def main(ssc):
zkQuorum, topic = sys.argv[1:]
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 1})
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()
def stream(ssc):
zkQuorum = "localhost:2181"
topic = "topic1"
tweets = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 1})
kstream = KafkaUtils.createDirectStream(ssc, topics = ['topic1'], kafkaParams = {"metadata.broker.list":"localhost:9092"})
tweets = tweets.map(lambda x: x[1].encode("ascii","ignore"))
return tweets
def main():
# Create a local StreamingContext with two working thread and batch interval of 5 second
sc = SparkContext("spark://ip-172-31-29-29:7077", "MyKafkaStream")
# stream interval of 5 seconds
ssc = StreamingContext(sc, 5)
kafkaStream = KafkaUtils.createStream(ssc, "52.3.61.194:2181", "GroupNameDoesntMatter", {"parking_sensor_data": 2})
messages = kafkaStream.flatMap(lambda s: create_tuple(s[1])).reduceByKey(lambda a,b: (int(a)+int(b))/2)
messages1 = messages.filter(lambda s: s[1] > 0)
messages1.pprint()
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
def bro_parse(zk,topic,db,db_table,num_of_workers):
app_name = "ONI-INGEST-{0}".format(topic)
wrks = int(num_of_workers)
# create spark context
sc = SparkContext(appName=app_name)
ssc = StreamingContext(sc,1)
sqc = HiveContext(sc)
# create DStream for each topic partition.
topic_dstreams = [ KafkaUtils.createStream(ssc, zk, app_name, {topic: 1}, keyDecoder=oni_decoder, valueDecoder=oni_decoder) for _ in range (wrks) ]
tp_stream = ssc.union(*topic_dstreams)
# Parallelism in Data Processing
#processingDStream = tp_stream(wrks)
# parse the RDD content.
proxy_logs = tp_stream.map(lambda x: proxy_parser(x[1]))
# save RDD into hive .
proxy_logs.foreachRDD(lambda x: save_to_hive(x,sqc,db,db_table,topic))
ssc.start()
ssc.awaitTermination()
def main():
if len(sys.argv) != 4:
print("Usage: kafka_wordcount.py <zk> <topic> <timeout>",
file=sys.stderr)
exit(-1)
sc = SparkContext(appName="PythonStreamingKafkaWordCount")
ssc = StreamingContext(sc, 1)
timeout = None
if len(sys.argv) == 4:
zk, topic, timeout = sys.argv[1:]
timeout = int(timeout)
else:
zk, topic = sys.argv[1:]
kvs = KafkaUtils.createStream(
ssc, zk, "spark-streaming-consumer", {topic: 1})
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()
kwargs = {}
if timeout:
kwargs['timeout'] = timeout
ssc.start()
ssc.awaitTermination(**kwargs)
def main():
conf = SparkConf().setAppName("kafka_source_mongo_sink_pymongo_filtered")
sc = SparkContext(conf=conf)
ssc = StreamingContext(sc, 1)
try:
kafka_streams = KafkaUtils.createStream(ssc, "localhost:2181", "spark-streaming-consumer", {"splash_json": 2})
kafka_streams.foreachRDD(process_rdd)
except Exception as e:
print e
ssc.start()
ssc.awaitTermination()
def test_kafka_stream(self):
"""Test the Python Kafka stream API."""
topic = self._randomTopic()
sendData = {"a": 3, "b": 5, "c": 10}
self._kafkaTestUtils.createTopic(topic)
self._kafkaTestUtils.sendMessages(topic, sendData)
stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(),
"test-streaming-consumer", {topic: 1},
{"auto.offset.reset": "smallest"})
self._validateStreamResult(sendData, stream)
def main(ssc):
zkQuorum, topic = sys.argv[1:]
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 2})
lines = kvs.map(lambda x: x[1])
# Convert RDDs of the words DStream to DataFrame and run SQL query
def process(time, rdd):
print("========= %s =========" % str(time))
try:
# Get the singleton instance of SQLContext
sqlContext = getSqlContextInstance(rdd.context)
# Convert RDD[String] to RDD[Row] to DataFrame
parts = rdd.map(lambda line: line.split(","))
delays_rdd= parts.map(lambda w: Row(carrier=w[0], origin=w[1], delay=float(w[2])))
delays = sqlContext.createDataFrame(delays_rdd, samplingRatio=1)
avg_delays = delays.groupBy("origin", "carrier").agg(F.avg(delays.delay).alias('average'))
avg_delays.write.format("org.apache.spark.sql.cassandra").\
options(table="task2_part2_group2_1", keyspace="mykeyspace").\
save(mode="append")
# Register as table
#dataFrame.registerTempTable("origin_carrier_delays")
# Do word count on table using SQL and print it
#carrier_delays_df = \
# sqlContext.sql("SELECT origin, carrier, avg(delay) AS average FROM origin_carrier_delays GROUP BY origin, carrier")
#carrier_delays_df.registerTempTable("origin_carrier_avg_delays")
#carrier_avg_delays_df = \
# sqlContext.sql("SELECT origin, carrier, avg_delay FROM origin_carrier_avg_delays GROUP BY origin ORDER BY avg_delay LIMIT 10")
#for i in carrier_delays_df.rdd.takeOrderedByKey(10, sortValue=lambda x: x[2], reverse=False).map(lambda x: x[1]).collect():
# print (i)
#dataFrame.select("origin", "carrier", "delay").write \
#carrier_delays_df.write \
# .format("org.apache.spark.sql.cassandra") \
# .options( table = "task2_part2_group2_1", keyspace = "mykeyspace") \
# .save(mode="append")
#carrier_delays_df.show()
except Exception as e: print (e)
#except:
# pass
#data = lines.map(lambda line: line.split(",")) \
# .map(lambda word: (word[0], float(word[1])) ) \
# .aggregateByKey((0,0), lambda a,b: (a[0] + b, a[1] + 1), lambda a,b: (a[0] + b[0], a[1] + b[1])) \
# .mapValues(lambda v: v[0]/v[1]) \
# .updateStateByKey(updateFunc) \
# .transform(lambda rdd: rdd.sortBy(lambda (word, count): -count))
#data.pprint()
lines.foreachRDD(process)
ssc.start()
ssc.awaitTermination()
def main(ssc):
zkQuorum, topic = sys.argv[1:]
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 1})
lines = kvs.map(lambda x: x[1])
data = lines.map(lambda line: line.split(",")) \
