Commit graph

27 commits

Author SHA1 Message Date
HyukjinKwon 4ad9bfd53b [SPARK-32138] Drop Python 2.7, 3.4 and 3.5
### What changes were proposed in this pull request?

This PR aims to drop Python 2.7, 3.4 and 3.5.

Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.

### Why are the changes needed?

 1. Unsupport EOL Python versions
 2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
 3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
 4. Users can use Python type hints with Pandas UDFs without thinking about Python version
 5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.

### Does this PR introduce _any_ user-facing change?

Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.

### How was this patch tested?

Manually tested and also tested in Jenkins.

Closes #28957 from HyukjinKwon/SPARK-32138.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-07-14 11:22:44 +09:00
HyukjinKwon fe75ff8bea [SPARK-28206][PYTHON] Remove the legacy Epydoc in PySpark API documentation
## What changes were proposed in this pull request?

Seems like we used to generate PySpark API documentation by Epydoc almost at the very first place (see 85b8f2c64f).

This fixes an actual issue:

Before:

![Screen Shot 2019-07-05 at 8 20 01 PM](https://user-images.githubusercontent.com/6477701/60720491-e9879180-9f65-11e9-9562-100830a456cd.png)

After:

![Screen Shot 2019-07-05 at 8 20 05 PM](https://user-images.githubusercontent.com/6477701/60720495-ec828200-9f65-11e9-8277-8f689e292cb0.png)

It seems apparently a bug within `epytext` plugin during the conversion between`param` and `:param` syntax. See also [Epydoc syntax](http://epydoc.sourceforge.net/manual-epytext.html).

Actually, Epydoc syntax violates [PEP-257](https://www.python.org/dev/peps/pep-0257/) IIRC and blocks us to enable some rules for doctest linter as well.

We should remove this legacy away and I guess Spark 3 is good timing to do it.

## How was this patch tested?

Manually built the doc and check each.

I had to manually find the Epydoc syntax by `git grep -r "{L"`, for instance.

Closes #25060 from HyukjinKwon/SPARK-28206.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
2019-07-05 10:08:22 -07:00
Liang-Chi Hsieh cb90617f89 [SPARK-25591][PYSPARK][SQL] Avoid overwriting deserialized accumulator
## What changes were proposed in this pull request?

If we use accumulators in more than one UDFs, it is possible to overwrite deserialized accumulators and its values. We should check if an accumulator was deserialized before overwriting it in accumulator registry.

## How was this patch tested?

Added test.

Closes #22635 from viirya/SPARK-25591.

Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
2018-10-08 15:18:08 +08:00
Takuya UESHIN 0df6bf8829 [BUILD] Fix lint-python.
## What changes were proposed in this pull request?

This pr fixes lint-python.

```
./python/pyspark/accumulators.py:231:9: E306 expected 1 blank line before a nested definition, found 0
./python/pyspark/accumulators.py:257:101: E501 line too long (107 > 100 characters)
./python/pyspark/accumulators.py:264:1: E302 expected 2 blank lines, found 1
./python/pyspark/accumulators.py:281:1: E302 expected 2 blank lines, found 1
```

## How was this patch tested?

Executed lint-python manually.

Author: Takuya UESHIN <ueshin@databricks.com>

Closes #21973 from ueshin/issues/build/1/fix_lint-python.
2018-08-03 03:18:46 +09:00
LucaCanali 15fc237226 Updates to Accumulators 2018-08-02 10:03:22 -05:00
Benjamin Peterson 7013eea11c [SPARK-23522][PYTHON] always use sys.exit over builtin exit
The exit() builtin is only for interactive use. applications should use sys.exit().

## What changes were proposed in this pull request?

All usage of the builtin `exit()` function is replaced by `sys.exit()`.

## How was this patch tested?

I ran `python/run-tests`.

