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From "Benjamin Zaitlen (JIRA)" <>
Subject [jira] [Commented] (SPARK-16367) Wheelhouse Support for PySpark
Date Tue, 10 Jan 2017 14:22:58 GMT


Benjamin Zaitlen commented on SPARK-16367:

[] do you also have plans on supporting conda packages and environments
as well ?

> Wheelhouse Support for PySpark
> ------------------------------
>                 Key: SPARK-16367
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: Deploy, PySpark
>    Affects Versions: 1.6.1, 1.6.2, 2.0.0
>            Reporter: Semet
>              Labels: newbie, python, python-wheel, wheelhouse
>   Original Estimate: 168h
>  Remaining Estimate: 168h
> *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. 
> This ticket proposes to allow users the ability to deploy their job as "Wheels" packages.
The Python community is strongly advocating to promote this way of packaging and distributing
Python application as a "standard way of deploying Python App". In other word, this is the
"Pythonic Way of Deployment".
> *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")
> First part of my proposal was to merge, in order to support wheels install and virtualenv
> *Virtualenv, wheel support and "Uber Fat Wheelhouse" for PySpark* 
> In Python, the packaging standard is now the "wheels" file format, which goes further
that good old ".egg" files. With a wheel file (".whl"), the package is already prepared for
a given architecture. You can have several wheels for a given package version, each specific
to an architecture, or environment. 
> For example, look at all the different version of
Wheel available. 
> The {{pip}} tools knows how to select the right wheel file matching the current system,
and how to install this package in a light speed (without compilation). Said otherwise, package
that requires compilation of a C module, for instance "numpy", does *not* compile anything
when installing from wheel file. 
> {{}} already provided wheels for major python version. It the wheel is
not available, pip will compile it from source anyway. Mirroring of Pypi is possible through
projects such as (untested) or the Pypi mirror support on Artifactory
(tested personnally). 
> {{pip}} also provides the ability to generate easily all wheels of all packages used
for a given project which is inside a "virtualenv". This is called "wheelhouse". You can even
don't mess with this compilation and retrieve it directly from 
> *Use Case 1: no internet connectivity* 
> Here my first proposal for a deployment workflow, in the case where the Spark cluster
does not have any internet connectivity or access to a Pypi mirror. In this case the simplest
way to deploy a project with several dependencies is to build and then send to complete "wheelhouse":

> - 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}}. I recommend to specify all package version. You can
use [pip-tools|] to maintain the requirements.txt 
> {code} 
> astroid==1.4.6 # via pylint 
> autopep8==1.2.4 
> click==6.6 # via pip-tools 
> colorama==0.3.7 # via pylint 
> enum34==1.1.6 # via hypothesis 
> findspark==1.0.0 # via spark-testing-base 
> first==2.0.1 # via pip-tools 
> hypothesis==3.4.0 # via spark-testing-base 
> lazy-object-proxy==1.2.2 # via astroid 
> linecache2==1.0.0 # via traceback2 
> pbr==1.10.0 
> pep8==1.7.0 # via autopep8 
> pip-tools==1.6.5 
> py==1.4.31 # via pytest 
> pyflakes==1.2.3 
> pylint==1.5.6 
> pytest==2.9.2 # via spark-testing-base 
> six==1.10.0 # via astroid, pip-tools, pylint, unittest2 
> spark-testing-base==0.0.7.post2 
> traceback2==1.4.0 # via unittest2 
> unittest2==1.1.0 # via spark-testing-base 
> wheel==0.29.0 
> wrapt==1.10.8 # via astroid 
> {code} 
> -- write a with some entry points or package. Use [PBR|]
it makes the jobs of maitaining a files really easy 
> -- create a virtualenv if not already in one: 
> {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} 
> This can take some times, but at the end you have all the .whl required *for your current
system* in a directory {{wheelhouse}}. 
> - zip it into a {{}}. 
> 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 --files /path/to/virtualenv/requirements.txt,/path/to/virtualenv/
--conf "spark.pyspark.virtualenv.enabled=true" ~/path/to/ 
> {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 wheelhouse deployment is triggered by the {{\-\-conf "spark.pyspark.virtualenv.enabled=true"
}} argument. The {{requirements.txt}} and {{}} are copied through {{--files}}.
The names of both files can be changed through {{\-\-conf}} arguments. I guess with a proper
documentation this might not be a problem 
> - you still need to define the path to {{requirement.txt}} and {{}} (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} 
> - on each node, a new virtualenv is created *at each deployment*. This has a cost, but
not so much, since the {{pip install}} will only install wheel, no compilation nor internet
connection will be required. The command line for installing the wheel on each node will be
> {code} 
> pip install --no-index --find-links=/path/to/node/wheelhouse -r requirements.txt 
> {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* 
> - creating a virtualenv at each execution takes time, not that much but still it can
take some seconds 
> - and disk space 
> - 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) 
> *Use Case 2: the Spark cluster has access to Pypi or a mirror of Pypi* 
> This is the more elegant situation. The Spark cluster (each node) can install the dependencies
of your project independently from the wheels provided by Pypi. Your internal dependencies
and your job project can also comes in independent wheel files as well. In this case the workflow
is much simpler: 
> - Turn your project into a Python module 
> - write {{requirements.txt}} and {{}} like in Use Case 1 
> - create the wheel with {{pip wheels}}. But now we will not send *ALL* the dependencies.
Only the one that are not on Pypi (current job project, other internal dependencies, etc).

