Application Lifecycle Management in a Serverless World
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Amazon API Gateway and AWS Lambda provide a new way of building applications by removing servers from the picture. But what does the removal of servers mean to tasks like deployment, monitoring, and debugging? How should you set up blue-green deployments or set alarms? Come learn all this and more, including how to use AWS services and tools like AWS CodePipeline, AWS CloudFormation, and Amazon CloudWatch to manage your serverless applications at high quality.
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Application Lifecycle Management in a Serverless World
- 1. ©2017, Amazon Web Services, Inc. or its affiliates. All rights reserved Application Lifecycle Management in a Serverless World Chris Munns – Senior Developer Advocate - Serverless
- 2. About me: Chris Munns - munns@amazon.com,@chrismunns – Senior Developer Advocate - Serverless – NewYorker – Previously: • Business Development Manager – DevOps, July ’15 - Feb ‘17 • AWS Solutions Architect Nov, 2011- Dec 2014 • Formerly on operations teams @Etsy and @Meetup • Little time at a hedge fund, Xerox and a few other startups – Rochester Institute of Technology: Applied Networking and Systems Administration ’05 – Internet infrastructure geek
- 4. Simple but usable primitives Scales with usage Never pay for idle Availability and fault tolerance built in Serverless means..
- 5. Serverless application EVENT SOURCE SERVICES (ANYTHING) Changes in data state Requests to endpoints Changes in resource state FUNCTION Node.js Python Java C#
- 6. Amazon S3 Amazon DynamoDB Amazon Kinesis AWS CloudFormation AWS CloudTrail Amazon CloudWatch Amazon Cognito Amazon SNSAmazon SES Cron events DATA STORES ENDPOINTS CONFIGURATION REPOSITORIES EVENT/MESSAGE SERVICES Event sources that trigger AWS Lambda … and the list will continue to grow! AWS CodeCommit Amazon API Gateway Amazon Alexa AWS IoT AWS Step Functions
- 7. Common use cases Web Applications • Static websites • Complex web apps • Packages for Flask and Express Data Processing • Real time • MapReduce • Batch Chatbots • Powering chatbot logic Backends • Apps & services • Mobile • IoT </></> Amazon Alexa • Powering voice- enabled apps • Alexa Skills Kit Autonomous IT • Policy engines • Extending AWS services • Infrastructure management
- 8. Serverless is growing quickly!
- 9. DEMO!
- 10. What is ALM? Application Lifecycle Management: developers customers react monitor delivery pipeline feedback loop productiontestbuildsource
- 11. • Integration tests with other systems • Load testing • UI tests • Penetration testing Release processes have four major phases Source Build Test Production • Check-in source code such as .java files. • Peer review new code • Compile code • Unit tests • Style checkers • Code metrics • Create container images • Deployment to production environments
- 12. Release processes levels Source Build Test Production Continuous integration Continuous delivery Continuous deployment
- 13. Serverless ALM checklist Application Build & Test Configuration & Management Deployment Monitoring,Tracing, & Troubleshooting
- 14. Managing continuous delivery Source Build Test Production Amazon S3 AWS Lambda (DIY) AWS CodeCommit GitHub AWS CodePipeline Scripts/PluginsJenkins AWS CodeBuild … OR …
- 15. Fully managed build service that compiles source code, runs tests, and produces software packages Scales continuously and processes multiple builds concurrently You can provide custom build environments suited to your needs via Docker images Only pay by the minute for the compute resources you use Launched with CodePipeline and Jenkins integration AWS CodeBuild
- 16. version: 0.1 environment_variables: plaintext: "INPUT_FILE": "saml.yaml” "S3_BUCKET": "” phases: install: commands: - npm install pre_build: commands: - eslint *.js build: commands: - npm test post_build: commands: - aws cloudformation package --template $INPUT_FILE --s3- bucket $S3_BUCKET --output-template post-saml.yaml artifacts: type: zip files: - post-saml.yaml - beta.json buildspec.yml Example