.flatMap(lambda word: [(word[0], 1), (word[1], 1)]) \
.reduceByKey(lambda a, b: a+b) \
.updateStateByKey(updateFunc) \
.transform(lambda rdd: rdd.sortBy(lambda (word, count): -count))
data.pprint()
ssc.start()
ssc.awaitTermination()
def main():
sc = SparkContext(appName="PythonStreamingKafkaWordCount")
ssc = StreamingContext(sc, 1)
zkQuorum = "localhost:2181"
topic = "twitter_raw"
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 1})
lines = kvs.map(lambda x: pickle.loads(x[1].decode("utf-8"))["text"]) # fetch the text
count = lines.map(lambda line: len(line.split())).reduce(add) # split into words and count
count.foreachRDD(publishToRedis) # publish to redis
count.pprint()
ssc.start()
ssc.awaitTermination()
def ss_kafka_bucket_counter(broker, topic, bucket_interval, output_msg, message_parse, valueDecoder=None):
"""Starts a Spark Streaming job from a Kafka input and parses message time
Args:
broker: the kafka broker that we look at for the topic
topic: the kafka topic for input
bucket_interval: the time interval in seconds (int) that the job will
bucket
output_msg: a function that takes in a spark SparkContext (sc) and
StreamingContext (ssc) and returns a function that takes a rdd that
performs the output task
message_parse: how the message is to be parsed
valueDecoder: same as Spark's valueDecoder
Returns:
None
"""
sc = SparkContext(appName="PythonKafkaBucketCounter")
ssc = StreamingContext(sc, bucket_interval + 5)
if valueDecoder:
kvs = KafkaUtils.createStream(ssc, broker, "spark-streaming-consumer", {topic: 1}, valueDecoder=valueDecoder)
else:
kvs = KafkaUtils.createStream(ssc, broker, "spark-streaming-consumer", {topic: 1})
# I assume that we do not store kafka keys
lines = kvs.map(lambda x: x[1])
interval_counts = lines.map(lambda line: (message_parse(line), 1)).reduceByKey(lambda a, b: a + b)
output_msg_func = output_msg(sc, ssc)
interval_counts.foreachRDD(output_msg_func)
ssc.start()
ssc.awaitTermination()
def consume(self):
messages = KafkaUtils.createStream(self.ssc, self.zookeeper, "spark-streaming-consumer", {self.topic: 1})
lines = messages.map(lambda x: x[1])
rows = lines.map(lambda x: {
"data": json.loads(x)['data'],
"time": json.loads(x)['time']
})
rows.foreachRDD(lambda x: {
self.check_and_write(x)
})
self.ssc.start()
self.ssc.awaitTermination()
def main():
#main function to execute code
sqlContext = SQLContext(sc)
zk_host = zk_ip+":2181"
consumer_group = "reading-consumer-group"
kafka_partitions={topic:1}
#create kafka stream
kvs = KafkaUtils.createStream(ssc,zk_host,consumer_group,kafka_partitions,valueDecoder=decoder)
lines = kvs.map(lambda x: x[1])
readings = lines.map(lambda x: Row(device_id=x["device_id"],\
metric_time=datetime.datetime.fromtimestamp(int(x["metric_time"])),\
metric_name=x["metric_name"],\
metric_value=float(x["metric_value"])))
readings.foreachRDD(process)
ssc.start()
ssc.awaitTermination()
def bluecoat_parse(zk,topic,db,db_table,num_of_workers,batch_size):
app_name = topic
wrks = int(num_of_workers)
# create spark context
sc = SparkContext(appName=app_name)
ssc = StreamingContext(sc,int(batch_size))
sqc = HiveContext(sc)
tp_stream = KafkaUtils.createStream(ssc, zk, app_name, {topic: wrks}, keyDecoder=spot_decoder, valueDecoder=spot_decoder)
proxy_data = tp_stream.map(lambda row: row[1]).flatMap(lambda row: row.split("\n")).filter(lambda row: rex_date.match(row)).map(lambda row: row.strip("\n").strip("\r").replace("\t", " ").replace(" ", " ")).map(lambda row: split_log_entry(row)).map(lambda row: proxy_parser(row))
saved_data = proxy_data.foreachRDD(lambda row: save_data(row,sqc,db,db_table,topic))
ssc.start();
ssc.awaitTermination()
def readSource(ssc, di_in_conf_with_ds_conf, app_conf):
sourceType = di_in_conf_with_ds_conf['source.type']
if sourceType == 'kafka':
kafkaSimpleConsumerApiUsed = app_conf.get('kafka.simple.consumer.api.used', True)
if kafkaSimpleConsumerApiUsed:
topics = di_in_conf_with_ds_conf['topics']
if not isinstance(topics, list):
raise TypeError("topic should be list")
brokers = di_in_conf_with_ds_conf['metadata.broker.list']
kafkaParams = {"metadata.broker.list": brokers}
stream = KafkaUtils.createDirectStream(ssc, topics, kafkaParams).map(lambda x: x[1])
else:
zkConnect = di_in_conf_with_ds_conf['zookeeper.connect']
groupId = app_conf['group.id']
numReceivers = app_conf.get('num.receivers', 1)
numConsumerFetchers = app_conf.get('num.consumer.fetchers')
topics = di_in_conf_with_ds_conf['topics']
topic_map = dict(zip(topics, numConsumerFetchers))
# streams = reduce(lambda x, y: x.union(y),
# map(KafkaUtils.createStream(ssc, zkConnect, groupId, topic_map),
# range(0, numReceivers)))
streams = [KafkaUtils.createStream(ssc, zkConnect, groupId, topic_map) for i in range(0, numReceivers)]
stream = ssc.union(streams).map(lambda x: x[1])
elif sourceType == 'hdfs':
path = di_in_conf_with_ds_conf['fs.defaultFS'] + '/' + di_in_conf_with_ds_conf['path']
stream = ssc.textFilesStream(path)
else:
raise Exception('Error: unsupported source.type = ' + sourceType)
num_repartition = app_conf.get('dataInterface.stream.repatition.partitions')
if num_repartition is None or not isinstance(num_repartition, int):
stream2 = stream
else:
stream2 = stream.repartition(num_repartition)
# 是否使用格式化插件类格式化
format_class_path = di_in_conf_with_ds_conf.get('format.class', '')
if format_class_path.strip() == '':
stream3 = stream2
else:
format_class_obj = get_class_obj(format_class_path)
stream3 = format_class_obj.format(stream2)
return stream3
def test_kafka_stream(self):
"""Test the Python Kafka stream API."""
