Final pass before the v1.0 release.
* Remove `VectorRDDs`
* Move `BinaryClassificationMetrics` from `evaluation.binary` to `evaluation`
* Change default value of `addIntercept` to false and allow to add intercept in Ridge and Lasso.
* Clean `DecisionTree` package doc and test suite.
* Mark model constructors `private[spark]`
* Rename `loadLibSVMData` to `loadLibSVMFile` and hide `LabelParser` from users.
* Add `saveAsLibSVMFile`.
* Add `appendBias` to `MLUtils`.
Author: Xiangrui Meng <meng@databricks.com>
Closes#524 from mengxr/mllib-cleaning and squashes the following commits:
295dc8b [Xiangrui Meng] update loadLibSVMFile doc
1977ac1 [Xiangrui Meng] fix doc of appendBias
649fcf0 [Xiangrui Meng] rename loadLibSVMData to loadLibSVMFile; hide LabelParser from user APIs
54b812c [Xiangrui Meng] add appendBias
a71e7d0 [Xiangrui Meng] add saveAsLibSVMFile
d976295 [Xiangrui Meng] Merge branch 'master' into mllib-cleaning
b7e5cec [Xiangrui Meng] remove some experimental annotations and make model constructors private[mllib]
9b02b93 [Xiangrui Meng] minor code style update
a593ddc [Xiangrui Meng] fix python tests
fc28c18 [Xiangrui Meng] mark more classes experimental
f6cbbff [Xiangrui Meng] fix Java tests
0af70b0 [Xiangrui Meng] minor
6e139ef [Xiangrui Meng] Merge branch 'master' into mllib-cleaning
94e6dce [Xiangrui Meng] move BinaryLabelCounter and BinaryConfusionMatrixImpl to evaluation.binary
df34907 [Xiangrui Meng] clean DecisionTreeSuite to use LocalSparkContext
c81807f [Xiangrui Meng] set the default value of AddIntercept to false
03389c0 [Xiangrui Meng] allow to add intercept in Ridge and Lasso
c66c56f [Xiangrui Meng] move tree md to package object doc
a2695df [Xiangrui Meng] update guide for BinaryClassificationMetrics
9194f4c [Xiangrui Meng] move BinaryClassificationMetrics one level up
1c1a0e3 [Xiangrui Meng] remove VectorRDDs because it only contains one function that is not necessary for us to maintain
This reopens https://github.com/apache/incubator-spark/pull/640 against the new repo
Author: Sandy Ryza <sandy@cloudera.com>
Closes#30 from sryza/sandy-spark-1004 and squashes the following commits:
89889d4 [Sandy Ryza] Move unzipping py4j to the generate-resources phase so that it gets included in the jar the first time
5165a02 [Sandy Ryza] Fix docs
fd0df79 [Sandy Ryza] PySpark on YARN
`RDD.pipe`'s doctest throws interrupted system call exception on Mac. It can be fixed by wrapping `pipe.stdout.readline` in an iterator.
Author: Xiangrui Meng <meng@databricks.com>
Closes#594 from mengxr/pyspark-pipe and squashes the following commits:
cc32ac9 [Xiangrui Meng] fix interrupted system call error in pyspark's RDD.pipe
Author: Michael Armbrust <michael@databricks.com>
Closes#585 from marmbrus/pythonCacheTable and squashes the following commits:
7ec1f91 [Michael Armbrust] Minor fix to python table caching API.
* Additions to the PySpark API to cancel jobs
* Monitor Thread in PythonRDD to kill Python workers if a task is interrupted
Author: Ahir Reddy <ahirreddy@gmail.com>
Closes#541 from ahirreddy/python-cancel and squashes the following commits:
dfdf447 [Ahir Reddy] Changed success -> completed and made logging message clearer
6c860ab [Ahir Reddy] PR Comments
4b4100a [Ahir Reddy] Success flag
adba6ed [Ahir Reddy] Destroy python workers
27a2f8f [Ahir Reddy] Start the writer thread...
d422f7b [Ahir Reddy] Remove unnecesssary vals
adda337 [Ahir Reddy] Busy wait on the ocntext.interrupted flag, and then kill the python worker
d9e472f [Ahir Reddy] Revert "removed unnecessary vals"
5b9cae5 [Ahir Reddy] removed unnecessary vals
07b54d9 [Ahir Reddy] Fix canceling unit test
8ae9681 [Ahir Reddy] Don't interrupt worker
7722342 [Ahir Reddy] Monitor Thread for python workers
db04e16 [Ahir Reddy] Added canceling api to PySpark
copying form previous pull request https://github.com/apache/spark/pull/462
Its probably better to let the underlying language implementation take care of the default . This was easier to do with python as the default value for seed in random and numpy random is None.
In Scala/Java side it might mean propagating an Option or null(oh no!) down the chain until where the Random is constructed. But, looks like the convention in some other methods was to use System.nanoTime. So, followed that convention.
Conflict with overloaded method in sql.SchemaRDD.sample which also defines default params.
sample(fraction, withReplacement=false, seed=math.random)
Scala does not allow more than one overloaded to have default params. I believe the author intended to override the RDD.sample method and not overload it. So, changed it.
If backward compatible is important, 3 new method can be introduced (without default params) like this
sample(fraction)
sample(fraction, withReplacement)
sample(fraction, withReplacement, seed)
Added some tests for the scala RDD takeSample method.
Author: Arun Ramakrishnan <smartnut007@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>
Closes#477 from smartnut007/master and squashes the following commits:
07bb06e [Arun Ramakrishnan] SPARK-1438 fixing more space formatting issues
b9ebfe2 [Arun Ramakrishnan] SPARK-1438 removing redundant import of random in python rddsampler
8d05b1a [Arun Ramakrishnan] SPARK-1438 RDD . Replace System.nanoTime with a Random generated number. python: use a separate instance of Random instead of seeding language api global Random instance.
