![\"输出结果\"](\"https://img.serverlesscloud.cn/2020114/1578989799994-part-00492-780.jpg\")
\n\nOnline Judge 系统（简称 OJ）是一个在线的判题系统。用户可以在线提交多种程序（如C、C++、Pascal）源代码，系统对源代码进行编译和执行，并通过预先设计的测试数据来检验程序源代码的正确性。
\n
随着时代的发展，OJ 已经真正地成为了测评工具，其作用不再局限为 ACM 备战，还有老师检测学生能力、学生入学考试、能力评测（例如 ZJU 的 PAT）、找工作刷题和面试（例如牛客）等，而目前 OJ 的开源框架也越来越多，但是很多 OJ 都是基于 HUSTOJ 进行定制或者二次开发。
\n无论是什么方法，在关于 OJ 的众多问题中，有一个就是：性能问题。
\n说实话，一些 OJ 群里，总会有人问：1 核 1G 的机器，可以同时判多少题目？可以有多少人同时用？如果比赛，大约有多少人需要多高性能的机器？那么『判题姬』是否只能存在传统的宿主机中，能否通过其他方式焕发新的生命力？
\n其中一种方法，就是和现有的云函数进行结合。
\n通过云函数实现在线编程的思路基本有两个：
\n这两种方法，第一种相对简单的，但是目前对于很多云函数服务商来说，函数数量有一定限制，而且每次执行这个操作相对比较繁琐。
\n所以，本文采用第二种策略，建立一个函数，每次执行，用户传入代码，系统执行，返回结果。
\n代码写入系统：
\ndef WriteCode(code):\n try:\n with open("/tmp/mytest.py", "w") as f:\n f.write(code)\n return True\n except Exception as e:\n print(e)\n return False执行代码：
\ndef RunCode(input_data=None):\n child = subprocess.Popen("python /tmp/mytest.py", stdin=input_data, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True, shell=True)\n error = child.stderr.read()\n output = child.stdout.read()\n return error, output代码和用例处理逻辑：
\ndef main_handler(event, context):\n if WriteCode(event["code"]):\n try:\n temp_list = []\n for eve in event["input"]:\n result = RunCode()\n temp_list.append({"error":result[0].decode("utf-8"),"result": result[1].decode("utf-8"), "exception":""})\n return json.dumps(temp_list)\n except Exception as e:\n return json.dumps({"error":"","result": "", "exception":str(e)})用户在传入数据的时候，需要注意事件为：
\n{\n "code": "print('hello')",\n "input": ["111","22222"]\n}这样就可以每次请求的时候把代码传入（code），每个测试用例的 input 就是 input 内容。
\n以本题输出结果：
\n
这样就实现了 Python 判题机的基本功能，此时通过 腾讯云云 API 实现参数传入，通过 Explorer 进行代码撰写，直接接入自己的 OJ 就可以了。
\n虽然这是一个简单的代码执行工具，但是这个小工具还可以应用在很多其他地方。本文只是抛砖引玉，例如我们做了一个 OJ，如果在本地跑代码可能性能和安全性都会受到挑战，那么此时，放入腾讯云云函数中，就会简单、安全、便捷得多，最主要的是腾讯云的函数调用免费额度很高。
\n此外，如果临时举办比赛，也不用费心费力扩容缩容，只要有云函数，后端的主要压力，都传给 Serverless 搞定，这也算是发挥了云函数的一个优势和特性。
\n那么，除了在 OJ 中使用的用途，它还有啥用？简单举两个例子：
\n除此之外，还有好多的用途，各位小伙伴们，快来自己挖掘吧！
\n\n\n传送门：
\n\n
\n- GitHub: github.com/serverless
\n- 官网：serverless.com
\n
欢迎访问：Serverless 中文网，您可以在 最佳实践 里体验更多关于 Serverless 应用的开发！
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