- 17. version: 0.1 environment_variables: plaintext: "INPUT_FILE": "saml.yaml” "S3_BUCKET": "” phases: install: commands: - npm install pre_build: commands: - eslint *.js build: commands: - npm test post_build: commands: - aws cloudformation package --template $INPUT_FILE --s3- bucket $S3_BUCKET --output-template post-saml.yaml artifacts: type: zip files: - post-saml.yaml - beta.json • Variables to be used by phases of build • Examples for what you can do in the phases of a build: • You can install packages or run commands to prepare your environment in ”install”. • Run syntax checking, commands in “pre_build”. • Execute your build tool/command in “build” • Test your app further or ship a container image to a repository in post_build • Create and store an artifact in S3 buildspec.yml Example
- 18. Building a deployment package Node.js & Python • .zip file consisting of your code and any dependencies • Use npm/pip to install libraries • All dependencies must be at root level Java • Either .zip file with all code/dependencies, or standalone .jar • Use Maven / Eclipse IDE plugins • Compiled class & resource files at root level, required jars in /lib directory C# (.NET Core) • Either .zip file with all code/dependencies, or a standalone .dll • Use NuGet / VisualStudio plugins • All assemblies (.dll) at root level
- 19. Where to Focus Your Tests: UI Service Unit 70% 20% 10%
- 20. What service and release step corresponds with which tests? UI Service Unit Third Party Tooling AWS CodeBuild BuildTest
- 21. Continuous delivery service for fast and reliable application updates Model and visualize your software release process Builds, tests, and deploys your code every time there is a code change Integrates with third-party tools and AWS AWS CodePipeline
- 22. Source Source GitHub Build CodeBuild AWS CodeBuild Deploy JavaApp Elastic Beanstalk Pipeline Stage Action Transition AWS CodePipeline MyApplication
- 23. Build CodeBuild AWS CodeBuild NotifyDevelopers Lambda Parallel actions Source Source GitHub Deploy JavaApp Elastic Beanstalk AWS CodePipeline MyApplication
- 24. Build CodeBuild AWS CodeBuild NotifyDevelopers Lambda TestAPI Runscope Sequential actions Deploy JavaApp Elastic Beanstalk Source Source GitHub AWS CodePipeline MyApplication
- 25. Build CodeBuild AWS CodeBuild Staging-Deploy JavaApp Elastic Beanstalk Prod-Deploy JavaApp Elastic Beanstalk QATeamReview Manual Approval Manual Approvals Review AWS CodePipeline MyApplication
- 26. DEMO!
- 27. Serverless ALM checklist Application Build & Test Configuration & Management Deployment Monitoring,Tracing, & Troubleshooting
- 28. Function versioning and aliases • Versions = immutable copies of code + configuration • Aliases = mutable pointers to versions • Development against $LATEST version • Each version/alias gets its own ARN • Enables rollbacks, staged promotions,“locked” behavior for client
- 29. Lambda Environment Variables • Key-value pairs that you can dynamically pass to your function • Available via standard environment variable APIs such as process.env for Node.js or os.environ for Python • Can optionally be encrypted via KMS – Allows you to specify in IAM what roles have access to the keys to decrypt the information • Useful for creating environments per stage (i.e. dev, testing, production)
- 30. API Gateway Stage Variables • Stage variables act like environment variables • Use stage variables to store configuration values • Stage variables are available in the $context object • Values are accessible from most fields in API Gateway • Lambda function ARN • HTTP endpoint • Custom authorizer function name • Parameter mappings
- 31. Stage variables and Lambda alias for stages Using Stage Variables in API Gateway together with Lambda function Aliases helps you manage a single API configuration and Lambda function for multiple stages myLambdaFunction 1 2 3 = prod 4 5 6 = beta 7 8 = dev My First API Stage variable = lambdaAlias Prod lambdaAlias = prod Beta lambdaAlias = beta Dev lambdaAlias = dev
