Primeros pasos en desarrollo serverless
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¿Qué es eso del desarrollo sin servidores? ¿Qué lenguajes puedo utilizar? ¿Cómo hago cosas como autenticación, o guardar en base de datos, o enviar notificaciones? ¿Esto escala? A todas estas preguntas, y a alguna más, intentaré dar respuesta en esta sesión, donde haré una pequeña demo de montar una app muy sencilla y desplegarla en la nube sin preocuparnos de gestionar infraestructura. Charla realizada por primera vez para AlcarriaConf 2021
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Primeros pasos en desarrollo serverless
- 1. Primeros pasos en desarrollo serverless Javier Ramírez Developer Advocate - Amazon Web Services @supercoco9 ramirez
- 2. We are witnessing a paradigm shift Experiment, innovate more often Release features faster Build better products Focus on business logic Decouple software systems Win customers Win customers 75% of organizations use or plan to use serverless technologies within the next two years.1 * 451 Research, The Journey to Serverless-First: Enterprise Stories https://aws.amazon.com/resources/analyst-reports/451-serverless-first-enterprises-on-aws/
- 3. Sumo Logic’s analysis of its AWS customers’ activities found a modest increase in those running Lambdas, going from 36% in 2019 to 39% in 2020. In early 2020, Datadog reported that more than 40% of its AWS customers had adopted Lambda, with that figure almost doubling among AWS customers that also used containers. https://thenewstack.io/adoption-of-aws-lambda-serverless-stalls/ https://www.sumologic.com/brief/continuous-intelligence-report/
- 4. Development transformation at Amazon (and everywhere else) 1994-2001 2002+ Monolithic architecture + hierarchical organization Decoupled services + Two-pizza teams
- 5. Amazon S3 at launch:
- 6. Amazon S3 @ re:Invent 2018: In 2020 S3 stored over 100 trillion (1014, or 100,000,000,000,000) objects, and regularly peaks at tens of millions of requests per second.
- 7. Deployment at scale 1000s of teams Micro- services CI/CD Serverless >60 million deployments a year*
- 8. Computing evolution – A paradigm shift LEVEL OF ABSTRACTION FOCUS ON BUSINESS LOGIC PHYSICAL MACHINES Requires “guess” planning Lives for years on-premises Heavy investments (capex) Low innovation factor Deploy in months
- 9. Computing evolution – A paradigm shift LEVEL OF ABSTRACTION FOCUS ON BUSINESS LOGIC VIRTUAL MACHINES Hardware independence Faster provisioning speed (minutes/hours) Trade capex for opex More scale Elastic resources Faster speed and agility Reduced maintenance
- 10. Computing evolution – A paradigm shift LEVEL OF ABSTRACTION FOCUS ON BUSINESS LOGIC CONTAINERIZATION Platform independence Consistent runtime environment Higher resource utilization Easier and faster deployments Isolation and sandboxing Start speed (deploy in seconds)
- 11. No server is easier to manage than "no server.” Werner Vogels—Amazon CTO
- 12. Computing evolution – A paradigm shift AWS Lambda AWS Fargate LEVEL OF ABSTRACTION FOCUS ON BUSINESS LOGIC Continuous scaling Fault tolerance built-in Pay for value Zero maintenance SERVERLESS
- 13. AWS operational responsibility models On-Premises Cloud Less More Compute Virtual Machine EC2 Elastic Beanstalk AWS Lambda Fargate Databases MySQL MySQL on EC2 RDS MySQL RDS Aurora Aurora Serverless DynamoDB Storage Storage S3 Messaging ESBs Amazon MQ Kinesis SQS / SNS Analytics Hadoop Hadoop on EC2 EMR Elasticsearch Service Athena
- 14. Serverless means … No servers to provision or manage Scales with usage Never pay for idle Availability and fault tolerance built in
- 15. AWS approach to reliability
- 16. Serverless means: Greater agility Less overhead Better focus Increased scale More flexibility Faster time to market
- 17. AWS Serverless Portfolio (some missing services) APPLICATION PRIMITIVES – COMPUTE AND DATASTORES AWS Lambda AWS Fargate Amazon API Gateway Amazon SNS Amazon MQ Amazon SQS AWS Step Functions APPLICATION INTEGRATION DEVELOPER TOOLS SECURITY AND ADMINISTRATION Amazon Aurora Serverless Amazon S3 Amazon DynamoDB AWS AppSync AWS IAM Amazon Cognito Amazon Inspector Amazon VPC Amazon GuardDuty AWS CloudFormation AWS Cloud9 AWS CloudTrail Amazon CloudWatch AWS X-Ray AWS CodePipeline AWS Config AWS SSO AWS Shield AWS WAF Amazon Kinesis AWS Serverless Application Repository Amazon EventBridge ANALYTICS Amazon Athena AWS Glue AWS Glue Elastic Views AWS Glue DataBrew Amazon Kinesis Amazon Quicksight Amazon Timestream
- 18. Introduction to AWS Lambda • Function-as-a-Service • Run code without provisioning or managing servers • Pay only for the compute time you consume • Automatically runs your code with high availability • Scale with usage
- 19. Lambda handles • Load balancing • Auto scaling • Handling failures • Security isolation • OS management • Managing utilization (and many other things) for you
- 20. Comparison of operational responsibility AWS Lambda Serverless functions AWS Fargate Serverless containers ECS/EKS Container-management as a service EC2 Infrastructure-as-a-Service More opinionated Less opinionated AWS manages Customer manages • Data source integrations • Physical hardware, software, networking, and facilities • Provisioning • Application code • Container orchestration, provisioning • Cluster scaling • Physical hardware, host OS/kernel, networking, and facilities • Application code • Data source integrations • Security config and updates, network config, management tasks • Container orchestration control plane • Physical hardware software, networking, and facilities • Application code • Data source integrations • Work clusters • Security config and updates, network config, firewall, management tasks • Physical hardware software, networking, and facilities • Application code • Data source integrations • Scaling • Security config and updates, network config, management tasks • Provisioning,managing scaling and patching of servers
