This document discusses serverless application lifecycle management (ALM) techniques. It provides an overview of common serverless use cases like web applications and data processing. It then outlines a serverless ALM checklist including configuration/management, deployment methods, and tracing/troubleshooting. Specific AWS services for packaging, deploying, automating deployment, and monitoring serverless applications like AWS Lambda, AWS Serverless Application Model (SAM), AWS CodePipeline, and AWS CloudWatch are also discussed. The document concludes with a call for feedback and further exploration of serverless ALM best practices.
4. Serverless application
EVENT SOURCE FUNCTION SERVICES (ANYTHING)
Changes in
data state
Requests to
endpoints
Changes in
resource state
Node.js
Python
Java
C#
NEW!
5. Author Package Test Deploy
AWS Lambda console
IDE plugins
3rd party toolsText editor
ALM – serverless apps
6. ALM – serverless apps
Author Package Test Deploy
• What if my serverless application consists of dozens or
hundreds of AWS resources?
• What if I have a large dev team?
• Use AWS SAM (powered by CloudFormation)
7. AWS CloudFormation
• Provision and manage a collection of related AWS
resources.
• Your application = CloudFormation stack
• Input .yaml file and output provisioned AWS resources
8. 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)
NEW!
16. AWS CodePipeline
• Continuous delivery service for fast and reliable
application updates
• Model and visualize your software release process
• Builds, tests and deploys your code with every git push
• Integrates with multiple AWS services and 3rd party tools
22. AWS CodeBuild
• Build and test code in the cloud
• Automatically scales to meet your build volume
• Curated build environments that include runtime and
testing tools for Python, Java, and Node.js
NEW!
27. CloudWatch Metrics
• Default (free) metrics:
• Invocations
• Duration
• Throttles
• Errors
• Create custom metrics for
health and status tracking
Metrics and logs
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
28. X-Ray + Lambda
• Collects data about requests that your application serves
• Provides diagnostic tools
• Visibility into the Lambda service
• Breakdown of your function’s performance
COMING
SOON!
31. X-Ray + Lambda
• Service map – identify where your errors or latency
problems are coming from
• Trace view – zoom in to determine the root cause
COMING
SOON!
33. 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.