def main():
elaticIP = "10.0.0.204"
elaticPort = 9200
url = "http://{}:{}/machan_laser/_search".format(elaticIP, elaticPort)
conf = SparkConf()
#conf = initSparkConfig(conf,"local",appName="StreamingKMeans")
conf = initSparkConfig(conf,
"spark://10.0.0.202:7077",
appName="StreamingKMeans")
sc = SparkContext(conf=conf)
sc.setLogLevel("WARN")
ssc = StreamingContext(sc, 1)
mqttData = MQTTUtils.createStream(ssc, brokerIP, topic)
programTime = []
programName = getInitData(url)
changeCount = 0
for i in programName:
subTime = getProcessTime(url, i)
subTime.append(i)
programTime.append(subTime)
print(programTime)
rProgramName = sc.parallelize(programTime)
trainData = rProgramName.map(toVector)
trainData.take(2)
def _startContext(self, topic):
# Start the StreamingContext and also collect the result
stream = MQTTUtils.createStream(self.ssc, "tcp://" + self._MQTTTestUtils.brokerUri(), topic)
result = []
def getOutput(_, rdd):
for data in rdd.collect():
result.append(data)
stream.foreachRDD(getOutput)
self.ssc.start()
return result
def main():
conf = SparkConf()
conf = initSparkConfig(conf, "local[3]", appName="mqttTest")
#conf = initSparkConfig(conf,"spark://10.0.0.202:7077",appName="StreamingKMeans")
sc = SparkContext(conf=conf)
sc.setLogLevel("WARN")
ssc = StreamingContext(sc, 3)
mqttData = MQTTUtils.createStream(ssc, brokerIP, topic)
r = mqttData.map(lambda x: x + "aa")
r.pprint()
ssc.start()
ssc.awaitTermination()
def _startContext(self, topic):
# Start the StreamingContext and also collect the result
stream = MQTTUtils.createStream(
self.ssc, "tcp://" + self._MQTTTestUtils.brokerUri(), topic)
result = []
def getOutput(_, rdd):
for data in rdd.collect():
result.append(data)
stream.foreachRDD(getOutput)
self.ssc.start()
return result
def _start_context_with_paired_stream(self, topics):
stream = MQTTUtils.createPairedStream(
self.ssc, "tcp://" + self._MQTTTestUtils.brokerUri(), topics)
# Keep a set because records can potentially be repeated.
result = set()
def getOutput(_, rdd):
for data in rdd.collect():
result.add(data)
stream.foreachRDD(getOutput)
self.ssc.start()
return result
def main():
with SparkContext(appName='MQTTstreaming') as sc:
ssc = StreamingContext(sc,2)
broker = 'tcp://' + sys.argv[1]
topic = sys.argv[2]
dataInput = MQTTUtils.createStream(ssc, broker, topic)
result = dataInput.map(lambda x: x.split(" ")).map(load).map(convert)
result.foreachRDD(lambda x: x.foreach(saveToEs))
result.pprint()
ssc.start()
ssc.awaitTermination()
ssc.stop()
#
# array_atual = m.calcular_total_de_pessoas_por_lugar_porcentagem(total_de_pessoas_3_locais,
# total_de_pessoas_em_cada_local)
# print(array_atual)
#ALGORITMO SPARK
if __name__ == "__main__":
sc = SparkContext()
ssc = StreamingContext(sc, 60)
brokerUrl = "tcp://localhost:1883"
topic1 = "hall"
topic2 = "praca"
lines_hall = MQTTUtils.createStream(ssc, brokerUrl, topic1)
# lines_hall.pprint(100)
# Split each line into macs
# macs = lines_hall.flatMap(lambda line: line.split(",")[1::3])
# macs.pprint(100)
# dic_hall = lines_hall.flatMap(lambda line: line.split(","))
# macs.pprint(100)
dic_hall = {}
keyPadrao = 'geral'
# values = lines_hall.map(lambda line: line.split(","))
values = lines_hall.map(lambda line: [keyPadrao, [json.loads(line)]])
# .map(lambda k, v: dic_hall.update({k: v}), keys)
# junk = map(lambda k, v: dic_hall.update({k: v}), keys, values[:])
`$ bin/run-example \
streaming-mqtt/examples/src/main/python/streaming/mqtt_wordcount.py tcp://localhost:1883 foo`
"""
import sys
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from mqtt import MQTTUtils
if __name__ == "__main__":
if len(sys.argv) != 3:
print >> sys.stderr, "Usage: mqtt_wordcount.py <broker url> <topic>"
exit(-1)
sc = SparkContext(appName="PythonStreamingMQTTWordCount")
ssc = StreamingContext(sc, 1)
brokerUrl = sys.argv[1]
topic = sys.argv[2]
lines = MQTTUtils.createStream(ssc, brokerUrl, topic)
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 getTimeQtd(tp1, tp2):
ts = []
ts.append(tp1[0])
ts.append(tp2[0])
return (max(ts), int(tp1[1]) + int(tp2[1]))
if __name__ == "__main__":
if len(sys.argv) != 3:
print >> sys.stderr, "Usage: mqtt_wordcount.py < > <>"
exit(-1)
sc = SparkContext(appName="PythonStreamingMQTTWordCount")
ssc = StreamingContext(sc, 120)
brokerUrl = sys.argv[1]
topic = sys.argv[2]
print (brokerUrl)
print (topic)
lines = MQTTUtils.createStream(ssc, brokerUrl, topic)
counts = lines.map(filtro)
windowedWordCounts = counts.reduceByKeyAndWindow(agrupaTS, None, 2760, 120)
windowedWordCounts = windowedWordCounts.map(getTimeMac)
windowedWordCounts = windowedWordCounts.filter(limpeza)
windowedWordCounts = windowedWordCounts.reduce(getTimeQtd)
windowedWordCounts = windowedWordCounts.map(contar)
windowedWordCounts.pprint()
ssc.start()
ssc.awaitTermination()
(time() * 1000) % 1000, interval.value, len(input)))
if last_batch:
ssc.stop()
# Initialize features to <number of sensors>-length array, filled with neutral initial sensor value
features = np.zeros(n_sensors)
features.fill(0.5)
# Initialize streaming for specified reporting interval
sc = SparkContext(appName="iotstream_lr_mqtt")
interval = sc.accumulator(0)
empty_intervals = sc.accumulator(0)
events = sc.accumulator(0)
ssc = StreamingContext(sc, reporting_interval)
sensor_stream = MQTTUtils.createStream(ssc, mqtt_URL, mqtt_topic)
# Load pre-computed model
model = LogisticRegressionModel.load(sc, modelname)
# Run model on each batch
#sensor_stream.pprint(10)
sensor_stream.foreachRDD(run_model)
# Start reading streaming data
ssc.start()
start_time = time()
ssc.awaitTermination()
finish_time = time()
elapsed_time = finish_time - start_time - empty_intervals.value * reporting_interval - 1.5 # Subtract off time waiting for events and 1.5 sec for termination
print(
