foreword
This article will discuss with you the actual project of python concurrent programming: Locust performance test (part 1, a total of N articles) . The series of articles will build the project from scratch, gradually improve the project, and finally make the project suitable for high concurrency scenarios Applications.
This article is the twenty-seventh article of python concurrent programming. The address of the previous article is as follows:
Python: Concurrent Programming (26)_Lion King's Blog-CSDN Blog
The address of the next article is as follows:
Python: Concurrent Programming (28)_Lion King's Blog-CSDN Blog
1. Locust
1. What is Locust?
Locust in Python is an open source performance testing tool and framework for simulating a large number of concurrent users and evaluating the performance of web applications or other network services. It was developed using the Python programming language to provide a simple yet powerful performance testing solution.
With Locust, you can write scripts that define user behavior, and then run those user scripts in a concurrent fashion. You can assign each user to perform specific tasks for a period of time, such as visiting a specific URL, submitting a form, or performing other actions. Locust provides a user-friendly web interface to monitor and control the testing process, including the number of concurrent users, request success rate, response time and other indicators.
Locust also supports distributed performance testing, allowing you to spread the load across multiple physical or virtual machines to simulate larger-scale user access. It also provides a wealth of statistics and graphs to help you analyze and understand the performance characteristics of your application.
The general steps for performance testing with Locust include scripting user behavior, defining tasks and load patterns, launching the test and monitoring the results, and then analyzing the collected performance data.
All in all, Locust in Python is a powerful and easy-to-use performance testing tool that helps developers and testers evaluate the performance and loadability of web applications.
2. Locust technology stack
Locust is based on multi-coroutines. It uses coroutines to implement concurrent performance testing. In Locust, each simulated user is considered a coroutine, and multiple coroutines can be executed simultaneously to simulate multiple concurrent users.
Locust uses the Greenlet library to implement coroutines. Greenlet is a lightweight coroutine library for Python that allows concurrent execution within a single thread. By using coroutines, Locust can simultaneously simulate a large number of concurrent users in one process without creating a thread or process for each user.
Coroutines provide a lightweight concurrency model because they involve no thread or process switching overhead. Compared with traditional multi-thread or multi-process models, coroutines can more efficiently handle a large number of concurrent users while reducing resource consumption.
By utilizing coroutines, Locust can achieve high-performance and high-scalability performance testing. It is capable of simulating large numbers of concurrent users and generating high loads while keeping resource footprint low. This makes Locust a powerful and efficient performance testing tool.
2. Is the coroutine really useful?
1. Concurrency comparison of specific scenarios of processes, threads, and coroutines
In the previous chapters, we haven't seen the ability of coroutines. Is it really useful? Now, let's feel it through the following code:
import asyncio
import multiprocessing
import threading
import time
# 多协程示例
async def coro_task(name):
print(f"Start task: {name}")
await asyncio.sleep(1)
print(f"Complete task: {name}")
async def coro_main():
tasks = []
for i in range(50):
tasks.append(coro_task(f"Task {i+1}"))
await asyncio.gather(*tasks)
# 多进程示例
def proc_task(name):
print(f"Start task: {name}")
time.sleep(1)
print(f"Complete task: {name}")
def proc_main():
processes = []
for i in range(50):
p = multiprocessing.Process(target=proc_task, args=(f"Task {i+1}",))
processes.append(p)
p.start()
for p in processes:
p.join()
# 多线程示例
def thread_task(name):
print(f"Start task: {name}")
time.sleep(1)
print(f"Complete task: {name}")
def thread_main():
threads = []
for i in range(50):
t = threading.Thread(target=thread_task, args=(f"Task {i+1}",))
threads.append(t)
t.start()
for t in threads:
t.join()
# 计算耗时
if __name__ == "__main__":
a = time.time()
# 多协程示例
asyncio.run(coro_main())
b = time.time()
# 多进程示例
proc_main()
c = time.time()
# 多线程示例
thread_main()
d = time.time()
print("协程：", b-a)
print("进程：", c-b)
print("线程：", d-c)
The above code shows examples of multi-coroutine, multi-process and multi-thread, and calculates their time consumption. The profiling section in the code compares the timing of each example.
In the code in the analysis section, the start time is first recorded (variable a), then the multi-coroutine example is run, and the end time is recorded (variable b). Next run the multi-process example, record the end time (variable c), and finally run the multi-threaded example and record the end time (variable d).
Then, by calculating the difference of the variables ba, cb and dc, the time consumption of each example is obtained. Finally, use the print statement to print out the time-consuming results of each example.
The purpose of this code is to compare the time-consuming situation of multi-coroutine, multi-process and multi-thread when executing 50 tasks. Through the printed results, you can observe the time-consuming difference of each concurrency model.
Note that exact timing results may vary by system and hardware. In addition, it should be noted that the multi-coroutine model may perform better when processing a large number of IO-intensive tasks due to its lightweight and efficient features, while the multi-process and multi-thread model may be better at processing CPU-intensive tasks or need to utilize It is more suitable for multi-core processor scenarios. Therefore, when choosing a concurrency model, it is necessary to comprehensively consider the characteristics and requirements of the application.
2. Advantages of multi-coroutine in certain scenarios
The multi-coroutine model has the following advantages over the multi-process and multi-thread models:
(1) Memory consumption: The multi-coroutine model consumes less memory than the multi-process and multi-thread model, because the coroutines share the same stack space.
(2) Context switching overhead: The context switching overhead of the multi-coroutine model is smaller, because only a small amount of context information needs to be saved and restored.
(3) Scalability: The multi-coroutine model can create a large number of coroutines in a single thread and perform high-concurrency execution, which has better scalability.
(4) IO-intensive tasks: The multi-coroutine model can achieve efficient IO processing and make full use of system resources through non-blocking IO operations and event loop mechanisms.
It should be noted that the choice of concurrency model should be based on specific application requirements and scenarios. In some cases, a multi-process or multi-thread model may be better suited to handle CPU-intensive tasks or specific system requirements. Therefore, make a choice according to the specific situation, and conduct performance tests and evaluations in actual applications to determine the best concurrency model.