In a bash terminal, can you do:
*export PYSPARK_DRIVER_PYTHON=/path/to/venv/bin/python*
and then:
run the *spark-shell* script ?
This should mimic the behaviour of jupyter in spark-shell and should be
fast (1-2mins similar to jupyter notebook)
This would confirm the guess that the python2.7 venv has some magic ^_^
On Wed, Sep 11, 2019 at 10:32 PM Dhrubajyoti Hati <dhruba.w...@gmail.com>
wrote:
> Also the performance remains identical when running the same script from
> jupyter terminal instead or normal terminal. In the script the spark
> context is created by
>
> spark = SparkSession \
> .builder \
> ..
> ..
> getOrCreate() command
>
>
> On Wed, Sep 11, 2019 at 10:28 PM Dhrubajyoti Hati <dhruba.w...@gmail.com>
> wrote:
>
>> 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
>>>>>
>>>>