69619c6 [Arun Ramakrishnan] SPARK-1438 fix spacing issue
0c247db [Arun Ramakrishnan] SPARK-1438 RDD language apis to support optional seed in RDD methods sample/takeSample
If there are no `transpose()` in `self.theta`, a
*ValueError: matrices are not aligned*
is occurring. The former test case just ignore this situation.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#463 from yinxusen/python-naive-bayes and squashes the following commits:
fcbe3bc [Xusen Yin] fix bugs of dot in python
I used the sbt-unidoc plugin (https://github.com/sbt/sbt-unidoc) to create a unified Scaladoc of our public packages, and generate Javadocs as well. One limitation is that I haven't found an easy way to exclude packages in the Javadoc; there is a SBT task that identifies Java sources to run javadoc on, but it's been very difficult to modify it from outside to change what is set in the unidoc package. Some SBT-savvy people should help with this. The Javadoc site also lacks package-level descriptions and things like that, so we may want to look into that. We may decide not to post these right now if it's too limited compared to the Scala one.
Example of the built doc site: http://people.csail.mit.edu/matei/spark-unified-docs/
Author: Matei Zaharia <matei@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>
Closes#457 from mateiz/better-docs and squashes the following commits:
a63d4a3 [Matei Zaharia] Skip Java/Scala API docs for Python package
5ea1f43 [Matei Zaharia] Fix links to Java classes in Java guide, fix some JS for scrolling to anchors on page load
f05abc0 [Matei Zaharia] Don't include java.lang package names
995e992 [Matei Zaharia] Skip internal packages and class names with $ in JavaDoc
a14a93c [Matei Zaharia] typo
76ce64d [Matei Zaharia] Add groups to Javadoc index page, and a first package-info.java
ed6f994 [Matei Zaharia] Generate JavaDoc as well, add titles, update doc site to use unified docs
acb993d [Matei Zaharia] Add Unidoc plugin for the projects we want Unidoced
Author: Michael Armbrust <michael@databricks.com>
Closes#447 from marmbrus/pythonInsert and squashes the following commits:
c7ab692 [Michael Armbrust] Keep docstrings < 72 chars.
ff62870 [Michael Armbrust] Add insertInto and saveAsTable to Python API.
Author: Reynold Xin <rxin@apache.org>
Closes#444 from rxin/pyspark and squashes the following commits:
fc11356 [Reynold Xin] Made the PySpark shell version checking compatible with Python 2.6.
571830b [Reynold Xin] Fixed broken pyspark shell.
https://issues.apache.org/jira/browse/SPARK-1483
From the original JIRA: " The parameter name is part of the public API in Scala and Python, since you can pass named parameters to a method, so we should name it to this more descriptive term. Everywhere else we refer to "splits" as partitions." - @mateiz
Author: CodingCat <zhunansjtu@gmail.com>
Closes#430 from CodingCat/SPARK-1483 and squashes the following commits:
4b60541 [CodingCat] deprecate defaultMinSplits
ba2c663 [CodingCat] Rename minSplits to minPartitions in public APIs
This will make the tests more stable when not running SQL tests.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#439 from pwendell/hive-tests and squashes the following commits:
88a6032 [Patrick Wendell] FIX: Don't build Hive in assembly unless running Hive tests.
Python alternative for https://github.com/apache/spark/pull/392; managed from shell.py
Author: AbhishekKr <abhikumar163@gmail.com>
Closes#399 from abhishekkr/pyspark_shell and squashes the following commits:
134bdc9 [AbhishekKr] pyspark require Python2, failing if system default is Py3 from shell.py
This makes it possible to create tables and insert into them using the DSL and SQL for the scala and java apis.
Author: Michael Armbrust <michael@databricks.com>
Closes#354 from marmbrus/insertIntoTable and squashes the following commits:
6c6f227 [Michael Armbrust] Create random temporary files in python parquet unit tests.
f5e6d5c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into insertIntoTable
765c506 [Michael Armbrust] Add to JavaAPI.
77b512c [Michael Armbrust] typos.
5c3ef95 [Michael Armbrust] use names for boolean args.
882afdf [Michael Armbrust] Change createTableAs to saveAsTable. Clean up api annotations.
d07d94b [Michael Armbrust] Add tests, support for creating parquet files and hive tables.
fa3fe81 [Michael Armbrust] Make insertInto available on JavaSchemaRDD as well. Add createTableAs function.
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes#341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
Currently it requires NumPy 1.7 due to using the copyto method (http://docs.scipy.org/doc/numpy/reference/generated/numpy.copyto.html) for extracting data out of an array.
Replace it with a fallback
Author: Sandeep <sandeep@techaddict.me>
Closes#391 from techaddict/1426 and squashes the following commits:
d365962 [Sandeep] SPARK-1426: Make MLlib work with NumPy versions older than 1.7 Currently it requires NumPy 1.7 due to using the copyto method (http://docs.scipy.org/doc/numpy/reference/generated/numpy.copyto.html) for extracting data out of an array. Replace it with a fallback
An initial API that exposes SparkSQL functionality in PySpark. A PythonRDD composed of dictionaries, with string keys and primitive values (boolean, float, int, long, string) can be converted into a SchemaRDD that supports sql queries.
