[SPARK-12334][SQL][PYSPARK] Support read from multiple input paths for orc file in DataFrameReader.orc

Beside the issue in spark api, also fix 2 minor issues in pyspark
- support read from multiple input paths for orc
- support read from multiple input paths for text

Author: Jeff Zhang <zjffdu@apache.org>

Closes #10307 from zjffdu/SPARK-12334.

2 files changed, 13 insertions, 6 deletions

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 45fb9b7591..4354345ebc 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -161,7 +161,7 @@ class DataFrameReader(OptionUtils):
              mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
              timeZone=None, wholeFile=None):
         """
-        Loads a JSON file and returns the results as a :class:`DataFrame`.
+        Loads JSON files and returns the results as a :class:`DataFrame`.
 
         `JSON Lines <http://jsonlines.org/>`_(newline-delimited JSON) is supported by default.
         For JSON (one record per file), set the `wholeFile` parameter to ``true``.
@@ -169,7 +169,7 @@ class DataFrameReader(OptionUtils):
         If the ``schema`` parameter is not specified, this function goes
         through the input once to determine the input schema.
 
-        :param path: string represents path to the JSON dataset,
+        :param path: string represents path to the JSON dataset, or a list of paths,
                      or RDD of Strings storing JSON objects.
         :param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema.
         :param primitivesAsString: infers all primitive values as a string type. If None is set,
@@ -252,7 +252,7 @@ class DataFrameReader(OptionUtils):
             jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
             return self._df(self._jreader.json(jrdd))
         else:
-            raise TypeError("path can be only string or RDD")
+            raise TypeError("path can be only string, list or RDD")
 
     @since(1.4)
     def table(self, tableName):
@@ -269,7 +269,7 @@ class DataFrameReader(OptionUtils):
 
     @since(1.4)
     def parquet(self, *paths):
-        """Loads a Parquet file, returning the result as a :class:`DataFrame`.
+        """Loads Parquet files, returning the result as a :class:`DataFrame`.
 
         You can set the following Parquet-specific option(s) for reading Parquet files:
             * ``mergeSchema``: sets whether we should merge schemas collected from all \
@@ -407,7 +407,7 @@ class DataFrameReader(OptionUtils):
 
     @since(1.5)
     def orc(self, path):
-        """Loads an ORC file, returning the result as a :class:`DataFrame`.
+        """Loads ORC files, returning the result as a :class:`DataFrame`.
 
         .. note:: Currently ORC support is only available together with Hive support.
 
@@ -415,7 +415,9 @@ class DataFrameReader(OptionUtils):
         >>> df.dtypes
         [('a', 'bigint'), ('b', 'int'), ('c', 'int')]
         """
-        return self._df(self._jreader.orc(path))
+        if isinstance(path, basestring):
+            path = [path]
+        return self._df(self._jreader.orc(_to_seq(self._spark._sc, path)))
 
     @since(1.4)
     def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 1b873e9578..f0a9a0400e 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -450,6 +450,11 @@ class SQLTests(ReusedPySparkTestCase):
                     Row(_c0=u'Hyukjin', _c1=u'25', _c2=u'I am Hyukjin\n\nI love Spark!')]
         self.assertEqual(ages_newlines.collect(), expected)
 
+    def test_read_multiple_orc_file(self):
+        df = self.spark.read.orc(["python/test_support/sql/orc_partitioned/b=0/c=0",
+                                  "python/test_support/sql/orc_partitioned/b=1/c=1"])
+        self.assertEqual(2, df.count())
+
     def test_udf_with_input_file_name(self):
         from pyspark.sql.functions import udf, input_file_name
         from pyspark.sql.types import StringType