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AWS reInvent 2018 Recap - Solutions Updates Part 2

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ReCap for Dr. Werner Vogels Key Notes
Young Yang, Solutions Architect
亞馬遜 AWS 於 2018 年 11 月底在美國拉斯維加斯所舉辦的第七屆 AWS re:Invent 2018 大會,在 AWS 客戶、合作夥伴、媒體人士、產業分析師及 AWS 員工共襄盛舉下,與會人數再創新高,超過 5 萬人。會中 AWS 發布超過 20 款雲端方案,且一半以上專攻雲端 AI、機器學習、物聯網,包括對 SageMaker 強化更多進階功能,推出第一款專用的機器學習推論晶片、加入深度的機器學習運算法支援,及其他包括儲存、資料庫、混合雲、邊緣運算 IoT 等解決方案。而具備微型機器學習能力的迷你自駕遙控車 DeepRacer 的現身,驚人之舉不僅抓人眼球,深入客戶體驗的用心,更成功抓住全球使用者的心。

為讓您與全球先進技術同步,共享最新趨勢資訊,解決您開發機器學習和發展 AIoT 所遇到的難題,AWS 台灣團隊將於 2019 年 1 月 31 日 (四) 舉辦《AWS re:Invent 2018 Recap 台北》,特別嚴選最適切國內諸位先進和企業需求的內容,從「技術創新」、「AIoT」兩大分組議程,發表 AWS 的新服務和新方案。大會除了邀請亞馬遜 AWS 大中華區首席雲計算企業顧問 (Principal Evangelist) 張俠博士分享 AWS 的解決方案藍圖外,眾多 AWS 資深專家也將分享包含機器學習、深度學習推理加速等新方案,完全託管的文件系統、資料庫,無伺服器、容器技術與安全性,以及大數據與分析、物聯網服務應用、儲存方案等最新技術。歡迎您親臨會場,全方位體驗 AWS 新服務將能為您創造的驚人創新之效益。

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AWS reInvent 2018 Recap - Solutions Updates Part 2

