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])))
def createStream(ssc,
kinesisAppName,
streamName,
endpointUrl,
regionName,
initialPositionInStream,
checkpointInterval,
storageLevel=StorageLevel.MEMORY_AND_DISK_2,
awsAccessKeyId=None,
awsSecretKey=None,
decoder=utf8_decoder):
"""
Create an input stream that pulls messages from a Kinesis stream. This uses the
Kinesis Client Library (KCL) to pull messages from Kinesis.
Note: The given AWS credentials will get saved in DStream checkpoints if checkpointing is
enabled. Make sure that your checkpoint directory is secure.
:param ssc: StreamingContext object
:param kinesisAppName: Kinesis application name used by the Kinesis Client Library (KCL) to
update DynamoDB
:param streamName: Kinesis stream name
:param endpointUrl: Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
:param regionName: Name of region used by the Kinesis Client Library (KCL) to update
DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
:param initialPositionInStream: In the absence of Kinesis checkpoint info, this is the
worker's initial starting position in the stream. The
values are either the beginning of the stream per Kinesis'
limit of 24 hours (InitialPositionInStream.TRIM_HORIZON) or
the tip of the stream (InitialPositionInStream.LATEST).
:param checkpointInterval: Checkpoint interval for Kinesis checkpointing. See the Kinesis
Spark Streaming documentation for more details on the different
types of checkpoints.
:param storageLevel: Storage level to use for storing the received objects (default is
StorageLevel.MEMORY_AND_DISK_2)
:param awsAccessKeyId: AWS AccessKeyId (default is None. If None, will use
DefaultAWSCredentialsProviderChain)
:param awsSecretKey: AWS SecretKey (default is None. If None, will use
DefaultAWSCredentialsProviderChain)
:param decoder: A function used to decode value (default is utf8_decoder)
:return: A DStream object
"""
jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
jduration = ssc._jduration(checkpointInterval)
try:
# Use KinesisUtilsPythonHelper to access Scala's KinesisUtils
helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\
.loadClass("org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper")
helper = helperClass.newInstance()
jstream = helper.createStream(ssc._jssc, kinesisAppName,
streamName, endpointUrl, regionName,
initialPositionInStream, jduration,
jlevel, awsAccessKeyId, awsSecretKey)
except Py4JJavaError as e:
if 'ClassNotFoundException' in str(e.java_exception):
KinesisUtils._printErrorMsg(ssc.sparkContext)
raise e
stream = DStream(jstream, ssc, NoOpSerializer())
return stream.map(lambda v: decoder(v))
def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
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
"""
kafkaParams.update({
"zookeeper.connect": zkQuorum,
"group.id": groupId,
"zookeeper.connection.timeout.ms": "10000",
})
if not isinstance(topics, dict):
raise TypeError("topics should be dict")
jtopics = MapConverter().convert(topics, ssc.sparkContext._gateway._gateway_client)
jparam = MapConverter().convert(kafkaParams, ssc.sparkContext._gateway._gateway_client)
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, jparam, jtopics, jlevel)
except Py4JJavaError as e:
# TODO: use --jar once it also work on driver
if 'ClassNotFoundException' in str(e.java_exception):
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> ...
________________________________________________________________________________________________
""" % (ssc.sparkContext.version, ssc.sparkContext.version))
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])))
def createStream(ssc, kinesisAppName, streamName, endpointUrl, regionName,
initialPositionInStream, checkpointInterval,
storageLevel=StorageLevel.MEMORY_AND_DISK_2,
awsAccessKeyId=None, awsSecretKey=None, decoder=utf8_decoder,
stsAssumeRoleArn=None, stsSessionName=None, stsExternalId=None):
"""
Create an input stream that pulls messages from a Kinesis stream. This uses the
Kinesis Client Library (KCL) to pull messages from Kinesis.
.. note:: The given AWS credentials will get saved in DStream checkpoints if checkpointing
is enabled. Make sure that your checkpoint directory is secure.
:param ssc: StreamingContext object
:param kinesisAppName: Kinesis application name used by the Kinesis Client Library (KCL) to
update DynamoDB
:param streamName: Kinesis stream name
:param endpointUrl: Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
:param regionName: Name of region used by the Kinesis Client Library (KCL) to update
DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
:param initialPositionInStream: In the absence of Kinesis checkpoint info, this is the
worker's initial starting position in the stream. The
values are either the beginning of the stream per Kinesis'
limit of 24 hours (InitialPositionInStream.TRIM_HORIZON) or
the tip of the stream (InitialPositionInStream.LATEST).
:param checkpointInterval: Checkpoint interval for Kinesis checkpointing. See the Kinesis
Spark Streaming documentation for more details on the different
types of checkpoints.
:param storageLevel: Storage level to use for storing the received objects (default is
StorageLevel.MEMORY_AND_DISK_2)
:param awsAccessKeyId: AWS AccessKeyId (default is None. If None, will use
DefaultAWSCredentialsProviderChain)
:param awsSecretKey: AWS SecretKey (default is None. If None, will use
DefaultAWSCredentialsProviderChain)
:param decoder: A function used to decode value (default is utf8_decoder)
:param stsAssumeRoleArn: ARN of IAM role to assume when using STS sessions to read from
the Kinesis stream (default is None).
:param stsSessionName: Name to uniquely identify STS sessions used to read from Kinesis
stream, if STS is being used (default is None).
:param stsExternalId: External ID that can be used to validate against the assumed IAM
role's trust policy, if STS is being used (default is None).
:return: A DStream object
"""
jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
jduration = ssc._jduration(checkpointInterval)
try:
# Use KinesisUtilsPythonHelper to access Scala's KinesisUtils
helper = ssc._jvm.org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper()
except TypeError as e:
if str(e) == "'JavaPackage' object is not callable":
KinesisUtils._printErrorMsg(ssc.sparkContext)
raise
jstream = helper.createStream(ssc._jssc, kinesisAppName, streamName, endpointUrl,
regionName, initialPositionInStream, jduration, jlevel,
awsAccessKeyId, awsSecretKey, stsAssumeRoleArn,
stsSessionName, stsExternalId)
stream = DStream(jstream, ssc, NoOpSerializer())
return stream.map(lambda v: decoder(v))
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)
return stream.map(lambda k_v:
(keyDecoder(k_v[0]), valueDecoder(k_v[1])))
def createStream(ssc, kinesisAppName, streamName, endpointUrl, regionName,
initialPositionInStream, checkpointInterval,
storageLevel=StorageLevel.MEMORY_AND_DISK_2,
awsAccessKeyId=None, awsSecretKey=None, decoder=utf8_decoder):
"""
Create an input stream that pulls messages from a Kinesis stream. This uses the
Kinesis Client Library (KCL) to pull messages from Kinesis.
Note: The given AWS credentials will get saved in DStream checkpoints if checkpointing is
enabled. Make sure that your checkpoint directory is secure.
:param ssc: StreamingContext object
:param kinesisAppName: Kinesis application name used by the Kinesis Client Library (KCL) to
update DynamoDB
:param streamName: Kinesis stream name
:param endpointUrl: Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
:param regionName: Name of region used by the Kinesis Client Library (KCL) to update
DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
:param initialPositionInStream: In the absence of Kinesis checkpoint info, this is the
worker's initial starting position in the stream. The
values are either the beginning of the stream per Kinesis'
limit of 24 hours (InitialPositionInStream.TRIM_HORIZON) or
the tip of the stream (InitialPositionInStream.LATEST).
:param checkpointInterval: Checkpoint interval for Kinesis checkpointing. See the Kinesis
Spark Streaming documentation for more details on the different
types of checkpoints.
