[
https://issues.apache.org/jira/browse/SPARK-16367?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Semet updated SPARK-16367:
--------------------------
Description:
*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")
First part of my proposal was to merge, in order to support wheels install and
virtualenv creation
*Virtualenv, wheel suppoer 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 https://pypi.python.org/pypi/numpy 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.
{{pypi.pypthon.org}} already provided wheels for major python version. It the
wheel is not available, pip will compile it from source anyway.
{{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 pypi.python.org.
*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.
- 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|https://github.com/nvie/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 setup.py with some entry points or package. Use
[PBR|http://docs.openstack.org/developer/pbr/] it makes the jobs of maitaining
a setup.py 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*
- 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 --files
/path/to/virtualenv/requirements.txt,/path/to/virtualenv/wheelhouse.zip --conf
"spark.pyspark.virtualenv.enabled=true" ~/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 wheelhouse deployment is triggered by the {{ --conf
"spark.pyspark.virtualenv.enabled=true" }} argument. The {{requirements.txt}}
and {{wheelhouse.zip}} 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
{{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}
- 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 like:
{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)
*code submission*
I already started working on this point, starting by merging the 2
mergerequests [#5408|https://github.com/apache/spark/pull/5408] and
[#13599|https://github.com/apache/spark/pull/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 concepts
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.
was:
*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")
First part of my proposal was to merge, in order to support wheels install and
virtualenv creation
*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, my proposal for on Pyspark developers point of
view:
- 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|https://github.com/nvie/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 setup.py with some entry points or package. Use
[PBR|http://docs.openstack.org/developer/pbr/] it makes the jobs of maitaining
a setup.py 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*
- 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 --files
/path/to/virtualenv/requirements.txt,/path/to/virtualenv/wheelhouse.zip --conf
"spark.pyspark.virtualenv.enabled=true" ~/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 wheelhouse deployment is triggered by the {{ --conf
"spark.pyspark.virtualenv.enabled=true" }} argument. The {{requirements.txt}}
and {{wheelhouse.zip}} 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
{{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}
- 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 like:
{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)
*code submission*
I already started working on this point, starting by merging the 2
mergerequests [#5408|https://github.com/apache/spark/pull/5408] and
[#13599|https://github.com/apache/spark/pull/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 concepts
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.
> Wheelhouse Support for PySpark
> ------------------------------
>
> Key: SPARK-16367
> URL: https://issues.apache.org/jira/browse/SPARK-16367
> 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.
> *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 creation
> *Virtualenv, wheel suppoer 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 https://pypi.python.org/pypi/numpy 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.
> {{pypi.pypthon.org}} already provided wheels for major python version. It the
> wheel is not available, pip will compile it from source anyway.
> {{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 pypi.python.org.
> *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.
> - 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|https://github.com/nvie/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 setup.py with some entry points or package. Use
> [PBR|http://docs.openstack.org/developer/pbr/] it makes the jobs of
> maitaining a setup.py 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*
> - 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 --files
> /path/to/virtualenv/requirements.txt,/path/to/virtualenv/wheelhouse.zip
> --conf "spark.pyspark.virtualenv.enabled=true" ~/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 wheelhouse deployment is triggered by the {{ --conf
> "spark.pyspark.virtualenv.enabled=true" }} argument. The {{requirements.txt}}
> and {{wheelhouse.zip}} 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
> {{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}
> - 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 like:
> {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)
> *code submission*
> I already started working on this point, starting by merging the 2
> mergerequests [#5408|https://github.com/apache/spark/pull/5408] and
> [#13599|https://github.com/apache/spark/pull/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 concepts
> 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.
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