aws(学习笔记第二十四课) 使用sam开发step functions

aws(学习笔记第二十四课)

• 使用sam开发step functions

学习内容：

• 生成sam的step functions实例程序
• 什么是SAM amazon Serverless Application Model
• SAM程序结构
• SAM执行程序

1. 生成sam的step functions实例程序

1. 参照文档
   这里参照AWS的官方文档SAM amazon Serverless Application Model
2. 什么是SAM amazon Serverless Application Model
   • 整体架构
     SAM就是一个基于Cloudformation的应用程序框架，主要目的正如名字(Serverless Application Model)，方便进行Serverless Application的开发。
     开发的一般步骤如下：
     • 开发lambda等serverless application。
     • 利用step functions等serverless application。当然，其中可以调用lambda。
     • 之后利用上面的serverless application，进行Cloudformation的template定义。
     • 最后经过sam build和sam deploy部署到AWS的环境中。
       

3. SAM程序结构

1. 开始使用SAM
   • 这里使用SAM提供的实例程序进行练习
     • HourlyTradingSchedule是一个AWS EventBridge的规则，这里定义了股票投资程序的调用周期。类似于linux的cron job。
     • StockTradingStateMachine就是股票交易的StateMachine。
     • 股票交易的StateMachine里面包括三个lambda
       • StockCheckerFunction这里随机产生股票的价格（进行简单的股票市场的模拟）
       • 中间其实有一个choice state，进行判断。这里没有画出来
       • 之后根据判断，如果股票高过某个固定价格，那么进行StockBuyerFunction的调用
       • 如果股票高过某个固定价格，那么进行StockSellerFunction的调用
       • 最后，不管买还是卖的操作，都进行TransactionTable的写入（使用DynamoDB记录交易）
         
   • 进行实际代码的实验
     • 实验环境
       这里还是使用非常给力的工具CloudShell。
       
     • 构建代码
       • 创建代码的父路径

         mkdir demo-sam
         cd demo-sam
         

       • 使用sam生成股票实例代码（这个代码是sam自带的）
         
         之后进行一些runtime的相关设定。
         
         到这里，代码就会被生成出来，而且cloudformation的template文件都是yaml格式的。
         
   • 将cloudshell环境中的代码通过S3取到本地
     • 创建传输文件的S3 bucket
       因为cloudshell不是很容易和本地传输文件，所以使用S3 bucket。
       
     • 将SAM代码打包，copy到S3 bucket

       tar zcvf demo-sam.tar.gz demo-sam/
       aws s3 cp demo-sam.tar.gz s3://finlay-cloudshell/
       

       
     • 将SAM init生成的实例程序代码，下载到本地
       
       本地文件夹如下所示。
       
     • 将SAM init生成的实例程序代码使用vscode打开（这里单纯的可以更加容易编辑代码）
       • template文件

         AWSTemplateFormatVersion: '2010-09-09'
         Transform: AWS::Serverless-2016-10-31
         Description: |demo-samSample SAM Template for demo-samResources:StockTradingStateMachine:Type: AWS::Serverless::StateMachine # More info about State Machine Resource: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-resource-statemachine.htmlProperties:DefinitionSubstitutions:StockCheckerFunctionArn: !GetAtt StockCheckerFunction.ArnStockSellerFunctionArn: !GetAtt StockSellerFunction.ArnStockBuyerFunctionArn: !GetAtt StockBuyerFunction.ArnDDBPutItem: !Sub arn:${AWS::Partition}:states:::dynamodb:putItemDDBTable: !Ref TransactionTableEvents:HourlyTradingSchedule:Type: Schedule # More info about Schedule Event Source: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-property-statemachine-schedule.htmlProperties:Description: Schedule to run the stock trading state machine every hourEnabled: false # This schedule is disabled by default to avoid incurring charges.Schedule: rate(1 hour)Policies:# Find out more about SAM policy templates: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html- LambdaInvokePolicy:FunctionName: !Ref StockCheckerFunction- LambdaInvokePolicy:FunctionName: !Ref StockSellerFunction- LambdaInvokePolicy:FunctionName: !Ref StockBuyerFunction- DynamoDBWritePolicy:TableName: !Ref TransactionTableDefinitionUri: statemachine/stock_trader.asl.jsonStockCheckerFunction:Type: AWS::Serverless::Function # More info about Function Resource: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-resource-function.htmlProperties:CodeUri: functions/stock_checker/Handler: app.lambda_handlerRuntime: python3.9Architectures:- x86_64StockSellerFunction:Type: AWS::Serverless::FunctionProperties:CodeUri: functions/stock_seller/Handler: app.lambda_handlerRuntime: python3.9Architectures:- x86_64StockBuyerFunction:Type: AWS::Serverless::FunctionProperties:CodeUri: functions/stock_buyer/Handler: app.lambda_handlerRuntime: python3.9Architectures:- x86_64TransactionTable:Type: AWS::Serverless::SimpleTable # More info about SimpleTable Resource: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-resource-simpletable.htmlProperties:PrimaryKey:Name: IdType: StringProvisionedThroughput:ReadCapacityUnits: 1WriteCapacityUnits: 1ApplicationResourceGroup:Type: AWS::ResourceGroups::GroupProperties:Name: !Sub ApplicationInsights-SAM-${AWS::StackName}ResourceQuery:Type: CLOUDFORMATION_STACK_1_0ApplicationInsightsMonitoring:Type: AWS::ApplicationInsights::ApplicationProperties:ResourceGroupName: !Ref ApplicationResourceGroupAutoConfigurationEnabled: 'true'
         Outputs:# StockTradingStateMachineHourlyTradingSchedule is an implicit Schedule event rule created out of Events key under Serverless::StateMachine# Find out more about other implicit resources you can reference within SAM# https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-specification-generated-resources.htmlStockTradingStateMachineArn:Description: Stock Trading State machine ARNValue: !Ref StockTradingStateMachineStockTradingStateMachineRoleArn:Description: IAM Role created for Stock Trading State machine based on thespecified SAM Policy TemplatesValue: !GetAtt StockTradingStateMachineRole.Arn
         

