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Posted to user@spark.apache.org by Subash Prabanantham <su...@gmail.com> on 2022/08/25 10:06:24 UTC
Profiling PySpark Pandas UDF
Hi All,
I was wondering if we have any best practices on using pandas UDF ?
Profiling UDF is not an easy task and our case requires some drilling down
on the logic of the function.
Our use case:
We are using func(Dataframe) => Dataframe as interface to use Pandas UDF,
while running locally only the function, it runs faster but when executed
in Spark environment - the processing time is more than expected. We have
one column where the value is large (BinaryType -> 600KB), wondering
whether this could make the Arrow computation slower ?
Is there any profiling or best way to debug the cost incurred using pandas
UDF ?
Thanks,
Subash
Re: Profiling PySpark Pandas UDF
Posted by Gourav Sengupta <go...@gmail.com>.
Hi,
I do send back those metrics in as columns in the pandas datagrams in case
required, but the true thing is that we need to finally be able to find out
the time for java object conversion along with the udf calls and actual
python memory and other details which we can all do by tweaking udf.
But I am 100 percent sure that your work will be useful and required.
Regards,
Gourav
On Mon, 29 Aug 2022, 10:36 Luca Canali, <lu...@cern.ch> wrote:
> Hi Abdeali,
>
>
>
> Thanks for the support. Indeed you can go ahead and test and review my
> latest PR for SPARK-34265
>
> (Instrument Python UDF execution using SQL Metrics) if you want to:
> https://github.com/apache/spark/pull/33559
>
> Currently I reduced the scope of the instrumentation to just 3 simple
> metrics to implement: "data sent to Python workers",
>
> "data returned from Python workers", "number of output rows".
> In a previous attempt I had also instrumented the time for UDF execution,
> although there are some subtle points there,
>
> and I may need to go back to testing that at a later stage.
>
> It definitely would be good to know if people using PySpark and Python
> UDFs find this proposed improvement useful.
>
> I see the proposed additional instrumentation as complementary to the
> Python/Pandas UDF Profiler introduced in Spark 3.3.
>
>
>
> Best,
>
> Luca
>
>
>
> *From:* Abdeali Kothari <ab...@gmail.com>
> *Sent:* Friday, August 26, 2022 15:59
> *To:* Luca Canali <lu...@cern.ch>
> *Cc:* Russell Jurney <ru...@gmail.com>; Gourav Sengupta <
> gourav.sengupta@gmail.com>; Sean Owen <sr...@gmail.com>; Takuya UESHIN <
> ueshin@happy-camper.st>; user <us...@spark.apache.org>; Subash
> Prabanantham <su...@gmail.com>
> *Subject:* Re: Profiling PySpark Pandas UDF
>
>
>
> Hi Luca, I see you pushed some code to the PR 3 hrs ago.
>
> That's awesome. If I can help out in any way - do let me know
>
> I think that's an amazing feature and would be great if it can get into
> spark
>
>
>
> On Fri, 26 Aug 2022, 12:41 Luca Canali, <lu...@cern.ch> wrote:
>
> @Abdeali as for “lightweight profiling”, there is some work in progress on
> instrumenting Python UDFs with Spark metrics, see
> https://issues.apache.org/jira/browse/SPARK-34265
>
> However it is a bit stuck at the moment, and needs to be revived I
> believe.
>
>
>
> Best,
>
> Luca
>
>
>
> *From:* Abdeali Kothari <ab...@gmail.com>
> *Sent:* Friday, August 26, 2022 06:36
> *To:* Subash Prabanantham <su...@gmail.com>
> *Cc:* Russell Jurney <ru...@gmail.com>; Gourav Sengupta <
> gourav.sengupta@gmail.com>; Sean Owen <sr...@gmail.com>; Takuya UESHIN <
> ueshin@happy-camper.st>; user <us...@spark.apache.org>
> *Subject:* Re: Profiling PySpark Pandas UDF
>
>
>
> The python profiler is pretty cool !
>
> Ill try it out to see what could be taking time within the UDF with it.
>
>
>
> I'm wondering if there is also some lightweight profiling (which does not
> slow down my processing) for me to get:
>
>
>
> - how much time the UDF took (like how much time was spent inside the UDF)
>
> - how many times the UDF was called
>
>
>
> I can see the overall time a stage took in the Spark UI - would be cool if
> I could find the time a UDF takes too
>
>
>
> On Fri, 26 Aug 2022, 00:25 Subash Prabanantham, <su...@gmail.com>
> wrote:
>
> Wow, lots of good suggestions. I didn’t know about the profiler either.
> Great suggestion @Takuya.
>
>
>
>
>
> Thanks,
>
> Subash
>
>
>
> On Thu, 25 Aug 2022 at 19:30, Russell Jurney <ru...@gmail.com>
> wrote:
>
> YOU know what you're talking about and aren't hacking a solution. You are
> my new friend :) Thank you, this is incredibly helpful!
>
>
>
>
> Thanks,
>
> Russell Jurney @rjurney <http://twitter.com/rjurney>
> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
> <http://facebook.com/jurney> datasyndrome.com
>
>
>
>
>
> On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ue...@happy-camper.st>
> wrote:
>
> Hi Subash,
>
> Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
> -
> https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
>
> Hope it can help you.
>
> Thanks.
>
>
>
> On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <ru...@gmail.com>
> wrote:
>
> Subash, I’m here to help :)
>
>
>
> I started a test script to demonstrate a solution last night but got a
> cold and haven’t finished it. Give me another day and I’ll get it to you.
> My suggestion is that you run PySpark locally in pytest with a fixture to
> generate and yield your SparckContext and SparkSession and the. Write tests
> that load some test data, perform some count operation and checkpoint to
> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
> checkpoint again or write some output to disk to make sure it finishes and
> then stop the timer and compute how long it takes. I’ll show you some code,
> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
> how much overhead there is using Pandera and Spark together to validate
> data: https://github.com/Graphlet-AI/graphlet
>
>
>
> I’ll respond by tomorrow evening with code in a fist! We’ll see if it gets
> consistent, measurable and valid results! :)
>
>
>
> Russell Jurney
>
>
>
> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>
> It's important to realize that while pandas UDFs and pandas on Spark are
> both related to pandas, they are not themselves directly related. The first
> lets you use pandas within Spark, the second lets you use pandas on Spark.
