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diff --git a/examples/src/main/python/sql/streaming/structured_kafka_wordcount.py b/examples/src/main/python/sql/streaming/structured_kafka_wordcount.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.
+#
+
+"""
+ Consumes messages from one or more topics in Kafka and does wordcount.
+ Usage: structured_kafka_wordcount.py <bootstrap-servers> <subscribe-type> <topics>
+   <bootstrap-servers> The Kafka "bootstrap.servers" configuration. A
+   comma-separated list of host:port.
+   <subscribe-type> There are three kinds of type, i.e. 'assign', 'subscribe',
+   'subscribePattern'.
+   |- <assign> Specific TopicPartitions to consume. Json string
+   |  {"topicA":[0,1],"topicB":[2,4]}.
+   |- <subscribe> The topic list to subscribe. A comma-separated list of
+   |  topics.
+   |- <subscribePattern> The pattern used to subscribe to topic(s).
+   |  Java regex string.
+   |- Only one of "assign, "subscribe" or "subscribePattern" options can be
+   |  specified for Kafka source.
+   <topics> Different value format depends on the value of 'subscribe-type'.
+
+ Run the example
+    `$ bin/spark-submit examples/src/main/python/sql/streaming/structured_kafka_wordcount.py \
+    host1:port1,host2:port2 subscribe topic1,topic2`
+"""
+from __future__ import print_function
+
+import sys
+
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import explode
+from pyspark.sql.functions import split
+
+if __name__ == "__main__":
+    if len(sys.argv) != 4:
+        print("""
+        Usage: structured_kafka_wordcount.py <bootstrap-servers> <subscribe-type> <topics>
+        """, file=sys.stderr)
+        exit(-1)
+
+    bootstrapServers = sys.argv[1]
+    subscribeType = sys.argv[2]
+    topics = sys.argv[3]
+
+    spark = SparkSession\
+        .builder\
+        .appName("StructuredKafkaWordCount")\
+        .getOrCreate()
+
+    # Create DataSet representing the stream of input lines from kafka
+    lines = spark\
+        .readStream\
+        .format("kafka")\
+        .option("kafka.bootstrap.servers", bootstrapServers)\
+        .option(subscribeType, topics)\
+        .load()\
+        .selectExpr("CAST(value AS STRING)")
+
+    # Split the lines into words
+    words = lines.select(
+        # explode turns each item in an array into a separate row
+        explode(
+            split(lines.value, ' ')
+        ).alias('word')
+    )
+
+    # Generate running word count
+    wordCounts = words.groupBy('word').count()
+
+    # Start running the query that prints the running counts to the console
+    query = wordCounts\
+        .writeStream\
+        .outputMode('complete')\
+        .format('console')\
+        .start()
+
+    query.awaitTermination()