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AWS Summit Seoul 2015 - AWS 최신 서비스 살펴보기 - Aurora, Lambda, EFS, Machine Learning, ECS

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2015년 4월 21일 서울 코엑스에서 열렸던 AWS Summit Seoul 2015의 첫번째 트랙에서 Thomas Park 아마존 웹서비스 솔루션스 아키텍트 총괄이 발표한 자료입니다.

AWS Summit Seoul 2015 - AWS 최신 서비스 살펴보기 - Aurora, Lambda, EFS, Machine Learning, ECS

  1. 1. Seoul
  2. 2. 신규 서비스 살펴보기 Thomas Park Head of Solutions Architecture, Korea
  3. 3. What if you could devote 30% more of your resources to your customers?
  4. 4. IT Map - Traditional IT E-mail, Productivity, Collaboration, HR, Finance, ERP Desktop Support, Device Management, Telephony, IT Support Information Security, CISO Encryption, Key Management, Identity Management, Firewalls, IDS, DDoS Business Applications Digital Products, Brand Websites, Mobile Applications, Point of Sale Systems, Commerce Corporate Applications End User Computing Infrastructure Servers, Storage, Networking, Databases, Data Warehousing, Data Centers
  5. 5. IT Map - Traditional IT with AWS Information Security, CISO Corporate Applications End User Computing Infrastructure Business Applications AWS Elastic Beanstalk, AWS Lambda, Amazon SQS, Amazon SNS, Amazon Mobile Analytics, Amazon CloudFront Amazon WorkMail, Amazon WorkDocs, AWS Marketplace, AWS Directory Service, SaaS Amazon WorkSpaces, Amazon AppStream, AWS Marketplace, AWS Mobile Services, SaaS AWS Identity and Access Management (IAM), AWS CloudHSM, AWS Key Management Service (AWS KMS), Security Groups, AWS Marketplace Amazon EC2, Amazon S3, Amazon RDS, Amazon VPC, Amazon Direct Connect, Directory Service, IAM, AWS Service Catalog
  6. 6. Enterprise Customers
  7. 7. IT Map - A Cloud-First Tomorrow Information Security, CISO Business Applications DevOps Corporate Applications End User Computing AWS Elastic Beanstalk, AWS Lambda, Amazon SQS, Amazon SNS, Amazon Mobile Analytics, Amazon CloudFront Amazon WorkMail, Amazon WorkDocs, AWS Marketplace, AWS Directory Service, SaaS Amazon WorkSpaces, Amazon AppStream, AWS Marketplace, AWS Mobile Services, SaaS Amazon EC2, Amazon S3, Amazon RDS, Amazon VPC, Amazon Direct Connect, Directory Service, IAM, AWS Service Catalog, AWS Code Services AWS Identity and Access Management (IAM), AWS CloudHSM, AWS Key Management Service (AWS KMS), Security Groups, AWS Marketplace
  8. 8. • Service-Oriented Architecture (SOA) • Everything gets a service interface • Primitives • “Microservices”
  9. 9. • Decentralized • Two-pizza teams • Agility, autonomy, accountability, and ownership • “DevOps”
  10. 10. • Deployment service • Zero downtime • Health checking • Versioned artifacts & rollbacks
  11. 11. • Continuous delivery • From check-in to production • CI/CD + release automation • >90% of teams Pipelines
  12. 12. DevOps Pipeline Source Developers commit changes Build Changes are built and unit tested Staging Code deployed to staging and load/UI tested Production Code is deployed to production Changes, Updates, and Fixes Ideas, Requests, and Bugs Developers Customers
  13. 13. = 50 million deployments a year Thousands of teams + Microservices architecture + Multiple environments + Continuous delivery
  14. 14. AWS Code services CodeCommit Private Beta CodePipeline Private Beta CodeDeploy Launched
  15. 15. What about the infrastructure?
  16. 16. Amazon EC2 Container Service
  17. 17. What are containers? • OS virtualization • Process isolation • Automation • ImagesServer Guest OS Bins/Libs Bins/Libs App2App1
  18. 18. Common Customer Challenges/Desires • Cluster Management • Configuration Management • Availability • Scalability (application and repository) • Scheduling • Monitoring • AWS integration – VPC, ELB, Auto Scaling, CloudWatch, etc.