topic = "topic1"
sendData = {"a": 3, "b": 5, "c": 10}
self._kafkaTestUtils.createTopic(topic)
self._kafkaTestUtils.sendMessages(topic, sendData)
stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(),
"test-streaming-consumer", {topic: 1},
{"auto.offset.reset": "smallest"})
result = {}
for i in chain.from_iterable(self._collect(stream.map(lambda x: x[1]),
sum(sendData.values()))):
result[i] = result.get(i, 0) + 1
self.assertEqual(sendData, result)
def main():
#main function to execute code
sc = SparkContext(appName="ReadingWriter")
ssc = StreamingContext(sc,10)
sqlContext = SQLContext(sc)
zk_host = zk_ip+":2181"
consumer_group = "reading-consumer-group"
kafka_partitions={"amtest":1}
#create kafka stream
kvs = KafkaUtils.createStream(ssc,zk_host,consumer_group,kafka_partitions,valueDecoder=decoder)
lines = kvs.map(lambda x: x[1])
#readings = lines.map(lambda x: {"device_id":x["device_id"],"metric_time":x["metric_time"],"metric_name":x["metric_name"],"metric_value":x["metric_value"]})
readings = lines.map(lambda x: {"device_id":x["device_id"],"metric_time":datetime.datetime.fromtimestamp(int(x["metric_time"])),"metric_name":x["metric_name"],"metric_value":float(x["metric_value"])})
readings.foreachRDD(lambda rdd: rdd.saveToCassandra("metrics", "raw_metrics"))
#readingdf.show()
#readings.pprint()
#lines.saveToCassandra("metrics", "raw_metrics")
ssc.start()
ssc.awaitTermination()
def main(self):
# loading configuration parameters (from a config file when working on a project)
zk, topic, app_name, batch_duration, master = self.setConfiguration()
# initiate the spark context / streaming context
conf = (SparkConf().setMaster(master))
sc = SparkContext(appName=app_name, conf=conf)
ssc = StreamingContext(sc, batch_duration)
# reading data to kafka
kvs = KafkaUtils.createStream(ssc, zk, "spark-streaming-consumer",
{topic: 1})
lines = kvs.map(lambda x: x[1])
lines.pprint()
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
sc.close()
def start():
# sc = SparkContext(appName='txt', conf=sconf)
sc = SparkContext("spark://192.168.1.148:7077", "NetworkWordCount")
ssc = StreamingContext(sc, 3)
brokers = "192.168.1.148:2181"
topic = 'taimei'
user_data = KafkaUtils.createStream(ssc, brokers,
"spark-streaming-consumer", {topic: 1})
# fromOffsets 设置从起始偏移量消费
# user_data = KafkaUtils.createDirectStream(ssc,[topic],kafkaParams={"metadata.broker.list":brokers},fromOffsets={TopicAndPartition(topic,partition):long(start)})
user_fields = user_data.map(lambda line: line[1].split('|'))
gender_users = user_fields.map(lambda fields: fields[3]).map(
lambda gender: (gender, 1)).reduceByKey(lambda a, b: a + b)
# user_data.foreachRDD(offset) # 存储offset信息
print("---------")
gender_users.pprint()
gender_users.foreachRDD(lambda rdd: rdd.foreach(echo)) # 返回元组
ssc.start()
ssc.awaitTermination()
def main():
#main function to execute code
sqlContext = SQLContext(sc)
zk_host = zk_ip + ":2181"
consumer_group = "reading-consumer-group"
kafka_partitions = {topic: 1}
#create kafka stream
kvs = KafkaUtils.createStream(ssc,
zk_host,
consumer_group,
kafka_partitions,
valueDecoder=decoder)
lines = kvs.map(lambda x: x[1])
readings = lines.map(lambda x: Row(device_id=x["device_id"],\
metric_time=datetime.datetime.fromtimestamp(int(x["metric_time"])),\
metric_name=x["metric_name"],\
metric_value=float(x["metric_value"])))
readings.foreachRDD(process)
ssc.start()
ssc.awaitTermination()
def __init__(self, config):
self._server = config.content["input"]["options"]["server"]
self._port = config.content["input"]["options"]["port"]
self._topic = config.content["input"]["options"]["topic"]
self._consumer_group = config.content["input"]["options"][
"consumer_group"]
self._batchDuration = config.content["input"]["options"][
"batchDuration"]
self._sep = config.content["input"]["options"]["sep"]
self._spark = SparkSession.builder.appName(
"StreamingDataKafka").getOrCreate()
sc = self._spark.sparkContext
sc.addFile(config.content["databases"]["country"])
sc.addFile(config.content["databases"]["city"])
sc.addFile(config.content["databases"]["asn"])
self._ssc = StreamingContext(sc, self._batchDuration)
list_conversion_function = list(
(map(lambda x: type_to_func(x.dataType),
config.data_structure_pyspark)))
ranked_pointer = list(enumerate(list_conversion_function))
functions_list = list(
map(lambda x: lambda list_string: x[1](list_string[x[0]]),
ranked_pointer))
function_convert = lambda x: list(
map(lambda func: func(x), functions_list))
try:
dstream = KafkaUtils.createStream(
self._ssc, "{0}:{1}".format(self._server, self._port),
self._consumer_group, {self._topic: 1})
self._dstream = dstream.map(
lambda x: function_convert(x[1].split(",")))
except:
raise KafkaConnectError(
"Kafka error: Connection refused: server={} port={} consumer_group={} topic={}"
.format(self._server, self._port, self._consumer_group,
self._topic))
def main():
#main function to execute code
sc = SparkContext(appName="CouponCounterPySpark")
ssc = StreamingContext(sc,10)
zk_host = "localhost:2181"
consumer_group = "coupon-event-consumers"
kafka_partitions={"test":1}
#create kafka stream
lines = KafkaUtils.createStream(ssc,zk_host,consumer_group,kafka_partitions)
events = lines.map(lambda line: line[1].split(','))
tmpagg = events.map(lambda event: ((event[1]),1) )
coupon_counts = tmpagg.reduceByKey(lambda x,y: x+y)
coupon_records = coupon_counts.map(lambda x: {"offer_id" : x[0], "bucket" : str(datetime.datetime.now().strftime("%s")), "count" : int(x[1])})
#coupon_records.pprint()
#coupon_records.registerTempTable("coupon_counters")
#coupon_records.select("offer_id","bucket","count").show()
#coupon_records = coupon_counts.map(lambda record: {"offer_id" : record[0],"bucket" : str(int(datetime.datetime.now().strftime("%s"))*1000),"count" : int(record[1])}
coupon_records.pprint()
coupon_records.foreachRDD(lambda rdd: rdd.saveToCassandra("loyalty","coupon_counters"))
ssc.start()
ssc.awaitTermination()
def createContext(zkQuorum, topic):
# If you do not see this printed, that means the StreamingContext has been loaded
# from the new checkpoint
print("Creating new context")
sc = SparkContext(appName="PythonStreamingRecoverableNetworkWordCount")
ssc = StreamingContext(sc, 1)
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer",
{topic: 1})
lines = kvs.map(lambda x: json.loads(x[1]))
#lines.pprint()
# hard one
#pairs=lines.map(lambda x: (x['uid'],x['topic']))
# simple one
pairs = lines.map(lambda x: (x['uid'], 1))
#pairs.pprint()
windowedWordCounts = pairs.reduceByKeyAndWindow(lambda x, y: x + y,
lambda x, y: x - y, 30, 10)
windowedWordCounts.pprint()
return ssc
def main():
global topic
topic = "topic_name"
global errortopic
errortopic = 'error_topic_data'
sc = CreateSparkContext()
ssc = StreamingContext(sc, 10)
try:
kafka_stream = KafkaUtils.createStream(ssc, "192.168.0.1:2181", "spark-streaming-consumer", {topic:12})
raw = kafka_stream.flatMap(lambda kafkaS: [kafkaS])
lines = raw.flatMap(lambda xs: xs[1].split(","))
counts = lines.map(lambda word: (str(datetime.now()), "api", word))
counts.foreachRDD(lambda k: saveToCassandra(k, sc, ssc))
except Exception, e:
print('error :'+str(e))
def main():
conf = SparkConf().setAppName("pyspark read")
sc = SparkContext(conf=conf)
ssc = StreamingContext(sc, 1)
kafkaStream = KafkaUtils.createStream(ssc, "localhost:2181", "spark-streaming-consumer", {"splash_json": 2})
stream = kafkaStream.map(lambda xs:xs)
stream.foreachRDD(lambda rdd: rdd.foreach(printRdd))
# stream.foreachRDD(lambda rdd: rdd.saveToMongodb(mongodb_uri))
# stream.pprint()
# filter out flights not departing from United States
# "depAirportCntry": "United States"
# messages = kafkaStream.map(lambda xs: json.load(xs))
# jsonmessages = messages.map(lambda x: json.loads(x))
# usdepartures = jsonmessages.map(lambda x: x['depAirportCntry'].filter(lambda x: "United States" in x))\
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
def test_kafka_stream(self):
"""Test the Python Kafka stream API."""
