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AWS re:Invent 2016: The State of Serverless Computing (SVR311)

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Join us to learn about the state of serverless computing from Dr. Tim Wagner, General Manager of AWS Lambda. Dr. Wagner discusses the latest developments from AWS Lambda and the serverless computing ecosystem. He talks about how serverless computing is becoming a core component in how companies build and run their applications and services, and he also discusses how serverless computing will continue to evolve.

Published in: Technology

AWS re:Invent 2016: The State of Serverless Computing (SVR311)

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Dr. Tim Wagner, General Manager, AWS Lambda and Amazon API Gateway December 1, 2016 Serverless Computing Mini Con State of the Union
  2. 2. Agenda
  3. 3. What is serverless? Build and run applications without thinking about servers
  4. 4. Let’s take a look at the evolution of computing Physical servers in datacenters Virtual servers in datacenters Virtual servers in the cloud
  5. 5. Each progressive step was better Physical Servers Datacenters Virtual Servers Datacenters • Higher utilization • Faster provisioning speed • Improved uptime • Disaster recovery • Hardware independence • Trade CAPEX for OPEX • More scale • Elastic resources • Faster speed and agility • Reduced maintenance • Better availability and fault tolerance Virtual servers in the cloud
  6. 6. But there are still limitations Physical Servers Datacenters Virtual Servers Datacenters • Trade CAPEX for OPEX • More scale • Elastic resources • Faster speed and agility • Reduced maintenance • Better availability and fault tolerance • Still need to administer virtual servers • Still need to manage capacity and utilization • Still need to size workloads • Still need to manage availability, fault tolerance • Still expensive to run intermittent jobs Virtual servers in the cloud
  7. 7. Evolving to serverless SERVERLESS Virtual servers in the cloud Physical servers in datacenters Virtual servers in datacenters
  8. 8. No server is easier to manage than no server All of these responsibilities go away Provisioning and utilization Availability and fault tolerance Scaling Operations and management
  9. 9. EVENT DRIVEN CONTINUOUS SCALING PAY BY USAGE Deliver on demand, never pay for idle
  10. 10. Building blocks for serverless applications AWS Lambda Amazon DynamoDB Amazon SNS Amazon API Gateway Amazon SQS Amazon Kinesis Amazon S3 Orchestration and State Management API Proxy Messaging and Queues Analytics Monitoring and Debugging Compute Storage Database AWS X-RayAWS Step Functions
  11. 11. Serverless changes how you deliver Speeds up time to market Dedicated time to innovation Increases developer productivity Eliminates operational complexity
  12. 12. Serverless today
  13. 13. Chatbots • Powering chatbot logic Amazon Alexa • Powering voice-enabled apps • Alexa Skills Kit Common use cases Web applications • Static websites • Dynamic web apps • Packages for Flask and Express Data processing • Real time • MapReduce • Batch Back ends • Apps & services • Mobile • IoT </></>
  14. 14. Customers innovating with serverless
  15. 15. Enterprises are achieving massive scale with Lambda • Thomson Reuters processes 4,000 requests per second • FINRA processes half a trillion validations of stock trades daily • Hearst reduced the time to ingest and process data for its analytics pipeline by 97% • Vevo can handle spikes of 80x normal traffic • Expedia triggers 1.2 billion Lambda requests each month
  16. 16. Content services • Re-architected the platform from the ground up • Uses microservices architecture • Can handle spikes of up to 80x normal traffic Serverless powers Vevo’s critical applications
  17. 17. Data services • Built its data platform starting from zero • Serverless architecture: AWS Lambda, Amazon Kinesis, Amazon Redshift, Amazon DynamoDB, Amazon S3, etc. • New platform went into production in <12 months Serverless powers Vevo’s critical applications
  18. 18. FINRA
  19. 19. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Tim Griesbach - Senior Director, FINRA December 1, 2016 SVR311 How We Perform ½ Trillion Validations Daily Serverless Computing at FINRA
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  22. 22. It’s more than just ½ Trillion validations… Volume Varies Daily & Hourly • Driven by world events in financial, government, etc. • Hourly volume varies; max of 75B elements per hour • Daily volume can vary 2-3x due to market events Validation rules expand over time • Data must meet a defined specification • SEC/FINRA adding/reviewing rules regularly • >200 rules today; >100+ derivations SLA Expectations Don’t Change • Timely feedback so data can be corrected to meet SLA • Invalid or late data = $$ fines 5
  23. 23. Traditional on premise solution • Static Hadoop Cluster, processing files in batches • Required to be available 24/5 • Map-only job scheduled every 10 minutes On premise data center NAS DB FTP Incoming Files 6
  24. 24. Challenges with our on premise solution • Not easily scalable = sized for peak demand • Idle 50% of time • Higher costs • Required ongoing costly server/software maintenance • Originally designed for batch processing 7
  25. 25. Goals for our new solution • Support volume spikes on-demand • Get out of infrastructure management • Don’t pay for peak all the time, pay for what we use • Can we improve the time of availability of data 8
  26. 26. Selecting Technology Technologies Considered • AWS Lambda • Apache Ignite / EC2 • Spark / EMR Evaluation Process • Define criteria • Create POC to validate Selection Criteria • Scalability • Security • Data Partitioning • Monitoring • Performance • Cost • Maintenance 9
