Cloud Architecture Patterns. Using Microsoft Azure (e-book) Pyskowice

If your team is investigating ways to design applications for the cloud, this concise book introduces 11 architecture patterns that can help you take advantage of cloud-platform services. You...ll learn how each of these platform-agnostic patterns work, when they might be useful in the cloud, and what impact they...ll have on your application architecture. You...ll also see an example of each pattern applied to an application built with Windows Azure.The patterns are organized into four major topics, such as scalability and handling failure, and primer chapters provide background on each topic. With the information in this book, you...ll be able to make informed decisions for designing effective cloud-native applications that maximize the value of cloud services, while also paying attention to user experience and operational efficiency.Learn about architectural patterns for:Scalability. Discover the advantages of horizontal scaling. Patterns covered include Horizontally Scaling Compute, Queue-Centric Workflow, and Auto-Scaling.Big data. Learn how to handle large amounts of data across a distributed system. Eventual consistency is explained, along with the MapReduce and Database Sharding patterns.Handling failure. Understand how multitenant cloud services and commodity hardware influence your applications. Patterns covered include Busy Signal and Node Failure.Distributed users. Learn how to overcome delays due to network latency when building applications for a geographically distributed user base. Patterns covered include Colocation, Valet Key, CDN, and Multi-Site Deployment. Spis treści: Cloud Architecture Patterns SPECIAL OFFER: Upgrade this ebook with OReilly Preface Audience Why This Book Exists Assumptions This Book Makes Contents of This Book Building Page of Photos on Windows Azure Terminology Conventions Used in This Book Using Code Examples Safari Books Online How to Contact Us Acknowledgments 1. Scalability Primer Scalability Defined Vertically Scaling Up Horizontally Scaling Out Describing Scalability The Scale Unit Resource Contention Limits Scalability Easing Resource Contention Scalability is a Business Concern The Cloud-Native Application Cloud Platform Defined Cloud-Native Application Defined Summary 2. Horizontally Scaling Compute Pattern Context Cloud Significance Impact Mechanics Cloud Scaling is Reversible Cloud scaling terminology Managing Session State Session state varies by application tier Sticky sessions in the web tier Stateful node challenges Session state without stateful nodes Stateless service nodes in the service tier Managing Many Nodes Efficient management enables horizontal scaling Capacity planning for large scale Sizing virtual machines Failure is partial Operational data collection Example: Building PoP on Windows Azure Web Tier Stateless Role Instances (or Nodes) Service Tier Operational Logs and Metrics Summary 3. Queue-Centric Workflow Pattern Context Cloud Significance Impact Mechanics Queues are Reliable Programming Model for Receiver Invisibility window and at-least-once processing Idempotent processing for repeat messages Poison messages handling for excessive repeats User Experience Implications Scaling Tiers Independently Example: Building PoP on Windows Azure User Interface Tier Service Tier Synopsis of Changes to Page of Photos System Summary 4. Auto-Scaling Pattern Context Cloud Significance Impact Mechanics Automation Based on Rules and Signals Separate Concerns Be Responsive to Horizontally Scaling Out Dont Be Too Responsive to Horizontally Scaling In Set Limits, Overriding as Needed Take Note of Platform-Enforced Scaling Limits Example: Building PoP on Windows Azure Throttling Auto-Scaling Other Resource Types Summary 5. Eventual Consistency Primer CAP Theorem and Eventual Consistency Eventual Consistency Examples Relational ACID and NoSQL BASE Impact of Eventual Consistency on Application Logic User Experience Concerns Programmatic Differences Summary 6. MapReduce Pattern Context Cloud Significance Impact Mechanics MapReduce Use Cases Beyond Custom Map and Reduce Functions More Than Map and Reduce Example: Building PoP on Windows Azure Summary 7. Database Sharding Pattern Context Cloud Significance Impact Mechanics Shard Identification Shard Distribution When Not to Shard Not All Tables Are Sharded Cloud Database Instances Example: Building PoP on Windows Azure Rebalancing Federations Fan-Out Queries Across Federations NoSQL Alternative Summary 8. Multitenancy and Commodity Hardware Primer Multitenancy Security Performance Management Impact of Multitenancy on Application Logic Commodity Hardware Shift in Emphasis from MTBF to MTTR Impact of Commodity Hardware on Application Logic Homogeneous Hardware Summary 9. Busy Signal Pattern Context Cloud Significance Impact Mechanics Transient Failures Result in Busy Signals Recognizing Busy Signals Responding to Busy Signals User Experience Impact Logging and Reducing Busy Signals Testing Example: Building PoP on Windows Azure Summary 10. Node Failure Pattern Context Cloud Significance Impact Mechanics Failure Scenarios Treat All Interruptions as Node Failures Maintain Sufficient Capacity for Failure with N+1 Rule Handling Node Shutdown Node shutdown with minimal impact to user experience Node shutdown without losing partially completed work Node shutdown without losing operational data Recovering From Node Failure Shielding interactive users from failures Resuming work-in-progress on backend systems Example: Building PoP on Windows Azure Preparing PoP for Failure N+1 rule Windows Azure fault domains Upgrade domains Handling PoP Role Instance Shutdown Web role instance shutdown Worker role instance shutdown Use controlled reboots Recovering PoP From Failure Summary 11. Network Latency Primer Network Latency Challenges Reducing Perceived Network Latency Reducing Network Latency Summary 12. Colocate Pattern Context Cloud Significance Impact Mechanics Automation Helps Cost Considerations Non-Technical Considerations Example: Building PoP on Windows Azure Affinity Groups Operational Logs and Metrics Summary 13. Valet Key Pattern Context Cloud Significance Impact Mechanics Public Access Granting Temporary Access Security Considerations Example: Building PoP on Windows Azure Public Read Access Shared Access Signatures Summary 14. CDN Pattern Context Cloud Significance Impact Mechanics Caches Can Be Inconsistent Example: Building PoP on Windows Azure Cost Considerations Security Considerations Additional Capabilities Summary 15. Multisite Deployment Pattern Context Cloud Significance Impact Mechanics Non-Technical Considerations in Data Center Selection Cost Implications Failover Across Data Centers Example: Building PoP on Windows Azure Choosing a Data Center Routing to the Closest Data Center Replicating User Data for Performance Replicating Identity Information for Account Owners Data Center Failover Colocation Alternatives Summary A. Further Reading Page of Photos (PoP) Sample Resources From Preface and Chapters Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Index About the Author SPECIAL OFFER: Upgrade this ebook with OReilly Copyright