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Serverless Architectural Patterns and Best Practices (ARC305-R2) - AWS re:Invent 2018

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As serverless architectures become more popular, customers need a framework of patterns to help them identify how to leverage AWS to deploy their workloads without managing servers or operating systems. This session describes reusable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We also discuss the considerations for moving an existing server-based workload to a serverless architecture. This session can help you recognize candidates for serverless architectures in your own organizations and understand areas of potential savings and increased agility.

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Serverless Architectural Patterns and Best Practices (ARC305-R2) - AWS re:Invent 2018

  1. 1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Serverless Architectural Patterns and Best Practices Drew Dennis Solutions Architect Amazon Web Services A R C 3 0 5 Maitreya Ranganath Solutions Architect Amazon Web Services
  2. 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Serverless foundations Web application Stream processing Data lake Machine learning
  3. 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Spectrum of AWS offerings AWS Lambda Amazon Kinesis Amazon S3 Amazon API Gateway Amazon SQS Amazon DynamoDB AWS IoT Amazon EMR Amazon ElastiCache Amazon RDS Amazon Redshift Amazon ES Managed Serverless Amazon EC2 Microsoft SQL Server “On Amazon Elastic Compute Cloud (Amazon EC2)” Amazon Cognito Amazon CloudWatch Amazon Athena AWS X-Ray AWS Step Functions Amazon MQ Amazon SageMaker Amazon Neptune
  4. 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Serverless means … No server or container management Flexible scaling No idle capacity $ High availability
  5. 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Bootstrap the runtime Start your code Lambda: The execution lifecycle Cold start Warm start Download your code Start new container Time
  6. 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Tune your function’s resources Lambda exposes only a memory control, with the % of CPU core and network capacity allocated to a function proportionally Is your code CPU, network or memory-bound? If so, it could be cheaper to choose more memory. > Memory, > Cores, > Network
  7. 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lambda Power Tuning Alex Casalboni https://github.com/alexcasalboni/aws- lambda-power-tuning
  8. 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lambda best practices • Minimize your package size. Use only needed SDK modules. • Put your dependency .jar files in a separate /lib directory. • Improve dependency injection with smaller and simpler IoC frameworks that load quickly on startup, like Dagger2. • Leverage smaller and faster frameworks like jackson-jr for Java data binding. • Use environment variables to modify operational behavior. • Make sure functions invoked by Amazon SQS don’t exceed the Visibility Timeout.
  9. 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Serverless Application Model (SAM) AWS CloudFormation extension optimized for serverless New serverless resource types: functions, APIs, and tables Global configurations Local testing with SAM CLI Open specification (Apache 2.0) 2018: API Gateway Authorizers, CORS, DDB SSE, managed policies github.com/awslabs/serverless-application-model
  10. 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cloud native IDE in AWS Cloud 9
  11. 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Source Build Test Deploy AWS CodeCommit AWS CodeBuild Third Party Tooling AWS CodeDeploy AWS CodePipeline AWS CodeStar AWS code services
  12. 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lambda alias traffic shifting & AWS SAM AutoPublishAlias By adding this property and specifying an alias name, AWS SAM will do the following: • Detect when new code is being deployed based on changes to the Amazon Simple Storage Service (Amazon S3) URI of the Lambda function • Create and publish an updated version of that function with the latest code • Create an alias with a name you provide (unless an alias already exists) and points to the updated version of the Lambda function Deployment preference type Canary10Percent30Minutes Canary10Percent5Minutes Canary10Percent10Minutes Canary10Percent15Minutes Linear10PercentEvery10Minutes Linear10PercentEvery1Minute Linear10PercentEvery2Minutes Linear10PercentEvery3Minutes AllAtOnce In AWS SAM
  13. 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lambda alias traffic shifting & AWS Step Functions http://amzn.to/2FjlWA7
  14. 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  15. 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Web application Data stored in Amazon DynamoDB Dynamic content in AWS Lambda Amazon API Gateway Browser Amazon CloudFront Amazon S3 Amazon Cognito
  16. 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Custom Ink reduces cost by 90% Custom Ink enables people to design and order custom t-shirts and gear for their clubs, companies, charities, family reunions, and more. "Custom Ink has now reduced their cost by a factor of 90% ... and increased service reliability ... The operations team can now focus on other areas since they no longer need to constantly manage the service." ” “ • Clipart and graphics service allows customers to apply a number of effects (flip, rotate, palette swaps), to their own artwork or any piece of clipart in the Custom Ink library • Leverages API Gateway and Lambda for autoscaling to handle peak loads • Migration completed in three months
  17. 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Web application Data stored in Amazon DynamoDB Dynamic content in AWS Lambda Amazon API Gateway Browser Amazon CloudFront Amazon S3 Amazon Cognito