>
>
>
> Hard to say with this info but you want to look at whether you are doing
> something expensive in each UDF call and consider amortizing it with the
> scalar iterator UDF pattern. Maybe.
>
>
>
> A pandas UDF is not spark code itself so no there is no tool in spark to
> profile it. Conversely any approach to profiling pandas or python would
> work here .
>
>
>
> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <go...@gmail.com>
> wrote:
>
> Hi,
>
>
>
> May be I am jumping to conclusions and making stupid guesses, but have you
> tried koalas now that it is natively integrated with pyspark??
>
>
>
> Regards
>
> Gourav
>
>
>
> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <su...@gmail.com>
> wrote:
>
> Hi All,
>
>
>
> I was wondering if we have any best practices on using pandas UDF ?
> Profiling UDF is not an easy task and our case requires some drilling down
> on the logic of the function.
>
>
>
>
>
> Our use case:
>
> We are using func(Dataframe) => Dataframe as interface to use Pandas UDF,
> while running locally only the function, it runs faster but when executed
> in Spark environment - the processing time is more than expected. We have
> one column where the value is large (BinaryType -> 600KB), wondering
> whether this could make the Arrow computation slower ?
>
>
>
> Is there any profiling or best way to debug the cost incurred using pandas
> UDF ?
>
>
>
>
>
> Thanks,
>
> Subash
>
>
>
> --
>
>
>
> Thanks,
>
> Russell Jurney @rjurney <http://twitter.com/rjurney>
> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
> <http://facebook.com/jurney> datasyndrome.com
>
>
>
>
> --
>
> Takuya UESHIN
>
>
RE: Profiling PySpark Pandas UDF
Posted by Luca Canali <lu...@cern.ch>.
Hi Abdeali,
Thanks for the support. Indeed you can go ahead and test and review my latest PR for SPARK-34265
(Instrument Python UDF execution using SQL Metrics) if you want to:
https://github.com/apache/spark/pull/33559
Currently I reduced the scope of the instrumentation to just 3 simple metrics to implement: "data sent to Python workers",
"data returned from Python workers", "number of output rows".
In a previous attempt I had also instrumented the time for UDF execution, although there are some subtle points there,
and I may need to go back to testing that at a later stage.
It definitely would be good to know if people using PySpark and Python UDFs find this proposed improvement useful.
I see the proposed additional instrumentation as complementary to the Python/Pandas UDF Profiler introduced in Spark 3.3.
Best,
Luca
From: Abdeali Kothari <ab...@gmail.com>
Sent: Friday, August 26, 2022 15:59
To: Luca Canali <lu...@cern.ch>
Cc: Russell Jurney <ru...@gmail.com>; Gourav Sengupta <go...@gmail.com>; Sean Owen <sr...@gmail.com>; Takuya UESHIN <ue...@happy-camper.st>; user <us...@spark.apache.org>; Subash Prabanantham <su...@gmail.com>
Subject: Re: Profiling PySpark Pandas UDF
Hi Luca, I see you pushed some code to the PR 3 hrs ago.
That's awesome. If I can help out in any way - do let me know
I think that's an amazing feature and would be great if it can get into spark
On Fri, 26 Aug 2022, 12:41 Luca Canali, <luca.canali@cern.ch <ma...@cern.ch> > wrote:
@Abdeali as for “lightweight profiling”, there is some work in progress on instrumenting Python UDFs with Spark metrics, see https://issues.apache.org/jira/browse/SPARK-34265
However it is a bit stuck at the moment, and needs to be revived I believe.
Best,
Luca
From: Abdeali Kothari <abdealikothari@gmail.com <ma...@gmail.com> >
Sent: Friday, August 26, 2022 06:36
To: Subash Prabanantham <subashprabakar@gmail.com <ma...@gmail.com> >
Cc: Russell Jurney <russell.jurney@gmail.com <ma...@gmail.com> >; Gourav Sengupta <gourav.sengupta@gmail.com <ma...@gmail.com> >; Sean Owen <srowen@gmail.com <ma...@gmail.com> >; Takuya UESHIN <ueshin@happy-camper.st <ma...@happy-camper.st> >; user <user@spark.apache.org <ma...@spark.apache.org> >
Subject: Re: Profiling PySpark Pandas UDF
The python profiler is pretty cool !
Ill try it out to see what could be taking time within the UDF with it.
I'm wondering if there is also some lightweight profiling (which does not slow down my processing) for me to get:
- how much time the UDF took (like how much time was spent inside the UDF)
- how many times the UDF was called
I can see the overall time a stage took in the Spark UI - would be cool if I could find the time a UDF takes too
On Fri, 26 Aug 2022, 00:25 Subash Prabanantham, <subashprabakar@gmail.com <ma...@gmail.com> > wrote:
Wow, lots of good suggestions. I didn’t know about the profiler either. Great suggestion @Takuya.
Thanks,
Subash
On Thu, 25 Aug 2022 at 19:30, Russell Jurney <russell.jurney@gmail.com <ma...@gmail.com> > wrote:
YOU know what you're talking about and aren't hacking a solution. You are my new friend :) Thank you, this is incredibly helpful!
Thanks,
Russell Jurney <http://twitter.com/rjurney> @rjurney <ma...@gmail.com> russell.jurney@gmail.com <http://linkedin.com/in/russelljurney> LI <http://facebook.com/jurney> FB <http://datasyndrome.com> datasyndrome.com
On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ueshin@happy-camper.st <ma...@happy-camper.st> > wrote:
Hi Subash,
Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
- https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
Hope it can help you.
Thanks.
