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.