  18. 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Choose the right API endpoint type Edge optimized: Designed to help you reduce client latency from anywhere on the Internet AWS Region API Gateway Internet edge location edge location edge location Amazon CloudFront Distribution API Gateway Managed
  19. 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Web application Data stored in Amazon DynamoDB Dynamic content in AWS Lambda Amazon API Gateway Browser Amazon CloudFront Amazon S3 Amazon Cognito Lambda@Edge
  20. 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lambda@Edge use cases and blueprints • Content customization • Based on user attributes, device properties • Visitor session validation • User-agent validation—add an Access-Control- Allow-Header • Validate access token to confirm authentication • URL customization • Re-write URLs, pretty URLs • A/B testing and cookie-based sticky sessions • “Flip a coin” to select a version of content displayed to each user • Security • Security header insertions (HSTS, X-Content- Type-Options, and more) • Bot handling
  21. 21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Choose the right API endpoint type Regional AWS us-east-2 API Gateway Internet AWS us-west-2 API Gateway Amazon Route 53 Lambda function Amazon DynamoDB Lambda function Amazon DynamoDB GlobalTables
  22. 22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Choose the right API endpoint type Regional AWS us-east-2 API Gateway Internet AWS us-west-2 API Gateway Amazon Route 53 Amazon CloudFront Amazon CloudFront Lambda function Amazon DynamoDB Lambda function Amazon DynamoDB GlobalTables
  23. 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Choose the right API endpoint type Regional AWS us-east-2 API Gateway Internet AWS us-west-2 API Gateway Amazon Route 53 Lambda function Amazon DynamoDB Lambda function Amazon DynamoDB GlobalTables Lambda@Edge Amazon CloudFront
  24. 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Choose the right API endpoint type Private: designed to expose APIs only inside your VPC AWS Region API Gateway YOUR VPC AWS Direct Connect On-premises
  25. 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon API Gateway AWS Lambda Amazon DynamoDB Amazon S3 Amazon CloudFront • Bucket Policies • ACLs • OAI • Geo-Restriction • Signed Cookies • Signed URLs • DDOS Protection IAM AuthZ Serverless web app security • Cross Account Lambda • Throttling (per Method) • Resource Policies • Usage Plans Browser Amazon Cognito • Encryption at Rest • VPC Endpoint • Function policies • Env Variables
  26. 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Authorization with Amazon Cognito Cognito User Pool (CUP) Amazon API Gateway IdP User A User B User C Cognito Identity Pool (CIP) /web /cip /cup AWS Lambda Amazon DynamoDB Token AWS Credentials User B Data IAM Authorization API Resources C C A A A B BB C
  27. 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Lambda Authorizer function Client Lambda function Amazon API Gateway Amazon DynamoDB AWS Identity & Access Management Lambda authorizers Two types: • TOKEN―authorization token passed in a header • REQUEST―all headers, query strings, paths, stage variables, or context variables
  28. 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. GraphQL: A query language for APIs Resources defined by a GraphQL schema Client sends query, server orchestrates data Multiple transports (HTTP, MQTT, WebSockets) Efficient (network bandwidth, dev time) Self-documenting (introspection with tooling)
  29. 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS AppSync DynamoDB Table Lambda Function Amazon ES GraphQL Schema Upload Schema GraphQL Query Mutation Subscription Real-time Online/Offline AppSync API Cognito User Pool Legacy Application Amazon RDS https:// HTTP Resolver
  30. 30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  31. 31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Streaming with Amazon Kinesis Easily collect, process, and analyze video and data streams in real time Capture, process, and store video streams Kinesis Video Streams Load data streams into data stores Kinesis Data Firehose SQL Analyze data streams with SQL Kinesis Data Analytics Capture, process, and store data streams Kinesis Data Streams
  32. 32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Streaming data ingestion Amazon CloudWatch: Delivery metrics Amazon S3: Buffered files Kinesis Agent Record producers Amazon Redshift: Table loads Amazon Elasticsearch Service: Domain loads Amazon S3: Source record backup AWS Lambda: Transformations & enrichment Amazon DynamoDB: Lookup tables Raw records Lookup Transformed records Transformed recordsRaw records Kinesis Data Firehose: Delivery stream
  33. 33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Streaming data ingestion Amazon CloudWatch: Delivery metrics Amazon S3: Buffered files Amazon Redshift: Table loads Amazon ElasticSearch Service: Domain loads Amazon S3: Source record backup AWS Lambda: Transformations & enrichment Amazon DynamoDB: Lookup tables Raw records Lookup Transformed records Transformed records Raw Records (HTTP POST/PUT) Amazon Kinesis Firehose: Delivery stream Amazon CloudFront Lambda@Edge Browser
  34. 34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Best practices Tune Kinesis Data Firehose buffer size and buffer interval Larger objects = fewer Lambda invocations, fewer Amazon S3 PUTs Enable compression to reduce storage costs Enable Source Record Backup for transformations Recover from transformation errors Follow Amazon Redshift best practices for loading data
  35. 35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Kinesis Data Streams and AWS Lambda • Number of Kinesis Data Streams shards corresponds to concurrent invocations of Lambda function • Batch size sets maximum number of records per Lambda function invocation Kinesis Data Streams: Stream AWS Lambda: Processor function Streaming source Other AWS services
  36. 36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Fan-out pattern Fan-out pattern trades strict message ordering versus higher throughput & lower latency Kinesis Data Streams: Stream Lambda: Dispatcher function Lambda: Processor function Increase throughput, reduce processing latency Streaming source
