Hi Maziyar, Mich

Do we have any ticket to track this? Any idea if this is going to be fixed in 
3.1.2?

Thanks and Regards,
Abhishek

From: Mich Talebzadeh <mich.talebza...@gmail.com>
Sent: Friday, April 9, 2021 2:11 PM
To: Maziyar Panahi <maziyar.pan...@iscpif.fr>
Cc: User <user@spark.apache.org>
Subject: Re: Why is Spark 3.0.x faster than Spark 3.1.x


Hi,

Regarding your point:

.... I won't be able to defend this request by telling Spark users the previous 
major release was and still is more stable than the latest major release ...

With the benefit of hindsight version 3.1.1 was released recently and the 
definition of stable (from a practical point of view) does not come into it 
yet. That is perhaps the reason why some vendors like Cloudera are few releases 
away from the latest version. In production what matters most is the 
predictability and stability. You are not doing anything wrong by rolling it 
back and awaiting further clarification and resolution on the error.

HTH


[https://docs.google.com/uc?export=download&id=1qt8nKd2bxgs6clwYFqGy-k84L3N79hW6&revid=0B1BiUVX33unjallLZWQwN1BDbGRMNTI5WUw3TlloMmJZRThjPQ]


 
[https://docs.google.com/uc?export=download&id=1-q7RFGRfLMObPuQPWSd9sl_H1UPNFaIZ&revid=0B1BiUVX33unjMWtVUWpINWFCd0ZQTlhTRHpGckh4Wlg4RG80PQ]
   view my Linkedin 
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On Fri, 9 Apr 2021 at 08:58, Maziyar Panahi 
<maziyar.pan...@iscpif.fr<mailto:maziyar.pan...@iscpif.fr>> wrote:
Thanks Mich, I will ask all of our users to use pyspark 3.0.x and will change 
all the notebooks/scripts to switch back from 3.1.1 to 3.0.2.

That's being said, I won't be able to defend this request by telling Spark 
users the previous major release was and still is more stable than the latest 
major release, something that made everything default to 3.1.1 (pyspark, 
downloads, etc.).

I'll see if I can open a ticket for this as well.


On 8 Apr 2021, at 17:27, Mich Talebzadeh 
<mich.talebza...@gmail.com<mailto:mich.talebza...@gmail.com>> wrote:

Well the normal course of action (considering laws of diminishing returns)  is 
that your mileage varies:

Spark 3.0.1 is pretty stable and good enough. Unless there is an overriding 
reason why you have to use 3.1.1, you can set it aside and try it when you have 
other use cases. For now I guess you can carry on with 3.0.1 as BAU.

HTH


 
[https://docs.google.com/uc?export=download&id=1-q7RFGRfLMObPuQPWSd9sl_H1UPNFaIZ&revid=0B1BiUVX33unjMWtVUWpINWFCd0ZQTlhTRHpGckh4Wlg4RG80PQ]
   view my Linkedin 
profile<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


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damage or destruction of data or any other property which may arise from 
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On Thu, 8 Apr 2021 at 16:19, Maziyar Panahi 
<maziyar.pan...@iscpif.fr<mailto:maziyar.pan...@iscpif.fr>> wrote:
I personally added the followings to my SparkSession in 3.1.1 and the result 
was exactly the same as before (local master). The 3.1.1 is still 4-5 times 
slower than 3.0.2 at least for that piece of code. I will do more investigation 
to see how it does with other stuff, especially anything without .transform or 
Spark ML related functions, but the small code I provided on any dataset that 
is big enough to take a minute to finish will show you the difference going 
from 3.0.2 to 3.1.1 by magnitude of 4-5:


.config("spark.sql.adaptive.coalescePartitions.enabled", "false")
.config("spark.sql.adaptive.enabled", "false")



On 8 Apr 2021, at 16:47, Mich Talebzadeh 
<mich.talebza...@gmail.com<mailto:mich.talebza...@gmail.com>> wrote:

spark 3.1.1

I enabled the parameter

spark_session.conf.set("spark.sql.adaptive.enabled", "true")

to see it effects

in yarn cluster mode, i.e spark-submit --master yarn --deploy-mode client

with 4 executors it crashed the cluster.

