Seeing more support for arrow based functions would be great.
Gives more control to application developers. And so pandas just becomes 1
of the available options.
On Fri, 3 Nov 2023, 21:23 Luca Canali, wrote:
> Hi Enrico,
>
>
>
> +1 on supporting Arrow on par with Pandas. Besides the frameworks
I feel this pain frequently
Something more interactive would be great
On Wed, 6 Sep 2023 at 4:34 PM, Santosh Pingale
wrote:
> Hey community
>
> Spark UI with the plan visualisation is an excellent resource for finding
> out crucial information about how your application is doing and what parts
I would definitely use it - is it's available :)
On Mon, 19 Jun 2023, 21:56 Jacek Laskowski, wrote:
> Hi Allison and devs,
>
> Although I was against this idea at first sight (probably because I'm a
> Scala dev), I think it could work as long as there are people who'd be
> interested in such an
y because the upgraded
> aqe.
>
> not sure whether this is expected though.
>
> On Thu, Jan 6, 2022 at 12:11 AM Abdeali Kothari
> wrote:
>
>> Just thought I'd do a quick bump and add the dev mailing list - in case
>> there is some insight there
>> Feels like th
Just thought I'd do a quick bump and add the dev mailing list - in case
there is some insight there
Feels like this should be categorized as a bug for spark 3.2.0
On Wed, Dec 29, 2021 at 5:25 PM Abdeali Kothari
wrote:
> Hi,
> I am using pyspark for some projects. And one of the thi
;> jupyter from org git repo as it was shared, so i do not know how the venv
>> was created or python for venv was created even.
>>
>> The os is CentOS release 6.9 (Final)
>>
>>
>>
>>
>>
>> *Regards,Dhrubajyoti Hati.Mob No: 9886428028/
import base64
>>>> import zlib
>>>>
>>>> def decompress(data):
>>>>
>>>> bytecode = base64.b64decode(data)
>>>> d = zlib.decompressobj(32 + zlib.MAX_WBITS)
>>>> decompressed_data = d.decompress(bytecode
Maybe you can try running it in a python shell or jupyter-console/ipython
instead of a spark-submit and check how much time it takes too.
Compare the env variables to check that no additional env configuration is
present in either environment.
Also is the python environment for both the exact
Was thinking that getting an estimated statistic of the number of issues
that would be closed if this is done would help.
Open issues: 3882 (project = SPARK AND status in (Open, "In Progress",
Reopened))
Open + Does not affect 3.0+ = 2795
Open + Does not affect 2.4+ = 2373
Open + Does not affect
gt; 2019년 3월 26일 (화) 오후 3:34, Reynold Xin 님이 작성:
>>
>> We have some early stuff there but not quite ready to talk about it in
>> public yet (I hope soon though). Will shoot you a separate email on it.
>>
>> On Mon, Mar 25, 2019 at 11:32 PM Abdeali Kothari <
>&g
out of some users. We are considering building
> a shim layer as a separate project on top of Spark (so we can make rapid
> releases based on feedback) just to test this out and see how well it could
> work in practice.
>
> On Mon, Mar 25, 2019 at 11:04 PM Abdeali Kothari
> wrote
Hi,
I was doing some spark to pandas (and vice versa) conversion because some
of the pandas codes we have don't work on huge data. And some spark codes
work very slow on small data.
It was nice to see that pyspark had some similar syntax for the common
pandas operations that the python community
I was trying to check out accumulators and see if I could use them for
anything.
I made a demo program and could not figure out how to add them up.
I found that I need to do a shuffle between all my python UDFs that I am
running for the accumulators to be run. Basically, if I do 5 withColumn()
I was writing some code to try to auto find a list of tables and databases
being used in a SparkSQL query. Mainly I was looking to auto-check the
permissions and owners of all the tables a query will be trying to access.
I was wondering whether PySpark has some method for me to directly use the
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