topic = "topic1"
sendData = {"a": 3, "b": 5, "c": 10}
jSendData = MapConverter().convert(
sendData, self.ssc.sparkContext._gateway._gateway_client)
self._kafkaTestUtils.createTopic(topic)
self._kafkaTestUtils.sendMessages(topic, jSendData)
stream = KafkaUtils.createStream(self.ssc,
self._kafkaTestUtils.zkAddress(),
"test-streaming-consumer", {topic: 1},
{"auto.offset.reset": "smallest"})
result = {}
for i in chain.from_iterable(
self._collect(stream.map(lambda x: x[1]),
sum(sendData.values()))):
result[i] = result.get(i, 0) + 1
self.assertEqual(sendData, result)
def KafkaWordCount(zkQuorum, group, topics, numThreads):
spark_conf = SparkConf().setAppName("KafkaWordCount")
sc = SparkContext(conf=spark_conf)
sc.setLogLevel("ERROR")
ssc = StreamingContext(sc, 1)
#ssc.checkpoint("file:///usr/local/spark/checkpoint")
# 这里表示把检查点文件写入分布式文件系统HDFS，所以要启动Hadoop
ssc.checkpoint(".")
topicAry = topics.split(",")
# 将topic转换为hashmap形式，而python中字典就是一种hashmap
topicMap = {}
for topic in topicAry:
topicMap[topic] = numThreads
lines = KafkaUtils.createStream(ssc, zkQuorum, group,
topicMap).map(lambda x: x[1])
words = lines.flatMap(lambda x: x.split(" "))
wordcount = words.map(lambda x: (x, 1)).reduceByKeyAndWindow(
(lambda x, y: x + y), (lambda x, y: x - y), 1, 1, 1)
wordcount.foreachRDD(lambda x: sendmsg(x))
wordcount.pprint()
ssc.start()
ssc.awaitTermination()
def main():
zkQuorum = "localhost:2181"
topic = "meetup-rsvps-topic"
sc = SparkContext("local[*]")
sc.setLogLevel("ERROR")
ssc = StreamingContext(sc, BATCH_DUR) # 5 sec batch duration
# utf-8 text stream from kafka
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark_consumer", {topic: 1}).cache()
# recent top N event stream
event_count = kvs.map(extract_event_count).filter(lambda line: line is not None)
event_count.reduceByKeyAndWindow(func=lambda x,y:x+y,
invFunc=lambda x,y:x-y,
windowDuration=WIN_DUR,
slideDuration=SLIDE_DUR) \
.filter(lambda pair: pair[1] > 0) \
.transform(take_top_rdd) \
.map(lambda pair: (pair[0][1], pair[1])) \
.foreachRDD(lambda rdd: rdd.foreachPartition(send_recent_top))
# running response count stream
response_count = kvs.map(extract_response).filter(lambda line: line is not None)
# TODO: may use countByValueAndWindow instead of updateStateByKey
response_count.updateStateByKey(update_count) \
.foreachRDD(lambda rdd: rdd.foreachPartition(send_response_count))
# count recent rsvps
rsvp_count = kvs.countByWindow(windowDuration=WIN_DUR, slideDuration=SLIDE_DUR) \
.foreachRDD(lambda rdd: rdd.foreachPartition(send_rsvp_count))
# event_count.pprint()
ssc.checkpoint("rsvps_checkpoint_dir")
ssc.start()
ssc.awaitTermination()
def initialize_and_parse_input_stream(input_zookeeper, input_topic, microbatch_duration):
"""
Initialize spark context, streaming context, input DStream and parse json from DStream.
:param input_zookeeper: input zookeeper hostname:port
:param input_topic: input kafka topic
:param microbatch_duration: duration of micro batches in seconds
:return sc, ssc, parsed_stream: initialized spark and streaming context, and json with data from DStream
"""
# Application name used as identifier
application_name = os.path.basename(sys.argv[0])
# Spark context initialization
sc = SparkContext(appName=application_name + ' ' + ' '.join(sys.argv[1:])) # Application name used as the appName
ssc = StreamingContext(sc, microbatch_duration)
# Initialize input DStream of flows from specified Zookeeper server and Kafka topic
input_stream = KafkaUtils.createStream(ssc, input_zookeeper, 'spark-consumer-' + application_name + str(time.time()),
{input_topic: 1})
# Parse input stream in the json format
parsed_stream = input_stream.map(lambda line: json.loads(line[1]))
return sc, ssc, parsed_stream
def streaming_profile_toDB(topic, re_table, conn, attributes, rule, job_type):
sc = spark.sparkContext
ssc = StreamingContext(sc, 5)
numThread = 3
print 'in streming profile to db'
print zkQuorum
print topic
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer",
{topic: numThread})
if job_type == 'accuracy':
print 'here in accuracy'
kvs.foreachRDD(lambda t, rdd: streaming_accuracy(
t, rdd, topic, re_table, conn, attributes, rule))
elif job_type == 'profile':
if rule == 'profile':
kvs.foreachRDD(lambda t, rdd: streaming_profile(
t, rdd, topic, re_table, conn, attributes))
else:
kvs.foreachRDD(lambda t, rdd: streaming_user_define_profile(
t, rdd, topic, re_table, conn, attributes, rule))
ssc.start()
ssc.awaitTermination()
def main():
conf = SparkConf().setAppName("pyspark read")
sc = SparkContext(conf=conf)
ssc = StreamingContext(sc, 1)
kafkaStream = KafkaUtils.createStream(ssc, "localhost:2181",
"spark-streaming-consumer",
{"splash_json": 2})
stream = kafkaStream.map(lambda xs: xs)
stream.foreachRDD(lambda rdd: rdd.foreach(printRdd))
# stream.foreachRDD(lambda rdd: rdd.saveToMongodb(mongodb_uri))
# stream.pprint()
# filter out flights not departing from United States
# "depAirportCntry": "United States"
# messages = kafkaStream.map(lambda xs: json.load(xs))
# jsonmessages = messages.map(lambda x: json.loads(x))
# usdepartures = jsonmessages.map(lambda x: x['depAirportCntry'].filter(lambda x: "United States" in x))\
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
def streaming_data(topic_name, window_size):
'''
Get data stream from Kafka broker