- 32. Manage MultipleVersions and Stages of your APIs Works like a source repository – clone your API to create a new version: API 1 (v1) Stage (dev) Stage (prod) API 2 (v2) Stage (dev)
- 33. Create templates of your infrastructure CloudFormation provisions AWS resources based on dependency needs Version control/replicate/update templates like code Integrates with development, CI/CD, management tools JSON andYAML supported AWS CloudFormation
- 34. AWSTemplateFormatVersion: '2010-09-09' Resources: GetHtmlFunctionGetHtmlPermissionProd: Type: AWS::Lambda::Permission Properties: Action: lambda:invokeFunction Principal: apigateway.amazonaws.com FunctionName: Ref: GetHtmlFunction SourceArn: Fn::Sub: arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${ServerlessRestApi}/Prod/ANY/* ServerlessRestApiProdStage: Type: AWS::ApiGateway::Stage Properties: DeploymentId: Ref: ServerlessRestApiDeployment RestApiId: Ref: ServerlessRestApi StageName: Prod ListTable: Type: AWS::DynamoDB::Table Properties: ProvisionedThroughput: WriteCapacityUnits: 5 ReadCapacityUnits: 5 AttributeDefinitions: - AttributeName: id AttributeType: S KeySchema: - KeyType: HASH AttributeName: id GetHtmlFunction: Type: AWS::Lambda::Function Properties: Handler: index.gethtml Code: S3Bucket: flourish-demo-bucket S3Key: todo_list.zip Role: Fn::GetAtt: - GetHtmlFunctionRole - Arn Runtime: nodejs4.3 GetHtmlFunctionRole: Type: AWS::IAM::Role Properties: ManagedPolicyArns: - arn:aws:iam::aws:policy/AmazonDynamoDBReadOnlyAccess - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole AssumeRolePolicyDocument: Version: '2012-10-17' Statement: - Action: - sts:AssumeRole Effect: Allow Principal: Service: - lambda.amazonaws.com ServerlessRestApiDeployment: Type: AWS::ApiGateway::Deployment Properties: RestApiId: Ref: ServerlessRestApi Description: 'RestApi deployment id: 127e3fb91142ab1ddc5f5446adb094442581a90d' StageName: Stage GetHtmlFunctionGetHtmlPermissionTest: Type: AWS::Lambda::Permission Properties: Action: lambda:invokeFunction Principal: apigateway.amazonaws.com FunctionName: Ref: GetHtmlFunction SourceArn: Fn::Sub: arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${ServerlessRestApi}/*/ANY/* ServerlessRestApi: Type: AWS::ApiGateway::RestApi Properties: Body: info: version: '1.0' title: Ref: AWS::StackName paths: "/{proxy+}": x-amazon-apigateway-any-method: x-amazon-apigateway-integration: httpMethod: ANY type: aws_proxy uri: Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03- 31/functions/${GetHtmlFunction.Arn}/invocations responses: {} swagger: '2.0' CloudFormation template
- 35. AWS Serverless Application Model (SAM) CloudFormation extension optimized for serverless New serverless resource types: functions, APIs, and tables Supports anything CloudFormation supports Open specification (Apache 2.0)
- 36. AWSTemplateFormatVersion: '2010-09-09' Resources: GetHtmlFunctionGetHtmlPermissionProd: Type: AWS::Lambda::Permission Properties: Action: lambda:invokeFunction Principal: apigateway.amazonaws.com FunctionName: Ref: GetHtmlFunction SourceArn: Fn::Sub: arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${ServerlessRestApi}/Prod/ANY/* ServerlessRestApiProdStage: Type: AWS::ApiGateway::Stage Properties: DeploymentId: Ref: ServerlessRestApiDeployment RestApiId: Ref: ServerlessRestApi StageName: Prod ListTable: Type: AWS::DynamoDB::Table Properties: ProvisionedThroughput: WriteCapacityUnits: 5 ReadCapacityUnits: 5 AttributeDefinitions: - AttributeName: id AttributeType: S KeySchema: - KeyType: HASH AttributeName: id GetHtmlFunction: Type: AWS::Lambda::Function Properties: Handler: index.gethtml Code: S3Bucket: flourish-demo-bucket S3Key: todo_list.zip Role: Fn::GetAtt: - GetHtmlFunctionRole - Arn Runtime: nodejs4.3 GetHtmlFunctionRole: Type: AWS::IAM::Role