aws(学习条记第二十四课) 使用sam开辟step functions
aws(学习条记第二十四课)[*] 使用sam开辟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
[*]参照文档
这里参照AWS的官方文档SAM amazon Serverless Application Model
[*]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，重要目的正如名字(Serverless Application Model)，方便进行Serverless Application的开辟。
开辟的一般步调如下：
[*]开辟lambda等serverless application。
[*]利用step functions等serverless application。当然，此中可以调用lambda。
[*]之后利用上面的serverless application，进行Cloudformation的template定义。
[*]末了经过sam build和sam deploy摆设到AWS的环境中。
https://i-blog.csdnimg.cn/direct/e0dce68272cc4b2cb6837abf74967c33.png
3. SAM程序结构
[*]开始使用SAM
[*]这里使用SAM提供的实例程序进行训练
[*]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（进行简朴的股票市场的模仿）
[*]中心其实有一个choice state，进行判定。这里没有画出来
[*]之后根据判定，假如股票高过某个固定价格，那么进行StockBuyerFunction的调用
[*]假如股票高过某个固定价格，那么进行StockSellerFunction的调用
[*]末了，不管买照旧卖的操作，都进行TransactionTable的写入（使用DynamoDB记载交易）
https://i-blog.csdnimg.cn/direct/cc84136bf92f4c0f87de1ff17026d589.png
[*]进行实际代码的实验
[*]实验环境
这里照旧使用非常给力的工具CloudShell。
https://i-blog.csdnimg.cn/direct/b29984c6d43a4a00854b1073d76c8403.png
[*]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）
https://i-blog.csdnimg.cn/direct/0f435a87535b4936b2529b67ba31eb62.png
之后进行一些runtime的相干设定。
https://i-blog.csdnimg.cn/direct/c33f3c5e27084857a95125e2a38ae594.png
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。
https://i-blog.csdnimg.cn/direct/9a34f777f5af49cd95cd0b944b56d30a.png
[*]将cloudshell环境中的代码通过S3取到本地
[*]创建传输文件的S3 bucket
由于cloudshell不是很轻易和本地传输文件，所以使用S3 bucket。
https://i-blog.csdnimg.cn/direct/a73ac09974734bc0b0c36a377c5dca94.png
[*]将SAM代码打包，copy到S3 buckettar zcvf demo-sam.tar.gz demo-sam/
aws s3 cp demo-sam.tar.gz s3://finlay-cloudshell/
https://i-blog.csdnimg.cn/direct/05d74f5ce83f410fad69b2a31e71ccdd.png
[*]将SAM init生成的实例程序代码，下载到本地
https://i-blog.csdnimg.cn/direct/cceb7b35353f4106b1439ea066e38044.png
本地文件夹如下所示。
https://i-blog.csdnimg.cn/direct/821bcc2bc9944b629e4ce5e579dd7851.png
[*]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轻易编辑代码）
[*]template文件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。
https://i-blog.csdnimg.cn/direct/19ab52fa7c8040729a2d7d82ed205ea5.png
[*]stock_checker文件 随机产生stock的价格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 假如价格低，选择买进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 假如价格高，选择抛出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{
"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
}
}
}
查抄vscode的sam预览功能
https://i-blog.csdnimg.cn/direct/1d7e5c5bb81e4d1ab97e7727667a5ecb.png
[*]回到cloudshell执行sam build和sam deploy摆设到AWSsam build
sam deploy
可以看到，本质上这个sam application照旧使用Cloudformation进行摆设。
https://i-blog.csdnimg.cn/direct/53c2b08fcbe24037b7790ae7cb775d82.png
检察Cloudformation进一步验证了想定结果。https://i-blog.csdnimg.cn/direct/8234d2a8b7154e3a80d4b1f70a669a44.png
4. 执行sam的step functions实例程序
[*] 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。
https://i-blog.csdnimg.cn/direct/43f5c30539eb44f282bd90f1ad5826ed.png
[*] 检察执行结果
可以看出，已经成功执行一次
https://i-blog.csdnimg.cn/direct/bd1bdb4f318a48c7967f2508f275565a.png
股票价格为2，执行了buy stock lambda
https://i-blog.csdnimg.cn/direct/6a2a094e18284ecbbc0d5e265b115ba3.png
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