On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <russell.jurney@gmail.com <ma...@gmail.com> > wrote:
Subash, I’m here to help :)
I started a test script to demonstrate a solution last night but got a cold and haven’t finished it. Give me another day and I’ll get it to you. My suggestion is that you run PySpark locally in pytest with a fixture to generate and yield your SparckContext and SparkSession and the. Write tests that load some test data, perform some count operation and checkpoint to ensure that data is loaded, start a timer, run your UDF on the DataFrame, checkpoint again or write some output to disk to make sure it finishes and then stop the timer and compute how long it takes. I’ll show you some code, I have to do this for Graphlet AI’s RTL utils and other tools to figure out how much overhead there is using Pandera and Spark together to validate data: https://github.com/Graphlet-AI/graphlet
I’ll respond by tomorrow evening with code in a fist! We’ll see if it gets consistent, measurable and valid results! :)
Russell Jurney
On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <srowen@gmail.com <ma...@gmail.com> > wrote:
It's important to realize that while pandas UDFs and pandas on Spark are both related to pandas, they are not themselves directly related. The first lets you use pandas within Spark, the second lets you use pandas on Spark.
Hard to say with this info but you want to look at whether you are doing something expensive in each UDF call and consider amortizing it with the scalar iterator UDF pattern. Maybe.
A pandas UDF is not spark code itself so no there is no tool in spark to profile it. Conversely any approach to profiling pandas or python would work here .
On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <gourav.sengupta@gmail.com <ma...@gmail.com> > wrote:
Hi,
May be I am jumping to conclusions and making stupid guesses, but have you tried koalas now that it is natively integrated with pyspark??
Regards
Gourav
On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <subashprabakar@gmail.com <ma...@gmail.com> > wrote:
Hi All,
I was wondering if we have any best practices on using pandas UDF ? Profiling UDF is not an easy task and our case requires some drilling down on the logic of the function.
Our use case:
We are using func(Dataframe) => Dataframe as interface to use Pandas UDF, while running locally only the function, it runs faster but when executed in Spark environment - the processing time is more than expected. We have one column where the value is large (BinaryType -> 600KB), wondering whether this could make the Arrow computation slower ?
Is there any profiling or best way to debug the cost incurred using pandas UDF ?
Thanks,
Subash
--
Thanks,
Russell Jurney <http://twitter.com/rjurney> @rjurney <ma...@gmail.com> russell.jurney@gmail.com <http://linkedin.com/in/russelljurney> LI <http://facebook.com/jurney> FB <http://datasyndrome.com> datasyndrome.com
--
Takuya UESHIN
Re: Profiling PySpark Pandas UDF
Posted by Abdeali Kothari <ab...@gmail.com>.
Hi Luca, I see you pushed some code to the PR 3 hrs ago.
That's awesome. If I can help out in any way - do let me know
I think that's an amazing feature and would be great if it can get into
spark
On Fri, 26 Aug 2022, 12:41 Luca Canali, <lu...@cern.ch> wrote:
> @Abdeali as for “lightweight profiling”, there is some work in progress on
> instrumenting Python UDFs with Spark metrics, see
> https://issues.apache.org/jira/browse/SPARK-34265
>
> However it is a bit stuck at the moment, and needs to be revived I
> believe.
>
>
>
> Best,
>
> Luca
>
>
>
> *From:* Abdeali Kothari <ab...@gmail.com>
> *Sent:* Friday, August 26, 2022 06:36
> *To:* Subash Prabanantham <su...@gmail.com>
> *Cc:* Russell Jurney <ru...@gmail.com>; Gourav Sengupta <
> gourav.sengupta@gmail.com>; Sean Owen <sr...@gmail.com>; Takuya UESHIN <
> ueshin@happy-camper.st>; user <us...@spark.apache.org>
> *Subject:* Re: Profiling PySpark Pandas UDF
>
>
>
> The python profiler is pretty cool !
>
> Ill try it out to see what could be taking time within the UDF with it.
>
>
>
> I'm wondering if there is also some lightweight profiling (which does not
> slow down my processing) for me to get:
>
>
>
> - how much time the UDF took (like how much time was spent inside the UDF)
>
> - how many times the UDF was called
>
>
>
> I can see the overall time a stage took in the Spark UI - would be cool if
> I could find the time a UDF takes too
>
>
>
> On Fri, 26 Aug 2022, 00:25 Subash Prabanantham, <su...@gmail.com>
> wrote:
>
> Wow, lots of good suggestions. I didn’t know about the profiler either.
> Great suggestion @Takuya.
>
>
>
>
>
> Thanks,
>
> Subash
>
>
>
> On Thu, 25 Aug 2022 at 19:30, Russell Jurney <ru...@gmail.com>
> wrote:
>
> YOU know what you're talking about and aren't hacking a solution. You are
> my new friend :) Thank you, this is incredibly helpful!
>
>
>
>
> Thanks,
>
> Russell Jurney @rjurney <http://twitter.com/rjurney>
> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
> <http://facebook.com/jurney> datasyndrome.com
>
>
>
>
>
> On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ue...@happy-camper.st>
> wrote:
>
> Hi Subash,
>
> Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
> -
> https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
>
> Hope it can help you.
>
> Thanks.
>
>
>
> On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <ru...@gmail.com>
> wrote:
>
> Subash, I’m here to help :)
>
>
>
> I started a test script to demonstrate a solution last night but got a
> cold and haven’t finished it. Give me another day and I’ll get it to you.
> My suggestion is that you run PySpark locally in pytest with a fixture to
> generate and yield your SparckContext and SparkSession and the. Write tests
> that load some test data, perform some count operation and checkpoint to
> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
> checkpoint again or write some output to disk to make sure it finishes and
> then stop the timer and compute how long it takes. I’ll show you some code,
> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
> how much overhead there is using Pandera and Spark together to validate
> data: https://github.com/Graphlet-AI/graphlet
>
>
>
> I’ll respond by tomorrow evening with code in a fist! We’ll see if it gets
> consistent, measurable and valid results! :)
>
>
>
> Russell Jurney
>
>
>
> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>
> It's important to realize that while pandas UDFs and pandas on Spark are
> both related to pandas, they are not themselves directly related. The first
> lets you use pandas within Spark, the second lets you use pandas on Spark.
>
>
>
> Hard to say with this info but you want to look at whether you are doing
> something expensive in each UDF call and consider amortizing it with the
> scalar iterator UDF pattern. Maybe.
>
>
>
> A pandas UDF is not spark code itself so no there is no tool in spark to
> profile it. Conversely any approach to profiling pandas or python would
> work here .
>
>
>
> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <go...@gmail.com>
> wrote:
>
> Hi,
>
>
>
> May be I am jumping to conclusions and making stupid guesses, but have you
> tried koalas now that it is natively integrated with pyspark??
