Caching for Microservices Architectures: Session I
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This document discusses how caching can help address performance, scalability, and autonomy challenges for microservices architectures. It introduces Pivotal Cloud Cache (PCC) as a caching solution for microservices on Pivotal Cloud Foundry. PCC provides an in-memory cache that can scale horizontally and increase performance. It also allows for data autonomy between microservices and teams while providing high availability. PCC offers an easy and cost-effective way to cache data and adopt microservices on Pivotal Cloud Foundry.
Caching for Microservices Architectures: Session I
- 1. Caching for Microservices Architectures Introducing Pivotal Cloud Cache Jagdish Mirani
- 2. Why Microservices Architectures Need a Cache Autonomy Availability Cost Reduction $ $ Performance
- 3. Why Microservices Architectures Need a Cache Autonomy Availability Cost Reduction $ $ Performance
- 4. Ability to handle a large number of concurrent requests Performance Drivers for Modern Applications ▪ More users of new mobile and web applications ▪ Users expect real-time response, even during peak usage ▪ Increasing number of requests from other applications ▪ New use cases from new data sources, ex: IoT and streaming data ▪ Scaling the application logic results in the need for scaling the data access layer
- 5. Caches Provide Blazing Fast Performance ▪ Memory is orders of magnitude faster than disk ▪ Caches can present a structural view of data optimized for performance ▪ Maximizing cache hits - Preloading cache (cache warming) - Expiration and eviction • Application driven • Time based • Notifications and events Microservice Instance Cache Database
- 6. Externalizing state is a requirement for microservice instances to scale Microservices Need Performance and Scalability ▪ Externalize microservices state for performance and scalability of the business logic - Store application state information in cache for fast retrieval - Adheres to 12-factor principles ▪ Dynamically change the number of application instances without losing state information
- 7. Microservices with large, frequently accessed data sets need a cache layer Microservices Need Performance and Scalability Performance and scalability of data ▪ Add servers to a shared cluster ▪ Reduces the pressure to scale rigid backing stores ▪ Enables availability and resilience
- 8. Why Microservices Architectures Need a Cache Autonomy Availability Cost Reduction $ $ Performance
- 9. Fosters an agile, dynamic application culture Team Autonomy Equates to Velocity ▪ Separate development and release cycles - Evolve each microservice independently - Independent development, test, production cycles - Continuous integration, continuous delivery ▪ Independent technology decisions, including data layer - Polyglot persistence - Independent data model decisions ▪ Changes should be non-breaking for other teams and microservices
- 10. Extreme ends of data sharing continuum present challenges Autonomy in the Context of the Data Layer Autonomous Distributed workload and data management challenges Shared Database Database Per Service No autonomy Development and runtime coupling
- 11. Data APIs Present Autonomous Views of Data ● Define a Data API that projects a data model to match the needs of the consuming microservices ● Data API is the access point to a microservice that’s primarily responsible for accessing data ● Data API provides a contract for accessing data ● Teams create data caches optimized for their microservices ● Allows more flexibility for (and isolation from) changes to backing stores Caches provide data for each autonomous view
- 12. Data API evolve in support of the evolution of the microservice(s) Versioned APIs Facilitate Change Management ▪ Analogous to the notion of versioned microservices ▪ Parallel deployment of versions creates the possibility of a managed evolution ▪ Allows for data transformations within the microservice as an alternative to changing the backing store(s) V1 V2
- 13. Caching Can Present an Autonomous View of Data ● Provides a surface area to: ○ implement access control ○ implement throttling ○ perform logging ○ enforce other policies Teams create data caches optimized for their microservices
- 14. Caching Can Present an Autonomous View of Data ● Data APIs project a bounded context ○ Each bounded context has a single, unified model ○ Relationships between models are explicitly defined ○ Teams are typically given full responsibility over one or more bounded contexts
- 15. Why Microservices Architectures Need a Cache Autonomy Availability Cost Reduction $ $ Performance
