Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/9313#discussion_r43350330
--- Diff: python/pyspark/context.py ---
@@ -806,6 +806,24 @@ def addPyFile(self, path):
import importlib
importlib.invalidate_caches()
+ def addJar(self, path):
+ """
+ Adds a JAR dependency for all tasks to be executed on this
SparkContext in the future.
+ The `path` passed can be either a local file, a file in HDFS (or
other Hadoop-supported
+ filesystems), an HTTP, HTTPS or FTP URI, or local:/path for a file
on every worker node.
+ """
+ self._jsc.sc().addJar(path)
+ self._jvm.PythonUtils.updatePrimaryClassLoader(self._jsc)
+
+ def _loadClass(self, className):
+ """
+ .. note:: Experimental
+
+ Loads a JVM class using the MutableClass loader used by spark.
+ This function exists because Py4J uses a different class loader.
+ """
+
self._jvm.java.lang.Thread.currentThread().getContextClassLoader().loadClass(className)
--- End diff --
based on
http://www.javaworld.com/article/2077344/core-java/find-a-way-out-of-the-classloader-maze.html
every thread has a classloader associated with it unless it was created by
native code. This is also the technique @tdas used for getting the class loader
in the python kafka utils. Although this did come up (in
https://issues.apache.org/jira/browse/SPARK-1403 ). I'll fix it here and make a
follow up JIRA to update the kafka utils pyspark to use the common methodlogy.
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