Properties: ManagedPolicyArns: - arn:aws:iam::aws:policy/AmazonDynamoDBReadOnlyAccess - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole AssumeRolePolicyDocument: Version: '2012-10-17' Statement: - Action: - sts:AssumeRole Effect: Allow Principal: Service: - lambda.amazonaws.com ServerlessRestApiDeployment: Type: AWS::ApiGateway::Deployment Properties: RestApiId: Ref: ServerlessRestApi Description: 'RestApi deployment id: 127e3fb91142ab1ddc5f5446adb094442581a90d' StageName: Stage GetHtmlFunctionGetHtmlPermissionTest: Type: AWS::Lambda::Permission Properties: Action: lambda:invokeFunction Principal: apigateway.amazonaws.com FunctionName: Ref: GetHtmlFunction SourceArn: Fn::Sub: arn:aws:execute-api:${AWS::Region}:${AWS::AccountId}:${ServerlessRestApi}/*/ANY/* ServerlessRestApi: Type: AWS::ApiGateway::RestApi Properties: Body: info: version: '1.0' title: Ref: AWS::StackName paths: "/{proxy+}": x-amazon-apigateway-any-method: x-amazon-apigateway-integration: httpMethod: ANY type: aws_proxy uri: Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03- 31/functions/${GetHtmlFunction.Arn}/invocations responses: {} swagger: '2.0' CloudFormation template
- 37. SAM template AWSTemplateFormatVersion: '2010-09-09’ Transform: AWS::Serverless-2016-10-31 Resources: GetHtmlFunction: Type: AWS::Serverless::Function Properties: CodeUri: s3://flourish-demo-bucket/todo_list.zip Handler: index.gethtml Runtime: nodejs4.3 Policies: AmazonDynamoDBReadOnlyAccess Events: GetHtml: Type: Api Properties: Path: /{proxy+} Method: ANY ListTable: Type: AWS::Serverless::SimpleTable
- 38. AWS commands – Package & Deploy Package • Creates a deployment package (.zip file) • Uploads deployment package to an Amazon S3 bucket • Adds a CodeUri property with S3 URI Deploy • Calls CloudFormation ‘CreateChangeSet’ API • Calls CloudFormation ‘ExecuteChangeSet’ API
- 39. Deploy via CodePipeline Pipeline flow: • Package in CodeBuild • Use CloudFormation actions in CodePipeline to create or update stacks via SAM templates • Optional: Make use of ChangeSets • Make use of specific stage/environment parameter files to pass in Lambda variables • Test our application between stages/environments • Optional: Make use of Manual Approvals
- 40. DEMO!
- 41. Serverless ALM checklist Application Build & Test Configuration & Management Deployment Monitoring,Tracing, & Troubleshooting
- 42. Metrics and logs CloudWatch Metrics • Default (free) metrics: • Invocations • Duration • Throttles • Errors • Create custom metrics for health and status tracking CloudWatch Logs • Every invocation generates START, END and REPORT entries to CW Logs • Emit your own log entries • Use 3rd party tools for aggregation and visualization
- 44. Tracing and tracking Integration with AWS X-Ray • Collects data about requests that your application serves • Visibility into the AWS Lambda service (dwell time, number of retries, latency and errors) • Detailed breakdown of your function’s performance, including calls made to downstream services and endpoints Integration with AWS CloudTrail • Captures calls made to AWS Lambda API; delivers log files to Amazon S3 • Tracks the request made to AWS Lambda,the source IP address from which the request was made, who made the request, when it was made • All control plane APIs can be tracked (no versioning/aliasing and invoke API) COMING SOON!
- 45. • Identify performance bottlenecks and errors • Pinpoint issues to specific service(s) in your application • Identify impact of issues on users of the application • Visualize the service call graph of your application AWS X-Ray
- 46. Service map
- 47. Trace view
- 48. DEMO!
- 49. Serverless ALM checklist Application Build & Test Configuration & Management Deployment Monitoring,Tracing, & Troubleshooting
- 50. Next steps • Explore the AWS SAM specification on GitHub • Visit the Lambda console, download a blueprint, and get started with AWS SAM • Send us your questions, comments, and feedback on the AWS Lambda Forums.
