We could do that after 1.5 released, it will have same release cycle as Spark in the future.
On Tue, Jul 28, 2015 at 5:52 AM, Olivier Girardot <o.girar...@lateral-thoughts.com> wrote: > +1 (once again :) ) > > 2015-07-28 14:51 GMT+02:00 Justin Uang <justin.u...@gmail.com>: >> >> // ping >> >> do we have any signoff from the pyspark devs to submit a PR to publish to >> PyPI? >> >> On Fri, Jul 24, 2015 at 10:50 PM Jeremy Freeman <freeman.jer...@gmail.com> >> wrote: >>> >>> Hey all, great discussion, just wanted to +1 that I see a lot of value in >>> steps that make it easier to use PySpark as an ordinary python library. >>> >>> You might want to check out this (https://github.com/minrk/findspark), >>> started by Jupyter project devs, that offers one way to facilitate this >>> stuff. I’ve also cced them here to join the conversation. >>> >>> Also, @Jey, I can also confirm that at least in some scenarios (I’ve done >>> it in an EC2 cluster in standalone mode) it’s possible to run PySpark jobs >>> just using `from pyspark import SparkContext; sc = SparkContext(master=“X”)` >>> so long as the environmental variables (PYTHONPATH and PYSPARK_PYTHON) are >>> set correctly on *both* workers and driver. That said, there’s definitely >>> additional configuration / functionality that would require going through >>> the proper submit scripts. >>> >>> On Jul 22, 2015, at 7:41 PM, Punyashloka Biswal <punya.bis...@gmail.com> >>> wrote: >>> >>> I agree with everything Justin just said. An additional advantage of >>> publishing PySpark's Python code in a standards-compliant way is the fact >>> that we'll be able to declare transitive dependencies (Pandas, Py4J) in a >>> way that pip can use. Contrast this with the current situation, where >>> df.toPandas() exists in the Spark API but doesn't actually work until you >>> install Pandas. >>> >>> Punya >>> On Wed, Jul 22, 2015 at 12:49 PM Justin Uang <justin.u...@gmail.com> >>> wrote: >>>> >>>> // + Davies for his comments >>>> // + Punya for SA >>>> >>>> For development and CI, like Olivier mentioned, I think it would be >>>> hugely beneficial to publish pyspark (only code in the python/ dir) on >>>> PyPI. >>>> If anyone wants to develop against PySpark APIs, they need to download the >>>> distribution and do a lot of PYTHONPATH munging for all the tools (pylint, >>>> pytest, IDE code completion). Right now that involves adding python/ and >>>> python/lib/py4j-0.8.2.1-src.zip. In case pyspark ever wants to add more >>>> dependencies, we would have to manually mirror all the PYTHONPATH munging >>>> in >>>> the ./pyspark script. With a proper pyspark setup.py which declares its >>>> dependencies, and a published distribution, depending on pyspark will just >>>> be adding pyspark to my setup.py dependencies. >>>> >>>> Of course, if we actually want to run parts of pyspark that is backed by >>>> Py4J calls, then we need the full spark distribution with either ./pyspark >>>> or ./spark-submit, but for things like linting and development, the >>>> PYTHONPATH munging is very annoying. >>>> >>>> I don't think the version-mismatch issues are a compelling reason to not >>>> go ahead with PyPI publishing. At runtime, we should definitely enforce >>>> that >>>> the version has to be exact, which means there is no backcompat nightmare >>>> as >>>> suggested by Davies in https://issues.apache.org/jira/browse/SPARK-1267. >>>> This would mean that even if the user got his pip installed pyspark to >>>> somehow get loaded before the spark distribution provided pyspark, then the >>>> user would be alerted immediately. >>>> >>>> Davies, if you buy this, should me or someone on my team pick up >>>> https://issues.apache.org/jira/browse/SPARK-1267 and >>>> https://github.com/apache/spark/pull/464? >>>> >>>> On Sat, Jun 6, 2015 at 12:48 AM Olivier Girardot >>>> <o.girar...