If you say that libraries are not transferred by default and in my case I
haven't used any --py-files then just because the driver python is
different I have facing 6x speed difference ? I am using client mode to
submit the program but the udfs and all are executed in the executors, then
why is the difference so much?
I tried the prints
For jupyter one the driver prints
../../jupyter-folder/venv
and executors print /usr
For spark-submit both of them print /usr
The cluster is created few years back and used organisation wide. So how
python 2.6.6 is installed, i honestly do not know. I copied the whole
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: 9886428028/9652029028*
On Wed, Sep 11, 2019 at 8:22 PM Abdeali Kothari <abdealikoth...@gmail.com>
wrote:
> The driver python may not always be the same as the executor python.
> You can set these using PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON
>
> The dependent libraries are not transferred by spark in any way unless you
> do a --py-files or .addPyFile()
>
> Could you try this:
> *import sys; print(sys.prefix)*
>
> on the driver, and also run this inside a UDF with:
>
> *def dummy(a):*
> * import sys; raise AssertionError(sys.prefix)*
>
> and get the traceback exception on the driver ?
> This would be the best way to get the exact sys.prefix (python path) for
> both the executors and driver.
>
> Also, could you elaborate on what environment is this ?
> Linux? - CentOS/Ubuntu/etc. ?
> How was the py 2.6.6 installed ?
> How was the py 2.7.5 venv created and how what the base py 2.7.5 installed
> ?
>
> Also, how are you creating the Spark Session in jupyter ?
>
>
> On Wed, Sep 11, 2019 at 7:33 PM Dhrubajyoti Hati <dhruba.w...@gmail.com>
> wrote:
>
>> But would it be the case for multiple tasks running on the same worker
>> and also both the tasks are running in client mode, so the one true is true
>> for both or for neither. As mentioned earlier all the confs are same. I
>> have checked and compared each conf.
>>
>> As Abdeali mentioned it must be because the way libraries are in both
>> the environments. Also i verified by running the same script for jupyter
>> environment and was able to get the same result using the normal script
>> which i was running with spark-submit.
>>
>> Currently i am searching for the ways the python packages are transferred
>> from driver to spark cluster in client mode. Any info on that topic would
>> be helpful.
>>
>> Thanks!
>>
>>
>>
>> On Wed, 11 Sep, 2019, 7:06 PM Patrick McCarthy, <pmccar...@dstillery.com>
>> wrote:
>>
>>> Are you running in cluster mode? A large virtualenv zip for the driver
>>> sent into the cluster on a slow pipe could account for much of that eight
>>> minutes.
>>>
>>> On Wed, Sep 11, 2019 at 3:17 AM Dhrubajyoti Hati <dhruba.w...@gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> I just ran the same script in a shell in jupyter notebook and find the
>>>> performance to be similar. So I can confirm this is because the libraries
>>>> used jupyter notebook python is different than the spark-submit python this
>>>> is happening.
>>>>
>>>> But now I have a following question. Are the dependent libraries in a
>>>> python script also transferred to the worker machines when executing a
>>>> python script in spark. Because though the driver python versions are
>>>> different, the workers machines will use their same python environment to
>>>> run the code. If anyone can explain this part, it would be helpful.
>>>>
>>>>
>>>>
>>>>
>>>> *Regards,Dhrubajyoti Hati.Mob No: 9886428028/9652029028*
>>>>
>>>>
>>>> On Wed, Sep 11, 2019 at 9:45 AM Dhrubajyoti Hati <dhruba.w...@gmail.com>
>>>> wrote:
>>>>
>>>>> Just checked from where the script is submitted i.e. wrt Driver, the
>>>>> python env are different. Jupyter one is running within a the virtual
>>>>> environment which is Python 2.7.5 and the spark-submit one uses 2.6.6. But
>>>>> the executors have the same python version right? I tried doing a
>>>>> spark-submit from jupyter shell, it fails to find python 2.7 which is not
>>>>> there hence throws error.
>>>>>
>>>>> Here is the udf which might take time:
>>>>>
>>>>> import base64
>>>>> import zlib
>>>>>
>>>>> def decompress(data):
>>>>>
>>>>> bytecode = base64.b64decode(data)
>>>>> d = zlib.decompressobj(32 + zlib.MAX_WBITS)
>>>>> decompressed_data = d.decompress(bytecode )
>>>>> return(decompressed_data.decode('utf-8'))
>>>>>
>>>>>
>>>>> Could this because of the two python environment mismatch from Driver
>>>>> side? But the processing
>>>>>
>>>>> happens in the executor side?
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> *Regards,Dhrub*
>>>>>
>>>>> On Wed, Sep 11, 2019 at 8:59 AM Abdeali Kothari <
>>>>> abdealikoth...@gmail.com> wrote:
>>>>>
>>>>>> 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 same? I ask because
>>>>>> it looks like you're using a UDF and if the Jupyter python has (let's
>>>>>> say)
>>>>>> numpy compiled with blas it would be faster than a numpy without it. Etc.
>>>>>> I.E. Some library you use may be using pure python and another may be
>>>>>> using
>>>>>> a faster C extension...
>>>>>>
>>>>>> What python libraries are you using in the UDFs? It you don't use
>>>>>> UDFs at all and use some very simple pure spark functions does the time
>>>>>> difference still exist?
>>>>>>
>>>>>> Also are you using dynamic allocation or some similar spark config
>>>>>> which could vary performance between runs because the same resources
>>>>>> we're
>>>>>> not utilized on Jupyter / spark-submit?
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 11, 2019, 08:43 Stephen Boesch <java...@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.w...@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, <
>>>>>>>> pmccar...@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.w...@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
>>>>>>>>>
>>>>>>>>
>>>
>>> --
>>>
>>>
>>> *Patrick McCarthy *
>>>
>>> Senior Data Scientist, Machine Learning Engineering
>>>
>>> Dstillery
>>>
>>> 470 Park Ave South, 17th Floor, NYC 10016
>>>
>>