'''
# Spark context
sc = SparkContext(appName="PythonSparkStreamingKafka")
# sc.setLogLevel("WARN")
# Streaming context
batch_duration = 60 # batch duration in seconds
stc = StreamingContext(sc, batch_duration)
# Connect to Kafka and get DStream of input stream data
kafkaStream = KafkaUtils.createStream(stc, 'localhost:2181',
'raw-event-streaming-consumer',
{topic_name: 1})
# windowed stream
windowedStream = kafkaStream.window(window_size)
# Start the streaming context
stc.start()
stc.awaitTermination()
def start():
sconf = SparkConf()
# sconf.set('spark.streaming.blockInterval','100')
sconf.set('spark.cores.max', 8)
sc = SparkContext(appName='KafkaWordCount', conf=sconf)
ssc = StreamingContext(sc, 2)
numStreams = 3
kafkaStreams = [
KafkaUtils.createStream(
ssc,
"server1-2-5-24-138:2181,server1-3-5-24-139:2181,server1-4-5-24-140:2181",
"streaming_test_group", {"spark_streaming_test_topic": 1})
for _ in range(numStreams)
]
unifiedStream = ssc.union(*kafkaStreams)
print unifiedStream
#统计生成的随机数的分布情况
result = unifiedStream.map(lambda x: (x[0], 1)).reduceByKey(
lambda x, y: x + y)
result.pprint()
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
def Stream(KafkaTopic, ssc):
#global Num
print(KafkaTopic)
StrTopic = KafkaTopic.split('-')
Num = StrTopic[0]
#print(Num)
kvs1 = KafkaUtils.createStream(
ssc, "localhost:2181", KafkaTopic,
{KafkaTopic: 1}) #接受来自kafka的数据， "localhost:2181"为zookeper的端口号
#kvs2 = KafkaUtils.createStream(ssc, "localhost:2181", KafkaTopic[1], {KafkaTopic[1]: 1}) #接受来自kafka的数据， "localhost:2181"为zookeper的端口号
lines1 = kvs1.map(lambda x: eval(x[1])['battery']) #x[1]代表接受kafka中的值其他都不行
#lines1.pprint()
#lines2 = kvs2.map(lambda x: eval(x[1])['battery']) #x[1]代表接受kafka中的值其他都不行
#val = lines.map(lambda k: (k['battery'])) #取字典中需要的参数组成一个tuple
batterychange1 = lines1.window(6, 3) #创建计算窗口
#batterychange2 = lines2.window(6,6)
batterymax1 = batterychange1.reduce(lambda x, y: x + y)
batteryfinal = batterymax1.map(lambda x: {"Exo_ID": Num, "battery": x})
#topic = kvs1.map(lambda x: x[0])
#batterymax2 = batterychange2.reduce(lambda x,y : x+y)
batteryfinal.pprint()
#topic.pprint()
#batterymax2.pprint()
batteryfinal.foreachRDD(lambda rdd: rdd.foreach(sendMsg)) #创建新的用于发送数据的RDD
def main():
try:
SetParameters()
sc = CreateSparkContext()
ssc = StreamingContext(sc, 5)
kafka_stream = KafkaUtils.createStream(ssc, "192.168.0.1:2181",
"topic", {topic: 12})
raw = kafka_stream.flatMap(lambda kafkaS: [kafkaS])
lines = raw.flatMap(lambda xs: xs[1].split(","))
counts = lines.map(lambda word: (str(datetime.now()), "api", word))
counts.foreachRDD(lambda k: saveToCassandra(k, sc, ssc))
ssc.start()
ssc.awaitTermination()
except Exception, e:
print('error:' + str(e))
def getStreamingData():
sc = SparkContext(appName="spark_temperature_processor")
ssc = StreamingContext(sc, 1)
zkQuorum = 'localhost:2181'
topic = 'test'
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer",
{topic: 1})
lines = kvs.map(lambda x: x[1])
lines.pprint()
fo.write(str(lines))
#counts = lines.flatMap(lambda line: line.split(" ")).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
#counts.pprint()
#.reduceByKey(lambda a,b : "hot" if int(b) > 90 else "cold")
#maxt.saveAsTextFile("kk.txt")
maxt = lines.map(lambda x: x[0])
maxt.pprint()
ssc.start()
ssc.awaitTermination() #terminate in 5 seconds
def createContext():
sc = SparkContext(appName="PythonSparkStreamingKafka_RM_02")
sc.setLogLevel("WARN")
ssc = StreamingContext(sc, 5)
# Define Kafka Consumer
kafkaStream = KafkaUtils.createStream(ssc, 'cdh57-01-node-01.moffatt.me:2181', 'spark-streaming2', {'twitter':1})
## --- Processing
# Extract tweets
parsed = kafkaStream.map(lambda v: json.loads(v[1]))
# Count number of tweets in the batch
count_this_batch = kafkaStream.count().map(lambda x:('Tweets this batch: %s' % x))
# Count by windowed time period
count_windowed = kafkaStream.countByWindow(60,5).map(lambda x:('Tweets total (One minute rolling count): %s' % x))
# Get authors
authors_dstream = parsed.map(lambda tweet: tweet['user']['screen_name'])
# Count each value and number of occurences
count_values_this_batch = authors_dstream.countByValue() .transform(lambda rdd:rdd .sortBy(lambda x:-x[1])) .map(lambda x:"Author counts this batch:\tValue %s\tCount %s" % (x[0],x[1]))
# Count each value and number of occurences in the batch windowed
count_values_windowed = authors_dstream.countByValueAndWindow(60,5) .transform(lambda rdd:rdd .sortBy(lambda x:-x[1])) .map(lambda x:"Author counts (One minute rolling):\tValue %s\tCount %s" % (x[0],x[1]))
# Write total tweet counts to stdout
# Done with a union here instead of two separate pprint statements just to make it cleaner to display
count_this_batch.union(count_windowed).pprint()
# Write tweet author counts to stdout
count_values_this_batch.pprint(5)
count_values_windowed.pprint(5)
return ssc
def create_dstream(ssc, zk_quorum, group_id, topics):
'''
Create an input stream that pulls ids packet messages from Kafka.
:param ssc : :class:`pyspark.streaming.context.StreamingContext` object.
:param zk_quorum: Zookeeper quorum (host[:port],...).
:param group_id : The group id for this consumer.
:param topics : Dictionary of topic -> numOfPartitions to consume. Each
partition is consumed in its own thread.
:returns : The schema of this :class:`DataFrame`.