Hinweis der Redaktion
- https://secure.flickr.com/photos/mgifford/4525333972
- 7 the Serverless computing approach that Lambda brings about isn’t just about “not having to manage servers”. Serverless means having a simple but usable primitive – your code as a Lambda function - with nothing that looks like a container or server. The programming model and APIs are all oriented around dealing with functions. Serverless means you only pay for work done, not for provisioning capacity. You don’t have to worry about utilization, because you never pay for idle. You only pay for compute time, that is, the time your function takes to run, in units of 100 ms. This is something most customers get excited about thinking about what paying 21 microcents for 100 ms of compute can do for their costs . For example, Nordstrom tells us switching to Lambda reduced the cost of their analytics pipeline by two orders of magnitude. A publishing company from Singapore tells us they saves over 30,000 per month by switching from a proprietary image processing solution to one built on Lambda for processing millions of images a day. Which brings me to the third aspect, that is Serverless means scaling is built in - you can never overprovision or under provision. Since your code is run in response to events, Lambda will automatically spin up as many instances of your function as required to handle any incoming event rate. Let me repeat this, any event rate. We have customers running backends handling in excess of 100, 000 TPS at peak, and others like Adroll who are processing over 55 B ad impressions a day through Lambda. And last but not the least - Serverless means that functions come with high availability and, depending on the workload, fault tolerance come built in. The combination of offloading these responsibilities can have significant impact on the way you own and operate applications running in the cloud. For example, Vidroll tells us what used to take them 10 engineers now takes them two, while handling twice the scale.
- But first, lets make sure we are all on the same page as to what a serverless application is. A Serverless application usually starts with an event. That event can be a write to a dynamoDB table, a PutObject to an S3 bucket, an HTTP call, or a host of other Lambda supported event sources. That event then triggers your Lambda function, which can be written in Node.js, Python, Java, or C#. Now remember, this is your code, and you can program it to do whatever you’d like. You could do things like call other downstream services to continue processing, return a result, or write metadata to database. Now, how would the lifecycle of such an application would look like?
- Recap the common use cases for serverless Web Applications: By combining AWS Lambda with other AWS services, developers can build powerful web applications that automatically scale up and down and run in a highly available configuration across multiple data centers – with zero administrative effort required for scalability, back-ups or multi-data center redundancy. Mention Flask and Express Backends: You can build serverless backends using AWS Lambda, Amazon API Gateway, and Amazon DynamoDB to handle web, mobile, Internet of Things (IoT) requests. Data Processing: You can build a variety of real-time data processing systems using AWS Lambda, Amazon Kinesis, Amazon S3, and Amazon DynamoDB.
- What companies are using serverless?
- Quick lambda how-to example in the console
- I want to take a moment to talk about different release processes. Each team’s release process takes a different shape to accommodate the needs of each team. Nearly all release processes can be simplified down to four stages – source, build, test and production. Each phase of the process provides increase confidence that the code being made available to customers will work in the way that was intended. During the source phase, developers check changes into a source code repository. Many teams require peer feedback on code changes before shipping code into production. Some teams use code reviews to provide peer feedback on the quality of code change. Others use pair programming as a way to provide real time peer feedback. During the Build phase an application’s source code is built and the quality of the code is tested on the build machine. The most common type of quality check are automated tests that do not require a server in order to execute and can be initiated from a test harness. Some teams extend their quality tests to include code metrics and style checks. There is an opportunity for automation any time a human is needed to make a decision on the code. The goal of the test phase is to perform tests that cannot be done on during the build phase and require the software to be deployed to a production like stages. Often these tests include testing integration with other live systems, load testing, UI testing and penetration testing. At Amazon we have many different pre-production stages we deploy to. A common pattern is for engineers to deploy builds to a personal development stage where an engineer can poke and prod their software running in a mini prod like stage to check that their automated tests are working correctly. Teams deploy to pre-production stages where their application interacts with other systems to ensure that the newly changed software work in an integrated environment. Finally code gets deployed to production. Different teams have different deployment strategies though we all share a goal of reducing risk when deploying new changes and minimizing the impact if a bad change does get out to production. Each of these steps can be automated without the entire release process being automated. There are several levels of release automation that I’ll step through.