  37. 37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Real-time analytics Kinesis Data Streams: Ingest stream Kinesis Data Analytics: Time window aggregation Kinesis Data Firehose: Error stream Amazon S3: Error records Record producers AWS Lambda: Alert function Amazon DynamoDB: Device thresholds Amazon SNS: Notifications
  38. 38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM" SELECT STREAM "device_id", STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '10' MINUTE) as "window_ts", SUM("measurement") as "sample_sum", COUNT(*) AS "sample_count" FROM "SOURCE_SQL_STREAM_001" GROUP BY "device_id", STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '10' MINUTE); Amazon Kinesis Data Analytics Aggregation 10-minute tumbling window Kinesis Data Analytics: Time window aggregation Source stream Destination stream
  39. 39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Customer Reference: Otonomo A dedicated platform for safe and simple exchange of connected car data to enable an ecosystem of services and applications • Ingesting 200M+ events per day • 500K+ events/sec at peak • Various consumer types for different use cases • Persisting large amount of data in Amazon S3 • Real-time sub-second processing optimized for stream processing • Kinesis Data Analytics • Anomaly detection / Insights / K-Top analysis • Easily run SQL / Windowing functions on stream Key Takeaways: • Amazon S3 for data lake • Decoupling of storage and compute • Scale independently • Serverless + managed • Focus on core business
  40. 40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Compare related services Amazon MQ Amazon Kinesis Data Streams Amazon SQS (Standard) Amazon SQS (FIFO) Amazon SNS Serverless No Yes Yes Yes Yes Guaranteed Ordering Yes Yes, within Shard No Yes, within Message Group No Message access Pull or Push Pull or Push (HTTP/2) Pull Pull Push Delivery Exactly-once At-least-once At-least-once Exactly-once Retries Data retention period Unlimited 7 days 14 days 14 days Until retries complete Parallel consumers Yes Multiple Consumers per Shard with independent iterators Multiple Readers (but only one message per Reader at a time) One consumer at a time Multiple Subscribers per topic Delivery destination types ActiveMQ clients Kinesis Consumers (KCL, Lambda, SDK) SQS Readers, Lambda SQS Readers HTTP/S, Mobile Push, SMS, Email, SQS, Lambda Row/object size Unlimited 1 MB 256 KB (TB with S3) 256 KB 256 KB
  41. 41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  42. 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data lake characteristics Collect, store, process, consume, and analyze all organizational data Structured, semi-structured, and unstructured data Decoupled compute and storage Fast automated ingestion Schema on read AI/ML and BI/analytical use cases Complementary to data warehouses
  43. 43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS serverless data lake Amazon S3 bucket(s) Amazon ESAWS Glue Amazon DynamoDB Catalog & search AWS Key Management Service (AWS KMS) AWS CloudTrail IAM Amazon Macie Security & auditing Amazon Cognito Amazon API Gateway IAM API/UI Amazon Athena Amazon QuickSight Amazon Redshift Spectrum Analytics & processing AWS Glue AWS Lambda Amazon Kinesis Data Streams Amazon Kinesis Data Firehose AWS Direct Connect Ingest AWS IoT
  44. 44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The foundation is Amazon S3 No need to run compute clusters for storage Virtually unlimited number of objects and volume Very high bandwidth – No aggregate throughput limit Multiple storage classes Versioning Encryption S3 Select CloudTrail Data Events S3 Analytics and Inventory S3 object tagging Block Public Access
  45. 45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Search and Data Catalog DynamoDB as metadata repository Amazon Elasticsearch Service AWS Lambda Metadata Index (DynamoDB) Search Index (Amazon ES) ObjectCreated ObjectDeleted PutItem Update Index S3 Bucket https://aws.amazon.com/answers/big-data/data-lake-solution/ Catalog & Search
  46. 46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Glue Crawlers AWS Glue Data Catalog Amazon QuickSight Amazon Redshift Spectrum Amazon Athena S3 Bucket(s) Catalog & Search Instantly query your data lake on Amazon S3
  47. 47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Analytics S3 Select on CSV, JSON and Apache Parquet objects Amazon QuickSight Amazon Athena AWS Lambda Predictions with Amazon SageMaker Amazon EMR AWS Glue (ETL) Analytics & processing
  48. 48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Athena―Serverless interactive query service 44.66 seconds...Data scanned: 169.53GB Cost: $5/TB or $0.005/GB = $0.85 SELECT gram, year, sum(count) FROM ngram WHERE gram = 'just say no' GROUP BY gram, year ORDER BY year ASC; Analytics & processing
  49. 49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Athena best practices Partition data s3://bucket/flight/parquet/year=2018/month=11/day=25/ Columnar formats – Apache Parquet, AVRO, ORC Compress files with splittable compression (bzip2) Optimize file sizes https://aws.amazon.com/blogs/big-data/top-10-performance-tuning- tips-for-amazon-athena/ Analytics & processing
  50. 50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Serverless batch processing AWS Lambda: Splitter Amazon S3 Object Amazon DynamoDB: Mapper Results AWS Lambda: Mappers …. …. AWS Lambda: Reducer Amazon S3 Results Analytics & Processing
  51. 51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Fannie Mae achieves 4x faster modeling One simulation run of ~ 20 million mortgages takes 1.5 hours, >4 times faster than the existing process. • Fannie Mae uses a financial modeling process for managing mortgage risk on a daily basis. • Monte-Carlo simulation process to project future cash flows. • 10 quadrillion (10𝑥1015 ) of cash flow projections each month in hundreds of economic scenarios.
