Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/14340#discussion_r72122440
--- Diff: python/pyspark/streaming/kafka010.py ---
@@ -0,0 +1,370 @@
+#
+# 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.
+#
+
+from pyspark.rdd import RDD
+from pyspark.serializers import AutoBatchedSerializer, PickleSerializer
+from pyspark.streaming import DStream
+from pyspark.streaming.kafka import KafkaDStream, KafkaRDD, OffsetRange
+
+__all__ = ['Assign', 'KafkaConsumerRecord', 'KafkaUtils', 'PreferBrokers',
'PreferConsistent',
+ 'PreferFixed', 'Subscribe', 'SubscribePattern',
'TopicPartition', 'utf8_decoder']
+
+
+def utf8_decoder(s):
+ """ Decode the unicode as UTF-8 """
+ if s is None:
+ return None
+ return s.decode('utf-8')
+
+
+class KafkaUtils(object):
+
+ @staticmethod
+ def createDirectStream(ssc, locationStrategy, consumerStrategy,
+ keyDecoder=utf8_decoder,
valueDecoder=utf8_decoder):
+ """
+ .. note:: Experimental
+
+ Create an input stream that directly pulls messages from Kafka
0.10 brokers with different
+ location strategy and consumer strategy.
+
+ This does not use Zookeeper to store offsets. The consumed offsets
are tracked
+ by the stream itself. For interoperability with Kafka monitoring
tools that depend on
+ Zookeeper, you have to update Kafka/Zookeeper yourself from the
streaming application.
+ You can access the offsets used in each batch from the generated
RDDs (see
+
+ To recover from driver failures, you have to enable checkpointing
in the StreamingContext.
+ The information on consumed offset can be recovered from the
checkpoint.
+ See the programming guide for details (constraints, etc.).
+
+ :param ssc: StreamingContext object,
+ :param locationStrategy: Strategy to schedule consumers for a
given TopicPartition on an
+ executor.
+ :param consumerStrategy: Choices of how to create and configure
underlying Kafka
+ Consumers on driver and executors.
+ :param keyDecoder: A function to decode key (default is
utf8_decoder).
+ :param valueDecoder: A function to decode value (default is
utf8_decoder).
+ :return: A DStream object.
+ """
+
+ helper = KafkaUtils._get_helper(ssc._sc)
+ ser = AutoBatchedSerializer(PickleSerializer())
+
+ jlocationStrategy = locationStrategy._jLocationStrategy(helper)
+ jconsumerStrategy = consumerStrategy._jConsumerStrategy(helper)
+
+ jstream = helper.createDirectStream(ssc._jssc, jlocationStrategy,
jconsumerStrategy)
+
+ def func(m):
+ m._set_key_deserializer(keyDecoder)
+ m._set_value_deserializer(valueDecoder)
+ return m
+
+ stream = DStream(jstream, ssc, ser).map(func)
+
+ return KafkaDStream(stream._jdstream, ssc,
stream._jrdd_deserializer)
+
+ @staticmethod
+ def createRDD(sc, kafkaParams, offsetRanges, locationStrategy,
+ keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
+ """
+ .. note:: Experimental
+
+ Create a Kafka RDD using offset ranges and location strategy.
+
+ :param sc: SparkContext object.
+ :param kafkaParams: Additional params for Kafka.
+ :param offsetRanges: list of offsetRange to specify
topic:partition:[start, end) to consume.
+ :param locationStrategy: Strategy to schedule consumers for a
given TopicPartition on an
+ executor.
+ :param keyDecoder: A function to decode key (default is
utf8_decoder).
+ :param valueDecoder: A function to decode value (default is
utf8_decoder).
+ :return: A RDD object.
+ """
+
+ if not isinstance(kafkaParams, dict):
+ raise TypeError("kafkaParams should be dict")
+
+ helper = KafkaUtils._get_helper(sc)
+ joffsetRanges = [o._jOffsetRange(helper) for o in offsetRanges]
+ jlocationStrategy = locationStrategy._jLocationStrategy(helper)
+
+ jrdd = helper.createRDD(sc._jsc, kafkaParams, joffsetRanges,
jlocationStrategy)
+
+ def func(m):
+ m._set_key_deserializer(keyDecoder)
+ m._set_value_deserializer(valueDecoder)
+ return m
+
+ rdd = RDD(jrdd, sc).map(func)
+
+ return KafkaRDD(rdd._jrdd, sc, rdd._jrdd_deserializer)
+
+ @staticmethod
+ def _get_helper(sc):
+ try:
+ helper =
sc._jvm.org.apache.spark.streaming.kafka010.KafkaUtilsPythonHelper()
+ KafkaRDD.set_helper(helper)
+ return helper
+ except TypeError as e:
+ if str(e) == "'JavaPackage' object is not callable":
+ KafkaUtils._printErrorMsg(sc)
+ raise
+
+ @staticmethod
+ def _printErrorMsg(sc):
+ print("""
+________________________________________________________________________________________________
+
+ Spark Streaming's Kafka libraries not found in class path. Try one of
the following.
+
+ 1. Include the Kafka library and its dependencies with in the
+ spark-submit command as
+
+ $ bin/spark-submit --packages
org.apache.spark:spark-streaming-kafka-0-10:%s ...
