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Serverless Architecture
The document discusses serverless computing and its implications. It defines serverless computing as deploying small amounts of functionality without concern for server maintenance or scalability. This relies on third parties to manage servers and services. Serverless computing leads to lower costs but more complicated operations, release management, and moving between platforms. Common triggers for serverless functions include program invocation, messages, file creation, APIs, and database events. The document also provides examples of serverless usage patterns and best practices for serverless applications.
Serverless Architecture
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- 2. HELLO! My nameis Dirk Weibel Technical Lead at Capital One Building cool stuff with great people dirk.weibel@gmail.com linkedin.com/in/dirkw 2
- 3. Agenda ▫ ReviewImportant Trends ▫ What is Serverless Computing? ▫ Rational for Serverless ▫ Ramifications of Serverless ▫ How Serverless works ▫ Serverless Architecture ▫ Code Examples3
- 4. Where are wegoing? Let’s look at the future state of enterprise computing.
- 5. Cloud Computing From TraditionalOps Teams To DevOps ▫ Breaking down the wall between developers and operations To NoOps ▫ No dedicated team to handle ops ▫ Developers must live with their code ▫ Incentivizes teams to build low maintenance systems5
- 6. Deployable Units are ShrinkingFrom Server-based Apps To VM-based Apps To Container-based Apps 6
- 7. Deployable Units are Shrinking FromMonolithic Applications To Service Oriented Architecture To Microservices 7
- 8. Deployable Units are ShrinkingSmaller deployment units: 1) Reduces code complexity 2) Increases our options for scalability 3) Increases environmental complexity 8
- 9. What if wecould pay by the function rather than pay by the server? 9
- 10. How far canwe go? 10 How far should we go?How fast should we go?
- 11. Serverless Architecture isthe next step But what does it mean to be Serverless?
- 12. Serverless Computing Dirk’s Definition Deploying smallamounts of functionality without concern for server maintenance or scalability 12 Sometimes called Function as a Service (FaaS)
- 13. Serverless Computing 13 Serverless Computing mustrely on a 3rd party or separate infrastructure to manage the servers and services.
- 14. Serverless Computing 14 Ramifications of Serverless: ▫Lower costs ▫ Operations are simpler (not effortless) ▫ Release Management is more complicated ▫ Moving between platforms is painful
- 15. Serverless Computing 15 Common Triggers forServerless functions ▫ Program invocation (i.e. Lambda SDK) ▫ Message (i.e. SQS, SNS, SES) ▫ File creation (i.e. S3) ▫ API gateway ▫ Database event (i.e. DynamoDB event)
- 16. Serverless Computing behind the curtain 16 ▫ Eachservice must run on a server ▫ Starting a service on a server takes time ▫ Once a service is started, it handles requests quickly ▫ A service may passivate between calls Any given request may have to wait for a service to either start or activate
- 17. Serverless Computing behind the curtain 17 ▫ Servicesmust be stateless ▫ Services are able to cache data ▫ Services may need to manage connections Passivate/Activate cycle may result in zombie connections
- 18. Serverless Computing 18 Serverless Operations ▫ Deploymentand rollbacks should be 100% automated ▫ Regression tests should be 100% automated ▫ Change management is more important than before serverless ▫ Monitoring is as important as ever
- 19. Don’t expect achange in technology to improve your teams’ discipline. 19
- 20. Serverless Computing 20 Serverless Designs ▫ Stateless ▫Single Purpose ▫ Event Driven Pipelines ▫ Microservices Mindset
- 21. Serverless Computing 21 Good Use-Cases forServerless ▫ Batch Processes ▫ Data Pipelines ▫ Backend Transactions ▫ Scheduled Jobs Bad Use-Case for Serverless ▫ RESTful Services
- 22. Serverless Computing Drawbacks 22 ▫ Vendor Control ▫Vendor Lock-In ▫ Startup Latency ▫ Security Concerns ▫ Lack of Tool Support ▫ Poor Support of Versioning ▫ Limited Error Handling
- 23. Serverless Usage Patterns Real-life exampleshow Serverless is used today.
- 24. “ Make things assimple as possible, but not simpler. - Albert Einstein 24
- 25. Usage Pattern Convert File 25 LambdaS3User S3 SES Lambda Upload File UploadEvent Save Converted File Saved File Event Initiate EmailSend Email
- 26. Usage Pattern Multiple File Conversion s 26 SNSS3User S3 Lambda Upload FileUpload Event Save Converted Files Saved File Event DynamoDB Lambda Lambda Lambda Upload Event Upsert Event
- 27. Usage Pattern Multiple File Conversions (continued) 27 Lambda User SES UpsertEvent Initiate Email Send Email DynamoDB
- 28. Usage Patterns ETL 28 SNS S3 Extract Process Redshift Upload File ReadFile Lambda Lambda Lambda Process Record Lambda Persist Command API RDS Command Enrich
- 29. Usage Pattern Website 29 Lambda CloudFrontBrowser DynamoDBLambda SPA APIs Static Files API Gateway LambdaDynamoDB DynamoDB S3 OAuth Service Authenticate
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- 31. Serverless Computing 31 ▫ Start small ▫Pick the right use-case ▫ Pick the right patterns ▫ Log extensively ▫ Know your dependencies ▫ Carefully consider release management Serverless Computing is the next step
- 32. 32 THANKS Any questions? Youcan find me at ▫ dirk.weibel@gmail.com ▫ linkedin.com/in/dirkw
Editor's Notes
- #8 Microservices lend themselves to rich internet applications.
- #14 The top row represents closed source and vendor lock-in. Customers have no control over vendor operations. The bottom row represent open source, granting consumers full control over their environments.
- #15 AWS uses Lambda as a loss leader, making money on other services. Microsoft and Google are following suit My prediction is that once companies start shifting major workloads into serverless, prices will go up, but will still be less than renting servers. Operations are simpler because you have less control. Your vendor won’t care as much about your application as you do. You’re at the mercy of the vendor’s level of service. We need better tooling in order to handle the different deployment model and higher complexity of release management.
- #18 AWS Lambda supports limited memory cache and limited amounts of disk writes. When a service is deactivated, those resources disappear
- #20 Don’t even try to go serverless unless your teams are already doing the right things. Go slow in order to go fast. Temporarily slow down the development of new features in order to build the skills required become a high performance team.