  52. 52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Pywren • PyWren Python library provides 10 TFLOPS of peak compute power with new default – 1000 concurrent functions • Achieve over 60 GB/sec of read and 50 GB/sec of write performance using Amazon S3 http://pywren.io/ Analytics & processing
  53. 53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  54. 54. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The AWS machine learning stack Frameworks KERAS Platforms Amazon SageMaker Amazon Mechanical Turk Serverless API-driven services A M A Z O N R E K O G N I T I O N A M A Z O N R E K O G N I T I O N V I D E O A M A Z O N P O L L Y A M A Z O N T R A N S C R I B E A M A Z O N T R A N S L A T E A M A Z O N C O M P R E H E N D A M A Z O N L E X Vision Language Conversational chatbots
  55. 55. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. 2. Submit image 4. DetectFaces 7. DetectText 1. Upload 3. Store image Lambda Step Functions 5. DetectLabels 6. DetectModerationLabels 8. Store metadata & analysis DynamoDB Amazon ES Image processing with Amazon Rekognition Image
  56. 56. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Media analysis solution S3: Web interface Amazon Cognito Amazon Rekognition Video: Detect objects, scenes, faces, & celebrities Amazon ES: Search index API Gateway: REST APIs https://aws.amazon.com/answers/media-entertainment/media-analysis-solution/ AWS Elemental MediaConvert: Transcode videos S3: Media storage Step Functions: Orchestrate analysis
  57. 57. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Connect Serverless contact center solution Real time and historical analytics High-quality voice capability Call recording Skills-based routing [Automatic Call Distribution (ACD)]
  58. 58. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Intelligent call center chatbot Amazon Connect Customer Amazon Lex AWS Lambda: Chatbot Processing DynamoDB: Customer Data Amazon SNS: SMS Messaging Customer calls Connect to reschedule an appointment Connect calls Lex chatbot Lex chatbot calls Lambda function to get customer preferences and fulfil Intents Lambda function sends text message confirmation via SNS Customer receives appointment confirmation text message Lambda function writes updates to DynamoDB
  59. 59. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Call center analytics Amazon Connect Customers Agents Contact trace records (CTRs) Kinesis Data Streams Kinesis Data Firehose S3: CTRs Call recordings S3: Call recordings S3: Call transcripts Step Functions: Orchestrate S3: Sentiment, key phrases, entities Step Functions: Orchestrate S3 Notifications for call transcripts Athena Amazon QuickSight
  60. 60. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Summary “No server is easier to manage than no server” - Werner Vogels, Amazon CTO Never pay for idle & scale as you grow Apply serverless patterns for common use-cases: Web applications Stream processing Data lake Machine learning What will you build with serverless?
  61. 61. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Related sessions ARC303 Architecture Patterns of Serverless Stream Processing at Scale Monday, November 26 5:30 PM - 6:30 PM | Aria West, Level 3, Starvine 10, Table 6 Tuesday, November 27 5:30 PM - 6:30 PM | Aria West, Level 3, Starvine 10, Table 4 ARC304 Architecture Patterns of Serverless Microservices Tuesday, November 27 2:30PM– 3:30PM | MGM, Level 3, North Concourse 306 ARC214 Using Containers and Serverless to Deploy Microservices Tuesday, November 27 10:00AM – 11:00AM | Aria East, Plaza Level, Orovada 3
  62. 62. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  63. 63. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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