```
from pyspark.context import SQLContext
sqlCtx = SQLContext(sc)
rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
srdd = sqlCtx.applySchema(rdd)
sqlCtx.registerRDDAsTable(srdd, "table1")
srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
srdd2.collect()
```
The last line yields ```[{"f1" : 1, "f2" : "row1"}, {"f1" : 2, "f2": "row2"}, {"f1" : 3, "f2": "row3"}]```
Author: Ahir Reddy <ahirreddy@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#363 from ahirreddy/pysql and squashes the following commits:
0294497 [Ahir Reddy] Updated log4j properties to supress Hive Warns
307d6e0 [Ahir Reddy] Style fix
6f7b8f6 [Ahir Reddy] Temporary fix MIMA checker. Since we now assemble Spark jar with Hive, we don't want to check the interfaces of all of our hive dependencies
3ef074a [Ahir Reddy] Updated documentation because classes moved to sql.py
29245bf [Ahir Reddy] Cache underlying SchemaRDD instead of generating and caching PythonRDD
f2312c7 [Ahir Reddy] Moved everything into sql.py
a19afe4 [Ahir Reddy] Doc fixes
6d658ba [Ahir Reddy] Remove the metastore directory created by the HiveContext tests in SparkSQL
521ff6d [Ahir Reddy] Trying to get spark to build with hive
ab95eba [Ahir Reddy] Set SPARK_HIVE=true on jenkins
ded03e7 [Ahir Reddy] Added doc test for HiveContext
22de1d4 [Ahir Reddy] Fixed maven pyrolite dependency
e4da06c [Ahir Reddy] Display message if hive is not built into spark
227a0be [Michael Armbrust] Update API links. Fix Hive example.
58e2aa9 [Michael Armbrust] Build Docs for pyspark SQL Api. Minor fixes.
4285340 [Michael Armbrust] Fix building of Hive API Docs.
38a92b0 [Michael Armbrust] Add note to future non-python developers about python docs.
337b201 [Ahir Reddy] Changed com.clearspring.analytics stream version from 2.4.0 to 2.5.1 to match SBT build, and added pyrolite to maven build
40491c9 [Ahir Reddy] PR Changes + Method Visibility
1836944 [Michael Armbrust] Fix comments.
e00980f [Michael Armbrust] First draft of python sql programming guide.
b0192d3 [Ahir Reddy] Added Long, Double and Boolean as usable types + unit test
f98a422 [Ahir Reddy] HiveContexts
79621cf [Ahir Reddy] cleaning up cruft
b406ba0 [Ahir Reddy] doctest formatting
20936a5 [Ahir Reddy] Added tests and documentation
e4d21b4 [Ahir Reddy] Added pyrolite dependency
79f739d [Ahir Reddy] added more tests
7515ba0 [Ahir Reddy] added more tests :)
d26ec5e [Ahir Reddy] added test
e9f5b8d [Ahir Reddy] adding tests
906d180 [Ahir Reddy] added todo explaining cost of creating Row object in python
251f99d [Ahir Reddy] for now only allow dictionaries as input
09b9980 [Ahir Reddy] made jrdd explicitly lazy
c608947 [Ahir Reddy] SchemaRDD now has all RDD operations
725c91e [Ahir Reddy] awesome row objects
55d1c76 [Ahir Reddy] return row objects
4fe1319 [Ahir Reddy] output dictionaries correctly
be079de [Ahir Reddy] returning dictionaries works
cd5f79f [Ahir Reddy] Switched to using Scala SQLContext
e948bd9 [Ahir Reddy] yippie
4886052 [Ahir Reddy] even better
c0fb1c6 [Ahir Reddy] more working
043ca85 [Ahir Reddy] working
5496f9f [Ahir Reddy] doesn't crash
b8b904b [Ahir Reddy] Added schema rdd class
67ba875 [Ahir Reddy] java to python, and python to java
bcc0f23 [Ahir Reddy] Java to python
ab6025d [Ahir Reddy] compiling
The Mesos backend uses this property when setting up a slave process. It is similarly set in the Scala repl (org.apache.spark.repl.SparkILoop), but I couldn't find any analogous for pyspark.
Author: Ivan Wick <ivanwick+github@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>
Closes#311 from ivanwick/master and squashes the following commits:
da0c3e4 [Ivan Wick] Set spark.executor.uri from environment variable (needed by Mesos)
Author: Sandeep <sandeep@techaddict.me>
Closes#356 from techaddict/1428 and squashes the following commits:
3bdf5f6 [Sandeep] SPARK-1428: MLlib should convert non-float64 NumPy arrays to float64 instead of complaining
Author: Holden Karau <holden@pigscanfly.ca>
Closes#242 from holdenk/spark-1320-cogroupandgroupshouldpassiterator and squashes the following commits:
f289536 [Holden Karau] Fix bad merge, should have been Iterable rather than Iterator
77048f8 [Holden Karau] Fix merge up to master
d3fe909 [Holden Karau] use toSeq instead
7a092a3 [Holden Karau] switch resultitr to resultiterable
eb06216 [Holden Karau] maybe I should have had a coffee first. use correct import for guava iterables
c5075aa [Holden Karau] If guava 14 had iterables
2d06e10 [Holden Karau] Fix Java 8 cogroup tests for the new API
11e730c [Holden Karau] Fix streaming tests
66b583d [Holden Karau] Fix the core test suite to compile
4ed579b [Holden Karau] Refactor from iterator to iterable
d052c07 [Holden Karau] Python tests now pass with iterator pandas
3bcd81d [Holden Karau] Revert "Try and make pickling list iterators work"
cd1e81c [Holden Karau] Try and make pickling list iterators work
c60233a [Holden Karau] Start investigating moving to iterators for python API like the Java/Scala one. tl;dr: We will have to write our own iterator since the default one doesn't pickle well
88a5cef [Holden Karau] Fix cogroup test in JavaAPISuite for streaming
a5ee714 [Holden Karau] oops, was checking wrong iterator
e687f21 [Holden Karau] Fix groupbykey test in JavaAPISuite of streaming
ec8cc3e [Holden Karau] Fix test issues\!
4b0eeb9 [Holden Karau] Switch cast in PairDStreamFunctions
fa395c9 [Holden Karau] Revert "Add a join based on the problem in SVD"
ec99e32 [Holden Karau] Revert "Revert this but for now put things in list pandas"
b692868 [Holden Karau] Revert
7e533f7 [Holden Karau] Fix the bug
8a5153a [Holden Karau] Revert me, but we have some stuff to debug
b4e86a9 [Holden Karau] Add a join based on the problem in SVD
c4510e2 [Holden Karau] Revert this but for now put things in list pandas
b4e0b1d [Holden Karau] Fix style issues
71e8b9f [Holden Karau] I really need to stop calling size on iterators, it is the path of sadness.
b1ae51a [Holden Karau] Fix some of the types in the streaming JavaAPI suite. Probably still needs more work
37888ec [Holden Karau] core/tests now pass
249abde [Holden Karau] org.apache.spark.rdd.PairRDDFunctionsSuite passes
6698186 [Holden Karau] Revert "I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy"
fe992fe [Holden Karau] hmmm try and fix up basic operation suite
172705c [Holden Karau] Fix Java API suite
caafa63 [Holden Karau] I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy
88b3329 [Holden Karau] Fix groupbykey to actually give back an iterator
4991af6 [Holden Karau] Fix some tests
be50246 [Holden Karau] Calling size on an iterator is not so good if we want to use it after
687ffbc [Holden Karau] This is the it compiles point of replacing Seq with Iterator and JList with JIterator in the groupby and cogroup signatures
This is the default mode for running spark-shell and pyspark, intended to allow users running spark for the first time to see the performance benefits of using multiple cores, while not breaking backwards compatibility for users who use "local" mode and expect exactly 1 core.
Author: Aaron Davidson <aaron@databricks.com>
Closes#182 from aarondav/110 and squashes the following commits:
a88294c [Aaron Davidson] Rebased changes for new spark-shell
a9f393e [Aaron Davidson] SPARK-1099: Introduce local[*] mode to infer number of cores
The reason it wasn't working was passing a bytearray to stream.write(), which is not supported in Python 2.6 but is in 2.7. (This array came from NumPy when we converted data to send it over to Java). Now we just convert those bytearrays to strings of bytes, which preserves nonprintable characters as well.
Author: Matei Zaharia <matei@databricks.com>
Closes#335 from mateiz/mllib-python-2.6 and squashes the following commits:
f26c59f [Matei Zaharia] Update docs to no longer say we need Python 2.7
a84d6af [Matei Zaharia] SPARK-1421. Make MLlib work on Python 2.6
Move the PR#468 of apache-incubator-spark to the apache-spark
"Adding an option to persist Spark RDD blocks into Tachyon."
Author: Haoyuan Li <haoyuan@cs.berkeley.edu>
Author: RongGu <gurongwalker@gmail.com>
Closes#158 from RongGu/master and squashes the following commits:
72b7768 [Haoyuan Li] merge master
9f7fa1b [Haoyuan Li] fix code style
ae7834b [Haoyuan Li] minor cleanup
a8b3ec6 [Haoyuan Li] merge master branch
e0f4891 [Haoyuan Li] better check offheap.
55b5918 [RongGu] address matei's comment on the replication of offHeap storagelevel
7cd4600 [RongGu] remove some logic code for tachyonstore's replication
51149e7 [RongGu] address aaron's comment on returning value of the remove() function in tachyonstore
8adfcfa [RongGu] address arron's comment on inTachyonSize
120e48a [RongGu] changed the root-level dir name in Tachyon
5cc041c [Haoyuan Li] address aaron's comments
9b97935 [Haoyuan Li] address aaron's comments
d9a6438 [Haoyuan Li] fix for pspark
77d2703 [Haoyuan Li] change python api.git status
3dcace4 [Haoyuan Li] address matei's comments
91fa09d [Haoyuan Li] address patrick's comments
589eafe [Haoyuan Li] use TRY_CACHE instead of MUST_CACHE
64348b2 [Haoyuan Li] update conf docs.
ed73e19 [Haoyuan Li] Merge branch 'master' of github.com:RongGu/spark-1
619a9a8 [RongGu] set number of directories in TachyonStore back to 64; added a TODO tag for duplicated code from the DiskStore
be79d77 [RongGu] find a way to clean up some unnecessay metods and classed to make the code simpler
49cc724 [Haoyuan Li] update docs with off_headp option
4572f9f [RongGu] reserving the old apply function API of StorageLevel
04301d3 [RongGu] rename StorageLevel.TACHYON to Storage.OFF_HEAP
c9aeabf [RongGu] rename the StorgeLevel.TACHYON as StorageLevel.OFF_HEAP
76805aa [RongGu] unifies the config properties name prefix; add the configs into docs/configuration.md
e700d9c [RongGu] add the SparkTachyonHdfsLR example and some comments
fd84156 [RongGu] use randomUUID to generate sparkapp directory name on tachyon;minor code style fix
939e467 [Haoyuan Li] 0.4.1-thrift from maven central
86a2eab [Haoyuan Li] tachyon 0.4.1-thrift is in the staging repo. but jenkins failed to download it. temporarily revert it back to 0.4.1
16c5798 [RongGu] make the dependency on tachyon as tachyon-0.4.1-thrift
eacb2e8 [RongGu] Merge branch 'master' of https://github.com/RongGu/spark-1
bbeb4de [RongGu] fix the JsonProtocolSuite test failure problem
6adb58f [RongGu] Merge branch 'master' of https://github.com/RongGu/spark-1
d827250 [RongGu] fix JsonProtocolSuie test failure
716e93b [Haoyuan Li] revert the version
ca14469 [Haoyuan Li] bump tachyon version to 0.4.1-thrift
2825a13 [RongGu] up-merging to the current master branch of the apache spark
6a22c1a [Haoyuan Li] fix scalastyle
8968b67 [Haoyuan Li] exclude more libraries from tachyon dependency to be the same as referencing tachyon-client.
