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

相关推荐
YYYYYY0202017 分钟前
材质及制作笔记
笔记·材质
相醉为友1 小时前
001 使用单片机实现的逻辑分析仪——吸收篇
笔记·单片机·嵌入式硬件·fpga开发·嵌入式
YuQiao03031 小时前
word光标一直闪的解决办法
笔记·其他
余多多_zZ2 小时前
HarmonyOSNext_API16_媒体查询
笔记·学习·华为·harmonyos·媒体
【云轩】2 小时前
《哪吒的混天绫FPGA》
笔记·嵌入式硬件·fpga开发
freexyn2 小时前
Matlab自学笔记四十九：类型识别：判断数据的类型和类别
数据结构·笔记·matlab
struggle20252 小时前
AWS Bedrock 多代理蓝图存储库使用 CDK、Streamlit 和 LangFuse 运行 AWS Bedrock 多代理 AI 协作的蓝图
运维·人工智能·自动化·云计算·aws
Revendell2 小时前
AWS Aurora存算分离架构
aws
V---scwantop---信3 小时前
复古半色调褶皱照片效果ps特效滤镜样机 Halftone Crumpled Paper Effect
笔记
SuperHeroWu73 小时前
【HarmonyOS 5】初学者如何高效的学习鸿蒙？
学习·华为·harmonyos·鸿蒙·入门·初学者·学习思路
热门推荐
01【笔记】MJ Prompt02RAG 实践- Ollama+RagFlow 部署本地知识库03DeepSeek各版本说明与优缺点分析04uniApp使用XR-Frame创建3D场景(3)光源投影的运用。05本地化部署AI知识库：基于Ollama+DeepSeek+AnythingLLM保姆级教程06从零安装 LLaMA-Factory 微调 Qwen 大模型成功及所有的坑07【通信中间件】Fdbus HelloWorld实例08苍穹外卖面试总结09如何在WPS和Word/Excel中直接使用DeepSeek功能10PyTorch量化技术教程：第四章 PyTorch在量化交易中的应用