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Benjamin Peterson <benjamin@python.org>

Closes #20682 from benjaminp/sys-exit.
2018-03-08 20:38:34 +09:00
Bryan Cutler 9bb239c8b1 [SPARK-23159][PYTHON] Update cloudpickle to v0.4.3
## What changes were proposed in this pull request?

The version of cloudpickle in PySpark was close to version 0.4.0 with some additional backported fixes and some minor additions for Spark related things.  This update removes Spark related changes and matches cloudpickle [v0.4.3](https://github.com/cloudpipe/cloudpickle/releases/tag/v0.4.3):

Changes by updating to 0.4.3 include:
* Fix pickling of named tuples https://github.com/cloudpipe/cloudpickle/pull/113
* Built in type constructors for PyPy compatibility [here](d84980ccaa)
* Fix memoryview support https://github.com/cloudpipe/cloudpickle/pull/122
* Improved compatibility with other cloudpickle versions https://github.com/cloudpipe/cloudpickle/pull/128
* Several cleanups https://github.com/cloudpipe/cloudpickle/pull/121 and [here](c91aaf1104)
* [MRG] Regression on pickling classes from the __main__ module https://github.com/cloudpipe/cloudpickle/pull/149
* BUG: Handle instance methods of builtin types https://github.com/cloudpipe/cloudpickle/pull/154
* Fix <span>#</span>129 : do not silence RuntimeError in dump() https://github.com/cloudpipe/cloudpickle/pull/153

## How was this patch tested?

Existing pyspark.tests using python 2.7.14, 3.5.2, 3.6.3

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #20373 from BryanCutler/pyspark-update-cloudpickle-42-SPARK-23159.
2018-03-08 20:19:55 +09:00
Josh Rosen 41afa16500 [SPARK-8652] [PYSPARK] Check return value for all uses of doctest.testmod()
This patch addresses a critical issue in the PySpark tests:

Several of our Python modules' `__main__` methods call `doctest.testmod()` in order to run doctests but forget to check and handle its return value. As a result, some PySpark test failures can go unnoticed because they will not fail the build.

Fortunately, there was only one test failure which was masked by this bug: a `pyspark.profiler` doctest was failing due to changes in RDD pipelining.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7032 from JoshRosen/testmod-fix and squashes the following commits:

60dbdc0 [Josh Rosen] Account for int vs. long formatting change in Python 3
8b8d80a [Josh Rosen] Fix failing test.
e6423f9 [Josh Rosen] Check return code for all uses of doctest.testmod().
2015-06-26 08:12:22 -07:00
Michael Nazario 1c5b19827a [SPARK-7899] [PYSPARK] Fix Python 3 pyspark/sql/types module conflict
This PR makes the types module in `pyspark/sql/types` work with pylint static analysis by removing the dynamic naming of the `pyspark/sql/_types` module to `pyspark/sql/types`.

Tests are now loaded using `$PYSPARK_DRIVER_PYTHON -m module` rather than `$PYSPARK_DRIVER_PYTHON module.py`. The old method adds the location of `module.py` to `sys.path`, so this change prevents accidental use of relative paths in Python.

Author: Michael Nazario <mnazario@palantir.com>

Closes #6439 from mnazario/feature/SPARK-7899 and squashes the following commits:

366ef30 [Michael Nazario] Remove hack on random.py
bb8b04d [Michael Nazario] Make doctests consistent with other tests
6ee4f75 [Michael Nazario] Change test scripts to use "-m"
673528f [Michael Nazario] Move _types back to types
2015-05-29 14:13:44 -07:00
Elisey Zanko 77176619a9 [SPARK-6661] Python type errors should print type, not object
Author: Elisey Zanko <elisey.zanko@gmail.com>

Closes #5361 from 31z4/spark-6661 and squashes the following commits:

73c5d79 [Elisey Zanko] Python type errors should print type, not object
2015-04-20 10:44:09 -07:00
Davies Liu 04e44b37cc [SPARK-4897] [PySpark] Python 3 support
This PR update PySpark to support Python 3 (tested with 3.4).

