Ok. Can't think of why that would happen.

Am Di., 10. Sept. 2019 um 20:26 Uhr schrieb Dhrubajyoti Hati <dhruba.work@gmail.com>:
As mentioned in the very first mail:
* same cluster it is submitted.
* from same machine they are submitted and also from same user
* each of them has 128 executors and 2 cores per executor with 8Gigs of memory each and both of them are getting that while running

to clarify more let me quote what I mentioned above. These data is taken from Spark-UI when the jobs are almost finished in both.
"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." which means per task time taken is much higher in spark-submit script than jupyter script. This is where I am really puzzled because they are the exact same code. why running them two different ways vary so much in the execution time.


Dhrubajyoti Hati.
Mob No: 9886428028/9652029028

On Wed, Sep 11, 2019 at 8:42 AM Stephen Boesch <javadba@gmail.com> wrote:
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:

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)) \

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?



Patrick McCarthy 

Senior Data Scientist, Machine Learning Engineering


470 Park Ave South, 17th Floor, NYC 10016