diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+from pyspark import SparkContext
+from pyspark.sql import SQLContext
+from pyspark.sql import Row, StructField, StructType, StringType, IntegerType
+
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="PythonSQL")
+    sqlContext = SQLContext(sc)
+
+    # RDD is created from a list of rows
+    some_rdd = sc.parallelize([Row(name="John", age=19),
+                              Row(name="Smith", age=23),
+                              Row(name="Sarah", age=18)])
+    # Infer schema from the first row, create a SchemaRDD and print the schema
+    some_schemardd = sqlContext.inferSchema(some_rdd)
+    some_schemardd.printSchema()
+
+    # Another RDD is created from a list of tuples
+    another_rdd = sc.parallelize([("John", 19), ("Smith", 23), ("Sarah", 18)])
+    # Schema with two fields - person_name and person_age
+    schema = StructType([StructField("person_name", StringType(), False),
+                        StructField("person_age", IntegerType(), False)])
+    # Create a SchemaRDD by applying the schema to the RDD and print the schema
+    another_schemardd = sqlContext.applySchema(another_rdd, schema)
+    another_schemardd.printSchema()
+    # root
+    #  |-- age: integer (nullable = true)
+    #  |-- name: string (nullable = true)
+
+    # A JSON dataset is pointed to by path.
+    # The path can be either a single text file or a directory storing text files.
+    path = os.environ['SPARK_HOME'] + "examples/src/main/resources/people.json"
+    # Create a SchemaRDD from the file(s) pointed to by path
+    people = sqlContext.jsonFile(path)
+    # root
+    #  |-- person_name: string (nullable = false)
+    #  |-- person_age: integer (nullable = false)
+
+    # The inferred schema can be visualized using the printSchema() method.
+    people.printSchema()
+    # root
+    #  |-- age: IntegerType
+    #  |-- name: StringType
+
+    # Register this SchemaRDD as a table.
+    people.registerAsTable("people")
+
+    # SQL statements can be run by using the sql methods provided by sqlContext
+    teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
+
+    for each in teenagers.collect():
+        print each[0]
+
+    sc.stop()