aws(学习笔记第二十四课) 使用sam开发step functions

sealaugh322025-01-15 1:09

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

          shell 复制代码 
          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

        shell 复制代码 
        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

          yaml 复制代码 
          AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: |
  demo-sam
  Sample SAM Template for demo-sam

Resources:
  StockTradingStateMachine:
    Type: AWS::Serverless::StateMachine # More info about State Machine Resource: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-resource-statemachine.html
    Properties:
      DefinitionSubstitutions:
        StockCheckerFunctionArn: !GetAtt StockCheckerFunction.Arn
        StockSellerFunctionArn: !GetAtt StockSellerFunction.Arn
        StockBuyerFunctionArn: !GetAtt StockBuyerFunction.Arn
        DDBPutItem: !Sub arn:${AWS::Partition}:states:::dynamodb:putItem
        DDBTable: !Ref TransactionTable
      Events:
        HourlyTradingSchedule:
          Type: Schedule # More info about Schedule Event Source: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-property-statemachine-schedule.html
          Properties:
            Description: Schedule to run the stock trading state machine every hour
            Enabled: 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 TransactionTable
      DefinitionUri: statemachine/stock_trader.asl.json

  StockCheckerFunction:
    Type: AWS::Serverless::Function # More info about Function Resource: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-resource-function.html
    Properties:
      CodeUri: functions/stock_checker/
      Handler: app.lambda_handler
      Runtime: python3.9
      Architectures:
        - x86_64

  StockSellerFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: functions/stock_seller/
      Handler: app.lambda_handler
      Runtime: python3.9
      Architectures:
        - x86_64

  StockBuyerFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: functions/stock_buyer/
      Handler: app.lambda_handler
      Runtime: python3.9
      Architectures:
        - x86_64

  TransactionTable:
    Type: AWS::Serverless::SimpleTable # More info about SimpleTable Resource: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/sam-resource-simpletable.html
    Properties:
      PrimaryKey:
        Name: Id
        Type: String
      ProvisionedThroughput:
        ReadCapacityUnits: 1
        WriteCapacityUnits: 1

  ApplicationResourceGroup:
    Type: AWS::ResourceGroups::Group
    Properties:
      Name: !Sub ApplicationInsights-SAM-${AWS::StackName}
      ResourceQuery:
        Type: CLOUDFORMATION_STACK_1_0
  ApplicationInsightsMonitoring:
    Type: AWS::ApplicationInsights::Application
    Properties:
      ResourceGroupName: !Ref ApplicationResourceGroup
      AutoConfigurationEnabled: '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.html
  StockTradingStateMachineArn:
    Description: Stock Trading State machine ARN
    Value: !Ref StockTradingStateMachine
  StockTradingStateMachineRoleArn:
    Description: IAM Role created for Stock Trading State machine based on the
      specified SAM Policy Templates
    Value: !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

          python 复制代码 
          from random import randint


def 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, required
        Input event to the Lambda function

    context: object, required
        Lambda Context runtime methods and attributes

    Returns
    ------
        dict: Object containing the current price of the stock
    """
    # Check current price of the stock
    stock_price = randint(
        0, 100
    )  # Current stock price is mocked as a random integer between 0 and 100
    return {"stock_price": stock_price}

        • stock_buyer文件 如果价格低，选择买进

          python 复制代码 
          from datetime import datetime
from random import randint
from uuid import uuid4


def lambda_handler(event, context):
    """Sample Lambda function which mocks the operation of buying a random number
    of 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, required
        Input event to the Lambda function

    context: object, required
        Lambda Context runtime methods and attributes

    Returns
    ------
        dict: Object containing details of the stock buying transaction
    """
    # Get the price of the stock provided as input
    stock_price = event["stock_price"]
    # Mocked result of a stock buying transaction
    transaction_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

          python 复制代码 
          from datetime import datetime
from random import randint
from uuid import uuid4


def lambda_handler(event, context):
    """Sample Lambda function which mocks the operation of selling a random number
    of 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, required
        Input event to the Lambda function

    context: object, required
        Lambda Context runtime methods and attributes

    Returns
    ------
        dict: Object containing details of the stock selling transaction
    """
    # Get the price of the stock provided as input
    stock_price = event["stock_price"]
    # Mocked result of a stock selling transaction
    transaction_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

          json 复制代码 
          {
    "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

      shell 复制代码 
      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

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