  1. 1. ReCap for Dr. Werner Vogels Key Notes Young Yang, Solutions Architect
  2. 2. Almost 14 years
  3. 3. Agenda 1. Reducing your blast radius 2. Cloud-native as a foundation for innovation 3. The right tool (DB) for the right job (workload) 4. Everyone wants to just focus on business logic 5. Are you well architected?
  4. 4. Reducing your blast radius
  5. 5. Reducing your blast radius
  6. 6. Blast radius • Who is impacted? • How many workloads? • What functionality? • How many locations?
  7. 7. Typical service application Compute Service Storage Compute Service Storage Compute Service Storage
  8. 8. Cell-based architecture Compute Cell 0 Compute Cell n Cell Router Service Storage Compute Cell 1 Storage Storage
  9. 9. System properties Cell 0 Cell router Service Cell 1 Cell n • Workload isolation • Failure containment • Scale-out vs. scale-up • Testability • Manageability Cell n+1 X
  10. 10. Cells and Availability zones Regional service Zone b Zone cZone a Zone b Zone cZone a Regional service cell Regional service cell Regional service cell Without Cells With Cells
  11. 11. Availability Zone failure Regional service Zone b Zone cZone a Zone b Zone cZone a Regional service cell Regional service cell Regional service cell Without Cells With Cells
  12. 12. Theoretical blast radius Regional service Zone b Zone cZone a Zone b Zone cZone a Regional service cell Regional service cell Regional service cell Without Cells With Cells
  13. 13. Cells and Availability Zones – zonal services Zone b Zone cZone a Zone b Zone cZone a Zonal service Zonal service Zonal service Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Without Cells With Cells
  14. 14. Availability zone failure Zone b Zone cZone a Zone b Zone cZone a Zonal service Zonal service Zonal service Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Without Cells With Cells
  15. 15. Partial availability zone failure Zone b Zone cZone a Zone b Zone cZone a Zonal service Zonal service Zonal service Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Without Cells With Cells
  16. 16. Theoretical blast radius Zone b Zone cZone a Zone b Zone cZone a Zonal service Zonal service Zonal service Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Zonal service cell Without Cells With Cells
  17. 17. Cloud-native as a foundation for innovation
  18. 18. SQL TRANSACTIONS CACHING LOGGING Relational databases: not designed for the cloud
  19. 19. Decompose the system into fundamental building blocks
  20. 20. Amazon Aurora Scaled-out distributed architecture A P P L I C A T I O N A P P L I C A T I O N A P P L I C A T I O N Shared Storage Volume S T O R A G E N O D E S W I T H S S D s SQL TRANSACTIONS CACHING SQL TRANSACTIONS CACHING SQL TRANSACTIONS CACHING
  21. 21. Fast Online DDL GeoSpatial indexing Load balancing & auto scaling Multi-region BacktrackBackup and restore Fast database cloning Instant crash recovery Serverless Global databases A m a z o n A u r o r a Cloud native database as a foundation for innovation
  22. 22. Aurora is the foundation for cloud native DB innovation R e d u c e r e a d l a t e n c y b y a v o i d i n g q u o r u m r e a d s M a k e t h e s t o r a g e s e r v i c e i n t e l l i g e n t b y b e i n g D B r e c o r d a w a r e R e d u c e w r i t e l a t e n c y r e i n v e n t i n g w h a t g e t s w r i t i n g H i g h l y r e l i a b l e a n d r e s i l i e n t t o c o m p l e x f a i l u r e s c e n a r i o s V e r y f a s t t i m e t o r e p a i r i n e x t r e m e f a i l u r e c a s e s D e c o u p l e i n t o a h i g h l y s c a l a b l e a n d r e l i a b l e s t o r a g e s e r v i c e
  23. 23. TodayD y n a m o w h i t e p a p e r 2 0 0 7 DynamoDB Launch CloudFormation Support Batch GetItem, Writeitem, update table Binary data types GSI query Fine grained access control Cross-region copy in data pipeline DDB local Geospatial library Low cost reads LSI launch JSON documents + expanded free-tier mobile console support cross-region import/export Cloudformation GSI/LSI Titan graph DB integration 1 minute CloudWatch metrics ElasticSearch integration Consistent scans CRR library CloudTrail integration Streams, Lambda triggers Focus on scalability, availability, and performance improvements Point-in-time recovery Service level agreement On-Demand Transactions Time to live (TTL) VPC Endpoints DynamoDB Accelerator (DAX) Auto scaling Global Tables On-demand backups Encryption at rest2 0 1 2 2 0 1 8 2 0 1 3 2 0 1 4 2 0 1 6 2 0 1 5 2 0 1 7
  24. 24. The four pillars of DynamoDB Fully-managed Performance at scale Comprehensive security Built for modern applications
  25. 25. DynamoDB Advancements over the last 12 months VPC endpoints April 2017 Auto scaling June 2017 DynamoDB Accelerator (DAX) April 2017 Time To Live (TTL) February 2017 Global tables On-demand backup Encryption at rest November 2017 November 2017 November 2017 Point-in-time recovery March 2018 SLA June 2018 99.999% SLA August 2018 Adaptive capacity ACID November 2018 Transactions November 2018 On-demand
  26. 26. DynamoDB transactions Simplify your code by executing multiple, all-or-nothing actions within and across tables with a single API call. Single API Call
  27. 27. DynamoDB on-demand Start at Zero No Limit Features • No capacity planning, provisioning, or reservations– simply make API calls • Pay only for the reads and writes you perform Key Benefits • Eliminates tradeoffs of over- or under- provisioning • Instantly accommodates your workload as traffic ramps up or down
  28. 28. Item and Offers Service https://www.amazon.co m Item Offer Relationships
  29. 29. Item Master Service (IMS) Blob Stores 4 Index Tables 7 Databases 24 Partitioning Hash Logical Partitions 256 Record Count ~600 Billion Update Rate ~5 Billion/Day 24 ORACLE DATABASES UPDAT ES PUBLI SH PARTITION MAP Oracle DB instance Oracle DB instance Oracle DB instance Oracle DB instance Websit e Sellers
  30. 30. Amazon Redshift is now 3.5x faster than 6 months ago Normalized Queries Per Hour (QPH) Assuming Amazon Redshift’s QPH 6 months ago=100% JUL 2018 AUG 2018 SEP 2018 OCT 2018 181% 237% 284% 350% 100% JUN 2018
  31. 31. 87% of Amazon Redshift customers don’t have significant wait times Daily cluster queue time per day Remaining 13% have bursts of activity averaging 10 minutes at a time
  32. 32. Consistently fast performance even with thousands of concurrent queries Amazon Redshift concurrency scaling A V A I L A B L E I N P R E V I E W T O D A Y NEW!
  33. 33. Machine learning / training data Surveillance video/closed circuit data lakesAutonomous vehicle data IoTeventlogs Logfiles High definition video Mapping data Documents Scanned forms Smar t Hom e data DNA sequences Oil & gas topography Backup s Video assets Compliance records Archives Genomic s data Origin content for CDNs Device sync and backup Customer callrecords Medical imagery and records Device backups Master media files WORM/compliance data Columnar-storage files (Parquet, etc.) Machine Learning training sets Historical record preservation data Telemetry data Pharmaceutical trial data HPC application data Transcode d content Seismic surveys CCTV data Thumbnai l Images Analytics results
  34. 34. Amazon S3
  35. 35. More than 235 distributed microservices
  36. 36. Designed for 11 9’s TIME TO FAIL TIME TO REPAIR
  37. 37. The right tool (DB) for the right job (workload)
  38. 38. AWS databases services Purpose-built for all your app needs NeptuneRDS Aurora CommercialCommunity Timestream QLDBElastiCache Relational Key-value Document In-memory Graph Time series Ledger DynamoDB DocumentDB NEW! Redis Memcached NEW!
  39. 39. Everyone wants to just focus on business logic
  40. 40. Lambda Innovation
  41. 41. Serverless allows you to build increasingly powerful applications, faster
  42. 42. Systems of systems
  43. 43. IDEs Languages AWS Cloud9 AWS Toolkit for PyCharm AWS Toolkit for IntelliJ AWS Toolkit for VS Code WebSocket support for API Gateway ALB support for Lambda Step Functions service integrations Managed Streaming for Kafka Custom Runtimes Workflows Programming Models AWS Lambda Layers AWS Nested Apps using Serverless Application Repository
  44. 44. Are you well architected?
  45. 45. | Benefits SECURITY 2 O P E R AT I O N A L E X C E L L E N C E 1 R E L I A B I L I T Y 3 C O S T O P T I M I Z AT I O N 5 P E R F O R M A N C E E F F I C I E N C Y 4
  46. 46. AWS Well- Architected Tool NEW! Measure and improve your architecture using AWS Well-Architected best practices G E N E R A L L Y A V A I L A B L E T O D A Y Implement workplans to improve your architecture Stay up to date as your architecture evolves Review workloads against best practices
  47. 47. Security Events Videos Resources Best Practices Improvement Plans
  48. 48. 95% of AWS features and services are built based on direct customer feedback
  49. 49. GO BUILD

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