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import pandas as pd
import pymysql
from datetime import datetime
from datetime import timedelta
import pickle
import time
from kafka import KafkaProducer
import json
from pyspark.streaming.kafka import KafkaUtils
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.streaming import StreamingContext
from pyspark import SparkConf
import redis
import sys
import os
import json
import pymysql
import numpy as np
import time
import datetime
import tensorflow as tf
import msgpack
import smtplib
import requests
from email.mime.text import MIMEText
from email.utils import formataddr
from email.mime.multipart import MIMEMultipart
from email.mime.application import MIMEApplication
# sys.path.append('/srv/apps/ftrl/Bandist_Streaming')
def send_email(app,id,e,extra_information = ''):
# 第三方 SMTP 服务
mail_host = 'smtp.exmail.qq.com' # 设置服务器
mail_user = "huangkai@igengmei.com" # 用户名
mail_pass = "UyhVobmDHa4r4ecV" # 口令
sender = 'huangkai@igengmei.com'
receivers = ['huangkai@igengmei.com'] # 接收邮件，可设置为你的QQ邮箱或者其他邮箱
e = str(e)
msg = MIMEMultipart()
part = MIMEText('app_id:'+id+':fail', 'plain', 'utf-8')
msg.attach(part)
msg['From'] = formataddr(["huangkai", sender])
# 括号里的对应收件人邮箱昵称、收件人邮箱账号
msg['To'] = ";".join(receivers)
# message['Cc'] = ";".join(cc_reciver)
msg['Subject'] = 'spark streaming:app_name:'+app
with open('error.txt','w') as f:
f.write(e)
f.write(extra_information)
f.close()
part = MIMEApplication(open('error.txt', 'r').read())
part.add_header('Content-Disposition', 'attachment', filename="error.txt")
msg.attach(part)
try:
smtpObj = smtplib.SMTP_SSL(mail_host, 465)
smtpObj.login(mail_user, mail_pass)
smtpObj.sendmail(sender, receivers, msg.as_string())
except smtplib.SMTPException:
print('error')
def ts_cal():
return 0
def cal_ctr(data):
a1 = datetime.datetime.now()
device_data = data[1]
device_id = device_data['device']['device_id']
db_eagle = pymysql.connect(host="172.16.40.158", port=4000, user="root", password="3SYz54LS9#^9sBvC",
db="eagle",
cursorclass=pymysql.cursors.DictCursor)
cursor = db_eagle.cursor()
sql = 'select id from online_api_service'
cursor.execute(sql)
results = cursor.fetchall()
device_meigou_ctr_key = 'device_meigou_ctr:device_id:'+str(device_id)
device_meigou_params_key = 'device_meigou_params:device_id:'+str(device_id)
redis_client = redis.StrictRedis.from_url('redis://:ReDis!GmTx*0aN6@172.16.40.133:6379')
meigou_index_dict = dict()
meigou_new_params_dict = dict()
index_value = 0
init_params_value = 1
model_param_a = list()
model_param_b = list()
if redis_client.exists(device_meigou_params_key):
meigou_params_dict = redis_client.hgetall(device_meigou_params_key)
for result in results:
if result['id'] in meigou_params_dict.keys():
meigou_index_dict.update({index_value:result['id']})
meigou_new_params_dict.update({result['id']:meigou_index_dict[result['id']]})
model_param_a.append(meigou_params_dict[result['id']]['a'])
model_param_b.append(meigou_params_dict[result['id']]['b'])
index_value += 1
else:
meigou_index_dict.update({index_value: result['id']})
meigou_new_params_dict.update({result['id']:{"a":init_params_value,"b":init_params_value}})
model_param_a.append(init_params_value)
model_param_b.append(init_params_value)
index_value +=1
else:
for result in results:
meigou_new_params_dict.update({result['id']:{"a":init_params_value,"b":init_params_value}})
meigou_index_dict.update({index_value: result['id']})
model_param_a.append(init_params_value)
model_param_b.append(init_params_value)
index_value += 1
a2 = datetime.datetime.now()
num_actions = len(results)
user_feature = np.array([1])
# hparams_nlinear = tf.contrib.training.HParams(num_actions=num_actions,
# context_dim=1,
# init_scale=0.3,
# activation=tf.nn.relu,
# layer_sizes=[1],
# batch_size=1,
# activate_decay=True,
# initial_lr=0.1,
# max_grad_norm=5.0,
# show_training=False,
# freq_summary=1000,
# buffer_s=-1,
# initial_pulls=0,
# reset_lr=True,
# lr_decay_rate=0.5,
# training_freq=1,
# training_freq_network=10000,
# training_epochs=100,
# a0=model_param_a,
# b0=model_param_b,
# lambda_prior=0.25)
# inital model
model = NeuralLinearPosteriorSampling('NeuralLinear',num_actions,model_param_a,model_param_b)
a2 =datetime.datetime.now()
vals = model.action(user_feature)
# model.update(user_feature,0,np.array(1))
max =vals.max()
min = vals.min()
ctr_0_1 = (vals-min)/(max-min)
meigou_ctr_dict = dict()
a3 =datetime.datetime.now()
for i in range(len(ctr_0_1)):
meigou_ctr_dict.update({meigou_index_dict[i]:ctr_0_1[i]})
redis_client.set(device_meigou_ctr_key,json.dumps(meigou_ctr_dict))
a4 = datetime.datetime.now()
send_email(str(a1),str(a2),str(a3),str(a4))
def choose_action():
return 0
def Filter_Data(data):
data_dict = data[1]
if b'content' in data_dict:
return False
elif 'type' in data_dict:
if data_dict['type'] == 'device_opened' and data_dict['device']['device_id'] == '8E699605-DC2A-46B6-8B47-E9E809353055':
return True
def write_to_kafka():
producer = KafkaProducer(bootstrap_servers=["172.16.44.25:9092","172.16.44.31:9092","172.16.44.45:9092"],
key_serializer=lambda k: json.dumps(k).encode('utf-8'),
value_serializer=lambda v: json.dumps(v).encode('utf-8'))
future = producer.send(topic="test_topic", key="hello", value="world")
try:
record_metadata = future.get(timeout=10)
print("send ok")
except kafka_errors as e:
print(str(e))
def Ctr(rdd):
try:
results = rdd
write_to_kafka()
return results
except:
print("fail")
def m_decoder(s):
if s is None:
return None
try:
data = json.loads(s)
return data
except:
data = msgpack.loads(s, encoding='utf-8')
return data
if __name__ == '__main__':
# Spark-Streaming-Kafka
sc = SparkContext(conf=SparkConf().setMaster("spark://nvwa01:7077").setAppName("kafka_test")
.set("spark.io.compression.codec", "lzf"))
ssc = SQLContext(sc)
ssc = StreamingContext(sc, 10)
sc.setLogLevel("WARN")
kafkaParams = {"metadata.broker.list": "172.16.44.25:9092,172.16.44.31:9092,172.16.44.45:9092",
"group.id": "kafka_test",
"socket.timeout.ms": "600000",
"auto.offset.reset": "largest"}
stream = KafkaUtils.createDirectStream(ssc, ["test_topic"], kafkaParams,
keyDecoder=m_decoder, valueDecoder=m_decoder)
transformstream = stream.transform(lambda x: Ctr(x))
transformstream.pprint()
ssc.start()
ssc.awaitTermination()