In addition to using performance testing tools for performance testing, we can also directly use python multithreading for performance testing.

Next, use these modules to test the performance of a query interface:

requests: send http request

json: the returned string is converted to json format

threading: multithreading

Time: Statistics time

See the code and notes for the specific implementation process.

import requests
import json
import threading
import time
# Define request base address
base_url = "http://127.0.0.1:8000"
success = 0
fail = 0
# Query thread
def get_guest_list_thread(start_user,end_user):
    for i in range(start_user,end_user):
        phone = 13800138000 + i 
        r = requests.get(base_url+'/api/get_guest_list/', params={'eid':1,'phone':phone})
        # print(r.status_code) # 200
        # print(r.content) # b'{"status": 200, "message": "success", "data": {"realname": "alen", "phone": "13800138000", "email": "alen@mail.com", "sign": false}}'
        # print(r.json()) # {'status': 200, 'message': 'success', 'data': {'realname': 'alen', 'phone': '13800138000', 'email': 'alen@mail.com', 'sign': False}}
        # print(type(r)) # < class' requests. Models. Response '> this type has json methods and does not need import json
        # print(r) # <Response [200]>
        # print(json.loads(r.content)) #You need to import json, {'status': 200,' message ':' success', 'data': {'realName': 'Alen', 'phone': '13800138000', 'email': 'Alen @ mail. Com', 'sign': false}}
        result = r.json()
        global success,fail
        try:
            if phone=='13800138000' or phone=='13800138001':
                assert result['status'] == 20
                success +=1
            else:
                assert result['status'] == 10022
                success +=1
        except AssertionError as e:
            print('get error:'+str(phone))
            fail +=1
 
# 5 threads, 25 data
# lists = {1:6, 6:11, 11:16, 16:21, 21:26} # You can write data like this, or you can generate it by
data = 25
n = 5
step = int(data/n)
lists = {}
for i in range(1,n+1):
    lists[(i-1)*step+1]=i*step + 1
print(lists)
 
 
# Create thread list
threads = []
# Create thread
for start_user,end_user in lists.items():
    t = threading.Thread(target=get_guest_list_thread,args=(start_user,end_user)) # args is a tuple
    threads.append(t)
if __name__ == '__main__':
    # start time
    start_time = time.time()
    # Startup thread
    for i in range(len(lists)):
        threads[i].start()
    for i in range(len(lists)):
        threads[i].join()
    # End time
    time.sleep(3) # In order to make it more obvious that the execution time of the use case is too long, sleep is added
    end_time = time.time()
    print("start time:"+str(start_time)+'>>>>>'+time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(start_time)))
    print("End time:"+str(end_time)+'>>>>>'+time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(end_time)))
    print('Total time consuming:' + str(end_time - start_time))
    print('Total time consuming:%.2f'%(end_time - start_time)) # Keep two decimal places
    print('Number of test cases passed:{}， Number of test failure cases:{}， The test pass rate is:{}'.format(success,fail,str(success*100/(success+fail))+'%'))

Result: