Thoughts:
Queue usage instructions:
- ()# Used for inter-process communication, a single master process cannot communicate with its children (try not to use this when using process pools)
- ().Queue()# for master-child process communication, through the process pool (pool) to create the process can be data-sharing
- ()# Used for inter-thread communication, data within the same process can be shared
1. Get the orders to be paid from the database
2. Will get out of the data added to the queue (()), and in the function to return the length of the message queue
3. According to the length of the queue to create the corresponding number of threads
4. Put the created thread in list
5. Start in order
6. Finally, wait for the main thread to finish executing the end of the statistics function running time
The code is as follows
import asyncio
import sys
from queue import Queue
("../")
from tool.__init__ import *
from tool.decorator_token import *
import time
from threading import Thread,Lock
class doWeChatNotify(BaseTest):
def __init__(self):
super().__init__()
self.limit_num=100 # of records queried
self.WeChatNotify_sql='''select order_id,order_sn from fw_order where `status`=0
and course_id=1569 ORDER BY create_time desc limit %d ;'''%(self.limit_num)
self.fwh_test_api=fwh_test_api
= self.my_op.sql_operation_fwh(self.WeChatNotify_sql)
self.fwh_order_dict = {}
= Queue()
@token_fwh# Verify token validity
def get_fwh_token_list(self):
token_list=self.fwh_token.loadTokenList()
return token_list
@token_crm#validate token has def get_crm_token_list(self) token_list=()
return token_list
def testDoWeChatNotify(self):
DoWeChatNotify_file='../tokenFileAndtxtFiles'+'/'+"DoWeChatNotify_asynchronousPay.txt"
with open(DoWeChatNotify_file,'a',encoding='utf=-8') as file:
str_first="order_id\t"+"order_sn\t\n" # First line of file data
(str_first)
fwh_order_id_list, fwh_order_sn_list = [], []
if !=():
for a in :
fwh_order_id=a['order_id']
fwh_order_sn=a['order_sn']
self.fwh_order_dict[fwh_order_id]=fwh_order_sn
with open(DoWeChatNotify_file,'a',encoding='utf-8') as file2:# File Write
str_DoWeChatNotifyInfo=str(fwh_order_id)+'\t'+str(fwh_order_sn)+'\t\n'
() # Clear the buffer
(str_DoWeChatNotifyInfo)
(self.fwh_order_dict)# Add data to the queue
# Close the database connection
# self.my_op.close_db_fwh()
# self.my_op.close_db()
return ()# of return queues
def asynchronousPay(self,order_id,order_sn):
count=1
count_num=50
token_list=self.get_fwh_token_list()
if (!=()):
headers_form_urlencoded['token']=token_list[0]
url_wechat_success_huidiao=self.fwh_test_api+'/index/Order/doWeChatNotify'
data_wechat_success_huidiao=self.data_to_str.requestDataToStr_firefoxAndChrome_fwh('''order_sn:{}
order_id:{}
meth_id:4
timestamp:157129653969
sign:0687b01b300b9e300d3996a9d2173f1380973e5a'''.format(order_sn,order_id))
request_wechat_success_huidiao=(url=url_wechat_success_huidiao,headers=headers_form_urlencoded,data=data_wechat_success_huidiao)
response_wechat_success_huidiao=request_wechat_success_huidiao.json()
if 'Order status error, not a pending order' in response_wechat_success_huidiao['msg']:
print(data_wechat_success_huidiao)
else:
print('Pending payment order is empty')
def run_multithreading(self):#Multithreading
threads = []#Store all the threads
nloops = list(range(()))# Get the number of queues
if len(nloops)>0:
for i,k in zip(nloops,().items()):#Create threads based on the number of queues
t = Thread(target=,args=(k[0],k[1]))
(t)
for s in nloops: # Starting multiple threads
threads[s].start()
for j in nloops: # Wait for all threads to finish
threads[j].join()
else:
print("The number of queues is empty.")
if __name__=="__main__":
start_time = () # Calculate program start time
wechfy=doWeChatNotify()
wechfy.run_multithreading()#Multithreading
print('program time consumption{:.2f}'.format(() - start_time)) # Calculate the total time spent on the program
Summary: pro-test running time will still be much faster, single-threaded payment of 100 orders four dozen seconds, multi-threaded run without seconds, with join eight seconds, there is still a lot of room for optimization, because the runtime will create 100 threads
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