CloudBluePrint-Chapter 1.7 : 云上应用技术架构-函数计算、Serverless、小程序

def handle(req):
    """handle a request to the function
    Args:
        req (str): request body
    """
    return req[::-1]

def reverse(event, context):
    return event['data'][::-1]

kubeless function deploy reverse --runtime python3.7 --from-file reverse.py --handler reverse.reverse

function main(params) {
    return {payload: params.payload.split("").reverse().join("")};
}

wsk action create reverse reverse.js

package main

import (
	"fmt"
	"net/http"
)

func reverseHandler(w http.ResponseWriter, r *http.Request) {
	msg := []rune(r.URL.Query().Get("msg"))
	for i, j := 0, len(msg)-1; i < j; i, j = i+1, j-1 {
		msg[i], msg[j] = msg[j], msg[i]
	}
	fmt.Fprint(w, string(msg))
}

func main() {
	http.HandleFunc("/", reverseHandler)
	http.ListenAndServe(":8080", nil)
}

apiVersion: serving.knative.dev/v1 # The version of the Knative API to use
kind: Service
metadata:
  name: reverse # The name of the app
  namespace: default # The namespace the app will use
spec:
  template:
    spec:
      containers:
        - image: gcr.io/my-project/reverse # The URL to the image of the app
          ports:
            - containerPort: 8080

def lambda_handler(event, context):
    message = event['message'][::-1]
    return {
        'message' : message
    }

module.exports.hello = async event => {
  return {
    statusCode: 200,
    body: JSON.stringify(
      {
        message: 'Go Serverless v1.0! Your function executed successfully!',
        input: event,
      },
      null,
      2
    ),
  };
};

import Amplify from 'aws-amplify';
import awsconfig from './aws-exports';

Amplify.configure(awsconfig);

// 在应用中使用 AWS 服务

exports.helloWorld = (req, res) => {
  res.send('Hello, World');
};

def hello_http(request):
    return 'Hello World!'

var firebaseConfig = {
  // ...
};
firebase.initializeApp(firebaseConfig);

module.exports = async function (context, req) {
    context.res = {
        body: "Hello, World"
    };
}

public static class Function1
{
    [FunctionName("Function1")]
    public static async Task<IActionResult> Run(
        [HttpTrigger(AuthorizationLevel.Function, "get", "post", Route = null)] HttpRequest req)
    {
        return new OkObjectResult("Hello, World");
    }
}

var client = new WindowsAzure.MobileServiceClient('https://your-service.azurewebsites.net');

def handler(event, context):
  print("hello world")
  return 'hello world'

exports.handler = function(event, context, callback) {
  callback(null, 'hello world');
};

var client = new OSS({
  region: '<Your region>',
  accessKeyId: '<Your AccessKeyId>',
  accessKeySecret: '<Your AccessKeySecret>',
});

exports.main = async (event, context) => {
  return event
}

module.exports.hello = async (event, context) => {
  return {
    message: 'Hello, Tencent Cloud!'
  }
}

wx.cloud.init({
  env: 'my-env-id',
  traceUser: true,
})

import os
from PIL import Image

def handle(event, context):
  # 获取上传的文件信息
  bucket_name = event['oss']['bucket']['name']
  object_key = event['oss']['object']['key']
  
  # 打开图片并进行处理
  with Image.open('/tmp/' + object_key) as img:
    # 进行图片压缩、裁剪等操作
    img.thumbnail((800, 800))
    img.save('/tmp/' + object_key)

  # 将处理后的图片上传到云存储
  s3_client.upload_file('/tmp/' + object_key, bucket_name, object_key)

import json
import boto3

def handle(event, context):
  # 获取日志事件
  log_events = event['awslogs']['data']

  # 对日志事件进行解析和处理
  for log_event in log_logs:
    # 进行实时分析，如用户行为统计等
    analysis_result = analyze(log_event)

  # 将分析结果保存到数据库或发送报告
  db_client.put_item(
    TableName='analysis_result',
    Item=analysis_result
  )

import boto3

def handle(event, context):
  # 获取当前时间
  current_time = context['time']

  # 清理过期数据
  db_client.delete_items(
    TableName='user_data',
    KeyConditionExpression='expire_time < :current_time',
    ExpressionAttributeValues={':current_time': current_time}
  )

  # 或者定时发送用户活跃度报告
  report = generate_report()
  email_client.send_email(
    Subject='Daily Active User Report',
    Body=report,
    ToAddresses=['admin@example.com']
  )

def handle(event, context):
  # 获取设备数据
  device_data = event['device_data']

  # 对设备数据进行处理，如温度监控和报警
  if device_data['temperature'] > THRESHOLD:
    send_alert(device_data['device_id'], 'Temperature is too high!')

def handle(event, context):
  # 获取上传的视频信息
  bucket_name = event['oss']['bucket']['name']
  object_key = event['oss']['object']['key']

  # 对视频进行处理，如转码或内容审核
  transcode_video(bucket_name, object_key)

import boto3

def handle(event, context):
  # 获取上传的图片信息
  bucket_name = event['s3']['bucket']['name']
  object_key = event['s3']['object']['key']

  # 调用 AI 模型进行预测
  prediction = predict_image(bucket_name, object_key)

  # 返回预测结果
  return {
    'statusCode': 200,
    'body': json.dumps(prediction)
  }

import boto3

def handle(event, context):
  # 获取订单信息
  order_info = event['Records'][0]['body']

  # 对订单进行处理，如库存检查和订单确认
  check_stock(order_info)
  confirm_order(order_info)