Decomposing a Monolith: Engineering and Operational strategies

1. Decomposing a Monolith
Engineering and Operational strategies we have
gleaned from our experience at Endurance

2. About Endurance
The business behind
small business
Web Hosting &
Builders
Domains
Email Marketing
And More..

3. Our APAC Story
Monolithic Orderbox platform
~$70 million revenue annually
~10 million active domains across 600+ TLDs
~1200 APIs and ~20k requests per minute
~500 GB live and ~1.5 TB archived Transactional data

4. Agenda
Need for micro-services
Common Mistakes
Managing the transition
Scaling, Redundancy and Deployments

5. Need for micro-services
Disrupt or be disrupted

6. Problems with Orderbox 1.0
Limited by vertical scaling of
resources
~400 DB connections
~6GB JVM Heap
Difficult to scale specific
functionality
Domain Lookup/Registration
Payments/Billing
HAProxy
User Management, Products Catalog,
Products Management, Order
Management, Payment and Billing
Monolith
Rate-Limiting
Authentication
Postgres

7. Micro-services to the rescue
Version, Release and
Scale individual services
Polyglot programming
Greater velocity
Postgres
Order
Management
(Java)
MongoDB
Products
Management
(Python)
MySQL
Billing
(NodeJS)

8. Common Mistakes

9. Sharing Database
Monolith
platform
Micro-service
Postgres
Maintenance of DB will
impact multiple services
Still a monolith with
additional steps

10. Duplication of Core
capabilities
Monolith platform with
Core API
Wholesale Orchestration Layer
Rate-Limiting Authentication
Rate-Limiting Authentication
CachingTransformations
Retail Orchestration Layer
Rate-Limiting Authentication
CachingTransformations

11. Managing the transition
Micro-services won’t happen overnight

12. Discover your first micro-
service
Apply ice-cream
scoop strategy
Prioritise
Business-critical
functions
Evaluate efforts
vs benefits

13. Modular Monolith
HAProxy
Rate-Limiting
Authentication
User
Management
Payments
Postgres
Billing Order
Management
Products
Management
Products
Catalog
Legacy interfaces/classes and frameworks
DB, JMS, Net utilsMaven modules
Facade pattern

14. Choose the right datastore
Polyglot Persistence
CQRS pattern
Orderbox 1.0
Monolith
PostgreSQL Cassandra
Domain Lookup
Service

15. Migrating traffic to the
micro-service
Orderbox 1.0
Services
HAProxy
Rate-Limiting
Authentication
Domain Lookup
Service
HAProxy
Rate-Limiting
Authentication
PostgreSQL Cassandra
2. Traffic from new end-point
1. Incremental
traffic from old
end-point

16. Monitoring the micro-service
Pro-actively
monitor
Availability
Correctness
Performance

17. Improved scale with the first
micro-service

18. Centralised Authentication
Multi-factor Auth
and Social Login
OAuth/OpenID
Connect with JWT
Inter-microservice
communication
Authentication
Service
Monolith
platform
Client
1. Request JWT
2. Pass JWT
Micro-service1

19. Scaling, Redundancy and
Deployments

20. Issues with Bare Metal
Deployments
Considerable time spent on
Installing application
dependencies and testing
Upgrading Tech stack
Under utilisation of resources
6 GB out of 24 GB RAM for
JVM
< 20% CPU utilisation across all
web nodes
Bare Metal
Server
Application
(RESTful API)
Application Dependencies
(Tomcat, JDK/JRE)
Operating System
(CentOS)

21. Containers & Orchestration
Faster tech stack
upgrades
Similar Staging and
Prod environments
Optimised infra
utilisation
Auto-scale, better
CI/CD, ..
Pod 1
Kube-proxyKubelet
Pod 2 Pod N
Node 1
Pod 1
Kube-proxyKubelet
Pod 2 Pod N
Node 2
DockerDocker
Kubernetes
Master
Node N

22. Target state of Orderbox 2.0
Kong
Client with JWT
1. Rate-Limiting
Keycloak
2. Authentication
4. Route
requests
Orderbox 1.0
Services
Domain
Lookup service
Micro-service2
3. Caching
In-Memory
Datastore
Redis

23. Takeaways
Analyse and figure out if there is a need for micro-services;
Identify your first micro-service by applying ice-cream scoop
strategy
Containerise the application for identical staging and prod
environments and faster tech stack upgrades
Use Kubernetes for optimised infra utilisation, auto-scaling,
better CI/CD, ..
Set up core capabilities like Authentication, Caching, Rate
Limiting through API Gateway to eliminate redundant efforts