@lateral-thoughts.com> wrote: >>>>> >>>>> Ok, I get it. Now what can we do to improve the current situation, >>>>> because right now if I want to set-up a CI env for PySpark, I have to : >>>>> 1- download a pre-built version of pyspark and unzip it somewhere on >>>>> every agent >>>>> 2- define the SPARK_HOME env >>>>> 3- symlink this distribution pyspark dir inside the python install dir >>>>> site-packages/ directory >>>>> and if I rely on additional packages (like databricks' Spark-CSV >>>>> project), I have to (except if I'm mistaken) >>>>> 4- compile/assembly spark-csv, deploy the jar in a specific directory >>>>> on every agent >>>>> 5- add this jar-filled directory to the Spark distribution's additional >>>>> classpath using the conf/spark-default file >>>>> >>>>> Then finally we can launch our unit/integration-tests. >>>>> Some issues are related to spark-packages, some to the lack of >>>>> python-based dependency, and some to the way SparkContext are launched >>>>> when >>>>> using pyspark. >>>>> I think step 1 and 2 are fair enough >>>>> 4 and 5 may already have solutions, I didn't check and considering >>>>> spark-shell is downloading such dependencies automatically, I think if >>>>> nothing's done yet it will (I guess ?). >>>>> >>>>> For step 3, maybe just adding a setup.py to the distribution would be >>>>> enough, I'm not exactly advocating to distribute a full 300Mb spark >>>>> distribution in PyPi, maybe there's a better compromise ? >>>>> >>>>> Regards, >>>>> >>>>> Olivier. >>>>> >>>>> Le ven. 5 juin 2015 à 22:12, Jey Kottalam <j...@cs.berkeley.edu> a écrit >>>>> : >>>>>> >>>>>> Couldn't we have a pip installable "pyspark" package that just serves >>>>>> as a shim to an existing Spark installation? Or it could even download >>>>>> the >>>>>> latest Spark binary if SPARK_HOME isn't set during installation. Right >>>>>> now, >>>>>> Spark doesn't play very well with the usual Python ecosystem. For >>>>>> example, >>>>>> why do I need to use a strange incantation when booting up IPython if I >>>>>> want >>>>>> to use PySpark in a notebook with MASTER="local[4]"? It would be much >>>>>> nicer >>>>>> to just type `from pyspark import SparkContext; sc = >>>>>> SparkContext("local[4]")` in my notebook. >>>>>> >>>>>> I did a test and it seems like PySpark's basic unit-tests do pass when >>>>>> SPARK_HOME is set and Py4J is on the PYTHONPATH: >>>>>> >>>>>> >>>>>> PYTHONPATH=$SPARK_HOME/python/:$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH >>>>>> python $SPARK_HOME/python/pyspark/rdd.py >>>>>> >>>>>> -Jey >>>>>> >>>>>> >>>>>> On Fri, Jun 5, 2015 at 10:57 AM, Josh Rosen <rosenvi...@gmail.com> >>>>>> wrote: >>>>>>> >>>>>>> This has been proposed before: >>>>>>> https://issues.apache.org/jira/browse/SPARK-1267 >>>>>>> >>>>>>> There's currently tighter coupling between the Python and Java halves >>>>>>> of PySpark than just requiring SPARK_HOME to be set; if we did this, I >>>>>>> bet >>>>>>> we'd run into tons of issues when users try to run a newer version of >>>>>>> the >>>>>>> Python half of PySpark against an older set of Java components or >>>>>>> vice-versa. >>>>>>> >>>>>>> On Thu, Jun 4, 2015 at 10:45 PM, Olivier Girardot >>>>>>> <o.girar...@lateral-thoughts.com> wrote: >>>>>>>> >>>>>>>> Hi everyone, >>>>>>>> Considering the python API as just a front needing the SPARK_HOME >>>>>>>> defined anyway, I think it would be interesting to deploy the Python >>>>>>>> part of >>>>>>>> Spark on PyPi in order to handle the dependencies in a Python project >>>>>>>> needing PySpark via pip. >>>>>>>> >>>>>>>> For now I just symlink the python/pyspark in my python install dir >>>>>>>> site-packages/ in order for PyCharm or other lint tools to work >>>>>>>> properly. >>>>>>>> I can do the setup.py work or anything. >>>>>>>> >>>>>>>> What do you think ? >>>>>>>> >>>>>>>> Regards, >>>>>>>> >>>>>>>> Olivier. >>>>>>> >>>>>>> >>>>>> >>> > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
