Downloaded 66 times
Uploaded byDataStax Academy
Microsoft: Building a Massively Scalable System with DataStax and Microsoft's Next Generation PaaS Infrastructure
This document discusses how to build massively scalable systems using DataStax and Microsoft's Azure Service Fabric platform. It addresses large-scale challenges like operations management, high density, deployment, updates, scalability, availability and failure recovery. It demonstrates how Service Fabric enables higher microservice density, faster deployments and upgrades, and fast scaling across clusters. Placement constraints and rolling upgrades are explored, along with handling failures and scaling services. Service Fabric provides declarative models, load balancing, and auto-scaling to simplify managing large, distributed systems.
More Related Content
Similar to Microsoft: Building a Massively Scalable System with DataStax and Microsoft's Next Generation PaaS Infrastructure
![Vibe Coding vs. Spec-Driven Development [Free Meetup]](https://cdn.slidesharecdn.com/ss_thumbnails/vibecodingvsspecdrivendevelopment-251209105622-43f455e7-thumbnail.jpg?width=640&height=640&fit=bounds)
Microsoft: Building a Massively Scalable System with DataStax and Microsoft's Next Generation PaaS Infrastructure
- 1. Building a massivelyscalable system with DataStax and Microsoft's next generation Paas infrastructure Rob Bagby Sr. Cloud Architect – Microsoft www.deveducate.com / www.robbagby.com Rob.Bagby@Microsoft.com @RBagby Jesus Aguilar Sr. Cloud Architect – Microsoft www.giventocode.com Jesus.Aguilar@microsoft.com @GivenToCode
- 2. How do Iscale my Data linearly? Does the VM have the required resources? Are there any cold VMs that I shouldn't be paying for? How do I move a service? Is the VM running?Is the VM too hot?How do I deploy across a part of the cluster? Does the VM have the required capacity? How do I roll back a deployment if it is does not work? How do I deploy across a part of the cluster? Can I have multiple versions running side-by-side? Where should I move it? Does my data have a single point of failure? Is my application running? Can I replicate data across data centers?
- 3. Large and DistributedChallenges Ops Management High Density Resource Governance Deployment and Packaging Rolling updates Scalability Hyper Scale Load balancing Scale Out Maintainability Granular versioning In-flight testing Microservices Availability Failure resilient Rolling updates Recovery from failures
- 4. Azure Service Fabric VNextPaaS offering Battle tested – internally used by +5 years in large systems State of the art orchestration and management Stateful and Stateless programming model
- 5. 271 CustomersService FabricPreview
- 6. Azure Service Fabric+ Cassandra Massively scalable system Cloud powered Modern architecture patterns Stateless Services Cassandra Cluster
- 7. Cassandra Deployment Options AzureMarketplace Templates GitHub Templates
- 8. Demo 1 –Cassandra on Azure
- 9. Large and DistributedChallenges Ops Management High Density Resource Governance Deployment and Packaging Rolling updates Scalability Hyper Scale Load balancing Scale Out Maintainability Granular versioning In-flight testing Microservices Availability Failure resilient Rolling updates Recovery from failures
- 10. Achieving High Densitywith Azure Service Fabric IaaS Approach 1 Cluster – 1 VM Azure Service Fabric (Stateless, stateful or Actor services) • 1 service instance per VM with uneven workloads • Lower compute density • Slow in deployment & upgrades • Slower in scaling and disaster recovery • Many microservices per VM • High microservices density • Fast deployment & upgrades • Fast scaling microservices across the cluster
- 11. Placement Constraints You controlyour topology Web Internal API N1 DMZ = False N2 DMZ = True N4 DMZ = True N5 DMZ = True N6 DMZ = False N3 DMZ = False Constraints = “(DMZ == False)” Constraints = “(DMZ == True)”
- 12. Demo 2 –The Sample Application
- 13. Large and DistributedChallenges Ops Management High Density Resource Governance Deployment and Packaging Rolling updates Scalability Hyper Scale Load balancing Scale Out Maintainability Granular versioning In-flight testing Microservices Availability Failure resilient Rolling updates Recovery from failures
- 14. FD2FD1 UD2 UD1 Nodes will godown. Fault Domains Same physical location, HW failures Upgrade Domains SW upgrades to the underlying compute resources Web Web API API
- 15. Rolling Updates UD 1 UD2 UD 3 N1 N2 N3 N4 N5 N6 V1 V1 V1 V2V2V2
- 16. Large and DistributedChallenges Ops Management High Density Resource Governance Deployment and Packaging Rolling updates Scalability Hyper Scale Load balancing Scale Out Maintainability Granular versioning In-flight testing Microservices Availability Failure resilient Rolling updates Recovery from failures
- 17. Demo 3 –Handling Failures & Rolling Upgrade
- 18. Large and DistributedChallenges Ops Management High Density Resource Governance Deployment and Packaging Rolling updates Scalability Hyper Scale Load balancing Scale Out Maintainability Granular versioning In-flight testing Microservices Availability Failure resilient Rolling updates Recovery from failures
- 19. Scaling and Balancing •Declarative Model – “I want the topology to look like this” • Service Fabric does the work • Balances Resources • Moves services when hot • Respects constraints • You provide guidance • These metrics are important • CPU • Memory • Etc. • Balancing Threshold • Activity Threshold
- 20. Demo 4 –Scalability
- 21.