         使用vscode打开template文件，可以看到AWS service的图形化结构。
         
       • stock_checker文件 随机产生stock的价格

         from random import randintdef lambda_handler(event, context):"""Sample Lambda function which mocks the operation of checking the current price of a stock.For demonstration purposes this Lambda function simply returns a random integer between 0 and 100 as the stock price.Parameters----------event: dict, requiredInput event to the Lambda functioncontext: object, requiredLambda Context runtime methods and attributesReturns------dict: Object containing the current price of the stock"""# Check current price of the stockstock_price = randint(0, 100)  # Current stock price is mocked as a random integer between 0 and 100return {"stock_price": stock_price}
         

       • stock_buyer文件 如果价格低，选择买进

         from datetime import datetime
         from random import randint
         from uuid import uuid4def lambda_handler(event, context):"""Sample Lambda function which mocks the operation of buying a random numberof shares for a stock.For demonstration purposes, this Lambda function does not actually perform any actual transactions. It simply returns a mocked result.Parameters----------event: dict, requiredInput event to the Lambda functioncontext: object, requiredLambda Context runtime methods and attributesReturns------dict: Object containing details of the stock buying transaction"""# Get the price of the stock provided as inputstock_price = event["stock_price"]# Mocked result of a stock buying transactiontransaction_result = {"id": str(uuid4()),  # Unique ID for the transaction"price": str(stock_price),  # Price of each share"type": "buy",  # Type of transaction (buy/sell)"qty": str(randint(1, 10)),  # Number of shares bought/sold (We are mocking this as a random integer between 1 and 10)"timestamp": datetime.now().isoformat(),  # Timestamp of the when the transaction was completed}return transaction_result
         

       • stock_seller文件 如果价格高，选择抛出

         from datetime import datetime
         from random import randint
         from uuid import uuid4def lambda_handler(event, context):"""Sample Lambda function which mocks the operation of selling a random numberof shares for a stock.For demonstration purposes, this Lambda function does not actually perform any actual transactions. It simply returns a mocked result.Parameters----------event: dict, requiredInput event to the Lambda functioncontext: object, requiredLambda Context runtime methods and attributesReturns------dict: Object containing details of the stock selling transaction"""# Get the price of the stock provided as inputstock_price = event["stock_price"]# Mocked result of a stock selling transactiontransaction_result = {"id": str(uuid4()),  # Unique ID for the transaction"price": str(stock_price),  # Price of each share"type": "sell",  # Type of transaction (buy/sell)"qty": str(randint(1, 10)),  # Number of shares bought/sold (We are mocking this as a random integer between 1 and 10)"timestamp": datetime.now().isoformat(),  # Timestamp of the when the transaction was completed}return transaction_result
         

       • step functions文件 将上面的lambda函数穿插起来，形成一个工作流程编排

         {"Comment": "A state machine that does mock stock trading.","StartAt": "Check Stock Value","States": {"Check Stock Value": {"Type": "Task","Resource": "${StockCheckerFunctionArn}","Retry": [{"ErrorEquals": ["States.TaskFailed"],"IntervalSeconds": 15,"MaxAttempts": 5,"BackoffRate": 1.5}],"Next": "Buy or Sell?"},"Buy or Sell?": {"Type": "Choice","Choices": [{"Variable": "$.stock_price","NumericLessThanEquals": 50,"Next": "Buy Stock"}],"Default": "Sell Stock"},"Sell Stock": {"Type": "Task","Resource": "${StockSellerFunctionArn}","Retry": [{"ErrorEquals": ["States.TaskFailed"],"IntervalSeconds": 2,"MaxAttempts": 3,"BackoffRate": 1}],"Next": "Record Transaction"},"Buy Stock": {"Type": "Task","Resource": "${StockBuyerFunctionArn}","Retry": [{"ErrorEquals": ["States.TaskFailed"],"IntervalSeconds": 2,"MaxAttempts": 3,"BackoffRate": 1}],"Next": "Record Transaction"},"Record Transaction": {"Type": "Task","Resource": "${DDBPutItem}","Parameters": {"TableName": "${DDBTable}","Item": {"Id": {"S.$": "$.id"},"Type": {"S.$": "$.type"},"Price": {"N.$": "$.price"},"Quantity": {"N.$": "$.qty"},"Timestamp": {"S.$": "$.timestamp"}}},"Retry": [{"ErrorEquals": ["States.TaskFailed"],"IntervalSeconds": 20,"MaxAttempts": 5,"BackoffRate": 10}],"End": true}}
         }
         

       检查vscode的sam预览功能
       
   • 回到cloudshell执行sam build和sam deploy部署到AWS

     sam build
     sam deploy
     

     可以看到，本质上这个sam application还是使用Cloudformation进行部署。
     
     查看Cloudformation进一步验证了想定结果。

4. 执行sam的step functions实例程序

1. 打开默认的aws eventbridge的规则设置
   默认是禁用的，编辑这里，打开禁用。
   

2. 查看执行结果
   可以看出，已经成功执行一次

   
   股票价格为2，执行了buy stock lambda