  19. 19. Amazon EC2 Container Service • Building Block Service • Cluster Management Made Easy • Flexible Scheduling • Performance at Scale • Security • Extensible
  20. 20. AWS Lambda
  21. 21. Amazon S3 Bucket Events Original image Thumbnailed image 1 2 3 Application Monitoring Security Deploy
  22. 22. Yes, you can do with EC2 instances… Amazon S3 Bucket Events Original image Thumbnailed image 1 2 3 Application Monitoring Security Deploy High Availability Scalability
  23. 23. PUT Original GET PUT Thumbnail Application Monitoring Security Deploy An event-driven computing service for dynamic applications High Availability Scalability
  24. 24. What is AWS Lambda? AWS Lambda is a compute service that runs your code in response to events such as image uploads, in-app activity, website clicks, or outputs from connected devices.
  25. 25. Data Triggers: Amazon S3 Amazon S3 Bucket Events AWS Lambda Original image Thumbnailed image 1 2 3
  26. 26. Amazon Machine Learning
  27. 27. Three types of data-driven development Retrospective analysis and reporting Here-and-now real-time processing and dashboards Predictions to enable smart applications Amazon Kinesis Amazon EC2 AWS Lambda Amazon Redshift, Amazon RDS Amazon S3 Amazon EMR
  28. 28. Machine learning and smart applications Machine learning is the technology that automatically finds patterns in your data and uses them to make predictions for new data points as they become available Your data + machine learning = smart applications
  29. 29. Building smart applications – a counter-pattern Dear Thomas, This awesome quadcopter is on sale for just $49.99!
  30. 30. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING o.date > GETDATE() – 30 We can start by sending the offer to all customers who placed an order in the last 30 days
  31. 31. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING AND o.date > GETDATE() – 30 … let’s narrow it down to just customers who bought toys
  32. 32. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING o.category = ‘toys’ AND (COUNT(*) > 2 AND ) … and expand the query to customers who purchased other toy helicopters recently
  33. 33. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING o.category = ‘toys’ AND (COUNT(*) > 2 AND SUM(o.price) > 200 AND o.date > GETDATE() – 30) ) Use machine learning technology to learn your business rules from data!
  34. 34. Why aren’t there more smart applications? 1. Machine learning expertise is rare 2. Building and scaling machine learning technology is hard 3. Closing the gap between models and applications is time-consuming and expensive
  35. 35. Introducing Amazon Machine Learning Easy to use, managed machine learning service built for developers Robust, powerful machine learning technology based on Amazon’s internal systems Create models using your data already stored in the AWS cloud Deploy models to production in seconds
  36. 36. Explore and understand your data
  37. 37. Explore model quality
  38. 38. Batch predictions with Amazon Redshift Structured data In Amazon Redshift Load predictions into Amazon Redshift -or- Read prediction results directly from S3 Predictions in S3 Query for predictions with Amazon ML batch API Your application
  39. 39. Real-time predictions for interactive applications Your application Query for predictions with Amazon ML real-time API
  40. 40. Unconstrained Big Data Growth • IT/Application server logs IT Infrastructure logs, Metering, Audit logs, Change logs • Websites/Mobile apps/Ads Clickstream, User Engagement • Sensor data/IoT Weather, Smart Grids, Wearables • Social media, user content 450MM+ Tweets/day GB TB PB ZB EB
  41. 41. Amazon RDS Aurora
  42. 42. Current DB architectures are monolithic Multiple layers of functionality all on a single box SQL Transactions Caching Logging
  43. 43. Current DB architectures are monolithic Even when you scale it out, you’re still replicating the same stack SQL Transactions Caching Logging SQL Transactions Caching Logging Application
  44. 44. Current DB architectures are monolithic SQL Transactions Caching Logging SQL Transactions Caching Logging Storage Application Even when you scale it out, you’re still replicating the same stack
  45. 45. This is a problem. For cost. For flexibility. And for availability.