- Continuous Integration Continuous Integration is the practice of checking in your code to the continuously and verifying each change with an automated build and test process. Over the past 10 years Continuous Integration has gained popularity in the software community. In the past developers were working in isolation for an extended period of time and only attempting to merge their changes into the mainline of their code once their feature was completed. Batching up changes to merge back into the mainline made not only merging the business logic hard, but it also made merging the test logic difficult. Continuous Integration practices have made teams more productive and allowed them to develop new features faster. Continuous Integration requires teams to write automated tests which, as we learned, improve the quality of the software being released and reduce the time it takes to validate that the new version of the software is good. There are different definitions of Continuous Integration, but the one we hear from our customers is that CI stops at the build stage, so I’m going to use that definition. Continuous Delivery Continuous Delivery extends Continuous Integration to include testing out to production-like stages and running verification testing against those deployments. Continuous Delivery may extend all the way to a production deployment, but they have some form of manual intervention between a code check-in and when that code is available for customers to use. Continuous Delivery is a big step forward over Continuous Integration allowing teams to be gain a greater level of certainty that their software will work in production. Continuous Deployment Continuous Deployment extends continuous delivery and is the automated release of software to customers from check in through to production without human intervention. Many of the teams at Amazon have reached a state of continuous deployment. Continuous Deployment reduces the time for your customers to get value from the code your team has just written, with the team getting faster feedback on the changes you’ve made. This fast customer feedback loop allow you to iterate quickly, allowing you to deliver more valuable software to your customers, quicker.
- Some of the most common pieces of feedback we get around ALM and tooling, are the 4 you see up on the screen. First, since a serverless application is essentially a collection of services and resources, how do I configure and manage them as one unit? As an application? Once I’ve defined and constructed an application, what’s the best way to consistently deploy the same application across different environments or accounts, with minimum effort? Once I know how to construct and deploy my serverless application, how do I automate that process? How do I set up a release process to automatically build, test and deploy my application to multiple environments? We are going to use the next hour or so to introduce best practices involving both new and existing services that provide answers to the challenges above
- The effort you put into the testing triangle should not be evenly distributed! Many experts in the industry recommend a 70,20,10 mix. (will need sources)
- The effort you put into the testing triangle should not be evenly distributed! Many experts in the industry recommend a 70,20,10 mix. (will need sources)
- Let’s take a look at an example Pipeline. I’ve created a simple 3 stage Pipeline to talk though my example. Source actions are special actions. They continuously poll the source providers, such as GitHub and S3, in order to detect changes. Once a change is detected, the new pipeline run is created and the new pipeline begins its run. The source actions retrieve a copy of the source information and place it into a customer owned S3 bucket. Once the source action is completed, the Source stage is marked as successful and we transition to the Build stage. In the Build Stage we have one action, Jenkins. Jenkins was integrated into CodePipeline as a CustomAction and has the same lifecycle as all custom actions. Talk through interaction Once the build action is completed, the Build stage is marked as successful and we transition to the Deploy stage The Deploy stage contains one action, an AWS Elastic Beanstalk deployment action. The Beanstalk action retrieves the build artifact from the customer’s S3 bucket and deploys it to the Elastic Beanstalk web container.
- Let’s take a look at an example Pipeline. I’ve created a simple 3 stage Pipeline to talk though my example. Source actions are special actions. They continuously poll the source providers, such as GitHub and S3, in order to detect changes. Once a change is detected, the new pipeline run is created and the new pipeline begins its run. The source actions retrieve a copy of the source information and place it into a customer owned S3 bucket. Once the source action is completed, the Source stage is marked as successful and we transition to the Build stage. In the Build Stage we have one action, Jenkins. Jenkins was integrated into CodePipeline as a CustomAction and has the same lifecycle as all custom actions. Talk through interaction Once the build action is completed, the Build stage is marked as successful and we transition to the Deploy stage The Deploy stage contains one action, an AWS Elastic Beanstalk deployment action. The Beanstalk action retrieves the build artifact from the customer’s S3 bucket and deploys it to the Elastic Beanstalk web container.
- Let’s take a look at an example Pipeline. I’ve created a simple 3 stage Pipeline to talk though my example. Source actions are special actions. They continuously poll the source providers, such as GitHub and S3, in order to detect changes. Once a change is detected, the new pipeline run is created and the new pipeline begins its run. The source actions retrieve a copy of the source information and place it into a customer owned S3 bucket. Once the source action is completed, the Source stage is marked as successful and we transition to the Build stage. In the Build Stage we have one action, Jenkins. Jenkins was integrated into CodePipeline as a CustomAction and has the same lifecycle as all custom actions. Talk through interaction Once the build action is completed, the Build stage is marked as successful and we transition to the Deploy stage The Deploy stage contains one action, an AWS Elastic Beanstalk deployment action. The Beanstalk action retrieves the build artifact from the customer’s S3 bucket and deploys it to the Elastic Beanstalk web container.
