Microservices

1. Microservices in Practice
Opportunities and challenges

2. Microservices
 Why adopt the Microservices paradigm?
 Increase enterprise agility
 Minimize catastrophic Failures
 For better resource utilization
 For building faster, smaller and cohesive applications

3. Microservices
Independent Observable
Business Domain Centric Resilient/Fail Safe
Decentralized
Decentralized • A single application failure will
not impact other applications
Observable • Constantly monitored and
measures taken to recover failed
application
Business Domain Centric • Cohesive
• Single Area of Focus
Resilient/Failsafe • Multiple instances running behind
load balancer
• Data is partitioned and replicated
• Ability to scale

4.  X scaling
 Load balancer
 Y scaling
 End point per concern
 Z scaling
 Data shards
Microservices

5. Technology
• Language: Java 8
• Framework: Spring Boot
• Database: PostgreSQL
• Cache: REDIS
• Internal Security: Spring Security
• Perimeter Security: APIGEE

6. Deployment
Architecture
• Load Balancer
• Variable Deployment
• Multiple Databases
• Accessed from Mobile applications,
servers and web applications

7. Case Study #1 – Central Data Repository
 Problem:
 Data Consolidation
 Not all data providers needed to scale to the same degree
 Backend Data Model definition
 Data migration
 API Data format definition for multiple consumers

9. Case Study #2: Rules Engine Microservices
 Problem Domain:
 Large data processing
 Avoiding total system failures
 Selective scaling
 Fast scaling

11. Automation

12. Automation
 Jenkins + Git + Salt for CI/CD
 Salt for configuration
management
 App Dynamics for monitoring
 JIRA + Slack for communication
 Docker for containerization
• Kubernetes at the Enterprise Level

13. Process
Red  Green
 Refactor
Code Review
Automated
Unit Tests
Salt
Deployment
to
Integration
Environment
Integration
Tests
User
Acceptance
Tests
Security
Audit
Load Tests Production

14. Git WorkFlow
 Each developer forks create a fork
 Code is reviewed prior to merging
to master
 Jenkins build from master
 Releases are tagged in master
 Feature / Release branches are
maintained

15. Challenges
DevOps Talent
Y Scaling
 How should a microservice be partitioned?
Z Scaling
 How should data be partitioned?
 No atomic transactions. Have to rely on eventual consistency.
 Achieving resiliency with on premises infrastructure

16. Conclusion
 Easier to build faster systems
 DevOps work can impact project timelines
 Elastic environments, like Amazon EC2, make it easier to achieve
scaling and resiliency
 Microservices like any other paradigm can be misused