  46. 46. Reimagining the relational database What if you were inventing the database today? You wouldn’t design it the way we did in 1970. At least not entirely. You’d build something that can scale out, that is self-healing, and that leverages existing AWS services.
  47. 47. Amazon Aurora is Easy to Use Amazon RDS Aurora
  48. 48. Aurora storage • Highly available by default – 6-way replication across 3 AZs – 4 of 6 write quorum • Automatic fallback to 3 of 4 if an AZ is unavailable – 3 of 6 read quorum • SSD, scale-out, multi-tenant storage – Seamless storage scalability – Up to 64 TB database size – Only pay for what you use • Log-structured storage – Many small segments, each with their own redo logs – Log pages used to generate data pages – Eliminates chatter between database and storage SQL Transactions AZ 1 AZ 2 AZ 3 Caching Amazon S3
  49. 49. Self-healing, fault-tolerant • Lose two copies or an AZ failure without read or write availability impact • Lose three copies without read availability impact • Automatic detection, replication, and repair SQL Transactio n AZ 1 AZ 2 AZ 3 Caching SQL Transactio n AZ 1 AZ 2 AZ 3 Caching Read and write availabilityRead availability
  50. 50. Survivable caches • We moved the cache out of the database process • Cache remains warm in the event of a database restart • Lets you resume fully loaded operations much faster • Instant crash recovery + survivable cache = quick and easy recovery from DB failures SQL Transactions Caching SQL Transactions Caching SQL Transactions Caching Caching process is outside the DB process and remains warm across a database restart
  51. 51. Simulate failures using SQL • To cause the failure of a component at the database node: ALTER SYSTEM CRASH [{INSTANCE | DISPATCHER | NODE}] • To simulate the failure of disks: ALTER SYSTEM SIMULATE percent_failure DISK failure_type IN [DISK index | NODE index] FOR INTERVAL interval • To simulate the failure of networking: ALTER SYSTEM SIMULATE percent_failure NETWORK failure_type [TO {ALL | read_replica | availability_zone}] FOR INTERVAL interval
  52. 52. Amazon Elastic File System (EFS)
  53. 53. Operating shared file storage today is a pain Application owner or developer IT administrator Business owner • Estimate demand • Procure hardware • Set aside physical space • Set up and maintain hardware (and network) • Manage access and security • Provide demand forecasts/business case • Add lead times and extra coordination to your schedule • Limit your flexibility and agility • Make up-front capital investments, over-buy, stay on a constant upgrade/refresh cycle • Sacrifice business agility • Distract your people from your business’s mission
  54. 54. We focused on changing the game EFS is simple EFS is elastic EFS is scalable 1 2 3
  55. 55. EFS is simple • Fully managed – No hardware, network, file layer – Create a scalable file system in seconds! • Seamless integration with existing tools and apps – NFS v4—widespread, open – Standard file system semantics – Works with standard OS file system APIs • Simple pricing = simple forecasting 1
  56. 56. EFS is elastic • File systems grow and shrink automatically as you add and remove files • No need to provision storage capacity or performance • You pay only for the storage space you use, with no minimum fee 2
  57. 57. • File systems can grow to petabyte scale • Throughput and IOPS scale automatically as file systems grow • Consistent low latencies regardless of file system size • Support for thousands of concurrent NFS connections EFS is scalable3
  58. 58. Cloud Has Become The New Normal
  59. 59. Infrastructure Regions Points of PresenceAvailability Zones Core Services Storage (Object, Block and Archival) Compute (VMs, Auto-scaling and Load Balancing) Databases (Relational, NoSQL, Caching) Networking (VPC, DX, DNS) CDN Access Control Usage Auditing Monitoring and Logs Administration & Security Key Storage Identity Management Platform Services Deployment & Management One-click web app deployment Dev/ops resource management Resource Templates Push Notifications Mobile Services Mobile Analytics Identity Sync App Services Workflow Transcoding Email Search Queuing & Notifications App streaming Analytics Hadoop Data Pipelines Data Warehouse Real-time Streaming Data Enterprise Applications Virtual Desktops Collaboration and Sharing More Functionality Than Any Other Infrastructure Provider
  60. 60. What if you could devote 30% more of your resources to your customers?
  61. 61. PLACE

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