- Let’s take a look at an example Pipeline. I’ve created a simple 3 stage Pipeline to talk though my example. Source actions are special actions. They continuously poll the source providers, such as GitHub and S3, in order to detect changes. Once a change is detected, the new pipeline run is created and the new pipeline begins its run. The source actions retrieve a copy of the source information and place it into a customer owned S3 bucket. Once the source action is completed, the Source stage is marked as successful and we transition to the Build stage. In the Build Stage we have one action, Jenkins. Jenkins was integrated into CodePipeline as a CustomAction and has the same lifecycle as all custom actions. Talk through interaction Once the build action is completed, the Build stage is marked as successful and we transition to the Deploy stage The Deploy stage contains one action, an AWS Elastic Beanstalk deployment action. The Beanstalk action retrieves the build artifact from the customer’s S3 bucket and deploys it to the Elastic Beanstalk web container.
- Demo of CodePipeline + CodeBuild off of a repo (could be github or CodeCommit).
- Some of the most common pieces of feedback we get around ALM and tooling, are the 4 you see up on the screen. First, since a serverless application is essentially a collection of services and resources, how do I configure and manage them as one unit? As an application? Once I’ve defined and constructed an application, what’s the best way to consistently deploy the same application across different environments or accounts, with minimum effort? Once I know how to construct and deploy my serverless application, how do I automate that process? How do I set up a release process to automatically build, test and deploy my application to multiple environments? We are going to use the next hour or so to introduce best practices involving both new and existing services that provide answers to the challenges above
- This how that template would look like. This is 5 times longer than the SAM template, and defines 8 separate resources. And this is what you had to write, before SAM existed.
- AWS SAM is a new specification that extends CloudFormation, and is optimized for serverless. It allows you to define 3 resource types commonly used in serverless applications, in a simpler and cleaner way: Lambda function, API Gateway APIs, and DynamoDB tables. It’s worth noting that SAM in its core, is a CloudFormation template. That means you can define any CloudFormation resource in your SAM template, to go along with your serverless resources.
- This how that template would look like. This is 5 times longer than the SAM template, and defines 8 separate resources. And this is what you had to write, before SAM existed.
- Let’s go over a SAM template to understand the specification better: First, we are defining a serverless function, which is transformed into a Lambda function under the covers. The first property specified is CodeUri. This property receives a URI that points to an S3 object. When CloudFormation creates my Lambda function it refers to this URI to retrieve the function’s deployment package. The next property I’d like you to pay attention to, is policies. The managed policies that you specify here will be included in the execution role that CloudFormation will generate for your Lambda function. Next, we are defining the function’s event source, which in this case is an API. Notice that I don’t need to explicitly define an API as a separate resource. Specifying an API as my function’s event source is sufficient for CloudFormation to generate an API with the specified characteristics for me. Lastly, I’m defining a DynamoDB table using the simpleTable. This shortcut will generate a DynamoDB table with a single attribute primary key, with a provisioned throughput of 5. The key piece that makes all of this possible, is the transform capability CloudFormation introduced. When you specify the serverless transform, then under the covers, CloudFormation turns this template into a regular CloudFormation template. CouldFormation then uses that template to generate my resources.
- First, After writing your template and defining your resources, you would have to point the CodeUri property to a deployment package located in S3. In order to do that, you would have to perform three steps: First, you would need to generate your deployment package, which includes your code. Second, you would need to upload that deployment package to S3. Third, you would fill in the CodeUri. Now, if you had 50 Lambda functions defined in your template, you would have to follow these steps for each of these functions. Luckily for us, the package command takes care of all these steps for us, and outputs a new SAM template that is identical to the old one, except it now has an updated CodeUri property that points to a deployment package in S3. After we have the new template, we need to provide it to CloudFormation so the service can provision the specified resources in our account. To do that, we can call the deploy command. The deploy command wraps to CloudFormation APIs – the first one is create change set, which unsurprisingly, creates a change set. A change set is essentially the delta between your existing stack, the template you are looking to deploy. If you are generating a new stack, then obviously the change set is going to include everything that’s defined in your template. After your change set has been created, the execute change set API will be called. This API simply applies the updates in your change set to your CloudFormation stack.
