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Architecting applications in the AWS cloud

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Architecting applications in the AWS cloud

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Architecting applications in the AWS cloud

  1. 1. Architecting Applications in the AWS Cloud1
  2. 2. Help business apps move to the cloud 2 Problems facing Enterprise IT ¨  ever-growing datasets ¨  unpredictable traffic patterns ¨  demand for faster response times ¨  budget constraints
  3. 3. Business Benefits of Cloud 3 ¨  With utility-style pricing - no fixed cost ¨  Just-in-Time Infrastructure ¨  More Efficient Resource Use ¨  Usage-Based Costing ¨  Reduced Time to Market
  4. 4. Technical Benefits of Cloud Computing 4 ¨  Scriptable Infrastructure ¨  Auto-scaling ¨  Proactive Scaling ¨  More Efficient Development Life Cycle ¨  Improved Testability ¨  Disaster Recovery and Business Continuity ¨  "Overflow" the Traffic to the Cloud
  5. 5. Understanding AWS Terminology 5 Your Application Payment AWS Worldwide Physical infrastructure (Geo Regions with multiple Availability Zones, Edge Locations) SimpleDBDomains SNSTopics SQSQueues CloudFront Simple Storage Service Objects and Buckets EC2 Instances (On-Demand, Spot, Reserved) Auto-scaling, Elastic LB, Cloud Watch SnapshotsEBS VolumesVPC RDS Elastic MapReduce JobFlows
  6. 6. IaaS Scalable Hardware Layer
  7. 7. IaaS Scalable Hardware Layer Software Infrastructure Layer Grid Service Storage Service Queue Service
  8. 8. Example: Storage Service Storage Service Storage Service Storage Service Storage Service New Server The data is automatically re-partitioned/re-balanced to take advantage of the new server added
  9. 9. EC2 Machine Image (OS + Apps) Usage: •  Create Machine Image •  Deploy the image to S3 •  Start 1 or more instances •  Use it as regular machine(s) Main Options: •  Dynamic/Static IP •  Choose cores •  Choose locations •  Persistence via EBS
  10. 10. Sample EC2 Use Cases Batch Processing §  All instances are configured with the same code §  Each instance operates on a subset of data §  Partitions are specified in a configuration file Web Service §  All instances are configured with the same code §  One or more instances are configured as load balancers (HAProxy for example) §  DNS Server distributes requests between load balancers
  11. 11. EC2 vs. Web Hosting Company Good §  Instantly add new instances §  Full-control over the machines and choice of the environment §  Likely cheaper (but depends on your exact situation) Bad §  Need to put the images together and manage instances §  No dedicated technical support (but there is premium support and RightScale like solutions)
  12. 12. S3 in a Nutshell Client Idea: •  Put/Get objects into buckets based on unique keys Main Features: •  Public/Private access •  Support for large objects Amazon S3 Bucket 1 Bucket N … Put object Get object
  13. 13. Sample S3 Use Cases Image/Video storage §  Put your media once on S3 and then serve it up §  Reads are 10 times cheaper than writes! Serialize your Java Objects §  Define unique key based on the object attributes §  Write out binary serialized version to a stream §  Write bytes to S3 §  Read them back when needed
  14. 14. Simple DB in a Nutshell Client Idea: •  Create flat database with auto-indexed tables Main Features: •  Each attribute is indexed •  Record structure is flexible •  Basic operators in queries •  Supports sorting Simple DB Domain Record 1 Put record Get record Query records Key1 Attributes: A1,A2… Record N Key2 Attributes: A1,A2… …
  15. 15. Sample SimpleDB Use Cases Index Media files stored on S3 §  Use the same key as on S3 §  Write the record with each metadata element as attribute Store flat objects §  Use SimpleDB as a storage for non-nested data
  16. 16. SQS in a Nutshell Writer Idea: •  Create an infinite asynchronous queue Main Features: •  Multiple queues •  Up to 4K messages •  Message Locking SQS Queue Message 1 Send Message Receive Message Message N … Reader
  17. 17. Sample SQS Use Cases Twitter Friend Update §  For each update generate a task to update friends §  Process updates in order Publish/Subscribe §  Post messages to the queue to inform multiple subscribers Process Pipeline §  Use different queues to put, for example, and order through a pipeline
  18. 18. One liner Descriptions ¨  Elastic IP: Allocate a static IP and assign to an instance ¨  CloudWatch: Monitor CPU utilization, disk r/w, & network traffic ¨  Auto-scaling group: Auto-scale based on metric from CloudWatch ¨  Elastic Load Balancing: Distribute incoming traffic to web instances ¨  Elastic Block Storage: network-attached persistent storage for EC2 ¨  Point-in-time EBS snapshots can be created and stored in S3 ¨  S3: distributed data store: store and retrieve objects in buckets ¨  Cloud Front: objects distributed & cached at multiple edge locations worldwide ¨  SimpleDB: a database w/ real-time querying of structured data ¨  RDS: a full-featured relational database in the cloud ¨  SQS: a reliable, scalable, hosted distributed queue for storing/retrieving messages ¨  Elastic MapReduce: a hosted Hadoop framework on EC2+S3 enabling custom JobFlows ¨  SNS: a way to notify applications or people from the cloud by creating Topics and using a publish-subscribe protocol ¨  VPC: extend your corporate network into a IPSEC private cloud contained within AWS ¨  Payment services: payment and billing services using Amazon's payment infrastructure. 18