- 16. Several points of failure Large Number of ‘Moving Parts’ ▪ Single request can touch several components: servers, distinct clusters, microservice instances ▪ Availability zones can fail ▪ Regions can become unstable ▪ Network is unreliable ▪ Cloud native architecture components are ephemeral by design - Instances added and removed dynamically Enterprise Readiness Requires the Ability to Tolerate Failures
- 17. Highly Available Caching Layer Offers Protection ● Cache serves as the ‘primary’ data store for the application ● High availability: copy data for failure protection ● Immune to lapses in backing store availability ● Backing stores kept up-to-date through the cache
- 18. Why Microservices Architectures Need a Cache Autonomy Availability Cost Reduction $ $ Performance
- 19. Expensive and Brittle Legacy Application Infrastructures ▪ High startup costs ▪ Steep pricing curve for adding capacity - Mainframe MIPS pricing - Legacy RDBMS data stores are expensive to scale ▪ Complex deployments ▪ Easily disrupted ▪ Points of failure ▪ Scalability bottlenecks
- 20. 20 Legacy Modernization is key to success $$$$ ROI Funds Transformation Replatform Modernize Migrate Runs on PCF Existing Workloads Cloud Native Built for PCF New Initiatives Cloud Native ModernizeReplatform Migrate
- 21. Legacy Systems: Part of a Cloud Native Evolution Create microservices around the edges of the legacy system ● Caching layer to mediates between the old and the new ● Optionally, re-platform the legacy application ● Optionally, reduce reliance on legacy application over time Microservices Legacy Middleware Legacy Application Monolithic Application
- 22. Introducing Pivotal Cloud Cache
- 23. Prepackaged for Simple Consumption ● Plans (use cases) based on caching patterns ● Look-aside pattern supported out of the box ○ Cache is controlled and managed by the application ○ Good for saving application state, microservices architectures, reducing load on legacy systems, etc.. ○ Perfect match for the Spring Framework @Cacheable annotation ● Other caching patterns and options to come (WAN replication, Session State Caching, Inline caching pattern, etc.). Look-Aside Cache Look-aside pattern supported out of the box App Instance Cache Database
- 24. Pivotal Cloud Cache • Easy accessibility through Marketplace • Instant Provisioning • Bind to apps through easy to use interface • Lifecycle management • Common access control and audit trails across services MySQL New Relic Single Sign- On RabbitMQ Config Server Service Directory Circuit Breaker Signal Sciences Crunchy PostgreSQL AND MORE Services Marketplace Pivotal Cloud Cache Dynatrace Extending the Pivotal Cloud Foundry Platform for Microservices Architectures
- 25. Easily Provisioned for Developer Self Service Operators create and register service plans with the Services Marketplace Create Service Plans Set Quotas Deploy PCC Broker Define VMs Define Memory Define CPU Define Disk Size Max Cluster Size Max # of Clusters OpsMan Tile Register with Marketplace
- 26. In-memory, and horizontally scalable for parallel execution Pivotal Cloud Cache Performance Grow cluster dynamically with no interruption of service or data loss Data is sharded or replicated across servers
- 27. This is how an in-memory cache can horizontally scale Partitioning (aka Sharding) Take advantage of the memory and network bandwidth of all members of the cluster
- 28. Replicated regions Partitioning and Co-location Replicated regions model many-to-many relationships
- 29. High Availability Spanning Servers and Availability Zones Stretched cluster across availability zones Replication for high availability of data in cache Pivotal Cloud Foundry resurrects lost VMs
- 30. Powerful Eventing System Publish/subscribe and Continuous Query built right in Put(key, value) Subscribers are automatically notified
- 31. Integrated Security Pre-configured Authentication and Authorization ● Role-based, configurable, authorization for administrative activities ● Pre-defined, pre-configured roles ● Consistent mechanism for authenticating and authorizing actions ● Every administrative function can require authorization ● Every data access can require authorization ● Some users can read/write data ● Others can start/stop servers ● Still others can configure cluster User 1 User 2 User 3 User 4 Group 1 Group 2 Role 1 Role 2 Role 3 Role 1A Role 1B Role 1C Role 2A Role 3A Role 3B Determines experience Determines permissions
- 32. Summary Speed up your apps on Pivotal Cloud Foundry ● PCC can overcome the performance, elasticity and scaling, challenges of microservices architectures ● Using PCC with data APIs can increase autonomy between teams ● PCC has rock solid availability and failure recovery ● PCC provides an evolutionary approach to adopting microservices that can extend the life of legacy systems ● The combination of PCC and PCF make it possible to get started quickly and easily adjust cache capacity as needed