS Code (in terms of capabilities ) as it is
becoming a standard tool available in Cloud workspaces like Amazon
workspace?

Mich



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<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>



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On Thu, 30 Sept 2021 at 15:43, Ali Behjati <bahja...@gmail.com> wrote:

> Not anything specific in my mind. Any IDE which is open to plugins can use
> it (e.g: VS Code and Jetbrains) to validate execution plans in the
> background and mark syntax errors based on the result.
>
> On Thu, Sep 30, 2021 at 4:40 PM Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> What IDEs do you have in mind?
>>
>>
>>
>>    view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* 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 from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Thu, 30 Sept 2021 at 15:20, Ali Behjati <bahja...@gmail.com> wrote:
>>
>>> Yeah it doesn't remove the need of testing on sample data. It would be
>>> more of syntax check rather than test. I have witnessed that syntax errors
>>> occur a lot.
>>>
>>> Maybe after having dry-run we will be able to create some automation
>>> around basic syntax checking for IDEs too.
>>>
>>> On Thu, Sep 30, 2021 at 4:15 PM Sean Owen <sro...@gmail.com> wrote:
>>>
>>>> If testing, wouldn't you actually want to execute things? even if at a
>>>> small scale, on a sample of data?
>>>>
>>>> On Thu, Sep 30, 2021 at 9:07 AM Ali Behjati <bahja...@gmail.com> wrote:
>>>>
>>>>> Hey everyone,
>>>>>
>>>>>
>>>>> By dry run I mean ability to validate the execution plan but not
>>>>> executing it within the code. I was wondering whether this exists in spark
>>>>> or not. I couldn't find it anywhere.
>>>>>
>>>>> If it doesn't exist I want to propose adding such a feature in spark.
>>>>>
>>>>> Why is it useful?
>>>>> 1. Faster testing: When using pyspark or spark on scala/java without
>>>>> DataSet we are prone to typos and mistakes about column names and other
>>>>> logical problems. Unfortunately IDEs won't help much and when dealing with
>>>>> Big Data, testing by running the code takes a lot of time. So this way we
>>>>> can understand typos very fast.
>>>>>
>>>>> 2. (Continuous) Integrity checks: When there are upstream and
>>>>> downstream pipelines, we can understand breaking changes much faster by
>>>>> running downstream pipelines in "dry run" mode.
>>>>>
>>>>> I believe it is not so hard to implement and I volunteer to work on it
>>>>> if the community approves this feature request.
>>>>>
>>>>> It can be tackled in different ways. I have two Ideas for
>>>>> implementation:
>>>>> 1. Noop (No Op) executor engine
>>>>> 2. On reads just infer schema and replace it with empty table with
>>>>> same schema
>>>>>
>>>>> Thanks,
>>>>> Ali
>>>>>
>>>>

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