[SPARK-9613] [CORE] Ban use of JavaConversions and migrate all existing uses to JavaConverters

Replace `JavaConversions` implicits with `JavaConverters`

Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.

Author: Sean Owen <sowen@cloudera.com>

Closes #8033 from srowen/SPARK-9613.

2 files changed, 14 insertions, 2 deletions

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 8af8637cf9..0948f9b27c 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -61,6 +61,18 @@ def _to_seq(sc, cols, converter=None):
     return sc._jvm.PythonUtils.toSeq(cols)
 
 
+def _to_list(sc, cols, converter=None):
+    """
+    Convert a list of Column (or names) into a JVM (Scala) List of Column.
+
+    An optional `converter` could be used to convert items in `cols`
+    into JVM Column objects.
+    """
+    if converter:
+        cols = [converter(c) for c in cols]
+    return sc._jvm.PythonUtils.toList(cols)
+
+
 def _unary_op(name, doc="unary operator"):
     """ Create a method for given unary operator """
     def _(self):
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 025811f519..e269ef4304 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -32,7 +32,7 @@ from pyspark.storagelevel import StorageLevel
 from pyspark.traceback_utils import SCCallSiteSync
 from pyspark.sql import since
 from pyspark.sql.types import _parse_datatype_json_string
-from pyspark.sql.column import Column, _to_seq, _to_java_column
+from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
 from pyspark.sql.types import *
 
@@ -494,7 +494,7 @@ class DataFrame(object):
             if w < 0.0:
                 raise ValueError("Weights must be positive. Found weight value: %s" % w)
         seed = seed if seed is not None else random.randint(0, sys.maxsize)
-        rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed))
+        rdd_array = self._jdf.randomSplit(_to_list(self.sql_ctx._sc, weights), long(seed))
         return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array]
 
     @property