spark-instrumented-optimizer/python/pyspark/streaming/kafka.py

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from py4j.protocol import Py4JJavaError

from pyspark.rdd import RDD
from pyspark.storagelevel import StorageLevel
from pyspark.serializers import AutoBatchedSerializer, PickleSerializer, PairDeserializer, \
    NoOpSerializer
from pyspark.streaming import DStream
from pyspark.streaming.dstream import TransformedDStream
from pyspark.streaming.util import TransformFunction

__all__ = ['Broker', 'KafkaMessageAndMetadata', 'KafkaUtils', 'OffsetRange',
           'TopicAndPartition', '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 createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
                     storageLevel=StorageLevel.MEMORY_AND_DISK_2,
                     keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
        """
        Create an input stream that pulls messages from a Kafka Broker.

        :param ssc:  StreamingContext object
        :param zkQuorum:  Zookeeper quorum (hostname:port,hostname:port,..).
        :param groupId:  The group id for this consumer.
        :param topics:  Dict of (topic_name -> numPartitions) to consume.
                        Each partition is consumed in its own thread.
        :param kafkaParams: Additional params for Kafka
        :param storageLevel:  RDD storage level.
        :param keyDecoder:  A function used to decode key (default is utf8_decoder)
        :param valueDecoder:  A function used to decode value (default is utf8_decoder)
        :return: A DStream object
        """
        if kafkaParams is None:
            kafkaParams = dict()
        kafkaParams.update({
            "zookeeper.connect": zkQuorum,
            "group.id": groupId,
            "zookeeper.connection.timeout.ms": "10000",
        })
        if not isinstance(topics, dict):
            raise TypeError("topics should be dict")
        jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
        helper = KafkaUtils._get_helper(ssc._sc)
        jstream = helper.createStream(ssc._jssc, kafkaParams, topics, jlevel)
        ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
        stream = DStream(jstream, ssc, ser)
        return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))

    @staticmethod
    def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None,
                           keyDecoder=utf8_decoder, valueDecoder=utf8_decoder,
                           messageHandler=None):
        """
        .. note:: Experimental

        Create an input stream that directly pulls messages from a Kafka Broker and specific offset.

        This is not a receiver based Kafka input stream, it directly pulls the message from Kafka
        in each batch duration and processed without storing.

        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 topics:  list of topic_name to consume.
        :param kafkaParams: Additional params for Kafka.
        :param fromOffsets: Per-topic/partition Kafka offsets defining the (inclusive) starting
                            point of the stream.
        :param keyDecoder:  A function used to decode key (default is utf8_decoder).
        :param valueDecoder:  A function used to decode value (default is utf8_decoder).
        :param messageHandler: A function used to convert KafkaMessageAndMetadata. You can assess
                               meta using messageHandler (default is None).
        :return: A DStream object
        """
        if fromOffsets is None:
            fromOffsets = dict()
        if not isinstance(topics, list):
            raise TypeError("topics should be list")
        if not isinstance(kafkaParams, dict):
            raise TypeError("kafkaParams should be dict")

        def funcWithoutMessageHandler(k_v):
            return (keyDecoder(k_v[0]), valueDecoder(k_v[1]))

        def funcWithMessageHandler(m):
            m._set_key_decoder(keyDecoder)
            m._set_value_decoder(valueDecoder)
            return messageHandler(m)

        helper = KafkaUtils._get_helper(ssc._sc)

        jfromOffsets = dict([(k._jTopicAndPartition(helper),
                              v) for (k, v) in fromOffsets.items()])
        if messageHandler is None:
            ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
            func = funcWithoutMessageHandler
            jstream = helper.createDirectStreamWithoutMessageHandler(
                ssc._jssc, kafkaParams, set(topics), jfromOffsets)
        else:
            ser = AutoBatchedSerializer(PickleSerializer())
            func = funcWithMessageHandler
            jstream = helper.createDirectStreamWithMessageHandler(
                ssc._jssc, kafkaParams, set(topics), jfromOffsets)

        stream = DStream(jstream, ssc, ser).map(func)
        return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)

    @staticmethod
    def createRDD(sc, kafkaParams, offsetRanges, leaders=None,
                  keyDecoder=utf8_decoder, valueDecoder=utf8_decoder,
                  messageHandler=None):
        """
        .. note:: Experimental

        Create an RDD from Kafka using offset ranges for each topic and partition.

        :param sc:  SparkContext object
        :param kafkaParams: Additional params for Kafka
        :param offsetRanges:  list of offsetRange to specify topic:partition:[start, end) to consume
        :param leaders: Kafka brokers for each TopicAndPartition in offsetRanges.  May be an empty
            map, in which case leaders will be looked up on the driver.
        :param keyDecoder:  A function used to decode key (default is utf8_decoder)
        :param valueDecoder:  A function used to decode value (default is utf8_decoder)
        :param messageHandler: A function used to convert KafkaMessageAndMetadata. You can assess
                               meta using messageHandler (default is None).
        :return: An RDD object
        """
        if leaders is None:
            leaders = dict()
        if not isinstance(kafkaParams, dict):
            raise TypeError("kafkaParams should be dict")
        if not isinstance(offsetRanges, list):
            raise TypeError("offsetRanges should be list")

        def funcWithoutMessageHandler(k_v):
            return (keyDecoder(k_v[0]), valueDecoder(k_v[1]))

        def funcWithMessageHandler(m):
            m._set_key_decoder(keyDecoder)
            m._set_value_decoder(valueDecoder)
            return messageHandler(m)

        helper = KafkaUtils._get_helper(sc)

        joffsetRanges = [o._jOffsetRange(helper) for o in offsetRanges]
        jleaders = dict([(k._jTopicAndPartition(helper),
                          v._jBroker(helper)) for (k, v) in leaders.items()])
        if messageHandler is None:
            jrdd = helper.createRDDWithoutMessageHandler(
                sc._jsc, kafkaParams, joffsetRanges, jleaders)
            ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
            rdd = RDD(jrdd, sc, ser).map(funcWithoutMessageHandler)
        else:
            jrdd = helper.createRDDWithMessageHandler(
                sc._jsc, kafkaParams, joffsetRanges, jleaders)
            rdd = RDD(jrdd, sc).map(funcWithMessageHandler)

        return KafkaRDD(rdd._jrdd, sc, rdd._jrdd_deserializer)

    @staticmethod
    def _get_helper(sc):
        try:
            return sc._jvm.org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper()
        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-0-8:%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-0-8-assembly, Version = %s.
     Then, include the jar in the spark-submit command as

     $ bin/spark-submit --jars <spark-streaming-kafka-0-8-assembly.jar> ...

________________________________________________________________________________________________

""" % (sc.version, sc.version))


class OffsetRange(object):
    """
    Represents a range of offsets from a single Kafka TopicAndPartition.
    """

    def __init__(self, topic, partition, fromOffset, untilOffset):
        """
        Create an OffsetRange to represent range of offsets
        :param topic: Kafka topic name.
        :param partition: Kafka partition id.
        :param fromOffset: Inclusive starting offset.
        :param untilOffset: Exclusive ending offset.
        """
        self.topic = topic
        self.partition = partition
        self.fromOffset = fromOffset
        self.untilOffset = untilOffset

    def __eq__(self, other):
        if isinstance(other, self.__class__):
            return (self.topic == other.topic
                    and self.partition == other.partition
                    and self.fromOffset == other.fromOffset
                    and self.untilOffset == other.untilOffset)
        else:
            return False

    def __ne__(self, other):
        return not self.__eq__(other)

    def __str__(self):
        return "OffsetRange(topic: %s, partition: %d, range: [%d -> %d]" \
               % (self.topic, self.partition, self.fromOffset, self.untilOffset)

    def _jOffsetRange(self, helper):
        return helper.createOffsetRange(self.topic, self.partition, self.fromOffset,
                                        self.untilOffset)


class TopicAndPartition(object):
    """
    Represents a specific topic and partition for Kafka.
    """

    def __init__(self, topic, partition):
        """
        Create a Python TopicAndPartition to map to the Java related object
        :param topic: Kafka topic name.
        :param partition: Kafka partition id.
        """
        self._topic = topic
        self._partition = partition

    def _jTopicAndPartition(self, helper):
        return helper.createTopicAndPartition(self._topic, self._partition)

    def __eq__(self, other):
        if isinstance(other, self.__class__):
            return (self._topic == other._topic
                    and self._partition == other._partition)
        else:
            return False

    def __ne__(self, other):
        return not self.__eq__(other)

    def __hash__(self):
        return (self._topic, self._partition).__hash__()


class Broker(object):
    """
    Represent the host and port info for a Kafka broker.
    """

