Serverless Architecture Patterns - Manoj Ganapathi - Serverless Summit
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The document outlines key principles and patterns in serverless architecture, emphasizing the use of stateless functions and various architectural patterns for different use cases. It describes patterns like messaging and command, fan out, and pipes and filter for managing tasks and services. Additionally, it references AWS and Azure function examples that demonstrate practical applications of these principles.
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Serverless Architecture Patterns - Manoj Ganapathi - Serverless Summit
- 1. CodeOps Technologies Serverless Architecture Patterns Manoj Ganapathi Chief Architect, CodeOps [email protected] @manojgr
- 2. Serverless: Core Principles ❖ Functions are the unit of deployment and scale ❖ Use Serverless to solve specific problems, not every application can be written as Serverless. ❖ Keep your functions Stateless – use cloud storage for WIP state ❖ Functions should: ❖ “Do One Thing”– Single Responsibility Principle ❖ Be Idempotent ❖ Finish as fast as possible “Rethink your application as an orchestra of autonomous functions”
- 3. Architectures •Compute as a Backend •Real-time Processing •Compute as a glue •Hybrid •Proxy Use Cases •App Backend •Data Pipelines •Bots and Skills •Legacy API Proxy •Scheduled/Event Driven services •Stream Processing Patterns •Messaging •Command •Pipes and Filter •Fan Out •Priority Queue Reference: Serverless Architectures on AWS
- 4. Key Patterns
- 5. Messaging and Command • Decouple services by introducing a queue • Typically in asynchronous messaging in web apps and services to take the load off the application • Also shows the Command pattern (encapsulated implementation for a standard interface) Reference: Serverless Architectures on AWS
- 6. Fan Out • Topic-Subscription scenario. • For e.g: Once a video is uploaded, you may want to encode them in multiple formats in parallel and also transcribe Reference: Serverless Architectures on AWS
- 7. Pipes and Filter • To manage a sequential series of tasks – like a data pipeline • Functions can work as a pipe (connector) or a filter (transformation) Reference: Serverless Architectures on AWS
- 8. Appendix AWS Lambda and Azure Functions examples
- 12. Azure Functions: Stream Processing https://www.slideshare.net/satonaoki/serverless-patterns-with-azure-functions
- 13. Azure Functions: Managing Dialog responses in Chatbots https://www.slideshare.net/satonaoki/serverless-patterns-with-azure-functions
- 14. Azure Functions: Integration and Web API https://www.slideshare.net/ServerlessConf/chris-anderson-and-yochay- kiriaty-serverless-patterns-with-azure-functions
- 15. AWS: Event Driven Automation https://www.slideshare.net/AmazonWebServices/aws-reinvent-2016-serverless- architectural-patterns-and-best-practices-arc402
- 16. Azure Functions: Service Event Processing https://www.slideshare.net/satonaoki/serverless-patterns-with-azure-functions