>
>
>
> Regards
>
> Gourav
>
>
>
> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <su...@gmail.com>
> wrote:
>
> Hi All,
>
>
>
> I was wondering if we have any best practices on using pandas UDF ?
> Profiling UDF is not an easy task and our case requires some drilling down
> on the logic of the function.
>
>
>
>
>
> Our use case:
>
> We are using func(Dataframe) => Dataframe as interface to use Pandas UDF,
> while running locally only the function, it runs faster but when executed
> in Spark environment - the processing time is more than expected. We have
> one column where the value is large (BinaryType -> 600KB), wondering
> whether this could make the Arrow computation slower ?
>
>
>
> Is there any profiling or best way to debug the cost incurred using pandas
> UDF ?
>
>
>
>
>
> Thanks,
>
> Subash
>
>
>
> --
>
>
>
> Thanks,
>
> Russell Jurney @rjurney <http://twitter.com/rjurney>
> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
> <http://facebook.com/jurney> datasyndrome.com
>
>
>
>
> --
>
> Takuya UESHIN
>
>
RE: Profiling PySpark Pandas UDF
Posted by Luca Canali <lu...@cern.ch>.
@Abdeali as for “lightweight profiling”, there is some work in progress on instrumenting Python UDFs with Spark metrics, see https://issues.apache.org/jira/browse/SPARK-34265
However it is a bit stuck at the moment, and needs to be revived I believe.
Best,
Luca
From: Abdeali Kothari <ab...@gmail.com>
Sent: Friday, August 26, 2022 06:36
To: Subash Prabanantham <su...@gmail.com>
Cc: Russell Jurney <ru...@gmail.com>; Gourav Sengupta <go...@gmail.com>; Sean Owen <sr...@gmail.com>; Takuya UESHIN <ue...@happy-camper.st>; user <us...@spark.apache.org>
Subject: Re: Profiling PySpark Pandas UDF
The python profiler is pretty cool !
Ill try it out to see what could be taking time within the UDF with it.
I'm wondering if there is also some lightweight profiling (which does not slow down my processing) for me to get:
- how much time the UDF took (like how much time was spent inside the UDF)
- how many times the UDF was called
I can see the overall time a stage took in the Spark UI - would be cool if I could find the time a UDF takes too
On Fri, 26 Aug 2022, 00:25 Subash Prabanantham, <subashprabakar@gmail.com <ma...@gmail.com> > wrote:
Wow, lots of good suggestions. I didn’t know about the profiler either. Great suggestion @Takuya.
Thanks,
Subash
On Thu, 25 Aug 2022 at 19:30, Russell Jurney <russell.jurney@gmail.com <ma...@gmail.com> > wrote:
YOU know what you're talking about and aren't hacking a solution. You are my new friend :) Thank you, this is incredibly helpful!
Thanks,
Russell Jurney <http://twitter.com/rjurney> @rjurney <ma...@gmail.com> russell.jurney@gmail.com <http://linkedin.com/in/russelljurney> LI <http://facebook.com/jurney> FB <http://datasyndrome.com> datasyndrome.com
On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ueshin@happy-camper.st <ma...@happy-camper.st> > wrote:
Hi Subash,
Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
- https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
Hope it can help you.
Thanks.
On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <russell.jurney@gmail.com <ma...@gmail.com> > wrote:
Subash, I’m here to help :)
I started a test script to demonstrate a solution last night but got a cold and haven’t finished it. Give me another day and I’ll get it to you. My suggestion is that you run PySpark locally in pytest with a fixture to generate and yield your SparckContext and SparkSession and the. Write tests that load some test data, perform some count operation and checkpoint to ensure that data is loaded, start a timer, run your UDF on the DataFrame, checkpoint again or write some output to disk to make sure it finishes and then stop the timer and compute how long it takes. I’ll show you some code, I have to do this for Graphlet AI’s RTL utils and other tools to figure out how much overhead there is using Pandera and Spark together to validate data: https://github.com/Graphlet-AI/graphlet
I’ll respond by tomorrow evening with code in a fist! We’ll see if it gets consistent, measurable and valid results! :)
Russell Jurney
On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <srowen@gmail.com <ma...@gmail.com> > wrote:
It's important to realize that while pandas UDFs and pandas on Spark are both related to pandas, they are not themselves directly related. The first lets you use pandas within Spark, the second lets you use pandas on Spark.
Hard to say with this info but you want to look at whether you are doing something expensive in each UDF call and consider amortizing it with the scalar iterator UDF pattern. Maybe.
A pandas UDF is not spark code itself so no there is no tool in spark to profile it. Conversely any approach to profiling pandas or python would work here .
On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <gourav.sengupta@gmail.com <ma...@gmail.com> > wrote:
Hi,
May be I am jumping to conclusions and making stupid guesses, but have you tried koalas now that it is natively integrated with pyspark??
Regards
Gourav
On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <subashprabakar@gmail.com <ma...@gmail.com> > wrote:
Hi All,
I was wondering if we have any best practices on using pandas UDF ? Profiling UDF is not an easy task and our case requires some drilling down on the logic of the function.
Our use case:
We are using func(Dataframe) => Dataframe as interface to use Pandas UDF, while running locally only the function, it runs faster but when executed in Spark environment - the processing time is more than expected. We have one column where the value is large (BinaryType -> 600KB), wondering whether this could make the Arrow computation slower ?
Is there any profiling or best way to debug the cost incurred using pandas UDF ?
Thanks,
Subash
--
Thanks,
Russell Jurney <http://twitter.com/rjurney> @rjurney <ma...@gmail.com> russell.jurney@gmail.com <http://linkedin.com/in/russelljurney> LI <http://facebook.com/jurney> FB <http://datasyndrome.com> datasyndrome.com
--
Takuya UESHIN
Re: Profiling PySpark Pandas UDF
Posted by Abdeali Kothari <ab...@gmail.com>.
The python profiler is pretty cool !
Ill try it out to see what could be taking time within the UDF with it.
