Explain the whole article how to perform performance testing based on Locust

1. LocustWhat is it?

Locust is an open source Python-basedPerformance Testingtool. It allows you to write and executeLoad Testing, simulates a large number of users accessing your system.

Summary of key points:

1. Conventional programming structure: In Locust, you can use Python's standard programming structures (such as loops, conditional statements, etc.) to define user behavior. This makes writing test scripts as easy as writing regular Python code.
2. Each user runs independently:Locust assigns a separate greenlet (lightweight process/coroutine) to each user, which allows your test scripts to work like normal blocking code without using callback functions or other asynchronous mechanisms.
3. Can be written and managed in the IDE: Because test scripts are pure Python code, you can write, debug, manage, and version control these test scripts in a familiar IDE instead of using some tools' XML or binary format.

Advantages:

• Concise and clear: Because Locust scripts are Python code, there is no additional learning cost, especially for developers who are familiar with Python.
• Collaborative and friendly: You can use a version control system (such as Git) to manage your test scripts, which makes team collaboration and code backtracking easier.

Through these features, Locust provides a powerful and flexible way to write load test scripts, allowing developers to express user behavior and test logic more naturally.

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2. Highlights of functions Greenlet

• The essence is to use high concurrency to simulate, and there will be no callback hell problem.

In Locust, each user runs independently in his own greenlet, which brings the following important benefits:

1. Writing code is more intuitive and simple

When each user is running in his own greenlet, you can write test scripts like you would write synchronous code without worrying about callback functions or asynchronous operations. This makes the code logic more intuitive, easy to understand and maintain. You can use Python's standard process control statements (e.g.if、forloops, etc.) to describe user behavior without switching context between asynchronous calls.

2. The code is more modular and readable

Since complex asynchronous patterns such as callbacks or promises are not required, your test code can be kept concise, modular and readable. You can write logic directly in the code without splitting the code into multiple parts. This not only reduces code complexity, but also improves the maintainability of the test script.

3. Reduces debugging difficulty

Asynchronous code usually introduces more debugging difficulties, as the problem may occur in any step of an asynchronous call. In Locust, user behavior is written as synchronized logic blocks, so when errors occur, positioning problems are simpler and more direct. You can use common Python debugging tools to troubleshoot problems step by step without dealing with the asynchronous call stack.

4. Better performance

greenlet is a lightweight coroutine that is very efficient in performance and resource usage. Greenlet consumes much less system resources than creating a thread or process, which means you can simulate more user behavior without significantly increasing the load on the system. This is especially beneficial for high concurrency testing, as you can simulate more users on the same machine.

5. Improve concurrent processing capabilities

Since each user runs independently in their own greenlet, you can take advantage ofConcurrent programmingThe advantages of simulating thousands of users without encountering common problems in traditional multithreaded programming (such as thread locks, deadlocks, etc.). This allows Locust to more efficiently simulate large-scale user behavior, suitable for performance testing in high-concurrency scenarios.

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3. Detailed explanation of the Locust parameter command form:

• The following is a command from the official website, which contains two concepts we need to understand first:locust --headless --users 10 --spawn-rate 1 -H <>

  When using Locust for load testing,--headlessand--spawn-rateare two important parameters that determine how the test runs and how quickly the user generates. Here is an explanation of these two parameters:

  1. -headless

  • explain：
    • -headlessThe parameter indicates that Locus is running in headless mode, which means that the test will be executed on the command line, rather than controlled and monitored through Locus's web interface.
  • Application scenarios：
    • This pattern is often used to automate tests, continuous integration (CI) pipelines, or to run tests in environments without GUIs (such as remote servers).
  • effect：
    • Start tests directly from the command line, suitable for scenarios where you want to perform load tests automatically in the background or in scripts.

  2. -spawn-rate

  • explain：

    • -spawn-rateThe parameter specifies the rate at which the user is generated, that is, how many users are generated per second.

  • Application scenarios：

    • This parameter allows you to control the growth rate of load tests instead of generating all users immediately, making it easier to gradually increase pressure and observe the system's response.

  • example：

    • In the commandlocust --headless --users 10 --spawn-rate 1middle,-spawn-rate 1It means that 1 user is generated per second, so it takes 10 seconds to generate 10 users in total.

  • effect：

    • Controlling the rate of generating users can help test the performance of the system under different loads. For example, slowly increasing the number of users can simulate gradually increasing real traffic, while fast generation of all users is suitable for stress testing.

    3. -usersparameter

    • explain：
      • -usersParameters specify what to generate in the testTotal number of users, that is, the number of simulated concurrent users.
    • Application scenarios：
      • This parameter is used to define the scale of load tests. For example, if you want to simulate 1000 concurrent users accessing the system, you can-usersSet to 1000.
    • example：
      • In the commandlocust --headless --users 10 --spawn-rate 1middle,-users 10Indicates that the test will generate a total of 10 concurrent users.
    • effect：
      • By setting-users, you can control the number of concurrent users in the test, and then simulate user traffic of different scales. This helps evaluate the performance of the system under different concurrent loads.

  Summarize

  • -headless: Run Locust in headless mode, avoiding the use of web interfaces, suitable for automated and GUI-free environments.
  • -spawn-rate: Specify the user-generated rate, control the growth rate of load, so as to gradually increase pressure and monitor system performance.
  • -usersParameters are used to specify the total number of users to simulate in the load test, helping you control the number of concurrent users to test the load capacity of the system.

  These three parameters can be used in combination to flexibly control the execution method of load tests and the increase speed of pressure.

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4. Implement Locust

• The prerequisite for this test is that you implement a hello interface

from locust import HttpUser, task



 




class HelloWorldUser(HttpUser):



    @task




    def hello_world(self):



        self.("/hello")

AI writing code

After running the locust command:

locust



[2021-07-24 09:58:46,215] .../INFO/: Starting web interface at <http://0.0.0.0:8089>




[2021-07-24 09:58:46,285] .../INFO/: Starting Locust 0.0.1.dev239


AI writing code

Open http://localhost:8089

You need to modify this host to change the cost address: For example, the address of hello's interface: localhost:8080

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