77be7e8 [RongGu] address mateiz's comment about the temp folder name problem. The implementation followed mateiz's advice.
1dcadf9 [Haoyuan Li] typo
bf278fa [Haoyuan Li] fix python tests
e82909c [Haoyuan Li] minor cleanup
776a56c [Haoyuan Li] address patrick's and ali's comments from the previous PR
8859371 [Haoyuan Li] various minor fixes and clean up
e3ddbba [Haoyuan Li] add doc to use Tachyon cache mode.
fcaeab2 [Haoyuan Li] address Aaron's comment
e554b1e [Haoyuan Li] add python code
47304b3 [Haoyuan Li] make tachyonStore in BlockMananger lazy val; add more comments StorageLevels.
dc8ef24 [Haoyuan Li] add old storelevel constructor
e01a271 [Haoyuan Li] update tachyon 0.4.1
8011a96 [RongGu] fix a brought-in mistake in StorageLevel
70ca182 [RongGu] a bit change in comment
556978b [RongGu] fix the scalastyle errors
791189b [RongGu] "Adding an option to persist Spark RDD blocks into Tachyon." move the PR#468 of apache-incubator-spark to the apache-spark
Also clarified comment on each file having to fit in memory
Author: Matei Zaharia <matei@databricks.com>
Closes#327 from mateiz/py-whole-files and squashes the following commits:
9ad64a5 [Matei Zaharia] SPARK-1414. Python API for SparkContext.wholeTextFiles
Since python does not have a library for max heap and usual tricks like inverting values etc.. does not work for all cases.
We have our own implementation of max heap.
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#97 from ScrapCodes/SPARK-1162/pyspark-top-takeOrdered2 and squashes the following commits:
35f86ba [Prashant Sharma] code review
2b1124d [Prashant Sharma] fixed tests
e8a08e2 [Prashant Sharma] Code review comments.
49e6ba7 [Prashant Sharma] SPARK-1162 added takeOrdered to pyspark
In PR https://github.com/apache/spark/pull/117, we added dense/sparse vector data model and updated KMeans to support sparse input. This PR is to replace all other `Array[Double]` usage by `Vector` in generalized linear models (GLMs) and Naive Bayes. Major changes:
1. `LabeledPoint` becomes `LabeledPoint(Double, Vector)`.
2. Methods that accept `RDD[Array[Double]]` now accept `RDD[Vector]`. We cannot support both in an elegant way because of type erasure.
3. Mark 'createModel' and 'predictPoint' protected because they are not for end users.
4. Add libSVMFile to MLContext.
5. NaiveBayes can accept arbitrary labels (introducing a breaking change to Python's `NaiveBayesModel`).
6. Gradient computation no longer creates temp vectors.
7. Column normalization and centering are removed from Lasso and Ridge because the operation will densify the data. Simple feature transformation can be done before training.
TODO:
1. ~~Use axpy when possible.~~
2. ~~Optimize Naive Bayes.~~
Author: Xiangrui Meng <meng@databricks.com>
Closes#245 from mengxr/vector and squashes the following commits:
eb6e793 [Xiangrui Meng] move libSVMFile to MLUtils and rename to loadLibSVMData
c26c4fc [Xiangrui Meng] update DecisionTree to use RDD[Vector]
11999c7 [Xiangrui Meng] Merge branch 'master' into vector
f7da54b [Xiangrui Meng] add minSplits to libSVMFile
da25e24 [Xiangrui Meng] revert the change to default addIntercept because it might change the behavior of existing code without warning
493f26f [Xiangrui Meng] Merge branch 'master' into vector
7c1bc01 [Xiangrui Meng] add a TODO to NB
b9b7ef7 [Xiangrui Meng] change default value of addIntercept to false
b01df54 [Xiangrui Meng] allow to change or clear threshold in LR and SVM
4addc50 [Xiangrui Meng] merge master
4ca5b1b [Xiangrui Meng] remove normalization from Lasso and update tests
f04fe8a [Xiangrui Meng] remove normalization from RidgeRegression and update tests
d088552 [Xiangrui Meng] use static constructor for MLContext
6f59eed [Xiangrui Meng] update libSVMFile to determine number of features automatically
3432e84 [Xiangrui Meng] update NaiveBayes to support sparse data
0f8759b [Xiangrui Meng] minor updates to NB
b11659c [Xiangrui Meng] style update
78c4671 [Xiangrui Meng] add libSVMFile to MLContext
f0fe616 [Xiangrui Meng] add a test for sparse linear regression
44733e1 [Xiangrui Meng] use in-place gradient computation
e981396 [Xiangrui Meng] use axpy in Updater
db808a1 [Xiangrui Meng] update JavaLR example
befa592 [Xiangrui Meng] passed scala/java tests
75c83a4 [Xiangrui Meng] passed test compile
1859701 [Xiangrui Meng] passed compile
834ada2 [Xiangrui Meng] optimized MLUtils.computeStats update some ml algorithms to use Vector (cont.)
135ab72 [Xiangrui Meng] merge glm
0e57aa4 [Xiangrui Meng] update Lasso and RidgeRegression to parse the weights correctly from GLM mark createModel protected mark predictPoint protected
d7f629f [Xiangrui Meng] fix a bug in GLM when intercept is not used
3f346ba [Xiangrui Meng] update some ml algorithms to use Vector
Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Prashant Sharma <scrapcodes@gmail.com>
Closes#262 from ScrapCodes/SPARK-1336/ReduceVerbosity and squashes the following commits:
87dfa54 [Prashant Sharma] Further reduction in noise and made pyspark tests to fail fast.
811170f [Prashant Sharma] Reducing the ouput of run-tests script.
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#235 from ScrapCodes/SPARK-1322/top-rev-sort and squashes the following commits:
f316266 [Prashant Sharma] Minor change in comment.
58e58c6 [Prashant Sharma] SPARK-1322, top in pyspark should sort result in descending order.
Doctest added for map in rdd.py
Author: Jyotiska NK <jyotiska123@gmail.com>
Closes#177 from jyotiska/pyspark_rdd_map_doctest and squashes the following commits:
a38527f [Jyotiska NK] Added doctest for map function in rdd.py
Here's the addition of min and max to statscounter.py and min and max methods to rdd.py.
Author: Dan McClary <dan.mcclary@gmail.com>
Closes#144 from dwmclary/SPARK-1246-add-min-max-to-stat-counter and squashes the following commits:
fd3fd4b [Dan McClary] fixed error, updated test
82cde0e [Dan McClary] flipped incorrectly assigned inf values in StatCounter
5d96799 [Dan McClary] added max and min to StatCounter repr for pyspark
21dd366 [Dan McClary] added max and min to StatCounter output, updated doc
1a97558 [Dan McClary] added max and min to StatCounter output, updated doc
a5c13b0 [Dan McClary] Added min and max to Scala and Java RDD, added min and max to StatCounter
ed67136 [Dan McClary] broke min/max out into separate transaction, added to rdd.py
1e7056d [Dan McClary] added underscore to getBucket
37a7dea [Dan McClary] cleaned up boundaries for histogram -- uses real min/max when buckets are derived
29981f2 [Dan McClary] fixed indentation on doctest comment
eaf89d9 [Dan McClary] added correct doctest for histogram
4916016 [Dan McClary] added histogram method, added max and min to statscounter
https://spark-project.atlassian.net/browse/SPARK-1240
It seems that the current implementation does not handle the empty RDD case when run takeSample
In this patch, before calling sample() inside takeSample API, I add a checker for this case and returns an empty Array when it's a empty RDD; also in sample(), I add a checker for the invalid fraction value
In the test case, I also add several lines for this case
Author: CodingCat <zhunansjtu@gmail.com>
Closes#135 from CodingCat/SPARK-1240 and squashes the following commits:
fef57d4 [CodingCat] fix the same problem in PySpark
36db06b [CodingCat] create new test cases for takeSample from an empty red
810948d [CodingCat] further fix
a40e8fb [CodingCat] replace if with require
ad483fd [CodingCat] handle the case with empty RDD when take sample
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#93 from ScrapCodes/SPARK-1162/pyspark-top-takeOrdered and squashes the following commits:
ece1fa4 [Prashant Sharma] Added top in python.
Author: prabinb <prabin.banka@imaginea.com>
Closes#92 from prabinb/python-api-rdd and squashes the following commits:
51129ca [prabinb] Added missing Python RDD functions Added __repr__ function to StorageLevel class. Added doctest for RDD.getStorageLevel().
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#115 from ScrapCodes/SPARK-1168/pyspark-foldByKey and squashes the following commits:
db6f67e [Prashant Sharma] SPARK-1168, Added foldByKey to pyspark.
Author: jyotiska <jyotiska123@gmail.com>
Closes#34 from jyotiska/pyspark_code and squashes the following commits:
c9439be [jyotiska] replaced dict with namedtuple
a6bf4cd [jyotiska] added callsite info for context.py
was raised earlier as a part of apache/incubator-spark#486
Author: Prabin Banka <prabin.banka@imaginea.com>
Closes#76 from prabinb/python-api-zip and squashes the following commits:
b1a31a0 [Prabin Banka] Added Python RDD.zip function
(Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615)
This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables:
SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY
The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public.
SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property.
SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory.
Other memory considerations:
- The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY.
- run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class).
This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however.
Author: Aaron Davidson <aaron@databricks.com>
Closes#99 from aarondav/sparkmem and squashes the following commits:
9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Prashant Sharma <scrapcodes@gmail.com>
Closes#80 from ScrapCodes/SPARK-1165/RDD.intersection and squashes the following commits:
9b015e9 [Prashant Sharma] Added a note, shuffle is required for intersection.
1fea813 [Prashant Sharma] correct the lines wrapping
d0c71f3 [Prashant Sharma] SPARK-1165 RDD.intersection in java
d6effee [Prashant Sharma] SPARK-1165 Implemented RDD.intersection in python.
The following Python APIs are added,
RDD.id()
SparkContext.setJobGroup()
SparkContext.setLocalProperty()
SparkContext.getLocalProperty()
SparkContext.sparkUser()
was raised earlier as a part of apache/incubator-spark#486
Author: Prabin Banka <prabin.banka@imaginea.com>
Closes#75 from prabinb/python-api-backup and squashes the following commits:
cc3c6cd [Prabin Banka] Added missing Python APIs
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#73 from ScrapCodes/SPARK-1109/wrong-API-docs and squashes the following commits:
1a55b58 [Prashant Sharma] SPARK-1109 wrong API docs for pyspark map function
This surroungs the complete worker code in a try/except block so we catch any error that arrives. An example would be the depickling failing for some reason
@JoshRosen
Author: Bouke van der Bijl <boukevanderbijl@gmail.com>
Closes#644 from bouk/catch-depickling-errors and squashes the following commits:
f0f67cc [Bouke van der Bijl] Lol indentation
0e4d504 [Bouke van der Bijl] Surround the complete python worker with the try block
Updated doctests for mapValues and flatMapValues in rdd.py
Author: jyotiska <jyotiska123@gmail.com>
Closes#621 from jyotiska/python_spark and squashes the following commits:
716f7cd [jyotiska] doctest updated for mapValues, flatMapValues in rdd.py
Fixed minor typo in worker.py
Author: jyotiska <jyotiska123@gmail.com>
Closes#630 from jyotiska/pyspark_code and squashes the following commits:
ee44201 [jyotiska] typo fixed in worker.py
Patch to allow PySpark to use existing JVM and Gateway. Changes to PySpark implementation of SparkConf to take existing SparkConf JVM handle. Change to PySpark SparkContext to allow subclass specific context initialization.