I'm wondering if there is also some lightweight profiling (which does not
slow down my processing) for me to get:
- how much time the UDF took (like how much time was spent inside the UDF)
- how many times the UDF was called
I can see the overall time a stage took in the Spark UI - would be cool if
I could find the time a UDF takes too
On Fri, 26 Aug 2022, 00:25 Subash Prabanantham, <su...@gmail.com>
wrote:
> Wow, lots of good suggestions. I didn’t know about the profiler either.
> Great suggestion @Takuya.
>
>
> Thanks,
> Subash
>
> On Thu, 25 Aug 2022 at 19:30, Russell Jurney <ru...@gmail.com>
> wrote:
>
>> YOU know what you're talking about and aren't hacking a solution. You are
>> my new friend :) Thank you, this is incredibly helpful!
>>
>>
>> Thanks,
>> Russell Jurney @rjurney <http://twitter.com/rjurney>
>> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
>> <http://facebook.com/jurney> datasyndrome.com
>>
>>
>> On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ue...@happy-camper.st>
>> wrote:
>>
>>> Hi Subash,
>>>
>>> Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
>>> -
>>> https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
>>>
>>> Hope it can help you.
>>>
>>> Thanks.
>>>
>>> On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <
>>> russell.jurney@gmail.com> wrote:
>>>
>>>> Subash, I’m here to help :)
>>>>
>>>> I started a test script to demonstrate a solution last night but got a
>>>> cold and haven’t finished it. Give me another day and I’ll get it to you.
>>>> My suggestion is that you run PySpark locally in pytest with a fixture to
>>>> generate and yield your SparckContext and SparkSession and the. Write tests
>>>> that load some test data, perform some count operation and checkpoint to
>>>> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
>>>> checkpoint again or write some output to disk to make sure it finishes and
>>>> then stop the timer and compute how long it takes. I’ll show you some code,
>>>> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
>>>> how much overhead there is using Pandera and Spark together to validate
>>>> data: https://github.com/Graphlet-AI/graphlet
>>>>
>>>> I’ll respond by tomorrow evening with code in a fist! We’ll see if it
>>>> gets consistent, measurable and valid results! :)
>>>>
>>>> Russell Jurney
>>>>
>>>> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>>>>
>>>>> It's important to realize that while pandas UDFs and pandas on Spark
>>>>> are both related to pandas, they are not themselves directly related. The
>>>>> first lets you use pandas within Spark, the second lets you use pandas on
>>>>> Spark.
>>>>>
>>>>> Hard to say with this info but you want to look at whether you are
>>>>> doing something expensive in each UDF call and consider amortizing it with
>>>>> the scalar iterator UDF pattern. Maybe.
>>>>>
>>>>> A pandas UDF is not spark code itself so no there is no tool in spark
>>>>> to profile it. Conversely any approach to profiling pandas or python would
>>>>> work here .
>>>>>
>>>>> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <
>>>>> gourav.sengupta@gmail.com> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> May be I am jumping to conclusions and making stupid guesses, but
>>>>>> have you tried koalas now that it is natively integrated with pyspark??
>>>>>>
>>>>>> Regards
>>>>>> Gourav
>>>>>>
>>>>>> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <
>>>>>> subashprabakar@gmail.com> wrote:
>>>>>>
>>>>>>> Hi All,
>>>>>>>
>>>>>>> I was wondering if we have any best practices on using pandas UDF ?
>>>>>>> Profiling UDF is not an easy task and our case requires some drilling down
>>>>>>> on the logic of the function.
>>>>>>>
>>>>>>>
>>>>>>> Our use case:
>>>>>>> We are using func(Dataframe) => Dataframe as interface to use Pandas
>>>>>>> UDF, while running locally only the function, it runs faster but when
>>>>>>> executed in Spark environment - the processing time is more than expected.
>>>>>>> We have one column where the value is large (BinaryType -> 600KB),
>>>>>>> wondering whether this could make the Arrow computation slower ?
>>>>>>>
>>>>>>> Is there any profiling or best way to debug the cost incurred using
>>>>>>> pandas UDF ?
>>>>>>>
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Subash
>>>>>>>
>>>>>>> --
>>>>
>>>> Thanks,
>>>> Russell Jurney @rjurney <http://twitter.com/rjurney>
>>>> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
>>>> <http://facebook.com/jurney> datasyndrome.com
>>>>
>>>
>>>
>>> --
>>> Takuya UESHIN
>>>
>>>
Re: Profiling PySpark Pandas UDF
Posted by Subash Prabanantham <su...@gmail.com>.
Wow, lots of good suggestions. I didn’t know about the profiler either.
Great suggestion @Takuya.
Thanks,
Subash
On Thu, 25 Aug 2022 at 19:30, Russell Jurney <ru...@gmail.com>
wrote:
> YOU know what you're talking about and aren't hacking a solution. You are
> my new friend :) Thank you, this is incredibly helpful!
>
>
> Thanks,
> Russell Jurney @rjurney <http://twitter.com/rjurney>
> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
> <http://facebook.com/jurney> datasyndrome.com
>
>
> On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ue...@happy-camper.st>
> wrote:
>
>> Hi Subash,
>>
>> Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
>> -
>> https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
>>
>> Hope it can help you.
>>
>> Thanks.
>>
>> On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <ru...@gmail.com>
>> wrote:
>>
>>> Subash, I’m here to help :)
>>>
>>> I started a test script to demonstrate a solution last night but got a
>>> cold and haven’t finished it. Give me another day and I’ll get it to you.
>>> My suggestion is that you run PySpark locally in pytest with a fixture to
>>> generate and yield your SparckContext and SparkSession and the. Write tests
>>> that load some test data, perform some count operation and checkpoint to
>>> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
>>> checkpoint again or write some output to disk to make sure it finishes and
>>> then stop the timer and compute how long it takes. I’ll show you some code,
>>> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
>>> how much overhead there is using Pandera and Spark together to validate
>>> data: https://github.com/Graphlet-AI/graphlet
>>>
>>> I’ll respond by tomorrow evening with code in a fist! We’ll see if it
>>> gets consistent, measurable and valid results! :)
>>>
>>> Russell Jurney
>>>
>>> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>>>
>>>> It's important to realize that while pandas UDFs and pandas on Spark
>>>> are both related to pandas, they are not themselves directly related. The
>>>> first lets you use pandas within Spark, the second lets you use pandas on
>>>> Spark.