:param storageLevel: Storage level to use for storing the received objects (default is
StorageLevel.MEMORY_AND_DISK_2)
:param awsAccessKeyId: AWS AccessKeyId (default is None. If None, will use
DefaultAWSCredentialsProviderChain)
:param awsSecretKey: AWS SecretKey (default is None. If None, will use
DefaultAWSCredentialsProviderChain)
:param decoder: A function used to decode value (default is utf8_decoder)
:return: A DStream object
"""
jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
jduration = ssc._jduration(checkpointInterval)
try:
# Use KinesisUtilsPythonHelper to access Scala's KinesisUtils
helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\
.loadClass("org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper")
helper = helperClass.newInstance()
jstream = helper.createStream(ssc._jssc, kinesisAppName, streamName, endpointUrl,
regionName, initialPositionInStream, jduration, jlevel,
awsAccessKeyId, awsSecretKey)
except Py4JJavaError as e:
if 'ClassNotFoundException' in str(e.java_exception):
KinesisUtils._printErrorMsg(ssc.sparkContext)
raise e
stream = DStream(jstream, ssc, NoOpSerializer())
return stream.map(lambda v: decoder(v))
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])))
def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
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 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)
return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
def _toPythonDStream(ssc, jstream, bodyDecoder):
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
stream = DStream(jstream, ssc, ser)
def func(event):
headersBytes = BytesIO(event[0]) if sys.version >= "3" else StringIO(event[0])
headers = {}
strSer = UTF8Deserializer()
for i in range(0, read_int(headersBytes)):
key = strSer.loads(headersBytes)
value = strSer.loads(headersBytes)
headers[key] = value
body = bodyDecoder(event[1])
return (headers, body)
return stream.map(func)
def _toPythonDStream(ssc, jstream, bodyDecoder):
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
stream = DStream(jstream, ssc, ser)
def func(event):
headersBytes = BytesIO(event[0]) if sys.version >= "3" else StringIO(event[0])
headers = {}
strSer = UTF8Deserializer()
for i in range(0, read_int(headersBytes)):
key = strSer.loads(headersBytes)
value = strSer.loads(headersBytes)
headers[key] = value
body = bodyDecoder(event[1])
return (headers, body)
return stream.map(func)
def createStream(ssc, hostname, port, queueName, threadNum,
storageLevel=StorageLevel.MEMORY_AND_DISK_2,
decoder=utf8_decoder):
"""
Create an input stream that pulls events from Memcache.
:param ssc: StreamingContext object
:param hostname: Hostname of the slave machine to which the flume data will be sent
:param port: Port of the slave machine to which the flume data will be sent
:param storageLevel: Storage level to use for storing the received objects
:return: A DStream object
"""
jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
helper = MQUtils._get_helper(ssc._sc)
jstream = helper.createStream(ssc._jssc, hostname, port, queueName, threadNum, jlevel)
stream = DStream(jstream, ssc, NoOpSerializer())
return stream.map(lambda v: decoder(v))
def createStream(ssc, arn, destination, awsRegion, awsKey, awsKecret,
initialPosition, storageLevel, maxRecord, interval):
sc = ssc.sparkContext
java_import(
sc._gateway.jvm,
"org.apache.spark.streaming.kinesis.DynamoDBStream.DynamoDBPythonUtils"
)
DynamoDBPythonUtils = sc._gateway.jvm.DynamoDBPythonUtils
jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
jstream = DynamoDBPythonUtils.DynamoDBStream(ssc._jssc, arn,
destination, awsRegion,
awsKey, awsKecret,
initialPosition, jlevel,
maxRecord, interval)
stream = DStream(jstream, ssc, NoOpSerializer())
return stream.map(lambda record: record.decode("utf-8")
if record is not None else None)
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])))
def createStream(ssc,
zkQuorum,
groupId,
topics,
kafkaParams={},
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
"""
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):
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> ...
________________________________________________________________________________________________
""" % (ssc.sparkContext.version, ssc.sparkContext.version))
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])))
ssc = StreamingContext(sc, 5)
helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader().loadClass("com.redhat.spark.streaming.jms.JMSUtilsPythonHelper")
helper = helperClass.newInstance()
jbrokerURL = "amqp://127.0.0.1:5672"
jqueuename = "default"
jlevel = ssc._sc._getJavaStorageLevel(StorageLevel.MEMORY_AND_DISK_SER_2)
jstream = helper.createStream(ssc._jssc, jbrokerURL, jqueuename, jlevel)
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
stream = DStream(jstream, ssc, ser)
utf8_decoder = lambda s: s and s.decode('utf-8')
keyDecoder = utf8_decoder
valueDecoder = utf8_decoder
a = stream.map(lambda (k, v): (keyDecoder(k), valueDecoder(v)))
def process(rdd):
print rdd.count()
def protect(func):
def _protect(rdd):
if rdd.take(1):
func(rdd)
return _protect
a.foreachRDD(protect(process))
ssc.start()
ssc.awaitTermination()
