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From Stephen Boesch <java...@gmail.com>
Subject Re: script running in jupyter 6-7x faster than spark submit
Date Wed, 11 Sep 2019 03:16:36 GMT
Sounds like you have done your homework to properly compare .   I'm
guessing the answer to the following is yes .. but in any case:  are they
both running against the same spark cluster with the same configuration
parameters especially executor memory and number of workers?

Am Di., 10. Sept. 2019 um 20:05 Uhr schrieb Dhrubajyoti Hati <
dhruba.work@gmail.com>:

> No, i checked for that, hence written "brand new" jupyter notebook. Also
> the time taken by both are 30 mins and ~3hrs as i am reading a 500  gigs
> compressed base64 encoded text data from a hive table and decompressing and
> decoding in one of the udfs. Also the time compared is from Spark UI not
> how long the job actually takes after submission. Its just the running time
> i am comparing/mentioning.
>
> As mentioned earlier, all the spark conf params even match in two scripts
> and that's why i am puzzled what going on.
>
> On Wed, 11 Sep, 2019, 12:44 AM Patrick McCarthy, <pmccarthy@dstillery.com>
> wrote:
>
>> It's not obvious from what you pasted, but perhaps the juypter notebook
>> already is connected to a running spark context, while spark-submit needs
>> to get a new spot in the (YARN?) queue.
>>
>> I would check the cluster job IDs for both to ensure you're getting new
>> cluster tasks for each.
>>
>> On Tue, Sep 10, 2019 at 2:33 PM Dhrubajyoti Hati <dhruba.work@gmail.com>
>> wrote:
>>
>>> Hi,
>>>
>>> I am facing a weird behaviour while running a python script. Here is
>>> what the code looks like mostly:
>>>
>>> def fn1(ip):
>>>    some code...
>>>     ...
>>>
>>> def fn2(row):
>>>     ...
>>>     some operations
>>>     ...
>>>     return row1
>>>
>>>
>>> udf_fn1 = udf(fn1)
>>> cdf = spark.read.table("xxxx") //hive table is of size > 500 Gigs with
>>> ~4500 partitions
>>> ddf = cdf.withColumn("coly", udf_fn1(cdf.colz)) \
>>>     .drop("colz") \
>>>     .withColumnRenamed("colz", "coly")
>>>
>>> edf = ddf \
>>>     .filter(ddf.colp == 'some_value') \
>>>     .rdd.map(lambda row: fn2(row)) \
>>>     .toDF()
>>>
>>> print edf.count() // simple way for the performance test in both
>>> platforms
>>>
>>> Now when I run the same code in a brand new jupyter notebook it runs 6x
>>> faster than when I run this python script using spark-submit. The
>>> configurations are printed and  compared from both the platforms and they
>>> are exact same. I even tried to run this script in a single cell of jupyter
>>> notebook and still have the same performance. I need to understand if I am
>>> missing something in the spark-submit which is causing the issue.  I tried
>>> to minimise the script to reproduce the same error without much code.
>>>
>>> Both are run in client mode on a yarn based spark cluster. The machines
>>> from which both are executed are also the same and from same user.
>>>
>>> What i found is the  the quantile values for median for one ran with
>>> jupyter was 1.3 mins and one ran with spark-submit was ~8.5 mins.  I am not
>>> able to figure out why this is happening.
>>>
>>> Any one faced this kind of issue before or know how to resolve this?
>>>
>>> *Regards,*
>>> *Dhrub*
>>>
>>
>>
>> --
>>
>>
>> *Patrick McCarthy  *
>>
>> Senior Data Scientist, Machine Learning Engineering
>>
>> Dstillery
>>
>> 470 Park Ave South, 17th Floor, NYC 10016
>>
>

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