- 21. Customer uses cases: Lambda at scale processes 4,000 requests per second processes half a trillion validations of stock trades daily triggers 1.2 billion Lambda requests each month Scaled up to 20,000 concurrent Lambda executions in testing
- 22. Anatomy of a Lambda Function
- 25. Serverless Applications Event source Function Node.js Python Java C# Go Ruby PowerShell Runtime API Changes in data state Requests to endpoints Changes in Resource state
- 27. Serverless Applications Event source Services Changes in data state Requests to endpoints Changes in Resource state Function Node.js Python Java C# Go Ruby PowerShell Runtime API
- 28. Lambda Execution Models Synchronous (push) Asynchronous (event) Stream (Poll-based) Amazon DynamoDB Amazon SNS /order Amazon S3 reqs Amazon Kinesis changes AWS Lambda service Amazon API Gateway Lambda function Lambda function function
- 29. Dead-Letter Queue • Asynchronous Lambda invocations are retried two more times (3 times total) • Lambda can forward payloads that were not processed to a dead-letter queue (IF configured!) • A mechanism to handle exceptions and failures gracefully
- 30. Monitoring and debugging Lambda functions • AWS Lambda console includes a dashboard for functions • Lists all Lambda functions • Easy editing of resources, event sources and other settings • At-a-glance metrics • Metrics automatically reported to Amazon CloudWatch for each Lambda function • Requests • Errors • Latency • Throttles
- 31. AWS X-Ray Profile and troubleshoot serverless applications: • Lambda instruments incoming requests for all supported languages and can capture calls made in code • API Gateway inserts a tracing header into HTTP calls as well as reports data back to X-Ray itself var AWSXRay = require(‘aws-xray-sdk-core‘); var AWS = AWSXRay.captureAWS(require(‘aws-sdk’)); S3Client = AWS.S3();
- 32. Anatomy of a Lambda Function Handler() function Function to be executed upon invocation Event object Data sent during Lambda function Invocation Context object Methods available to interact with runtime information (request ID, log group, more) import json def lambda_handler(event, context): # TODO implement return { 'statusCode': 200, 'body': json.dumps('Hello World!') }
- 33. Lambda Layers Lets functions easily share code: Upload layer once, reference within any function Promote separation of responsibilities, lets developers iterate faster on writing business logic Built in support for secure sharing by ecosystem
- 34. Lambda Layers: Uses cases • Custom code, that is used by more than one function • Libraries, modules, frameworks to simplify the implementation of your business logic • Security/monitoring service • Shared code that does not change frequently • Bring your own Runtime • C++ • Rust • PHP
- 35. Fine-grained Pricing Buy compute time in 1ms increments Low request charge No hourly, daily, or monthly minimums No per-device fees Never pay for idle Free Tier 1M requests and 400,000 GBs of compute. Every month, every customer.
- 36. Let’s see this in action
- 37. AWS Compute Offerings Service I want to configure servers, storage, networking, and my OS I want to run servers, configure applications, and control scaling Run my code when it’s needed How do I choose? Amazon EC2 Amazon ECS AWS Lambda AWS Fargate I want to run my containers
- 38. Serverless compute engine for containers Long-running Bring existing code Fully-managed orchestration AWS Fargate The two Serverless compute options Serverless event-driven code execution Short-lived All language runtimes Data source integrations AWS Lambda
- 39. Picking a Compute Platform: Containers vs. Lambda 10 GB
- 40. But distributed systems can be harder to coordinate and debug
- 41. Orchestration for serverless apps “I want to sequence functions” “I want to select functions based on data” “I want to run functions in parallel” “I want to retry functions” “I want to try/catch/finally” “I want to run code for hours” AWS Step Functions
- 42. AWS Step Functions Visualize in the Console Define in JSON Monitor Executions Easily coordinate multiple Lambda functions using visual workflows
- 43. Processing a new bank account application requires some service coordination Data checking Verify identity documents Check address Human review List flagged applications Handle human decisions Account applications Accept new application Consolidate data checks Human review? Approve or reject
- 44. Example workflow: opening an account Wait for a callback Parallel steps Branching choice Tasks
- 45. There are still a lot of things to think about • Deployment and Infrastructure as code • Secrets/configuration management • Simplifying code management • Debugging/troubleshooting • Performance controls • Security
- 47. Para saber más • https://aws.amazon.com/serverless • https://bit.ly/ServerlessWorkshopIL • https://step-functions-workshop.go-aws.com/
- 49. Gracias Javier Ramírez Developer Advocate - Amazon Web Services @supercoco9 ramirez