Author: Ahir Reddy <ahirreddy@gmail.com>
Closes#622 from ahirreddy/pyspark-existing-jvm and squashes the following commits:
a86f457 [Ahir Reddy] Patch to allow PySpark to use existing JVM and Gateway. Changes to PySpark implementation of SparkConf to take existing SparkConf JVM handle. Change to PySpark SparkContext to allow subclass specific context initialization.
Added example Python code for sort
I added an example Python code for sort. Right now, PySpark has limited examples for new people willing to use the project. This example code sorts integers stored in a file. I was able to sort 5 million, 10 million and 25 million integers with this code.
Author: jyotiska <jyotiska123@gmail.com>
== Merge branch commits ==
commit 8ad8faf6c8e02ae1cd68565d98524edf165f54df
Author: jyotiska <jyotiska123@gmail.com>
Date: Sun Feb 9 11:00:41 2014 +0530
Added comments in code on collect() method
commit 6f98f1e313f4472a7c2207d36c4f0fbcebc95a8c
Author: jyotiska <jyotiska123@gmail.com>
Date: Sat Feb 8 13:12:37 2014 +0530
Updated python example code sort.py
commit 945e39a5d68daa7e5bab0d96cbd35d7c4b04eafb
Author: jyotiska <jyotiska123@gmail.com>
Date: Sat Feb 8 12:59:09 2014 +0530
Added example python code for sort
Version number to 1.0.0-SNAPSHOT
Since 0.9.0-incubating is done and out the door, we shouldn't be building 0.9.0-incubating-SNAPSHOT anymore.
@pwendell
Author: Mark Hamstra <markhamstra@gmail.com>
== Merge branch commits ==
commit 1b00a8a7c1a7f251b4bb3774b84b9e64758eaa71
Author: Mark Hamstra <markhamstra@gmail.com>
Date: Wed Feb 5 09:30:32 2014 -0800
Version number to 1.0.0-SNAPSHOT
This fixes SPARK-1043, a bug introduced in 0.9.0
where PySpark couldn't serialize strings > 64kB.
This fix was written by @tyro89 and @bouk in #512.
This commit squashes and rebases their pull request
in order to fix some merge conflicts.
Fix PySpark hang when input files are deleted (SPARK-1025)
This pull request addresses [SPARK-1025](https://spark-project.atlassian.net/browse/SPARK-1025), an issue where PySpark could hang if its input files were deleted.
Remove Typesafe Config usage and conf files to fix nested property names
With Typesafe Config we had the subtle problem of no longer allowing
nested property names, which are used for a few of our properties:
http://apache-spark-developers-list.1001551.n3.nabble.com/Config-properties-broken-in-master-td208.html
This PR is for branch 0.9 but should be added into master too.
(cherry picked from commit 34e911ce9a)
Signed-off-by: Patrick Wendell <pwendell@gmail.com>
This helps in case the exception happened while serializing a record to
be sent to Java, leaving the stream to Java in an inconsistent state
where PythonRDD won't be able to read the error.
We've used camel case in other Spark methods so it felt reasonable to
keep using it here and make the code match Scala/Java as much as
possible. Note that parameter names matter in Python because it allows
passing optional parameters by name.
- Added a Python wrapper for Naive Bayes
- Updated the Scala Naive Bayes to match the style of our other
algorithms better and in particular make it easier to call from Java
(added builder pattern, removed default value in train method)
- Updated Python MLlib functions to not require a SparkContext; we can
get that from the RDD the user gives
- Added a toString method in LabeledPoint
- Made the Python MLlib tests run as part of run-tests as well (before
they could only be run individually through each file)
SPARK-991: Report information gleaned from a Python stacktrace in the UI
Scala:
- Added setCallSite/clearCallSite to SparkContext and JavaSparkContext.
These functions mutate a LocalProperty called "externalCallSite."
- Add a wrapper, getCallSite, that checks for an externalCallSite and, if
none is found, calls the usual Utils.formatSparkCallSite.
- Change everything that calls Utils.formatSparkCallSite to call
getCallSite instead. Except getCallSite.
- Add wrappers to setCallSite/clearCallSite wrappers to JavaSparkContext.
Python:
- Add a gruesome hack to rdd.py that inspects the traceback and guesses
what you want to see in the UI.
- Add a RAII wrapper around said gruesome hack that calls
setCallSite/clearCallSite as appropriate.
- Wire said RAII wrapper up around three calls into the Scala code.
I'm not sure that I hit all the spots with the RAII wrapper. I'm also
not sure that my gruesome hack does exactly what we want.
One could also approach this change by refactoring
runJob/submitJob/runApproximateJob to take a call site, then threading
that parameter through everything that needs to know it.
One might object to the pointless-looking wrappers in JavaSparkContext.
Unfortunately, I can't directly access the SparkContext from
Python---or, if I can, I don't know how---so I need to wrap everything
that matters in JavaSparkContext.
Conflicts:
core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
Also replaced SparkConf.getOrElse with just a "get" that takes a default
value, and added getInt, getLong, etc to make code that uses this
simpler later on.