:rtype : :class:`pyspark.sql.types.StructType`
'''
from pyspark.streaming.kafka import KafkaUtils
from ..serializer import deserialize
dstream = KafkaUtils.createStream(ssc,
zk_quorum,
group_id,
topics,
keyDecoder=lambda x: x,
valueDecoder=deserialize)
return dstream.map(lambda x: x[1]).flatMap(lambda x: x).map(
lambda x: x.split(','))
def main(tag):
sc = create_sc("UnitTest")
sc.setLogLevel("INFO")
print('SPARK CONTEXT INFO :')
print(' VERSION :', sc.version)
print(' DRIVER MEMORY :', sc._conf.get('spark.driver.memory'))
stream = StreamingContext(sc, 60)
kafka_stream = KafkaUtils.createStream(
stream, 'localhost:2181', 'spark-historian-consumer',
{'historian-topic-CRY-TGBT-NORMAL-CRY-act-cons-pow': 1})
#kafka_stream = KafkaUtils.createStream(stream, 'victoria.com:2181', 'spark-streaming', {'imagetext':1})
#parsed = kafka_stream.map(lambda v: json.loads(v[1]))
parsed = kafka_stream.map(lambda v: analyzeLog(v[1]))
#parsed.pprint()
parsed.foreachRDD(lambda k: process(k))
parsed.pprint()
stream.start()
stream.awaitTermination()
def main():
global zookeeper_IP
global cassandra_IP
global cassandra_keyspace
global cassandra_table
global kafka_handling_api
global seconds_per_job
global topic
global c_dao
zookeeper_IP = GetConfig('zookeeper_IP')
cassandra_IP = GetConfig('cassandra_IP')
cassandra_keyspace = GetConfig('cassandra_keyspace')
cassandra_table = GetConfig('cassandra_table')
kafka_handling_api = GetConfig('kafka_handling_api')
seconds_per_job = GetConfig('seconds_per_job')
topic = GetConfig('topic')
c_dao = CassandraDAO(CassandraType.PRODUCTION)
sc = CreateSparkContext(cassandra_IP)
ssc = StreamingContext(sc, int(float(seconds_per_job)))
try:
kafka_stream = KafkaUtils.createStream(ssc, zookeeper_IP,
'spark-streaming-consumer',
{topic: 12})
raw = kafka_stream.flatMap(lambda kafkaS: [kafkaS])
lines = raw.filter(lambda xs: xs[1].split(','))
counts = lines.map(lambda word: (str(datetime.now()), 'api', word[1]))
counts.foreachRDD(lambda k: saveToCassandra(sc, ssc, k))
except Exception, e:
print('error:' + str(e))
def streaming():
os.environ[
'PYSPARK_SUBMIT_ARGS'] = '--jars spark-streaming-kafka-assembly_2.10-1.6.0.jar pyspark-shell'
spark = SparkSession.builder.master("spark://t3.dev:7077").appName("test") \
.config('spark.jars.packages', 'org.apache.spark:spark-streaming-kafka-0-8-assembly_2.11:2.4.5') \
.getOrCreate()
print(dir(spark._jvm))
ssc = StreamingContext(spark.sparkContext, 5)
kafkaParams = {
"bootstrap_servers": "t3.dev:9092",
"kafka.bootstrap.servers": "t3.dev:9092",
"brokers": "t3.dev:9092",
"host": "t3.dev:9092"
}
topics = {'spark-test': 1}
lines = KafkaUtils.createStream(ssc, 't3.dev:2181', 'local-test', topics,
kafkaParams)
print(lines.pprint(10))
ssc.start()
ssc.awaitTermination(60)
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext, HiveContext, SparkSession
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
conf = SparkConf().setMaster("local[1]").setAppName("StreamProcessor_1")
sc = SparkContext(conf=conf)
print "Setting LOG LEVEL as ERROR"
sc.setLogLevel("ERROR")
ssc = StreamingContext(sparkContext=sc, batchDuration=1)
kafkaStream = KafkaUtils.createStream(ssc=ssc,
zkQuorum='localhost:2181',
topics='test')
print(sc)
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
if __name__ == "__main__":
# Create the Spark context
sc = SparkContext(appName="DataIngestionApp")
log4j = sc._jvm.org.apache.log4j
log4j.LogManager.getRootLogger().setLevel(log4j.Level.WARN)
# Create the Spark Streaming Context with 10 seconds batch interval
ssc = StreamingContext(sc, 10)
# Check point directory setting
ssc.checkpoint("\tmp")
# Zookeeper host
zooKeeperQuorum="localhost"
# Kaka message group
messageGroup="sfb-consumer-group"
# Kafka topic where the programming is listening for the data
# Reader TODO: Here only one topic is included, it can take a comma separated string containing the list of topics.
# Reader TODO: When using multiple topics, use your own logic to extract the right message and persist to its data store
topics = "message"
numThreads = 1
# Create a Kafka DStream
kafkaStream = KafkaUtils.createStream(ssc, zooKeeperQuorum, messageGroup, {topics: numThreads})
messageLines = kafkaStream.map(lambda x: x[1])
# This is where the messages are printed to the console. Instead of this, implement your own persistence logic
messageLines.pprint()
# Start the streaming
ssc.start()
# Wait till the application is terminated
ssc.awaitTermination()
kstream = KafkaUtils.createDirectStream(ssc, topics = ['topic1'], kafkaParams = {"metadata.broker.list":"localhost:9092"})
tweets = tweets.map(lambda x: x[1].encode("ascii","ignore"))
return tweets
def process_rdd_queue(twitter_stream):
# Create the queue through which RDDs can be pushed to
# a QueueInputDStream
rddQueue = []
for i in range(3):
rddQueue += [ssc.sparkContext.parallelize([get_next_tweet(twitter_stream)], 5)]
lines = ssc.queueStream(rddQueue)
lines.pprint()
if __name__ == "__main__":
sc = SparkContext(appName="PythonStreamingQueueStream")
ssc = StreamingContext(sc, 10)
# Instantiate the twitter_stream
#twitter_stream = connect_twitter()
# Get RDD queue of the streams json or parsed
#process_rdd_queue(twitter_stream)
zkQuorum = "localhost:2181"
topic = "topic1"
tweets = KafkaUtils.createStream(ssc, zkQuorum, "PythonStreamingQueueStream", {topic: 1})
#tweets = stream(ssc)
#process_rdd_queue(twitter_stream)
tweets.pprint()
ssc.start()
time.sleep(100)
ssc.stop(stopSparkContext=True, stopGraceFully=True)
rdd.foreachPartition(output_partition)
if __name__ == "__main__":
client = pyhdfs.HdfsClient(hosts="10.120.14.120,9000",
user_name="cloudera")
#ser producer for topic "utime"
topic = "utime"
broker_list = '10.120.14.120:9092,10.120.14.120:9093'
sc = SparkContext()
ssc = StreamingContext(sc, 3)
#ser consumer kafkastream take from topic Pdata
lines = KafkaUtils.createStream(ssc, "10.120.14.120:2182",
"Pdata_for_model", {"Pdata": 3})
load_file = open(
"/home/cloudera/HA_ML_prdict_project/predict_model/rfr_0910_df.pkl",
'rb')
MRI_Model = joblib.load(load_file)
load_file.close()
rfr_bc = sc.broadcast(MRI_Model)
r = lines.map(lambda x: x[0])
r0 = lines.map(lambda x: x[1])
r1 = r0.map(lambda x: (int(x.split(",")[0]),int(x.split(",")[1]),int(x.split(",")[2]),int(x.split(",")[3]),int(x.split(",")[4]),\
int(x.split(",")[5]),int(x.split(",")[6]),int(x.split(",")[7])))
r2 = r1.map(lambda x: np.array(x, dtype=int))
r3 = r2.map(lambda x: x.reshape(1, -1))
RDB_HOST = os.environ.get('RDB_HOST')
RDB_PORT = os.environ.get('RDB_PORT')
RDB_DB = "avrotopic1db"
zkQuorum, topic, stream_window, RDB_TABLE = sys.argv[1:]
stream_window = int(stream_window)
sc = SparkContext(appName="PythonStreamingKafkaSums")
ssc = StreamingContext(sc, batchDuration=stream_window)
streams = []
schema = avro.schema.parse(open("WaterSensor.avsc").read())
reader = DatumReader(schema)
numStreams = 4
kafkaStreams = [KafkaUtils.createStream(ssc=ssc, zkQuorum=zkQuorum, groupId="avro-topic1-consumer", valueDecoder=io.BytesIO, topics={topic: 1}) for _ in range (numStreams)]
#kvs = kafkaStreams[1]
#kkvvss = ssc.union(*kafkaStreams)#.partitionBy(numPartitions=20)
#kvs.print()
#kvs = KafkaUtils.createStream(ssc, zkQuorum, "my-topic2-consumer", {topic: 1})
def sendRDDCount(count):
#print('index: ' + str(index))
connection = createNewConnection()#todo: use-connection-pool
#print('count' + str(count))
#r.table(RDB_TABLE).filter(r.row["partition"] == index).update({"count": count}).run(connection)
r.table(RDB_TABLE).insert({"count": count, "time":time.time()}).run(connection)
connection.close()
def sendPartitionCount(index, count):
try:
s = db.session.query(Station).filter(Station.id == station['station_id'])
s.update({Station.num_bikes_available: station['num_bikes_available'],
Station.num_docks_available: station['num_docks_available']})
db.session.commit()
except exc.IntegrityError:
db.session.rollback()
def consume_data():
sc = SparkContext(appName="Lets Go")
ssc = StreamingContext(sc, 1)
zkQuorum, topic = sys.argv[1:]
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 1})
lines = kvs.map(lambda x: x[1])
counts = lines.flatMap(lambda line: line.split(" ")) \
.map(lambda row: (row, update_station_status())) \
.reduceByKey(lambda a, b: a+b)
ssc.start()
ssc.awaitTermination()
def system_alerts():
"""Get alerts about the system.
