Locust performance test - environment construction and use

1. What is? Locust？

     Locust Is an easy to use , Scriptable and extensible performance testing tool . You can use the general Python Code defines user behavior , This makes Locust It can be expanded infinitely , And very friendly to developers .

2. features

• Distributed and scalable - Support hundreds of thousands of users

Locust Make it easy to run load tests on multiple computers . It's event based （ Use gevent）, This allows a single process to handle thousands of concurrent users . Although there may be other tools that can perform more requests per second on a given hardware , But every one of them Locust The user's low overhead makes it ideal for testing high concurrency workloads .

• be based on Web Of UI

Locust Have user-friendly Web Interface , It can display the test progress in real time . You can even change the load while the test runs . It can also be in the absence of UI Operation in case of , So that it can be easily used for CI / CD test .

• Can test any system

Even if Locust Main content and website / Services are used together , It can also be used to test almost any system or protocol . just Write a client for what you want to test , Or browse some clients created by the community .

3. Environmental installation

Official documents

https://docs.locust.io/en/1.4.4/what-is-locust.html

install Python 3.6 Or later .

 Use pip install Locust.pip install locust

 Verify the installation and display locust Version number  pip show locust
(venv) E:\workspace\LocustConcurrent>pip show locustName: locustVersion: 1.4.4Summary: Developer friendly load testing frameworkHome-page: https://locust.io/

4. Quick start

stay Locust in , You can use Python Code defines user behavior . then , You can use the locust Command and （ Optional ） Its Web The interface generates and simulates a large number of these users as it collects request statistics .

locust_cnblogs.py

''''Create by dell on 2021/4/13Author ：wenchengdescribe ：'''from locust import HttpUser, task, betweenclass QuickstartUser(HttpUser):    wait_time = between(1, 2.5)
    @task    def bky_deom(self):        self.client.get("/")
    @task(3)    def bky_demo2(self):        self.client.get("/wen-cheng")        if __name__ == '__main__':    import os
    os.system('locust -f locust_cnblogs.py -H https://www.cnblogs.com')

5. Startup and operation locust

open locust Of web Interface , Browser access ：http://127.0.0.1:8089

Set the number of virtual users 20, Start every second 5 Users , Click on Start swarming Began to run

locust Of web Interface analysis ：

• Number of users to simulate It's the number of simulated users

• Hatch rate (users spawned/second Represents the speed at which simulated users are generated

• Start swarming Start testing

6. Analyze test results

• Type： Request type ;

• Name： Request path ;

• Requests： Number of current requests ;

• Fails： Number of current request failures ;

• Median： In the middle , Unit millisecond , Generally, the server response time is lower than this value , The other half is higher than that ;

• Average： Average response time for all requests , millisecond ;

• Min： Minimum server response time for requests , millisecond ;

• Max： Maximum server response time for a request , millisecond ;

• Content Size： The size of a single request , Unit byte ;

• Reqs/sec： Number of requests per second .

Interface chart display

 throughput / Number of response transactions per second （rps） Real time statistics 
 Mean response time / Real time statistics of average transactions 
 Number of virtual users running 

Derived data

The above is the whole content of this article , I hope the content of this article has a certain reference value for your study or work , If you have any questions, you can leave a message , Thank you for your support .

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Learning resource sharing

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