Source code for pyspark.streaming.kafka
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from py4j.java_gateway import Py4JJavaError
from pyspark.rdd import RDD
from pyspark.storagelevel import StorageLevel
from pyspark.serializers import PairDeserializer, NoOpSerializer
from pyspark.streaming import DStream
from pyspark.streaming.dstream import TransformedDStream
from pyspark.streaming.util import TransformFunction
__all__ = ['Broker', 'KafkaUtils', 'OffsetRange', 'TopicAndPartition', 'utf8_decoder']
[docs]def utf8_decoder(s):
""" Decode the unicode as UTF-8 """
if s is None:
return None
return s.decode('utf-8')
[docs]class KafkaUtils(object):
@staticmethod
[docs] def createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
storageLevel=StorageLevel.MEMORY_AND_DISK_SER_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)
try:
# Use KafkaUtilsPythonHelper to access Scala's KafkaUtils (see SPARK-6027)
helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\
.loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper")
helper = helperClass.newInstance()
jstream = helper.createStream(ssc._jssc, kafkaParams, topics, jlevel)
except Py4JJavaError as e:
# TODO: use --jar once it also work on driver
if 'ClassNotFoundException' in str(e.java_exception):
KafkaUtils._printErrorMsg(ssc.sparkContext)
raise e
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
stream = DStream(jstream, ssc, ser)
return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
@staticmethod
[docs] def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None,
keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
"""
.. 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).
: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")
try:
helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
.loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper")
helper = helperClass.newInstance()
jfromOffsets = dict([(k._jTopicAndPartition(helper),
v) for (k, v) in fromOffsets.items()])
jstream = helper.createDirectStream(ssc._jssc, kafkaParams, set(topics), jfromOffsets)
except Py4JJavaError as e:
if 'ClassNotFoundException' in str(e.java_exception):
KafkaUtils._printErrorMsg(ssc.sparkContext)
raise e
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
stream = DStream(jstream, ssc, ser) \
.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)
@staticmethod
[docs] def createRDD(sc, kafkaParams, offsetRanges, leaders=None,
keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
"""
.. note:: Experimental
Create a 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)
:return: A 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")
try:
helperClass = sc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
.loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper")
helper = helperClass.newInstance()
joffsetRanges = [o._jOffsetRange(helper) for o in offsetRanges]
jleaders = dict([(k._jTopicAndPartition(helper),
v._jBroker(helper)) for (k, v) in leaders.items()])
jrdd = helper.createRDD(sc._jsc, kafkaParams, joffsetRanges, jleaders)
except Py4JJavaError as e:
if 'ClassNotFoundException' in str(e.java_exception):
KafkaUtils._printErrorMsg(sc)
raise e
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
rdd = RDD(jrdd, sc, ser).map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
return KafkaRDD(rdd._jrdd, rdd.ctx, rdd._jrdd_deserializer)
@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:%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-assembly, Version = %s.
Then, include the jar in the spark-submit command as
$ bin/spark-submit --jars <spark-streaming-kafka-assembly.jar> ...
________________________________________________________________________________________________
""" % (sc.version, sc.version))
[docs]class OffsetRange(object):
"""
Represents a range of offsets from a single Kafka TopicAndPartition.
"""
def __init__(self, topic, partition, fromOffset, untilOffset):
"""
Create a 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)
[docs]class TopicAndPartition(object):
"""
Represents a specific top 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)
[docs]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
"""
try:
helperClass = self.ctx._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
.loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper")
helper = helperClass.newInstance()
joffsetRanges = helper.offsetRangesOfKafkaRDD(self._jrdd.rdd())
except Py4JJavaError as e:
if 'ClassNotFoundException' in str(e.java_exception):
KafkaUtils._printErrorMsg(self.ctx)
raise e
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