> - no need to create a wheelhouse. You can still copy the wheels either with {{--py-files}}
(will be automatically installed) or inside a wheelhouse named {{}} 
> Deployment becomes: 
> Now comes the time to submit the project: 
> {code} 
> bin/spark-submit --master master --deploy-mode client --files /path/to/project/requirements.txt
--py-files /path/to/project/internal_dependency_1.whl,/path/to/project/internal_dependency_2.whl,/path/to/project/current_project.whl
--conf "spark.pyspark.virtualenv.enabled=true" --conf "spark.pyspark.virtualenv.index_url="
> {code} 
> or with a wheelhouse that only contains internal dependencies and current project wheels:

> {code} 
> bin/spark-submit --master master --deploy-mode client --files /path/to/project/requirements.txt,/path/to/project/
--conf "spark.pyspark.virtualenv.enabled=true" --conf "spark.pyspark.virtualenv.index_url="
> {code} 
> or if you want to use the official Pypi or have configured {{pip.conf}} to hit the internal
pypi mirror (see doc bellow): 
> {code} 
> bin/spark-submit --master master --deploy-mode client --files /path/to/project/requirements.txt,/path/to/project/
--conf "spark.pyspark.virtualenv.enabled=true" ~/path/to/ 
> {code} 
> On each node, the deployment will be done with a command such as: 
> {code} 
> pip install --index-url --find-links=/path/to/node/wheelhouse
-r requirements.txt 
> {code} 
> Note: 
> - {{\-\-conf "spark.pyspark.virtualenv.index_url="}} allows
to specify a Pypi mirror, for example a mirror internal to your company network. If not provided,
the default Pypi mirror ( will be requested 
> - to send a wheelhouse, use {{\-\-files}}. To send individual wheels, use {{\-\-py-files}}.
With the latter, all wheels will be installed. For multiple architecture cluster, prepare
all needed wheels for all architecture and use a wheelhouse archive, this allows {{pip}} to
choose the right version of the wheel automatically. 
> *code submission* 
> I already started working on this point, starting by merging the 2 mergerequests [#5408|]
and [#13599|] 
> I'll upload a patch asap for review. 
> I see two major interogations: 
> - I don't know that much YARN or MESOS, so I might require some help for the final integration

> - documentation should really be carefully crafted so users are not lost in all these
> I really think having this "wheelhouse" support for spark will really helps using, maintaining,
and evolving Python scripts on Spark. Python has a rich set of mature libraries Spark should
do anythink to help developers easily access and use them in their everyday job. 
> *Important notes about some complex package such as numpy* 
> Numpy is the kind of package that take several minutes to deploy and we want to avoid
having all nodes install it each time. Pypi provides several precompiled wheel but it may
occurs that the wheel are not right for your platform or the platform fo your cluster. 
> Wheels are *not* cached for pip version < 7.0. From pip v7.0 and +, wheel are automatically
cached when built (if needed), so the first installation might take some time, but after the
installation will be straight forward.
> On most of my machines, numpy is installed without any compilation thanks to wheels
> *Certificate* 
> pip does not use system ssl certificate. If you use a local pypi mirror behind https
with internal certificate, you'll have to setup pypi correctly with the following content
in {{~/.pip/pip.conf}}: 
> {code} 
> [global] 
> cert = /path/to/your/internal/certificates.pem 
> {code} 
> First creation might take some times, but pip will automatically cache the wheel for
your system in {{~/.cache/pip/wheels}}. You can of course recreate the wheel with {{pip wheel}}
or find the wheel in {{~/.cache/pip/wheels}}. You can use {{pip -v install numpy}} to see
where it has placed the wheel in cache. 
> If you use Artifactory, you can upload your wheels at a local, central cache that can
be shared accross all your slave. See [this documentation|]
to see how this works. This way, you can insert wheels in this local cache and it will be
seens as if it has been uploaded to the official repository (local cache + remote cache can
be "merged" into a virtual repository with artifactory) 
> *Set use of internal pypi mirror* 
> Ask your IT to update the {{~/.pip/pip.conf}} of the node to point by default to the
internal mirror: 
> {code} 
> [global] 
> ; Low timeout 
> timeout = 20 
> index-url = https://&lt;user&gt;:&lt;pass&gt; 
> {code} 
> Now, no more need to specify the {{\-\-conf "spark.pyspark.virtualenv.index_url="}}
in your Spark submit command line 
> Note: this will not work when installing package with {{python install}} syntax.
In this case you need to update {{~/.pypirc}} and use the {{-r}} argument. This syntax is
not used in spark-submit
> *Extra*
> Approach vulgarized at the [following blog post|]

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