I then reduced the number of executors to 2 and this time it ran OK but the 
performance is worse

I assume it adds some overhead?



 
[https://docs.google.com/uc?export=download&id=1-q7RFGRfLMObPuQPWSd9sl_H1UPNFaIZ&revid=0B1BiUVX33unjMWtVUWpINWFCd0ZQTlhTRHpGckh4Wlg4RG80PQ]
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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 
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On Thu, 8 Apr 2021 at 15:05, Maziyar Panahi 
<maziyar.pan...@iscpif.fr<mailto:maziyar.pan...@iscpif.fr>> wrote:
Thanks Sean,

I have already tried adding that and the result is absolutely the same.

The reason that config cannot be the reason (at least not alone) it's because 
my comparison is between Spark 3.0.2 and Spark 3.1.1. This config has been set 
to true the beginning of 3.0.0 and hasn't changed:

- 
https://spark.apache.org/docs/3.1.1/sql-performance-tuning.html#adaptive-query-execution
- 
https://spark.apache.org/docs/3.0.2/sql-performance-tuning.html#adaptive-query-execution
- 
https://spark.apache.org/docs/3.0.1/sql-performance-tuning.html#adaptive-query-execution
- 
https://spark.apache.org/docs/3.0.0/sql-performance-tuning.html#adaptive-query-execution

So it can't be a good thing for 3.0.2 and a bad thing for 3.1.1, unfortunately 
the issue is some where else.


On 8 Apr 2021, at 15:54, Sean Owen <sro...@gmail.com<mailto:sro...@gmail.com>> 
wrote:

Right, you already established a few times that the difference is the number of 
partitions. Russell answered with what is almost surely the correct answer, 
that it's AQE. In toy cases it isn't always a win.
Disable it if you need to. It's not a problem per se in 3.1; AQE speeds up more 
realistic workloads in general.

On Thu, Apr 8, 2021 at 8:52 AM maziyar 
<maziyar.pan...@iscpif.fr<mailto:maziyar.pan...@iscpif.fr>> wrote:
So this is what I have in my Spark UI for 3.0.2 and 3.1.1: For pyspark==3.0.2 
(stage "showString at NativeMethodAccessorImpl.java:0"): 
[http://apache-spark-user-list.1001560.n3.nabble.com/file/t8277/Screenshot_2021-04-08_at_15.png]
 Finished in 10 seconds For pyspark==3.1.1 (same stage "showString at 
NativeMethodAccessorImpl.java:0"): 
[http://apache-spark-user-list.1001560.n3.nabble.com/file/t8277/Screenshot_2021-04-08_at_15.png]
 Finished the same stage in 39 seconds As you can see everything is literally 
the same between 3.0.2 and 3.1.1, number of stages, number of tasks, Input, 
Output, Shuffle Read, Shuffle Write, except the 3.0.2 runs all 12 tasks 
together while the 3.1.1 finishes 10/12 and the other 2 are the processing of 
the actual task which I shared previously: 3.1.1 
[http://apache-spark-user-list.1001560.n3.nabble.com/file/t8277/114009725-af969e00-9863-11eb-8e5b-07ce53e8f5f3.png]
 3.0.2 
[http://apache-spark-user-list.1001560.n3.nabble.com/file/t8277/114009712-ac9bad80-9863-11eb-9e55-c797833bdbba.png]
 PS: I have just made the same test in Databricks with 1 worker 8.1 (includes 
Apache Spark 3.1.1, Scala 2.12): 
[http://apache-spark-user-list.1001560.n3.nabble.com/file/t8277/Screenshot_2021-04-08_at_15.png]
 7.6 (includes Apache Spark 3.0.1, Scala 2.12) 
[http://apache-spark-user-list.1001560.n3.nabble.com/file/t8277/Screenshot_2021-04-08_at_15.png]
 There is still a difference, over 20 seconds which when it comes to the whole 
process being within a minute that is a big bump. Not sure what it is, but 
until further notice, I will advise our users to not use Spark/PySpark 3.1.1 
locally or in Databricks. (there are other optimizations, maybe it's not 
noticeable, but this is such a simple code and it can become a bottleneck 
quickly in larger pipelines)
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