>>>>
>>>> Hard to say with this info but you want to look at whether you are
>>>> doing something expensive in each UDF call and consider amortizing it with
>>>> the scalar iterator UDF pattern. Maybe.
>>>>
>>>> A pandas UDF is not spark code itself so no there is no tool in spark
>>>> to profile it. Conversely any approach to profiling pandas or python would
>>>> work here .
>>>>
>>>> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <
>>>> gourav.sengupta@gmail.com> wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> May be I am jumping to conclusions and making stupid guesses, but have
>>>>> you tried koalas now that it is natively integrated with pyspark??
>>>>>
>>>>> Regards
>>>>> Gourav
>>>>>
>>>>> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <
>>>>> subashprabakar@gmail.com> wrote:
>>>>>
>>>>>> Hi All,
>>>>>>
>>>>>> I was wondering if we have any best practices on using pandas UDF ?
>>>>>> Profiling UDF is not an easy task and our case requires some drilling down
>>>>>> on the logic of the function.
>>>>>>
>>>>>>
>>>>>> Our use case:
>>>>>> We are using func(Dataframe) => Dataframe as interface to use Pandas
>>>>>> UDF, while running locally only the function, it runs faster but when
>>>>>> executed in Spark environment - the processing time is more than expected.
>>>>>> We have one column where the value is large (BinaryType -> 600KB),
>>>>>> wondering whether this could make the Arrow computation slower ?
>>>>>>
>>>>>> Is there any profiling or best way to debug the cost incurred using
>>>>>> pandas UDF ?
>>>>>>
>>>>>>
>>>>>> Thanks,
>>>>>> Subash
>>>>>>
>>>>>> --
>>>
>>> Thanks,
>>> Russell Jurney @rjurney <http://twitter.com/rjurney>
>>> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
>>> <http://facebook.com/jurney> datasyndrome.com
>>>
>>
>>
>> --
>> Takuya UESHIN
>>
>>
Re: Profiling PySpark Pandas UDF
Posted by Russell Jurney <ru...@gmail.com>.
YOU know what you're talking about and aren't hacking a solution. You are
my new friend :) Thank you, this is incredibly helpful!
Thanks,
Russell Jurney @rjurney <http://twitter.com/rjurney>
russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
<http://facebook.com/jurney> datasyndrome.com
On Thu, Aug 25, 2022 at 10:52 AM Takuya UESHIN <ue...@happy-camper.st>
wrote:
> Hi Subash,
>
> Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
> -
> https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
>
> Hope it can help you.
>
> Thanks.
>
> On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <ru...@gmail.com>
> wrote:
>
>> Subash, I’m here to help :)
>>
>> I started a test script to demonstrate a solution last night but got a
>> cold and haven’t finished it. Give me another day and I’ll get it to you.
>> My suggestion is that you run PySpark locally in pytest with a fixture to
>> generate and yield your SparckContext and SparkSession and the. Write tests
>> that load some test data, perform some count operation and checkpoint to
>> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
>> checkpoint again or write some output to disk to make sure it finishes and
>> then stop the timer and compute how long it takes. I’ll show you some code,
>> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
>> how much overhead there is using Pandera and Spark together to validate
>> data: https://github.com/Graphlet-AI/graphlet
>>
>> I’ll respond by tomorrow evening with code in a fist! We’ll see if it
>> gets consistent, measurable and valid results! :)
>>
>> Russell Jurney
>>
>> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>>
>>> It's important to realize that while pandas UDFs and pandas on Spark are
>>> both related to pandas, they are not themselves directly related. The first
>>> lets you use pandas within Spark, the second lets you use pandas on Spark.
>>>
>>> Hard to say with this info but you want to look at whether you are doing
>>> something expensive in each UDF call and consider amortizing it with the
>>> scalar iterator UDF pattern. Maybe.
>>>
>>> A pandas UDF is not spark code itself so no there is no tool in spark to
>>> profile it. Conversely any approach to profiling pandas or python would
>>> work here .
>>>
>>> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <
>>> gourav.sengupta@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> May be I am jumping to conclusions and making stupid guesses, but have
>>>> you tried koalas now that it is natively integrated with pyspark??
>>>>
>>>> Regards
>>>> Gourav
>>>>
>>>> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <
>>>> subashprabakar@gmail.com> wrote:
>>>>
>>>>> Hi All,
>>>>>
>>>>> I was wondering if we have any best practices on using pandas UDF ?
>>>>> Profiling UDF is not an easy task and our case requires some drilling down
>>>>> on the logic of the function.
>>>>>
>>>>>
>>>>> Our use case:
>>>>> We are using func(Dataframe) => Dataframe as interface to use Pandas
>>>>> UDF, while running locally only the function, it runs faster but when
>>>>> executed in Spark environment - the processing time is more than expected.
>>>>> We have one column where the value is large (BinaryType -> 600KB),
>>>>> wondering whether this could make the Arrow computation slower ?
>>>>>
>>>>> Is there any profiling or best way to debug the cost incurred using
>>>>> pandas UDF ?
>>>>>
>>>>>
>>>>> Thanks,
>>>>> Subash
>>>>>
>>>>> --
>>
>> Thanks,
>> Russell Jurney @rjurney <http://twitter.com/rjurney>
>> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
>> <http://facebook.com/jurney> datasyndrome.com
>>
>
>
> --
> Takuya UESHIN
>
>
Re: Profiling PySpark Pandas UDF
Posted by Sean Owen <sr...@gmail.com>.
Oh whoa I didn't realize we had this! I stand corrected
On Thu, Aug 25, 2022, 12:52 PM Takuya UESHIN <ue...@happy-camper.st> wrote:
> Hi Subash,
>
> Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
> -
> https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
>
> Hope it can help you.
>
> Thanks.
>
> On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <ru...@gmail.com>
> wrote:
>
>> Subash, I’m here to help :)
>>
>> I started a test script to demonstrate a solution last night but got a
>> cold and haven’t finished it. Give me another day and I’ll get it to you.
