![](\"https://img.serverlesscloud.cn/202033/1583244075920-1.png\")
在上一篇文章《Serverless 的资源评估与成本探索》中，我们对性能和成本探索进行了些思考，在此就引出一个新的问题：当我们使用 Serverless 架构的时候，如何设置运行内存和超时时间呢？这里分享下我的评估方法供大家参考。
\n首先在函数上线时，选择一个稍微大一点的内存。例如，这里执行一次函数，得到下图结果：
\n
那么将我的函数设置为 128M 或者 256M，超时时间设置成 3S。
\n让函数跑一段时间，例如该接口每天触发约为 4000 次：
\n![](\"https://img.serverlesscloud.cn/202033/1583244075839-1.png\")
将这个函数的日志捞出来写成脚本，做统计：
\n import json, time, numpy, base64\n import matplotlib.pyplot as plt\n from matplotlib import font_manager\n from tencentcloud.common import credential\n from tencentcloud.common.profile.client_profile import ClientProfile\n from tencentcloud.common.profile.http_profile import HttpProfile\n from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException\n from tencentcloud.scf.v20180416 import scf_client, models\n \n secretId = ""\n secretKey = ""\n region = "ap-guangzhou"\n namespace = "default"\n functionName = "course"\n \n font = font_manager.FontProperties(fname="./fdbsjw.ttf")\n \n try:\n cred = credential.Credential(secretId, secretKey)\n httpProfile = HttpProfile()\n httpProfile.endpoint = "scf.tencentcloudapi.com"\n \n clientProfile = ClientProfile()\n clientProfile.httpProfile = httpProfile\n client = scf_client.ScfClient(cred, region, clientProfile)\n \n req = models.GetFunctionLogsRequest()\n \n strTimeNow = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(time.time())))\n strTimeLast = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(time.time()) - 86400))\n params = {\n "FunctionName": functionName,\n "Limit": 500,\n "StartTime": strTimeLast,\n "EndTime": strTimeNow,\n "Namespace": namespace\n }\n req.from_json_string(json.dumps(params))\n \n resp = client.GetFunctionLogs(req)\n \n durationList = []\n memUsageList = []\n \n for eveItem in json.loads(resp.to_json_string())["Data"]:\n durationList.append(eveItem['Duration'])\n memUsageList.append(eveItem['MemUsage'] / 1024 / 1024)\n \n durationDict = {\n "min": min(durationList), # 运行最小时间\n "max": max(durationList), # 运行最大时间\n "mean": numpy.mean(durationList) # 运行平均时间\n }\n memUsageDict = {\n "min": min(memUsageList), # 内存最小使用\n "max": max(memUsageList), # 内存最大使用\n "mean": numpy.mean(memUsageList) # 内存平均使用\n }\n \n plt.figure(figsize=(10, 15))\n plt.subplot(4, 1, 1)\n plt.title('运行次数与运行时间图', fontproperties=font)\n x_data = range(0, len(durationList))\n plt.plot(x_data, durationList)\n plt.subplot(4, 1, 2)\n plt.title('运行时间直方分布图', fontproperties=font)\n plt.hist(durationList, bins=20)\n plt.subplot(4, 1, 3)\n plt.title('运行次数与内存使用图', fontproperties=font)\n x_data = range(0, len(memUsageList))\n plt.plot(x_data, memUsageList)\n plt.subplot(4, 1, 4)\n plt.title('内存使用直方分布图', fontproperties=font)\n plt.hist(memUsageList, bins=20)\n\n\n with open("/tmp/result.png", "rb") as f:\n base64_data = base64.b64encode(f.read())\n \n\n print("-" * 10 + "运行时间相关数据" + "-" * 10)\n print("运行最小时间:\\t", durationDict["min"], "ms")\n print("运行最大时间:\\t", durationDict["max"], "ms")\n print("运行平均时间:\\t", durationDict["mean"], "ms")\n \n print("\\n")\n \n print("-" * 10 + "内存使用相关数据" + "-" * 10)\n print("内存最小使用:\\t", memUsageDict["min"], "MB")\n print("内存最大使用:\\t", memUsageDict["max"], "MB")\n print("内存平均使用:\\t", memUsageDict["mean"], "MB")\n \n print("\\n")\n \n plt.show(dpi=200)\n except TencentCloudSDKException as err:\n print(err)运行结果：
\n ----------运行时间相关数据----------\n 运行最小时间:\t 6.02 ms\n 运行最大时间:\t 211.22 ms\n 运行平均时间:\t 54.79572 ms\n \n ----------内存使用相关数据----------\n 内存最小使用:\t 17.94921875 MB\n 内存最大使用:\t 37.21875190734863 MB\n 内存平均使用:\t 24.83201559448242 MB![](\"https://img.serverlesscloud.cn/202033/1583244075858-1.png\")
通过该结果可以清楚看出，近 500 次，每次函数的时间消耗和内存使用。
\n可以看到时间消耗基本在 1S 以下，所以此处「超时时间」设置成 1S 比较合理；而内存使用基本是 64M 以下，所以此时内存设置成 64M 就可以。
\n再举个例子，对于另外一个函数：
\n ----------运行时间相关数据----------\n 运行最小时间:\t 63445.13 ms\n 运行最大时间:\t 442629.12 ms\n 运行平均时间:\t 91032.31301886792 ms\n\n \n ----------内存使用相关数据----------\n 内存最小使用:\t 26.875 MB\n 内存最大使用:\t 58.69140625 MB\n 内存平均使用:\t 36.270415755937684 MB![](\"https://img.serverlesscloud.cn/202033/1583244076054-1.png\")
假如说上一个函数，是一个非常平稳和光滑的函数，很容易预估资源使用率，那么这个函数则可以很明显看出波动。
\n运行时间绝大部分在 150S 以下，部分不到 200S，最高峰值近 450S。这个时候，我们就可以业务需求来判定，450S 的请求波峰是否可以被中止。此时，我推荐将这个函数的超时时间设置为 200S。
\n至于内存部分，可以看到绝大部分都在 40MB 以内，部分出现在 45-55MB，最高未超过 60MB，所以此时可以将函数设置为 64MB。
\n就目前来说，云函数在执行时可能会有一定的波动。因此内存使用或超时时间在范围内波动是很正常的，我们可以根据业务需求来做一些设置，将资源使用量压到最低，节约成本。
\n我的做法基本就是分为两步走：
\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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