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.