- Add CloudFormation deploy of application.
- Some of the most common pieces of feedback we get around ALM and tooling, are the 4 you see up on the screen. First, since a serverless application is essentially a collection of services and resources, how do I configure and manage them as one unit? As an application? Once I’ve defined and constructed an application, what’s the best way to consistently deploy the same application across different environments or accounts, with minimum effort? Once I know how to construct and deploy my serverless application, how do I automate that process? How do I set up a release process to automatically build, test and deploy my application to multiple environments? We are going to use the next hour or so to introduce best practices involving both new and existing services that provide answers to the challenges above
- Today, you can monitor your application with CloudWatch metrics. Lambda functions come with 4 metrics, out of the box – number of Invocations, average duration of your function, number of throttles, and number of errors (4XX specifically). If you wish, you could always create custom metrics to track the health and status of your application. For your debugging needs, Lambda integrates with CloudWatch logs. Every invocation generates a start and end timestamp, as well as a report entry. That includes information such as function duration, billed duration, and the amount of memory used by your function. In addition customers can emit as many log entries as they want, to track their function’s operations. Log-based debugging is a great solution that provides visibility into your function’s operations, but it doesn’t provide any visibility into the lambda service itself, and the overhead it introduces. In addition, with logs, there is only so much information that you can get for downstream calls that your function made.
- First, it is going to provide you with something they call a service map. A service map is a visual representation of how the request flows through your application. In this example, you see three nodes. Each node includes three data points – avg. latency, number of req. per minute, and errors. The first two are shown inside the node, while errors will be represented by the node’s color. 200s will always be green, and 400s, and 500s will be displayed in different colors. In this example, you see a Lambda function that made a downstream call to a DynamoDB table. The first node is the Lambda service. The timing info written inside represents the time the request spent in the Lambda service, from the time it hits our FE, to the time it leaves Lambda. The second node represents your function’s execution. The timing info represents the function’s execution time. The third node represents a downstream call to Dynamo DB, and the timing info shows you time that passed from when your function made the call, till it received a response. Looking at a visualization such as this one allows you to gain insights in a matter of seconds: Where are latency issues or errors coming from? Are they caused by the Lambda service, by my function, or more specifically, from which specific downstream call?
- Now, let’s say I’ve looked at the service map, and identified a latency issue. I could quickly conclude that the problem stems from the Lambda service itself. But which part exactly? That’s when you would want to switch to the trace view. The trace view allows us to zoom in into your request and where it spent time in Lambda. This request is the same request we saw in the service map – an async request that makes a call to DynamoDB. The first segment you see, represents the entire time spent in Lambda, from hitting the FE, till leaving the service. The dwell time segment shows you how much time was spent in the Lambda queue. All async requests to Lambda are put into a queue before being processed. Next, you see Lambda’s attempt to invoke your function. You could have up to 3 attempts for async requests, as Lambda retries your function up to three times before it fails. As you can see, this request succeeded on the first attempt, and returned a 200. Next, you can see actual time it took your function to execute. The difference between attempt 1 and your function segment, the gap over there, is the time it took Lambda to start your function. Next, you can see the downstream call your function made. In this case, a putitem to test-table, which took 31ms and returned a 200. If any of these calls returned an error, you would be able to access your error information from the trace view. With X-ray, you now have visibility into the Lambda service that you never had before.
- Add CloudFormation deploy of application.
- Some of the most common pieces of feedback we get around ALM and tooling, are the 4 you see up on the screen. First, since a serverless application is essentially a collection of services and resources, how do I configure and manage them as one unit? As an application? Once I’ve defined and constructed an application, what’s the best way to consistently deploy the same application across different environments or accounts, with minimum effort? Once I know how to construct and deploy my serverless application, how do I automate that process? How do I set up a release process to automatically build, test and deploy my application to multiple environments? We are going to use the next hour or so to introduce best practices involving both new and existing services that provide answers to the challenges above
- https://secure.flickr.com/photos/dullhunk/202872717/