https://gbfs.citibikenyc.com/gbfs/en/system_alerts.json"""
pass
"mapreduce.job.output.value.class": "org.apache.hadoop.io.Writable"}
keyConv = "org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter"
valueConv = "org.apache.spark.examples.pythonconverters.StringListToPutConverter"
#execute send
rdd.map(writeHbase).saveAsNewAPIHadoopDataset(
conf=conf,
keyConverter=keyConv,
valueConverter=valueConv)
if __name__ == "__main__":
sc = SparkContext(appName = "Hbase")
ssc = StreamingContext(sc, 1)
# ssc.checkpoint(checkpointDir)
# ssc = StreamingContext.getOrCreate(checkpointDir, buildContext)
zkQuorum = "localhost:2181"
kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {KAFKA_TOPIC: 1})
#data stream of data dictionaries
ds = kvs.map(lambda data: ast.literal_eval(data[1]))
ds.pprint()
if ds is not None:
ds.foreachRDD(sendRecord)
ssc.start()
ssc.awaitTermination()
# sumstats = ds.map(partitionCount).updateStateByKey(partitionCount)
# ssc.stop(stopGraceFully=True)
schema=['MinPrice', 'Direct', 'OutboundLeg'])
df.show()
df.write.saveAsTable(name="default.flights",
format="hive",
mode="append")
sc = SparkContext("local[*]", "FlightData")
ssc = StreamingContext(sc, 5)
ss = SparkSession.builder.appName("FlightData").config(
"spark.sql.warehouse.dir", "/user/hive/warehouse").config(
"hive.metastore.uris",
"thrift://localhost:9083").enableHiveSupport().getOrCreate()
kafkaStream = KafkaUtils.createStream(ssc, 'localhost:2181', 'Flights',
{'flights': 1})
parsed = kafkaStream.map(lambda v: json.loads(v[1]))
#user_counts = parsed.map(lambda tweet: (tweet['user']["screen_name"], 1)).reduceByKey(lambda x,y: x + y)
#user_counts.pprint()
longest_duration = parsed.flatMap(lambda v: v.get("Quotes"))
#longest_duration.pprint()
table = longest_duration.map(
lambda v: (v.get("MinPrice"), v.get("Direct"), v.get("OutboundLeg")))
longest_duration.pprint()
import sys
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
sc = SparkContext(appName="kafkaTest")
ssc = StreamingContext(sc,5)
kvs = KafkaUtils.createStream(ssc, "localhost:2181", "spark_streaming", {"inter_transact": 1})
kvs.pprint(10)
ssc.start()
ssc.awaitTermination()
'''
Created on Jul 7, 2016
@author: rbhat
'''
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
# Create a local StreamingContext with two working thread and batch interval of 2 second
sc = SparkContext("local[*]", "MyKafkaStream")
ssc = StreamingContext(sc, 1)
kafkaStream = KafkaUtils.createStream(ssc, "deepc04.acis.ufl.edu:2181", "GroupNameDoesntMatter", {"test": 2})
messages = kafkaStream.map(lambda xs:xs)
messages.pprint()
ssc.start() # Start the computation
ssc.awaitTermination() # Wait for the computation to terminate
stream_window = int(stream_window)
sc = SparkContext(appName="PythonStreamingKafkaJSONSums")
ssc = StreamingContext(sc, batchDuration=stream_window)
def createNewConnection():
return r.connect(host=RDB_HOST, port=RDB_PORT, db=RDB_DB)
#delete any data in table
connection = createNewConnection()
r.table(RDB_TABLE).delete().run(connection)
connection.close()
streams = []
numStreams = 6 #read parallelism
kafkaStreams = [KafkaUtils.createStream(ssc, zkQuorum, "JSON-consumer", {topic: 1}) for _ in range (numStreams)]
#set up kafkaStreams into a list
def sendRDDCount(count):
connection = createNewConnection()
r.table(RDB_TABLE).insert(count).run(connection)
connection.close()
for idx,kvs in enumerate(kafkaStreams):
countsDstream=kvs.count()
countsDstream = countsDstream.map(lambda x: {"count":x, "time":time.time()})
records = kvs.map(lambda x: bytesDecoder(x[1]))
sums = records.map(lambda obj: (obj['unique_id'], obj['quantity'])) \
.reduceByKey(lambda a, b: a+b)
countsDstream.foreachRDD(lambda rdd: sendRDDCount(rdd.take(1)))
ssc.start()
ssc.awaitTermination()
# 6.2.4절 예제 6-12
from pyspark import SparkContext, SparkConf, storagelevel
from pyspark.streaming.context import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