Bug fixes for file input stream and checkpointing
- Fixed bugs in the file input stream that led the stream to fail due to transient HDFS errors (listing files when a background thread it deleting fails caused errors, etc.)
- Updated Spark's CheckpointRDD and Streaming's CheckpointWriter to use SparkContext.hadoopConfiguration, to allow checkpoints to be written to any HDFS compatible store requiring special configuration.
- Changed the API of SparkContext.setCheckpointDir() - eliminated the unnecessary 'useExisting' parameter. Now SparkContext will always create a unique subdirectory within the user specified checkpoint directory. This is to ensure that previous checkpoint files are not accidentally overwritten.
- Fixed bug where setting checkpoint directory as a relative local path caused the checkpointing to fail.
The test in context.py created two different instances of the
SparkContext class by copying "globals", so that some tests can have a
global "sc" object and others can try initializing their own contexts.
This led to two JVM gateways being created since SparkConf also looked
at pyspark.context.SparkContext to get the JVM.
Python bindings for mllib
This pull request contains Python bindings for the regression, clustering, classification, and recommendation tools in mllib.
For each 'train' frontend exposed, there is a Scala stub in PythonMLLibAPI.scala and a Python stub in mllib.py. The Python stub serialises the input RDD and any vector/matrix arguments into a mutually-understood format and calls the Scala stub. The Scala stub deserialises the RDD and the vector/matrix arguments, calls the appropriate 'train' function, serialises the resulting model, and returns the serialised model.
ALSModel is slightly different since a MatrixFactorizationModel has RDDs inside. The Scala stub returns a handle to a Scala MatrixFactorizationModel; prediction is done by calling the Scala predict method.
I have tested these bindings on an x86_64 machine running Linux. There is a risk that these bindings may fail on some choose-your-own-endian platform if Python's endian differs from java.nio.ByteBuffer's idea of the native byte order.
Add collectPartition to JavaRDD interface.
This interface is useful for implementing `take` from other language frontends where the data is serialized. Also remove `takePartition` from PythonRDD and use `collectPartition` in rdd.py.
Thanks @concretevitamin for the original change and tests.
Change the implementation to use runJob instead of PartitionPruningRDD.
Also update the unit tests and the python take implementation
to use the new interface.
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().
If we support custom serializers, the Python
worker will know what type of input to expect,
so we won't need to wrap Tuple2 and Strings into
pickled tuples and strings.
The constructor for SparkContext should pass in self so that we track
the current context and produce errors if another one is created. Add
a doctest to make sure creating multiple contexts triggers the
exception.
Add a new classmethod to SparkContext to set system properties like is
possible in Scala/Java. Unlike the Java/Scala implementations, there's
no access to System until the JVM bridge is created. Since
SparkContext handles that, move the initialization of the JVM
connection to a separate classmethod that can safely be called
repeatedly as long as the same instance (or no instance) is provided.
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).
Conflicts:
bagel/pom.xml
core/pom.xml
core/src/test/scala/org/apache/spark/ui/UISuite.scala
examples/pom.xml
mllib/pom.xml
pom.xml
project/SparkBuild.scala
repl/pom.xml
streaming/pom.xml
tools/pom.xml
In scala 2.10, a shorter representation is used for naming artifacts
so changed to shorter scala version for artifacts and made it a property in pom.
Currently PythonPartitioner determines partition ID by hashing a
byte-array representation of PySpark's key. This PR lets
PythonPartitioner use the actual partition ID, which is required e.g.
for sorting via PySpark.
The sc.StorageLevel -> StorageLevel pathway is a bit janky, but otherwise
the shell would have to call a private method of SparkContext. Having
StorageLevel available in sc also doesn't seem like the end of the world.
There may be a better solution, though.
As for creating the StorageLevel object itself, this seems to be the best
way in Python 2 for creating singleton, enum-like objects:
http://stackoverflow.com/questions/36932/how-can-i-represent-an-enum-in-python
This commit makes Spark invocation saner by using an assembly JAR to
find all of Spark's dependencies instead of adding all the JARs in
lib_managed. It also packages the examples into an assembly and uses
that as SPARK_EXAMPLES_JAR. Finally, it replaces the old "run" script
with two better-named scripts: "run-examples" for examples, and
"spark-class" for Spark internal classes (e.g. REPL, master, etc). This
is also designed to minimize the confusion people have in trying to use
"run" to run their own classes; it's not meant to do that, but now at
least if they look at it, they can modify run-examples to do a decent
job for them.
As part of this, Bagel's examples are also now properly moved to the
examples package instead of bagel.
This addresses SPARK-885, a usability issue where PySpark's
Java gateway process would be killed if the user hit ctrl-c.
Note that SIGINT still won't cancel the running s
This fix is based on http://stackoverflow.com/questions/5045771
This fixes SPARK-832, an issue where PySpark
would not work when the master and workers used
different SPARK_HOME paths.
This change may potentially break code that relied
on the master's PYTHONPATH being used on workers.
To have custom PYTHONPATH additions used on the
workers, users should set a custom PYTHONPATH in
spark-env.sh rather than setting it in the shell.
One unfortunate consequence of this fix is that we materialize any
collections that are given to us as generators, but this seems necessary
to get reasonable behavior on small collections. We could add a
batchSize parameter later to bypass auto-computation of batch size if
this becomes a problem (e.g. if users really want to parallelize big
generators nicely)
The problem was that the gateway was being initialized whenever the
pyspark.context module was loaded. The fix uses lazy initialization
that occurs only when SparkContext instances are actually constructed.
I also made the gateway and jvm variables private.
This change results in ~3-4x performance improvement when running the
PySpark unit tests.
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.
PythonPartitioner did not take the Python-side partitioning function
into account when checking for equality, which might cause problems
in the future.