+
+ 2. Download the JAR of the artifact from Maven Central
http://search.maven.org/,
+ Group Id = org.apache.spark, Artifact Id =
spark-streaming-kafka-0-10-assembly, Version = %s.
+ Then, include the jar in the spark-submit command as
+
+ $ bin/spark-submit --jars <spark-streaming-kafka-0-10-assembly.jar>
...
+
+________________________________________________________________________________________________
+
+""" % (sc.version, sc.version))
+
+
+class LocationStrategy(object):
+ """
+ .. note:: Experimental
+
+ A python wrapper of Scala LocationStrategy.
+ """
+
+ def _jLocationStrategy(self, helper):
+ pass
+
+
+class PreferBrokers(LocationStrategy):
+ """
+ .. note:: Experimental
+
+ Use this only if your executors are on the same nodes as your kafka
brokers.
+
+ """
+ def _jLocationStrategy(self, helper):
+ return helper.createPreferBrokers()
+
+
+class PreferConsistent(LocationStrategy):
+ """
+ .. note:: Experimental
+
+ Use this in most cases, it will consistently distribute partitions
across all executors.
+ """
+
+ def _jLocationStrategy(self, helper):
+ return helper.createPreferConsistent()
+
+
+class PreferFixed(LocationStrategy):
+ """
+ .. note:: Experimental
+
+ Use this to place particular TopicPartitions on particular hosts if
your load is uneven. Any
+ TopicPartition not specified in the map will use a consistent location.
+ """
+
+ def __init__(self, hostMap):
+ """
+ Python wrapper of Scala PreferFixed.
+
+ :param hostMap: A dict of TopicPartition to hostname.
+ """
+ self.hostMap = hostMap
+
+ def _jLocationStrategy(self, helper):
+ jhostMap = dict([(k._jTopicPartition(helper), v) for (k, v) in
self.hostMap.items()])
+ return helper.createPreferFixed(jhostMap)
+
+
+class ConsumerStrategy(object):
+ """
+ .. note:: Experimental
+
+ A python wrapper of Scala ConsumerStrategy.
+ """
+
+ def _jConsumerStrategy(self, helper):
+ pass
+
+
+class Subscribe(ConsumerStrategy):
+ """
+ .. note:: Experimental
+
+ Subscribe to a collection of topics.
+ """
+
+ def __init__(self, topics, kafkaParams, offsets=None):
+ """
+ Subscribe to a collection of topics.
+
+ :param topics: List of topics to subscribe.
+ :param kafkaParams: Kafka parameters.
+ :param offsets: offsets to begin at on initial startup. If no
offset is given for a
+ TopicPartition, the committed offset (if applicable) or
kafka param
+ auto.offset.reset will be used.
+ """
+ self.topics = set(topics)
+ self.kafkaParams = kafkaParams
+ self.offsets = dict() if offsets is None else offsets
+
+ def _jConsumerStrategy(self, helper):
+ jOffsets = dict([k._jTopicPartition(helper), v] for (k, v) in
self.offsets.items())
+ return helper.createSubscribe(self.topics, self.kafkaParams,
jOffsets)
+
+
+class SubscribePattern(ConsumerStrategy):
+ """
+ .. note:: Experimental
+
+ Subscribe to all topics matching specified pattern to get dynamically
assigned partitions.
+ """
+
+ def __init__(self, pattern, kafkaParams, offsets=None):
+ """
+ Subscribe to all topics matching specified pattern to get
dynamically assigned partitions.
+
+ :param pattern: pattern to subscribe to.
+ :param kafkaParams: Kafka parameters.
+ :param offsets: offsets to begin at on initial startup. If no
offset is given for a
+ TopicPartition, the committed offset (if applicable) or
kafka param
+ auto.offset.reset will be used.
+ """
+ self.pattern = pattern
+ self.kafkaParams = kafkaParams
+ self.offsets = dict() if offsets is None else offsets
+
+ def _jConsumerStrategy(self, helper):
+ jOffsets = dict([k._jTopicPartition(helper), v] for (k, v) in
self.offsets.items())
+ return helper.createSubscribePattern(self.pattern,
self.kafkaParams, jOffsets)
+
+
+class Assign(ConsumerStrategy):
+ """
+ .. note:: Experimental
+
+ Assign a fixed collection of TopicPartitions.
+ """
+
+ def __init__(self, topicPartitions, kafkaParams, offsets=None):
+ """
+ Assign a fixed collection of TopicPartitions.
+
+ :param topicPartitions: List of TopicPartitions to assign.
+ :param kafkaParams: kafka parameters.
+ :param offsets: offsets to begin at on initial startup. If no
offset is given for a
+ TopicPartition, the committed offset (if applicable) or
kafka param
+ auto.offset.reset will be used.
+ """
+ self.topicPartitions = set(topicPartitions)
+ self.kafkaParams = kafkaParams
+ self.offsets = dict() if offsets is None else offsets
+
+ def _jConsumerStrategy(self, helper):
+ jTopicPartitions = [i._jTopicPartition(helper) for i in
self.topicPartitions]
+ jOffsets = dict([k._jTopicPartition(helper), v] for (k, v) in
self.offsets.items())
+ return helper.createAssign(set(jTopicPartitions),
self.kafkaParams, jOffsets)
+
+
+class TopicPartition(object):
+ """
+ Represents a specific top and partition for Kafka.
--- End diff --
top should be topic
---
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