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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Structuring a PySpark Application
Date Thu, 01 Jul 2021 11:14:33 GMT
Hi Kartik,

I parameterized your shell script and tested on a stob python file and
looks OK, ensuring that the shell script is more robust


#!/bin/bash
set -e

#cd "$(dirname "${BASH_SOURCE[0]}")/../"

pyspark_venv="pyspark_venv"
source_zip_file="DSBQ.zip"
[ -d ${pyspark_venv} ] && rm -r -d ${pyspark_venv}
[ -f ${pyspark_venv}.tar.gz ] && rm -r -f ${pyspark_venv}.tar.gz
[ -f ${source_zip_file} ] && rm -r -f ${source_zip_file}

python3 -m venv ${pyspark_venv}
source ${pyspark_venv}/bin/activate
pip install -r requirements_spark.txt
pip install venv-pack
venv-pack -o ${pyspark_venv}.tar.gz

export PYSPARK_DRIVER_PYTHON=python
export PYSPARK_PYTHON=./${pyspark_venv}/bin/python
spark-submit \
        --master local[4] \
        --conf
"spark.yarn.dist.archives"=${pyspark_venv}.tar.gz#${pyspark_venv} \
        /home/hduser/dba/bin/python/dynamic_ARRAY_generator_parquet.py


HTH


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>



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On Wed, 30 Jun 2021 at 19:21, Kartik Ohri <kartikohri13@gmail.com> wrote:

> Hi Mich!
>
> We use this in production but indeed there is much scope for improvements,
> configuration being one of those :).
>
> Yes, we have a private on-premise cluster. We run Spark on YARN (no
> airflow etc.) which controls the scheduling and use HDFS as a datastore.
>
> Regards
>
> On Wed, Jun 30, 2021 at 11:41 PM Mich Talebzadeh <
> mich.talebzadeh@gmail.com> wrote:
>
>> Thanks for the details Kartik.
>>
>> Let me go through these. The code itself and indentation looks good.
>>
>> One minor thing I noticed is that you are not using a yaml file
>> (config.yml) for your variables and you seem to embed them in your
>> config.py code. That is what I used to do before :) a friend advised me to
>> initialise with yaml and read them in python file. However, I guess that is
>> a personal style.
>>
>> Overall looking neat. I believe you are running all these on-premises and
>> not using airflow or composer for your scheduling.
>>
>>
>> Cheers
>>
>>
>> Mich
>>
>>
>>    view my Linkedin 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 relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Wed, 30 Jun 2021 at 18:39, Kartik Ohri <kartikohri13@gmail.com> wrote:
>>
>>> Hi Mich!
>>>
>>> Thanks for the reply.
>>>
>>> The zip file contains all of the spark related
>>> code, particularly contents of this folder
>>> <https://github.com/metabrainz/listenbrainz-server/tree/master/listenbrainz_spark>
>>> .
>>> The requirements_spark.txt
>>> <https://github.com/metabrainz/listenbrainz-server/blob/master/requirements_spark.txt>
is
>>> contained in the project and it contains the non-spark dependencies of the
>>> python code.
>>> The tar.gz file is created according to Pyspark docs
>>> <https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html#using-virtualenv>
for
>>> dependency management. The spark.yarn.dist.archives also comes from
>>> there.
>>>
>>> This is the python file
>>> <https://github.com/metabrainz/listenbrainz-server/blob/master/spark_manage.py>
>>> invoked by the spark-submit to start the "RequestConsumer".
>>>
>>> Regards,
>>> Kartik
>>>
>>>
>>> On Wed, Jun 30, 2021 at 9:02 PM Mich Talebzadeh <
>>> mich.talebzadeh@gmail.com> wrote:
>>>
>>>> Hi Kartik,
>>>>
>>>> Can you explain how you create your zip file? Does that include all in
>>>> your top project directory as per PyCharm etc.
>>>>
>>>> The rest looks Ok as you are creating a Python Virtual Env
>>>>
>>>> python3 -m venv pyspark_venv
>>>> source pyspark_venv/bin/activate
>>>>
>>>> How do you create that requirements_spark.txt file?
>>>>
>>>> pip install -r requirements_spark.txt
>>>> pip install venv-pack
>>>>
>>>>
>>>> Where is this gz file used?
>>>> venv-pack -o pyspark_venv.tar.gz
>>>>
>>>> Because I am not clear about below line
>>>>
>>>> --conf "spark.yarn.dist.archives"=pyspark_venv.tar.gz#environment \
>>>>
>>>> It helps if you walk us through the shell itself for clarification HTH,
>>>>
>>>> Mich
>>>>
>>>>
>>>>
>>>>
>>>>    view my Linkedin 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 relying on this email's technical content is explicitly
>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>> arising from such loss, damage or destruction.
>>>>
>>>>
>>>>
>>>>
>>>> On Wed, 30 Jun 2021 at 15:47, Kartik Ohri <kartikohri13@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi all!
>>>>>
>>>>> I am working on a Pyspark application and would like suggestions on
>>>>> how it should be structured.
>>>>>
>>>>> We have a number of possible jobs, organized in modules. There is also
>>>>> a "RequestConsumer
>>>>> <https://github.com/metabrainz/listenbrainz-server/blob/master/listenbrainz_spark/request_consumer/request_consumer.py>"
>>>>> class which consumes from a messaging queue. Each message contains the
name
>>>>> of the job to invoke and the arguments to be passed to it. Messages are
put
>>>>> into the message queue by cronjobs, manually etc.
>>>>>
>>>>> We submit a zip file containing all python files to a Spark cluster
>>>>> running on YARN and ask it to run the RequestConsumer. This
>>>>> <https://github.com/metabrainz/listenbrainz-server/blob/master/docker/start-spark-request-consumer.sh#L23-L34>
>>>>> is the exact spark-submit command for the interested. The results of
the
>>>>> jobs are collected
>>>>> <https://github.com/metabrainz/listenbrainz-server/blob/master/listenbrainz_spark/request_consumer/request_consumer.py#L120-L122>
>>>>> by the request consumer and pushed into another queue.
>>>>>
>>>>> My question is whether this type of structure makes sense. Should the
>>>>> Request Consumer instead run independently of Spark and invoke spark-submit
>>>>> scripts when it needs to trigger a job? Or is there another recommendation?
>>>>>
>>>>> Thank you all in advance for taking the time to read this email and
>>>>> helping.
>>>>>
>>>>> Regards,
>>>>> Kartik.
>>>>>
>>>>>
>>>>>

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