>> My suggestion is that you run PySpark locally in pytest with a fixture to
>> generate and yield your SparckContext and SparkSession and the. Write tests
>> that load some test data, perform some count operation and checkpoint to
>> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
>> checkpoint again or write some output to disk to make sure it finishes and
>> then stop the timer and compute how long it takes. I’ll show you some code,
>> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
>> how much overhead there is using Pandera and Spark together to validate
>> data: https://github.com/Graphlet-AI/graphlet
>>
>> I’ll respond by tomorrow evening with code in a fist! We’ll see if it
>> gets consistent, measurable and valid results! :)
>>
>> Russell Jurney
>>
>> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>>
>>> It's important to realize that while pandas UDFs and pandas on Spark are
>>> both related to pandas, they are not themselves directly related. The first
>>> lets you use pandas within Spark, the second lets you use pandas on Spark.
>>>
>>> Hard to say with this info but you want to look at whether you are doing
>>> something expensive in each UDF call and consider amortizing it with the
>>> scalar iterator UDF pattern. Maybe.
>>>
>>> A pandas UDF is not spark code itself so no there is no tool in spark to
>>> profile it. Conversely any approach to profiling pandas or python would
>>> work here .
>>>
>>> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <
>>> gourav.sengupta@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> May be I am jumping to conclusions and making stupid guesses, but have
>>>> you tried koalas now that it is natively integrated with pyspark??
>>>>
>>>> Regards
>>>> Gourav
>>>>
>>>> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <
>>>> subashprabakar@gmail.com> wrote:
>>>>
>>>>> Hi All,
>>>>>
>>>>> I was wondering if we have any best practices on using pandas UDF ?
>>>>> Profiling UDF is not an easy task and our case requires some drilling down
>>>>> on the logic of the function.
>>>>>
>>>>>
>>>>> Our use case:
>>>>> We are using func(Dataframe) => Dataframe as interface to use Pandas
>>>>> UDF, while running locally only the function, it runs faster but when
>>>>> executed in Spark environment - the processing time is more than expected.
>>>>> We have one column where the value is large (BinaryType -> 600KB),
>>>>> wondering whether this could make the Arrow computation slower ?
>>>>>
>>>>> Is there any profiling or best way to debug the cost incurred using
>>>>> pandas UDF ?
>>>>>
>>>>>
>>>>> Thanks,
>>>>> Subash
>>>>>
>>>>> --
>>
>> Thanks,
>> Russell Jurney @rjurney <http://twitter.com/rjurney>
>> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
>> <http://facebook.com/jurney> datasyndrome.com
>>
>
>
> --
> Takuya UESHIN
>
>
Re: Profiling PySpark Pandas UDF
Posted by Takuya UESHIN <ue...@happy-camper.st>.
Hi Subash,
Have you tried the Python/Pandas UDF Profiler introduced in Spark 3.3?
-
https://spark.apache.org/docs/latest/api/python/development/debugging.html#python-pandas-udf
Hope it can help you.
Thanks.
On Thu, Aug 25, 2022 at 10:18 AM Russell Jurney <ru...@gmail.com>
wrote:
> Subash, I’m here to help :)
>
> I started a test script to demonstrate a solution last night but got a
> cold and haven’t finished it. Give me another day and I’ll get it to you.
> My suggestion is that you run PySpark locally in pytest with a fixture to
> generate and yield your SparckContext and SparkSession and the. Write tests
> that load some test data, perform some count operation and checkpoint to
> ensure that data is loaded, start a timer, run your UDF on the DataFrame,
> checkpoint again or write some output to disk to make sure it finishes and
> then stop the timer and compute how long it takes. I’ll show you some code,
> I have to do this for Graphlet AI’s RTL utils and other tools to figure out
> how much overhead there is using Pandera and Spark together to validate
> data: https://github.com/Graphlet-AI/graphlet
>
> I’ll respond by tomorrow evening with code in a fist! We’ll see if it gets
> consistent, measurable and valid results! :)
>
> Russell Jurney
>
> On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
>
>> It's important to realize that while pandas UDFs and pandas on Spark are
>> both related to pandas, they are not themselves directly related. The first
>> lets you use pandas within Spark, the second lets you use pandas on Spark.
>>
>> Hard to say with this info but you want to look at whether you are doing
>> something expensive in each UDF call and consider amortizing it with the
>> scalar iterator UDF pattern. Maybe.
>>
>> A pandas UDF is not spark code itself so no there is no tool in spark to
>> profile it. Conversely any approach to profiling pandas or python would
>> work here .
>>
>> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <go...@gmail.com>
>> wrote:
>>
>>> Hi,
>>>
>>> May be I am jumping to conclusions and making stupid guesses, but have
>>> you tried koalas now that it is natively integrated with pyspark??
>>>
>>> Regards
>>> Gourav
>>>
>>> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <
>>> subashprabakar@gmail.com> wrote:
>>>
>>>> Hi All,
>>>>
>>>> I was wondering if we have any best practices on using pandas UDF ?
>>>> Profiling UDF is not an easy task and our case requires some drilling down
>>>> on the logic of the function.
>>>>
>>>>
>>>> Our use case:
>>>> We are using func(Dataframe) => Dataframe as interface to use Pandas
>>>> UDF, while running locally only the function, it runs faster but when
>>>> executed in Spark environment - the processing time is more than expected.
>>>> We have one column where the value is large (BinaryType -> 600KB),
>>>> wondering whether this could make the Arrow computation slower ?
>>>>
>>>> Is there any profiling or best way to debug the cost incurred using
>>>> pandas UDF ?
>>>>
>>>>
>>>> Thanks,
>>>> Subash
>>>>
>>>> --
>
> Thanks,
> Russell Jurney @rjurney <http://twitter.com/rjurney>
> russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
> <http://facebook.com/jurney> datasyndrome.com
>
--
Takuya UESHIN
Re: Profiling PySpark Pandas UDF
Posted by Russell Jurney <ru...@gmail.com>.