Known issue: unpickle array from Pyrolite is broken in Python 3, those tests are skipped.

TODO: ec2/spark-ec2.py is not fully tested with python3.

Author: Davies Liu <davies@databricks.com>
Author: twneale <twneale@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #5173 from davies/python3 and squashes the following commits:

d7d6323 [Davies Liu] fix tests
6c52a98 [Davies Liu] fix mllib test
99e334f [Davies Liu] update timeout
b716610 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
cafd5ec [Davies Liu] adddress comments from @mengxr
bf225d7 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
179fc8d [Davies Liu] tuning flaky tests
8c8b957 [Davies Liu] fix ResourceWarning in Python 3
5c57c95 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
4006829 [Davies Liu] fix test
2fc0066 [Davies Liu] add python3 path
71535e9 [Davies Liu] fix xrange and divide
5a55ab4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
125f12c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ed498c8 [Davies Liu] fix compatibility with python 3
820e649 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
e8ce8c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ad7c374 [Davies Liu] fix mllib test and warning
ef1fc2f [Davies Liu] fix tests
4eee14a [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
20112ff [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
59bb492 [Davies Liu] fix tests
1da268c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ca0fdd3 [Davies Liu] fix code style
9563a15 [Davies Liu] add imap back for python 2
0b1ec04 [Davies Liu] make python examples work with Python 3
d2fd566 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
a716d34 [Davies Liu] test with python 3.4
f1700e8 [Davies Liu] fix test in python3
671b1db [Davies Liu] fix test in python3
692ff47 [Davies Liu] fix flaky test
7b9699f [Davies Liu] invalidate import cache for Python 3.3+
9c58497 [Davies Liu] fix kill worker
309bfbf [Davies Liu] keep compatibility
5707476 [Davies Liu] cleanup, fix hash of string in 3.3+
8662d5b [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
f53e1f0 [Davies Liu] fix tests
70b6b73 [Davies Liu] compile ec2/spark_ec2.py in python 3
a39167e [Davies Liu] support customize class in __main__
814c77b [Davies Liu] run unittests with python 3
7f4476e [Davies Liu] mllib tests passed
d737924 [Davies Liu] pass ml tests
375ea17 [Davies Liu] SQL tests pass
6cc42a9 [Davies Liu] rename
431a8de [Davies Liu] streaming tests pass
78901a7 [Davies Liu] fix hash of serializer in Python 3
24b2f2e [Davies Liu] pass all RDD tests
35f48fe [Davies Liu] run future again
1eebac2 [Davies Liu] fix conflict in ec2/spark_ec2.py
6e3c21d [Davies Liu] make cloudpickle work with Python3
2fb2db3 [Josh Rosen] Guard more changes behind sys.version; still doesn't run
1aa5e8f [twneale] Turned out `pickle.DictionaryType is dict` == True, so swapped it out
7354371 [twneale] buffer --> memoryview  I'm not super sure if this a valid change, but the 2.7 docs recommend using memoryview over buffer where possible, so hoping it'll work.
b69ccdf [twneale] Uses the pure python pickle._Pickler instead of c-extension _pickle.Pickler. It appears pyspark 2.7 uses the pure python pickler as well, so this shouldn't degrade pickling performance (?).
f40d925 [twneale] xrange --> range
e104215 [twneale] Replaces 2.7 types.InstsanceType with 3.4 `object`....could be horribly wrong depending on how types.InstanceType is used elsewhere in the package--see http://bugs.python.org/issue8206
79de9d0 [twneale] Replaces python2.7 `file` with 3.4 _io.TextIOWrapper
2adb42d [Josh Rosen] Fix up some import differences between Python 2 and 3
854be27 [Josh Rosen] Run `futurize` on Python code:
7c5b4ce [Josh Rosen] Remove Python 3 check in shell.py.
2015-04-16 16:20:57 -07:00
Yandu Oppacher 3bead67d59 [SPARK-4387][PySpark] Refactoring python profiling code to make it extensible
This PR is based on #3255 , fix conflicts and code style.