## pyspark에서 실행할 경우 sparkContext는 생성하지 않습니다!
# ./pyspark --packages org.apache.spark:spark-streaming-kafka-0-8-assembly_2.11:2.0.2
conf = SparkConf()
sc = SparkContext(master="local[*]", appName="KafkaSample", conf=conf)
ssc = StreamingContext(sc, 3)
ds1 = KafkaUtils.createStream(ssc, "localhost:2181", "test-consumer-group1", {"test": 3})
ds2 = KafkaUtils.createDirectStream(ssc, ["test"], {"metadata.broker.list": "localhost:9092"})
ds1.pprint()
ds2.pprint()
ssc.start()
ssc.awaitTermination()
record_id = colleciton.insert(push_user)
colleciton = mongodb.notify_record
notify_user = {"createtime": int(time.time()), "sendid": sendid, "type": channel,"purchaseinfoid":purchaseinfoid,"recordid":record_id}
colleciton.insert(notify_user)
#print sendid, str(num),purchaseinfo["name"].encode("utf8"),purchaseinfo["price"].encode("utf8"),purchaseinfo["unit"].encode("utf8"),str(purchaseinfo["purchaseid"]),sendtype
#reply_wx_notify(sendid, str(num), purchaseinfo["name"],purchaseinfo["price"], purchaseinfo["unit"], str(purchaseinfoid),str(purchaseinfo["purchaseid"]))
thread.start_new_thread(reply_wx_notify, (sendid, str(num), purchaseinfo["name"],purchaseinfo["price"], purchaseinfo["unit"], str(purchaseinfoid),str(purchaseinfo["purchaseid"]),uuid,sendtype))
pass
def handlestream(kvs):
parsed = kvs.map(lambda (k, v): json.loads(v))#获取消息的json格式
#处理发送任务
send=parsed.filter(lambda x: True if x["messagetype"] == 2 else False)
send.foreachRDD(sendPush)
if __name__ == "__main__":
sc = SparkContext(appName="sendKafka")
ssc = StreamingContext(sc, 1)
kvs = KafkaUtils.createStream(ssc, zk_server, "send-group", {send_task_topic: 1})
handlestream(kvs)
ssc.start()
ssc.awaitTermination()
colleciton.insert(notify_user)
#print sendid, str(num),purchaseinfo["name"].encode("utf8"),purchaseinfo["price"].encode("utf8"),purchaseinfo["unit"].encode("utf8"),str(purchaseinfo["purchaseid"]),sendtype
#reply_wx_notify(sendid, str(num), purchaseinfo["name"],purchaseinfo["price"], purchaseinfo["unit"], str(purchaseinfoid),str(purchaseinfo["purchaseid"]))
thread.start_new_thread(
reply_wx_notify,
(sendid, str(num),
purchaseinfo["name"],
purchaseinfo["price"],
purchaseinfo["unit"],
str(purchaseinfoid),
str(purchaseinfo["purchaseid"]), uuid,
sendtype))
pass
def handlestream(kvs):
parsed = kvs.map(lambda (k, v): json.loads(v)) #获取消息的json格式
#处理发送任务
send = parsed.filter(lambda x: True if x["messagetype"] == 2 else False)
send.foreachRDD(sendPush)
if __name__ == "__main__":
sc = SparkContext(appName="sendKafka")
ssc = StreamingContext(sc, 1)
kvs = KafkaUtils.createStream(ssc, zk_server, "send-group",
{send_task_topic: 1})
handlestream(kvs)
ssc.start()
ssc.awaitTermination()
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: script.py <zk> <topic>", file=sys.stderr)
exit(-1)
zkQuorum, topic = sys.argv[1:]
sc = SparkContext(appName="KafkaSparkStreaming")
sc.setLogLevel("WARN")
ssc = StreamingContext(sc, 10)
ssc.checkpoint("checkpoint")
ks = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 42})
def processInput(line):
fields = line[1].split("\t")
return ((str(fields[6]), 1), (str(fields[7]), 1))
def updateFunction(newValues, runningCount):
return sum(newValues, runningCount or 0)
digest = ks.flatMap(processInput)\
.updateStateByKey(updateFunction)\
.transform(lambda rdd: rdd.sortBy(lambda x: x[1], ascending=False)\
.map(lambda (x, y): y).zipWithIndex().map(lambda (x, y): (y, x))
)
def toCSVLine(data):
return cv2.imdecode(np.frombuffer(s, dtype=np.uint8), -1)
# the dtype of the input data (.jpg) is np.uint8
def write_img(rdd):
imgs = rdd.collect()
for img in imgs:
cv2.imwrite('res1.jpg', img)
if __name__ == '__main__':
player_dict = getDict()
# spark = SparkSession.builder.getOrCreate()
sc = SparkContext()
ssc = StreamingContext(sc, 1)
raw_stream = KafkaUtils.createStream(ssc, 'localhost:2182', 'dl', {'dl_input':3}, valueDecoder=imdecoder)
# Kafka default valueDecoder is str.decode('utf-8')
imgs = raw_stream.map(lambda x:x[1])
# load model
model = get_testing_model()
keras_weights_file = 'model/keras/model.h5'
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
# process as rdd
info_list,temp = imgs.transform(body_and_num_recog)
temp.foreachRDD(write_img)
# num = temp.map(lambda x:int(x[1]))
if __name__ == "__main__":
# SparkContext represents connection to a Spark cluster.
conf = SparkConf()
conf.setAppName("Kafka Spark App")
conf.setMaster('local[2]')
sc = SparkContext(conf=conf)
sc.setLogLevel("WARN")
# StreamingContext represents connection to a Spark cluster from existing SparkContext.
ssc = StreamingContext(
sc, 60) # the number indicates how many seconds each batch lasts.
# Creates an input stream that pulls events from Kafka.
kvs = KafkaUtils.createStream(ssc, "streamsetApp:2181",
"spark-streaming-consumer", {"NETFLOW": 1})
parsed = kvs.map(lambda x: json.loads(x[1]))
# Get only elements that are needed and rename to make it clear.
netflow_dict = parsed.map(lambda x: ({
'srcAddr': x['srcaddr_s'],
'srcPort': x['srcport'],
'dstAddr': x['dstaddr_s'],
'dstPort': x['dstport'],
'tcpFlags': x['tcp_flags'],
'protocol': x['proto'],
'timestampStart': x['first'],
'timestampEnd': x['last'],
'numBytes': x['dOctets'],
'numFlows': x['count']
}))
def my_decoder(s):
return s
def eye_aspect_ratio(eye):
A = distance.euclidean(eye[1], eye[5])
B = distance.euclidean(eye[2], eye[4])
C = distance.euclidean(eye[0], eye[3])
ear = (A + B) / (2.0 * C)
return ear
kafkaStream = KafkaUtils.createStream(ssc,
brokers,
'test-consumer-group-1',
{input_topic: 15},
valueDecoder=my_decoder)
producer = KafkaProducer(bootstrap_servers='G01-01:9092',
compression_type='gzip',
batch_size=163840,
buffer_memory=33554432,
max_request_size=20485760)
thresh = 0.25
frame_check = 20
detect = dlib.get_frontal_face_detector()
predict = dlib.shape_predictor(
predictor_path) # Dat file is the crux of the code
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["right_eye"]
virtualMachine = 'local'
if socket.gethostname() == 'ubuntu':
virtualMachine = socket.gethostname()
if virtualMachine == 'local':
dirTrainingModel = config.get('StreamingProperties', 'URLTrainingModelLocal')
else:
dirTrainingModel = config.get('StreamingProperties', 'URLTrainingModelHDFS')
if virtualMachine == 'ubuntu':
ssc = StreamingContext(sc, 2)
brokers = "localhost:2181"
kvs = KafkaUtils.createStream(ssc, \
"localhost:2181", \
topicName,
{"topic":1})
#kvs = KafkaUtils.createDirectStream (ssc, [topicName], {"metadata.broker.list": brokers})
kvs.pprint()
#kvs.foreachRDD (saveData)
#brokers = "localhost:9092"
#kvs = KafkaUtils.createDirectStream (ssc, [topicName], {"metadata.broker.list": brokers})
#KafkaUtils.createStream(scc, )
#kvs.pprint()
#kvs.foreachRDD (saveStream)
#rowData = data.map(lambda row: row.asDict())
#rowData.saveToMongoDB(mongodb_connection + 'test.resultsStreaming')
ssc.start()
