old_contents: #
  # 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.
  #
  
  """
   Counts words in UTF8 encoded, '\n' delimited text directly received from Kafka in every 2 seconds.
   Usage: direct_kafka_wordcount.py <broker_list> <topic>
  
   To run this on your local machine, you need to setup Kafka and create a producer first, see
   http://kafka.apache.org/documentation.html#quickstart
  
   and then run the example
      `$ bin/spark-submit --jars \
        external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \
        examples/src/main/python/streaming/direct_kafka_wordcount.py \
        localhost:9092 test`
  """
  
  import sys
  
  from pyspark import SparkContext
  from pyspark.streaming import StreamingContext
  from pyspark.streaming.kafka import KafkaUtils
  
  if __name__ == "__main__":
      if len(sys.argv) != 3:
          #print("Usage: direct_kafka_wordcount.py <broker_list> <topic>", file=sys.stderr)
          exit(-1)
  
      sc = SparkContext(appName="PythonStreamingDirectKafkaWordCount")
      ssc = StreamingContext(sc, 2)
  
      brokers, topic = sys.argv[1:]
      kvs = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers})
      lines = kvs.map(lambda x: x[1])
      counts = lines.flatMap(lambda line: line.split(" ")) \
          .map(lambda word: (word, 1)) \
          .reduceByKey(lambda a, b: a+b)
      counts.pprint()
  
      ssc.start()
      ssc.awaitTermination()
  
new_contents: #
  # 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.
  #
  
  """
   Counts words in UTF8 encoded, '\n' delimited text directly received from Kafka in every 2 seconds.
   Usage: direct_kafka_wordcount.py <broker_list> <topic>
  
   To run this on your local machine, you need to setup Kafka and create a producer first, see
   http://kafka.apache.org/documentation.html#quickstart
  
   and then run the example
      `$ bin/spark-submit --jars \
        external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \
        examples/src/main/python/streaming/direct_kafka_wordcount.py \
        localhost:9092 test`
  """
  
  import sys
  
  from pyspark import SparkContext
  from pyspark.streaming import StreamingContext
  from pyspark.streaming.kafka import KafkaUtils
  
  if __name__ == "__main__":
      if len(sys.argv) != 3:
          print("Usage: direct_kafka_wordcount.py <broker_list> <topic>", file=sys.stderr)
          exit(-1)
  
      sc = SparkContext(appName="PythonStreamingDirectKafkaWordCount")
      ssc = StreamingContext(sc, 2)
  
      brokers, topic = sys.argv[1:]
      kvs = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers})
      lines = kvs.map(lambda x: x[1])
      counts = lines.flatMap(lambda line: line.split(" ")) \
          .map(lambda word: (word, 1)) \
          .reduceByKey(lambda a, b: a+b)
      counts.pprint()
  
      ssc.start()
      ssc.awaitTermination()
  
subject: Update direct_kafka_wordcount.py