Closes #3255.

Author: Yandu Oppacher <yandu.oppacher@jadedpixel.com>
Author: Davies Liu <davies@databricks.com>

Closes #3901 from davies/refactor-python-profile-code and squashes the following commits:

b4a9306 [Davies Liu] fix tests
4b79ce8 [Davies Liu] add docstring for profiler_cls
2700e47 [Davies Liu] use BasicProfiler as default
349e341 [Davies Liu] more refactor
6a5d4df [Davies Liu] refactor and fix tests
31bf6b6 [Davies Liu] fix code style
0864b5d [Yandu Oppacher] Remove unused method
76a6c37 [Yandu Oppacher] Added a profile collector to accumulate the profilers per stage
9eefc36 [Yandu Oppacher] Fix doc
9ace076 [Yandu Oppacher] Refactor of profiler, and moved tests around
8739aff [Yandu Oppacher] Code review fixes
9bda3ec [Yandu Oppacher] Refactor profiler code
2015-01-28 13:48:06 -08:00
Davies Liu c5414b6818 [SPARK-3478] [PySpark] Profile the Python tasks
This patch add profiling support for PySpark, it will show the profiling results
before the driver exits, here is one example:

```
============================================================
Profile of RDD<id=3>
============================================================
         5146507 function calls (5146487 primitive calls) in 71.094 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  5144576   68.331    0.000   68.331    0.000 statcounter.py:44(merge)
       20    2.735    0.137   71.071    3.554 statcounter.py:33(__init__)
       20    0.017    0.001    0.017    0.001 {cPickle.dumps}
     1024    0.003    0.000    0.003    0.000 t.py:16(<lambda>)
       20    0.001    0.000    0.001    0.000 {reduce}
       21    0.001    0.000    0.001    0.000 {cPickle.loads}
       20    0.001    0.000    0.001    0.000 copy_reg.py:95(_slotnames)
       41    0.001    0.000    0.001    0.000 serializers.py:461(read_int)
       40    0.001    0.000    0.002    0.000 serializers.py:179(_batched)
       62    0.000    0.000    0.000    0.000 {method 'read' of 'file' objects}
       20    0.000    0.000   71.072    3.554 rdd.py:863(<lambda>)
       20    0.000    0.000    0.001    0.000 serializers.py:198(load_stream)
    40/20    0.000    0.000   71.072    3.554 rdd.py:2093(pipeline_func)
       41    0.000    0.000    0.002    0.000 serializers.py:130(load_stream)
       40    0.000    0.000   71.072    1.777 rdd.py:304(func)
       20    0.000    0.000   71.094    3.555 worker.py:82(process)
```

Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk
by `sc.dump_profiles(path)`, such as

```python
>>> sc._conf.set("spark.python.profile", "true")
>>> rdd = sc.parallelize(range(100)).map(str)
>>> rdd.count()
100
>>> sc.show_profiles()
============================================================
Profile of RDD<id=1>
============================================================
         284 function calls (276 primitive calls) in 0.001 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        4    0.000    0.000    0.000    0.000 serializers.py:198(load_stream)
        4    0.000    0.000    0.000    0.000 {reduce}
     12/4    0.000    0.000    0.001    0.000 rdd.py:2092(pipeline_func)
        4    0.000    0.000    0.000    0.000 {cPickle.loads}
        4    0.000    0.000    0.000    0.000 {cPickle.dumps}
      104    0.000    0.000    0.000    0.000 rdd.py:852(<genexpr>)
        8    0.000    0.000    0.000    0.000 serializers.py:461(read_int)
       12    0.000    0.000    0.000    0.000 rdd.py:303(func)
```
The profiling is disabled by default, can be enabled by "spark.python.profile=true".

Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump"

This is bugfix of #2351 cc JoshRosen

Author: Davies Liu <davies.liu@gmail.com>

Closes #2556 from davies/profiler and squashes the following commits:

e68df5a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
858e74c [Davies Liu] compatitable with python 2.6
7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles()
2b0daf2 [Davies Liu] fix docs
7a56c24 [Davies Liu] bugfix
cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext
fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
09d02c3 [Davies Liu] Merge branch 'master' into profiler
c23865c [Davies Liu] Merge branch 'master' into profiler
15d6f18 [Davies Liu] add docs for two configs
dadee1a [Davies Liu] add docs string and clear profiles after show or dump
4f8309d [Davies Liu] address comment, add tests
0a5b6eb [Davies Liu] fix Python UDF
4b20494 [Davies Liu] add profile for python
2014-09-30 18:24:57 -07:00
Josh Rosen f872e4fb80 Revert "[SPARK-3478] [PySpark] Profile the Python tasks"
This reverts commit 1aa549ba98.
2014-09-26 14:47:14 -07:00
Davies Liu 1aa549ba98 [SPARK-3478] [PySpark] Profile the Python tasks
This patch add profiling support for PySpark, it will show the profiling results
before the driver exits, here is one example:

```
============================================================
Profile of RDD<id=3>
============================================================
         5146507 function calls (5146487 primitive calls) in 71.094 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  5144576   68.331    0.000   68.331    0.000 statcounter.py:44(merge)
       20    2.735    0.137   71.071    3.554 statcounter.py:33(__init__)
       20    0.017    0.001    0.017    0.001 {cPickle.dumps}
     1024    0.003    0.000    0.003    0.000 t.py:16(<lambda>)
       20    0.001    0.000    0.001    0.000 {reduce}
       21    0.001    0.000    0.001    0.000 {cPickle.loads}
       20    0.001    0.000    0.001    0.000 copy_reg.py:95(_slotnames)
       41    0.001    0.000    0.001    0.000 serializers.py:461(read_int)
       40    0.001    0.000    0.002    0.000 serializers.py:179(_batched)
       62    0.000    0.000    0.000    0.000 {method 'read' of 'file' objects}
       20    0.000    0.000   71.072    3.554 rdd.py:863(<lambda>)
       20    0.000    0.000    0.001    0.000 serializers.py:198(load_stream)
    40/20    0.000    0.000   71.072    3.554 rdd.py:2093(pipeline_func)
       41    0.000    0.000    0.002    0.000 serializers.py:130(load_stream)
       40    0.000    0.000   71.072    1.777 rdd.py:304(func)
       20    0.000    0.000   71.094    3.555 worker.py:82(process)
```

Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk
by `sc.dump_profiles(path)`, such as

```python
>>> sc._conf.set("spark.python.profile", "true")
>>> rdd = sc.parallelize(range(100)).map(str)
>>> rdd.count()
100
>>> sc.show_profiles()
============================================================
Profile of RDD<id=1>
============================================================
         284 function calls (276 primitive calls) in 0.001 seconds

   Ordered by: internal time, cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        4    0.000    0.000    0.000    0.000 serializers.py:198(load_stream)
        4    0.000    0.000    0.000    0.000 {reduce}
     12/4    0.000    0.000    0.001    0.000 rdd.py:2092(pipeline_func)
        4    0.000    0.000    0.000    0.000 {cPickle.loads}
        4    0.000    0.000    0.000    0.000 {cPickle.dumps}
      104    0.000    0.000    0.000    0.000 rdd.py:852(<genexpr>)
        8    0.000    0.000    0.000    0.000 serializers.py:461(read_int)
       12    0.000    0.000    0.000    0.000 rdd.py:303(func)
```
The profiling is disabled by default, can be enabled by "spark.python.profile=true".

Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump"

Author: Davies Liu <davies.liu@gmail.com>

Closes #2351 from davies/profiler and squashes the following commits:

7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles()
2b0daf2 [Davies Liu] fix docs
7a56c24 [Davies Liu] bugfix
cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext
fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler
09d02c3 [Davies Liu] Merge branch 'master' into profiler
c23865c [Davies Liu] Merge branch 'master' into profiler
15d6f18 [Davies Liu] add docs for two configs
dadee1a [Davies Liu] add docs string and clear profiles after show or dump
4f8309d [Davies Liu] address comment, add tests
0a5b6eb [Davies Liu] fix Python UDF
4b20494 [Davies Liu] add profile for python
2014-09-26 09:27:42 -07:00
Davies Liu 6481d27425 [SPARK-3309] [PySpark] Put all public API in __all__
Put all public API in __all__, also put them all in pyspark.__init__.py, then we can got all the documents for public API by `pydoc pyspark`. It also can be used by other programs (such as Sphinx or Epydoc) to generate only documents for public APIs.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2205 from davies/public and squashes the following commits:

c6c5567 [Davies Liu] fix message
f7b35be [Davies Liu] put SchemeRDD, Row in pyspark.sql module
7e3016a [Davies Liu] add __all__ in mllib
6281b48 [Davies Liu] fix doc for SchemaRDD
6caab21 [Davies Liu] add public interfaces into pyspark.__init__.py
2014-09-03 11:49:45 -07:00
Nicholas Chammas d614967b0b [SPARK-2627] [PySpark] have the build enforce PEP 8 automatically
As described in [SPARK-2627](https://issues.apache.org/jira/browse/SPARK-2627), we'd like Python code to automatically be checked for PEP 8 compliance by Jenkins. This pull request aims to do that.

Notes:
* We may need to install [`pep8`](https://pypi.python.org/pypi/pep8) on the build server.
* I'm expecting tests to fail now that PEP 8 compliance is being checked as part of the build. I'm fine with cleaning up any remaining PEP 8 violations as part of this pull request.
* I did not understand why the RAT and scalastyle reports are saved to text files. I did the same for the PEP 8 check, but only so that the console output style can match those for the RAT and scalastyle checks. The PEP 8 report is removed right after the check is complete.
* Updates to the ["Contributing to Spark"](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark) guide will be submitted elsewhere, as I don't believe that text is part of the Spark repo.

Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>

Closes #1744 from nchammas/master and squashes the following commits:

274b238 [Nicholas Chammas] [SPARK-2627] [PySpark] minor indentation changes
983d963 [nchammas] Merge pull request #5 from apache/master
1db5314 [nchammas] Merge pull request #4 from apache/master
0e0245f [Nicholas Chammas] [SPARK-2627] undo erroneous whitespace fixes
bf30942 [Nicholas Chammas] [SPARK-2627] PEP8: comment spacing
6db9a44 [nchammas] Merge pull request #3 from apache/master
7b4750e [Nicholas Chammas] merge upstream changes
91b7584 [Nicholas Chammas] [SPARK-2627] undo unnecessary line breaks
44e3e56 [Nicholas Chammas] [SPARK-2627] use tox.ini to exclude files
b09fae2 [Nicholas Chammas] don't wrap comments unnecessarily
bfb9f9f [Nicholas Chammas] [SPARK-2627] keep up with the PEP 8 fixes
9da347f [nchammas] Merge pull request #2 from apache/master
aa5b4b5 [Nicholas Chammas] [SPARK-2627] follow Spark bash style for if blocks
d0a83b9 [Nicholas Chammas] [SPARK-2627] check that pep8 downloaded fine
dffb5dd [Nicholas Chammas] [SPARK-2627] download pep8 at runtime
a1ce7ae [Nicholas Chammas] [SPARK-2627] space out test report sections
21da538 [Nicholas Chammas] [SPARK-2627] it's PEP 8, not PEP8
6f4900b [Nicholas Chammas] [SPARK-2627] more misc PEP 8 fixes
fe57ed0 [Nicholas Chammas] removing merge conflict backups
9c01d4c [nchammas] Merge pull request #1 from apache/master
9a66cb0 [Nicholas Chammas] resolving merge conflicts
a31ccc4 [Nicholas Chammas] [SPARK-2627] miscellaneous PEP 8 fixes
beaa9ac [Nicholas Chammas] [SPARK-2627] fail check on non-zero status
723ed39 [Nicholas Chammas] always delete the report file
0541ebb [Nicholas Chammas] [SPARK-2627] call Python linter from run-tests
12440fa [Nicholas Chammas] [SPARK-2627] add Scala linter
61c07b9 [Nicholas Chammas] [SPARK-2627] add Python linter
75ad552 [Nicholas Chammas] make check output style consistent
2014-08-06 12:58:24 -07:00
Aaron Davidson ef4ff00f87 SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
Prior to this change, every PySpark task completion opened a new socket to the accumulator server, passed its updates through, and then quit. I'm not entirely sure why PySpark always sends accumulator updates, but regardless this causes a very rapid buildup of ephemeral TCP connections that remain in the TCP_WAIT state for around a minute before being cleaned up.

