[jira] [Created] (SPARK-16367) Wheelhouse Support for PySpark

["Semet (JIRA)" <ji...@apache.org>]

Semet created SPARK-16367:
-----------------------------

             Summary: Wheelhouse Support for PySpark
                 Key: SPARK-16367
                 URL: https://issues.apache.org/jira/browse/SPARK-16367
             Project: Spark
          Issue Type: Improvement
          Components: Deploy, PySpark
    Affects Versions: 1.6.2, 1.6.1, 2.0.0
            Reporter: Semet


*Rational*
Is it recommended, in order to deploying Scala packages written in Scala, to build big fat jar files. This allows to have all dependencies on one package so the only "cost" is copy time to deploy this file on every Spark Node.

On the other hand, Python deployment is more difficult once you want to use external packages, and you don't really want to mess with the IT to deploy the packages on the virtualenv of each nodes.

*Previous approaches*
I based the current proposal over the two following bugs related to this point:
- SPARK-6764 ("Wheel support for PySpark")
- SPARK-13587("Support virtualenv in PySpark")

So here is my proposal:

*Uber Fat Wheelhouse for Python Deployment*
In Python, the packaging standard is now "wheels", which goes further that old good ".egg" files. With a wheel file (".whl"), the package is already prepared for a given architecture. You can have several wheel, each specific to an architecture, or environment. 

The {{pip}} tools now how to select the package matching the current system, how to install this package in a light speed. Said otherwise, package that requires compilation of a C module, for instance, does *not* compile anything when installing from wheel file.

{{pip}} also provides the ability to generate easily all wheel of all packages used for a given module (inside a "virtualenv"). This is called "wheelhouse". You can even don't mess with this compilation and retrieve it directly from pypi.python.org.

*Developer workflow*
Here is, in a more concrete way, how my proposal will be for developers:

- you are writing a PySpark script that increase in term of size and dependencies. Deploying on Spark for example requires to build numpy or Theano and other dependencies
- to use "Big Fat Wheelhouse" support of Pyspark, you need to turn his script into a standard Python package:
-- write a requirements.txt.
-- write a setup.py. Use [PBR|http://docs.openstack.org/developer/pbr/] it makes the jobs of maitaining a setup.py files really easy
-- use [pip-tools|https://github.com/nvie/pip-tools] to maintain the requirements.txt
-- create a virtualenv if not already:
{code}
virtualenv env
{code}
-- Work on your environment, define the requirement you need in {{requirements.txt}}, do all the {{pip install}} you need.
- create the wheelhouse for your current project
{code}
pip install wheelhouse
pip wheel . --wheel-dir wheelhouse
{code}
- zip it into a {{wheelhouse.zip}}.

Note that you can have your own package (for instance 'my_package') be generated into a wheel and so installed by {{pip}} automatically.

Now comes the time to submit the project:
{code}
bin/spark-submit  --master master --deploy-mode client --conf "spark.pyspark.virtualenv.enabled=true" --conf "spark.pyspark.virtualenv.type=native" --conf "spark.pyspark.virtualenv.requirements=/path/to/virtualenv/requirements.txt" --conf "spark.pyspark.virtualenv.bin.path=virtualenv" "spark.pyspark.virtualenv.wheelhouse=/path/to/virtualenv/wheelhouse.zip"  ~/path/to/launcher_script.py
{code}

You can see that:
- no extra argument is add in the command line. All configuration goes through {{--conf}} argument (this has been directly taken from SPARK-13587). According to the history on spark source code, I guess the goal is to simplify the maintainance of the various command line interface, by avoiding too many specific argument.
- the command line is pretty complex indeed. I guess with a proper documentation this might not be a problem
- you still need to define the path to {{requirement.txt}} and {{wheelhouse.zip}} (they will be automatically copied to each node). This is important since this will allow {{pip install}}, running of each node, to pick only the wheels he needs. For example, if you have a package compiled on 32 bits and 64 bits, you will have 2 wheels, and on each node, {{pip}} will only select the right one
- I have choosen to keep the script at the end of the command line, but for me it is just a launcher script, it can only be 4 lines:
{code}
/#!/usr/bin/env python	

from mypackage import run
run()
{code}

*advantages*
- quick installation, since there is no compilation
- no Internet connectivity support, no need mess with the corporate proxy or require a local mirroring of pypi.
- package versionning isolation (two spark job can depends on two different version of a given library)

*disadvantages*
- slighly more complex to setup than sending a simple python script, but this feature is not lost
- support of heterogenous Spark nodes (ex: 32 bits, 64 bits) is possible but one has to send all wheels flavours and ensure pip is able to install in every environment. The complexity of this task is on the hands of the developer and no more on the IT persons! (TMHO, this is an advantage)



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org