    def __init__(self, host, port):
        """
        Create a Python Broker to map to the Java related object.
        :param host: Broker's hostname.
        :param port: Broker's port.
        """
        self._host = host
        self._port = port

    def _jBroker(self, helper):
        return helper.createBroker(self._host, self._port)


class KafkaRDD(RDD):
    """
    A Python wrapper of KafkaRDD, to provide additional information on normal RDD.
    """

    def __init__(self, jrdd, ctx, jrdd_deserializer):
        RDD.__init__(self, jrdd, ctx, jrdd_deserializer)

    def offsetRanges(self):
        """
        Get the OffsetRange of specific KafkaRDD.
        :return: A list of OffsetRange
        """
        helper = KafkaUtils._get_helper(self.ctx)
        joffsetRanges = helper.offsetRangesOfKafkaRDD(self._jrdd.rdd())
        ranges = [OffsetRange(o.topic(), o.partition(), o.fromOffset(), o.untilOffset())
                  for o in joffsetRanges]
        return ranges


class KafkaDStream(DStream):
    """
    A Python wrapper of KafkaDStream
    """

    def __init__(self, jdstream, ssc, jrdd_deserializer):
        DStream.__init__(self, jdstream, ssc, jrdd_deserializer)

    def foreachRDD(self, func):
        """
        Apply a function to each RDD in this DStream.
        """
        if func.__code__.co_argcount == 1:
            old_func = func
            func = lambda r, rdd: old_func(rdd)
        jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer) \
            .rdd_wrapper(lambda jrdd, ctx, ser: KafkaRDD(jrdd, ctx, ser))
        api = self._ssc._jvm.PythonDStream
        api.callForeachRDD(self._jdstream, jfunc)

    def transform(self, func):
        """
        Return a new DStream in which each RDD is generated by applying a function
        on each RDD of this DStream.

        `func` can have one argument of `rdd`, or have two arguments of
        (`time`, `rdd`)
        """
        if func.__code__.co_argcount == 1:
            oldfunc = func
            func = lambda t, rdd: oldfunc(rdd)
        assert func.__code__.co_argcount == 2, "func should take one or two arguments"

        return KafkaTransformedDStream(self, func)


class KafkaTransformedDStream(TransformedDStream):
    """
    Kafka specific wrapper of TransformedDStream to transform on Kafka RDD.
    """

    def __init__(self, prev, func):
        TransformedDStream.__init__(self, prev, func)

    @property
    def _jdstream(self):
        if self._jdstream_val is not None:
            return self._jdstream_val

        jfunc = TransformFunction(self._sc, self.func, self.prev._jrdd_deserializer) \
            .rdd_wrapper(lambda jrdd, ctx, ser: KafkaRDD(jrdd, ctx, ser))
        dstream = self._sc._jvm.PythonTransformedDStream(self.prev._jdstream.dstream(), jfunc)
        self._jdstream_val = dstream.asJavaDStream()
        return self._jdstream_val


class KafkaMessageAndMetadata(object):
    """
    Kafka message and metadata information. Including topic, partition, offset and message
    """

    def __init__(self, topic, partition, offset, key, message):
        """
        Python wrapper of Kafka MessageAndMetadata
        :param topic: topic name of this Kafka message
        :param partition: partition id of this Kafka message
        :param offset: Offset of this Kafka message in the specific partition
        :param key: key payload of this Kafka message, can be null if this Kafka message has no key
                    specified, the return data is undecoded bytearry.
        :param message: actual message payload of this Kafka message, the return data is
                        undecoded bytearray.
        """
        self.topic = topic
        self.partition = partition
        self.offset = offset
        self._rawKey = key
        self._rawMessage = message
        self._keyDecoder = utf8_decoder
        self._valueDecoder = utf8_decoder

    def __str__(self):
        return "KafkaMessageAndMetadata(topic: %s, partition: %d, offset: %d, key and message...)" \
               % (self.topic, self.partition, self.offset)

    def __repr__(self):
        return self.__str__()

    def __reduce__(self):
        return (KafkaMessageAndMetadata,
                (self.topic, self.partition, self.offset, self._rawKey, self._rawMessage))

    def _set_key_decoder(self, decoder):
        self._keyDecoder = decoder

    def _set_value_decoder(self, decoder):
        self._valueDecoder = decoder

    @property
    def key(self):
        return self._keyDecoder(self._rawKey)

    @property
    def message(self):
        return self._valueDecoder(self._rawMessage)