  19. 19. Building Scalable Architectures ¨  Cloud is infinitely scalable: Architect for scalability ¨  Identify the monolithic components and bottlenecks in your architecture ¨  Identify the areas where you cannot leverage the on-demand provisioning capabilities in your architecture ¨  Refactor your application in order to leverage the scalable infrastructure and take advantage of the cloud ¨  Characteristics of a truly scalable application ¤  Increasing resources results in a proportional increase in performance ¤  Cost per unit reduces as the number of units increases ¤  Handles heterogeneity ¤  Is operationally efficient and resilient 19
  20. 20. Fear No Constraints ¨  Cloud might not have the exact specification of the resource that you have on-premise ¤  e.g., "Cloud does not provide X amount of RAM in a server" or "My database needs to have more IOPS than what I can get in a single instance” ¨  Even though you might not get an exact replica of your hardware in the cloud environment, you have the ability to get more of those resources in the cloud to compensate that need ¤  e.g., if the cloud does not give N GB RAM in a server, n  use a distributed cache like memcached or n  partition your data across multiple servers ¤  e.g., if your database need more read-heavy IOPS than what cloud offers, n  distribute the read load across a fleet of synchronized slaves or n  use a sharding algorithm that routes the data where it needs to be or n  use a database clustering solution ¨  Apparent constraints can be broken in ways that will improve the scalability and performance 20
  21. 21. Cloud Administration ¨  SysAdmins/WebMasters transition into CloudMASTERS ¤  Tasks performed become even more interesting as CloudMASTERS learn more about applications and decide what's best for the business ¨  CloudMASTERS don’t need to provision servers and install software and wire up network devices ¤  Cloud infrastructure is programmable and encourages automation ¤  Grunt is replaced by few clicks and command line calls ¨  CloudMASTERS move up the technology stack and learn how to manage abstract cloud resources using scripts ¤  Learn new deployment methods and embrace new models (query parallelization, geo-redundancy, and asynchronous replication), ¤  rethink the architectural approach for data (sharding, horizontal partitioning, federating), and ¤  leverage different storage options available in the cloud for different types of datasets ¨  When architecting applications, businesses encourage more cross-pollination of knowledge between the two 21 ¨  app developers may not work closely with the sysadmin/webmasters who may not have a clue apps ¨  requires close cooperation between app devs and CloudMASTERS Traditional enterprise Cloud enterprise
  22. 22. Design for auto recovery from Failure ¨  Failure will happen (period) ¨  Automated recovery from failure ¤  always design, implement, & deploy for auto recovery 22
  23. 23. Be a pessimist ¨  Assume that ¤  hardware will fail ¤  outages will occur ¤  some disaster will strike ¤  your app will be slammed with more than expected load some day ¤  with time your application software will fail too ¨  Plan auto-recovery during design time 23
  24. 24. Fault-tolerant Cloud Architecture ¨  What if a node in your system fails? ¤  How do you recognize failure? ¤  How do I replace that node? ¨  What are my app’s single points of failure? ¤  what if load balancer fails? n  a load balancer sits in front of an array of application servers ¤  What if the master node fails in a master/slave system? n  How does the failover occur? n  How is a new slave instantiated n  How does new slave sync with the master? 24
  25. 25. Fault-tolerant Cloud Architecture ¨  What happens to my app if the dependent services changes its interface? ¨  What if downstream service times out or returns an exception? ¨  What if the cache keys grow beyond memory limit of an instance? 25
  26. 26. Mechanism to handle failure ¨  Have a coherent backup and restore strategy for your data and automate it ¨  Build process threads that resume on reboot ¨  Allow the state of the system to re-sync by reloading messages from queues ¨  Keep preconfigured and pre-optimized virtual images to support strategies 2 and 3 on launch/boot ¨  Avoid in-memory sessions or user states; use data stores 26
  27. 27. Impervious to reboots/re-launches ¨  If a controller instance dies, ¤  its brought up, and ¤  resumed to previous state ¤  as if no evil had happened 27
  28. 28. 28 Thank you.
  29. 29. Appendix29
  30. 30. greptheweb (using SQS and SimpleDB) ¨  greptheweb enables search query that gets Million Search Results (MSR) back as output 30 grep is a unix utility to search patterns hence the name greptheweb Input dataset regex getstatus ¨  output is filtered using regular expressions to narrow based on criteria

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