Rather than trying to allow these sockets to be cleaned up faster, this patch simply reuses the connection between tasks completions (since they're fed updates in a single-threaded manner by the DAGScheduler anyway).

The only tricky part here was making sure that the AccumulatorServer was able to shutdown in a timely manner (i.e., stop polling for new data), and this was accomplished via minor feats of magic.

I have confirmed that this patch eliminates the buildup of ephemeral sockets due to the accumulator updates. However, I did note that there were still significant sockets being created against the PySpark daemon port, but my machine was not able to create enough sockets fast enough to fail. This may not be the last time we've seen this issue, though.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1503 from aarondav/accum and squashes the following commits:

b3e12f7 [Aaron Davidson] SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
2014-07-31 15:31:53 -07:00
Josh Rosen cbb7f04aef Add custom serializer support to PySpark.
For now, this only adds MarshalSerializer, but it lays the groundwork
for other supporting custom serializers.  Many of these mechanisms
can also be used to support deserialization of different data formats
sent by Java, such as data encoded by MsgPack.

This also fixes a bug in SparkContext.union().
2013-11-10 16:45:38 -08:00
Ewen Cheslack-Postava 7eaa56de7f Add an add() method to pyspark accumulators.
Add a regular method for adding a term to accumulators in
pyspark. Currently if you have a non-global accumulator, adding to it
is awkward. The += operator can't be used for non-global accumulators
captured via closure because it's involves an assignment. The only way
to do it is using __iadd__ directly.

Adding this method lets you write code like this:

def main():
    sc = SparkContext()
    accum = sc.accumulator(0)

    rdd = sc.parallelize([1,2,3])
    def f(x):
        accum.add(x)
    rdd.foreach(f)
    print accum.value

where using accum += x instead would have caused UnboundLocalError
exceptions in workers. Currently it would have to be written as
accum.__iadd__(x).
2013-10-19 19:55:39 -07:00
Matei Zaharia af3c9d5042 Add Apache license headers and LICENSE and NOTICE files 2013-07-16 17:21:33 -07:00
Josh Rosen e61729113d Remove unnecessary doctest __main__ methods. 2013-02-03 21:29:40 -08:00
Josh Rosen b47d054cfc Remove use of abc.ABCMeta due to cloudpickle issue.
cloudpickle runs into issues while pickling subclasses of AccumulatorParam,
which may be related to this Python issue:

    http://bugs.python.org/issue7689

This seems hard to fix and the ABCMeta wasn't necessary, so I removed it.
2013-01-23 11:47:27 -08:00
Josh Rosen c75ae3622e Make AccumulatorParam an abstract base class. 2013-01-21 22:32:57 -08:00
Josh Rosen 17035db159 Add __repr__ to Accumulator; fix bug in sc.accumulator 2013-01-20 11:58:57 -08:00
Matei Zaharia a23ed25f3c Add a class comment to Accumulator 2013-01-20 02:10:25 -08:00
Matei Zaharia 8e7f098a2c Added accumulators to PySpark 2013-01-20 01:57:44 -08:00