Subash, I’m here to help :)
I started a test script to demonstrate a solution last night but got a cold
and haven’t finished it. Give me another day and I’ll get it to you. My
suggestion is that you run PySpark locally in pytest with a fixture to
generate and yield your SparckContext and SparkSession and the. Write tests
that load some test data, perform some count operation and checkpoint to
ensure that data is loaded, start a timer, run your UDF on the DataFrame,
checkpoint again or write some output to disk to make sure it finishes and
then stop the timer and compute how long it takes. I’ll show you some code,
I have to do this for Graphlet AI’s RTL utils and other tools to figure out
how much overhead there is using Pandera and Spark together to validate
data: https://github.com/Graphlet-AI/graphlet
I’ll respond by tomorrow evening with code in a fist! We’ll see if it gets
consistent, measurable and valid results! :)
Russell Jurney
On Thu, Aug 25, 2022 at 10:00 AM Sean Owen <sr...@gmail.com> wrote:
> It's important to realize that while pandas UDFs and pandas on Spark are
> both related to pandas, they are not themselves directly related. The first
> lets you use pandas within Spark, the second lets you use pandas on Spark.
>
> Hard to say with this info but you want to look at whether you are doing
> something expensive in each UDF call and consider amortizing it with the
> scalar iterator UDF pattern. Maybe.
>
> A pandas UDF is not spark code itself so no there is no tool in spark to
> profile it. Conversely any approach to profiling pandas or python would
> work here .
>
> On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <go...@gmail.com>
> wrote:
>
>> Hi,
>>
>> May be I am jumping to conclusions and making stupid guesses, but have
>> you tried koalas now that it is natively integrated with pyspark??
>>
>> Regards
>> Gourav
>>
>> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <su...@gmail.com>
>> wrote:
>>
>>> Hi All,
>>>
>>> I was wondering if we have any best practices on using pandas UDF ?
>>> Profiling UDF is not an easy task and our case requires some drilling down
>>> on the logic of the function.
>>>
>>>
>>> Our use case:
>>> We are using func(Dataframe) => Dataframe as interface to use Pandas
>>> UDF, while running locally only the function, it runs faster but when
>>> executed in Spark environment - the processing time is more than expected.
>>> We have one column where the value is large (BinaryType -> 600KB),
>>> wondering whether this could make the Arrow computation slower ?
>>>
>>> Is there any profiling or best way to debug the cost incurred using
>>> pandas UDF ?
>>>
>>>
>>> Thanks,
>>> Subash
>>>
>>> --
Thanks,
Russell Jurney @rjurney <http://twitter.com/rjurney>
russell.jurney@gmail.com LI <http://linkedin.com/in/russelljurney> FB
<http://facebook.com/jurney> datasyndrome.com
Re: Profiling PySpark Pandas UDF
Posted by Sean Owen <sr...@gmail.com>.
It's important to realize that while pandas UDFs and pandas on Spark are
both related to pandas, they are not themselves directly related. The first
lets you use pandas within Spark, the second lets you use pandas on Spark.
Hard to say with this info but you want to look at whether you are doing
something expensive in each UDF call and consider amortizing it with the
scalar iterator UDF pattern. Maybe.
A pandas UDF is not spark code itself so no there is no tool in spark to
profile it. Conversely any approach to profiling pandas or python would
work here .
On Thu, Aug 25, 2022, 11:22 AM Gourav Sengupta <go...@gmail.com>
wrote:
> Hi,
>
> May be I am jumping to conclusions and making stupid guesses, but have you
> tried koalas now that it is natively integrated with pyspark??
>
> Regards
> Gourav
>
> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <su...@gmail.com>
> wrote:
>
>> Hi All,
>>
>> I was wondering if we have any best practices on using pandas UDF ?
>> Profiling UDF is not an easy task and our case requires some drilling down
>> on the logic of the function.
>>
>>
>> Our use case:
>> We are using func(Dataframe) => Dataframe as interface to use Pandas UDF,
>> while running locally only the function, it runs faster but when executed
>> in Spark environment - the processing time is more than expected. We have
>> one column where the value is large (BinaryType -> 600KB), wondering
>> whether this could make the Arrow computation slower ?
>>
>> Is there any profiling or best way to debug the cost incurred using
>> pandas UDF ?
>>
>>
>> Thanks,
>> Subash
>>
>>
Re: Profiling PySpark Pandas UDF
Posted by Andrew Melo <an...@gmail.com>.
Hi Gourav,
Since Koalas needs the same round-trip to/from JVM and Python, I
expect that the performance should be nearly the same for UDFs in
either API
Cheers
Andrew
On Thu, Aug 25, 2022 at 11:22 AM Gourav Sengupta
<go...@gmail.com> wrote:
>
> Hi,
>
> May be I am jumping to conclusions and making stupid guesses, but have you tried koalas now that it is natively integrated with pyspark??
>
> Regards
> Gourav
>
> On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <su...@gmail.com> wrote:
>>
>> Hi All,
>>
>> I was wondering if we have any best practices on using pandas UDF ? Profiling UDF is not an easy task and our case requires some drilling down on the logic of the function.
>>
>>
>> Our use case:
>> We are using func(Dataframe) => Dataframe as interface to use Pandas UDF, while running locally only the function, it runs faster but when executed in Spark environment - the processing time is more than expected. We have one column where the value is large (BinaryType -> 600KB), wondering whether this could make the Arrow computation slower ?
>>
>> Is there any profiling or best way to debug the cost incurred using pandas UDF ?
>>
>>
>> Thanks,
>> Subash
>>
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Re: Profiling PySpark Pandas UDF
Posted by Gourav Sengupta <go...@gmail.com>.
Hi,
May be I am jumping to conclusions and making stupid guesses, but have you
tried koalas now that it is natively integrated with pyspark??
Regards
Gourav
On Thu, 25 Aug 2022, 11:07 Subash Prabanantham, <su...@gmail.com>
wrote:
> Hi All,
>
> I was wondering if we have any best practices on using pandas UDF ?
> Profiling UDF is not an easy task and our case requires some drilling down
> on the logic of the function.
>
>
> Our use case:
> We are using func(Dataframe) => Dataframe as interface to use Pandas UDF,
> while running locally only the function, it runs faster but when executed
> in Spark environment - the processing time is more than expected. We have
> one column where the value is large (BinaryType -> 600KB), wondering
> whether this could make the Arrow computation slower ?
>
> Is there any profiling or best way to debug the cost incurred using pandas
> UDF ?
>
>
> Thanks,
> Subash
>
>