  27. 27. Our Serverless Cloud Solution 10
  28. 28. Lambda centered AWS Solution • Validator implemented using Lambda • Queues provide input/output notification from system • Controller manages data feeds into Lambda and notifications out of Lambda • Data on S3 in Herd On premise data center NAS FTP Incoming Files Validation Lambda Account Controller Consumer … 11
  29. 29. Our Results – Faster, Cheaper, More Scalable • Reduced cost, only pay for what we use • Reduce by over 50% • Processing times have been reduced • Goal is regardless of volume, any file avail in <1m • Errors handled on a finer grained basis • Horizontal scaling improved • We have seen volume spikes >3x with no impact • Less infrastructure to manage 12
  30. 30. FINRA’s Future Plans • Eliminate the EC2 based controller • Considering for other ETL processing within FINRA • Rethink how we can use Lambda for other applications • Continue push to provide near real-time throughput 17
  31. 31. Thank you!
  32. 32. Serverless is a core component of modern apps
  33. 33. AWS: The Complete Serverless Platform
  34. 34. Capabilities of a serverless platform
  35. 35. Cloud logic layer
  36. 36. NEW Cloud logic layer C# C# language support • Powered by .NET Core 1.0 release • Supports functions packaged as DLLs • Built in support for NuGet packages • Author and deploy C# functions using Visual Studio to AWS Lambda
  37. 37. NEW Cloud logic layer Amazon API Gateway binary encoding • Serve images, audio, video or other binary data through APIs • Encode to Base64 or decode to binary • Automatic encoding to Base64 for AWS Lambda functions
  38. 38. NEW Cloud logic layer Amazon API Gateway documentation generation • Document your APIs for better developer experience • Support for documentation per Swagger specifications • Support for separate workflows – tech writer vs API developer
  39. 39. NEW Cloud logic layer Amazon Lex • Chatbots for Facebook and AWS Mobile Hub • Powered by the same technology as Alexa • Efficient and intuitive tools to build conversations • Scales automatically • Connects to enterprise systems • Slack and Twilio integration coming soon
  40. 40. Responsive data sources
  41. 41. Lambda event sources – AWS services Amazon API Gateway Amazon S3 Amazon DynamoDB Amazon Aurora Amazon Simple Notification Service Amazon Simple Email Service Amazon Cognito Amazon CloudWatch Amazon Kinesis Streams AWS CodeCommit AWS CloudFormation AWS Config Amazon Lex
  42. 42. Integrations library
  43. 43. NEW Integrations library Amazon API Gateway listings in the AWS Marketplace
  44. 44. Reliability and performance
  45. 45. NEW Reliability and performance Dead Letter Queues • Automatically capture events after exhausting retries • Build even more reliable event processing applications • Target Amazon SQS queues or Amazon SNS topics
  46. 46. Application modeling framework Monolithic application Microservices
  47. 47. But what happens when you have an entire app made up of many functions? Composing serverless applications
  48. 48. Meet SAM
  49. 49. AWS Serverless Application Model (SAM) Standard model for representing serverless applications on AWS Functions, APIs, event sources, and data stores Simplifies deployment and management for serverless applications
  50. 50. AWS Serverless Application Model (SAM) • Natively supported by AWS CloudFormation • Export any function as a SAM template • Package and deploy SAM templates using AWS CLI • Open spec under Apache 2.0 for community extensions
  51. 51. Developer ecosystem — AWS </> AWS CodePipeline + SAM GitHub Amazon S3 AWS CodeCommit AWS CodeBuild AWS CodeBuild third-party tools AWS CloudFormation Commit Build Test Deploy to Prod
  52. 52. Developer ecosystem — AWS How do you debug distributed applications made of multiple functions or services? How do you gain insights into how your functions are performing or behaving?
  53. 53. Introducing AWS X-Ray (in preview) • Analyze and debug distributed apps in production • Visualize service call graph of your app • Identify performance bottlenecks and errors • Pinpoint service-specific issues • Identify impact of issues on users of the app • Lambda support coming soon
  54. 54. Developer ecosystem Write code Deploy to customer Build and test Receive feedback
  55. 55. Developer ecosystem — commercial MonitoringDeploymentIntegrationsCode libraries APN skills
  56. 56. Developer ecosystem — open source Chalice Framework
  57. 57. Orchestration and state management
  58. 58. 1. A concept used by CompSci profs for torturing undergrads, full of arcane math 2. A practical way to build and manage modern serverless apps “State machine” (noun)
  59. 59. “I want to sequence functions” “I want to call functions based on data” “I want more control over retries” “I want try/catch/finally” “I have a workflow that runs for hours” “I want to run functions in parallel” Orchestration and state management
  60. 60. Introducing AWS Step Functions Run cloud state machines Coordinate components of multi- step apps Visualize application as a series of steps Handles thousands of workflows and millions of simultaneous steps
  61. 61. Lambda everywhere We’ve expanded our coverage to most major regions in the AWS global infrastructure
  62. 62. Introducing Lambda@Edge (in preview) Lambda is now available in all Amazon CloudFront edge locations • Low-latency request/response customization • Supporters viewer and origin events
  63. 63. Introducing AWS Greengrass (in preview) • Extends AWS processing on devices • Low latency, near-real time
  64. 64. Serverless is a fundamental component of modern applications
  65. 65. Conclusion Lambda is a fundamental component of modern application architectures It has a place in everything from data processing to simple web apps

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