The document discusses challenges in traditional application modernization approaches, including risks related to upfront planning, project focus, and technical debt. It proposes using an event streaming architecture to evolve system design, emphasizing asynchronous communication and domain-driven design principles. The approach aims to quickly implement functional capabilities while maintaining legacy parity and adapting to operational goals.

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Application
Modernization
With Event Streaming
Architecture
David Wadden
Staff Engineer
July 20th, 2020
@ronmervis

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David Wadden
Staff Engineer
@ronmervis
picture

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➔ Big upfront planning. You’re assessing the entire portfolio at
once before starting the work.
Risk: Fail to get quick wins to build momentum.
➔ Horizontally focused project work. Often we address painful
symptoms in an IT-centric “horizontal” manner.
Risk: You miss root cause and don’t deliver business impact
➔ Your mess for less. Instead of making meaningful changes,
you’re moving apps from one host to the next.
Risk: Won’t get the business-centric benefits you desire.
➔ One-time project versus competency development.
Efforts attempt to “finish” versus build a practice.
Risk: Not set up to continuously keep technical debt low.
Traditional
Modernizatio
n
Approaches

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We Start with Events First

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Service Business capability service
e.g. “Order service”
Queue
Topic
Messaging Q / T
Async communication
Service A
Service B
Service C Service D
Service E
Service F
External Connected external system
e.g. “Weather service”
External 2
External 1
UI
UI
Q Q
T
User
Interface
Entry points
relevant in the narrative
Synchronous
… service calls
Asynchronous
Boris Diagram - Workshop

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Event
Storming
2. Event Storm the system
Thin
Slice
3. Select “Thin Slice(s)” of functionalities
OKRs
1. Define Objectives & Key Results
Tested
Code
6. Produce tested and working code
Boris
4. Drafts the desired notional architecture
Snap-E 5. Fill the backlog
API DATA
PUB / SUB EXT
STORIES UI RISKS
SERVICE A
Deconstructing Monoliths

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Business Domain
Decomposition

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Get to Function Fast!
Evolve architecture and implementation from simple to production ready
Goals
● Spike risky interactions identified from Boris exercise
● Validate notional architecture with simplest implementation
● Maintain legacy function parity

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Get to Function Fast!
Continued...
Anti-goals
● Building for resilience or scale at this stage
● Over-thinking the microservices design
● Implement final state architecture or future proofing
● Use-cases outside of the thin slice

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Get to Function Fast!
Continued...
Technical Strategies
● Separate reads and writes
● Spend the time on interface design
● Keep technical complexity minimal
● Implement anti-corruption layer to protect legacy

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● React to domain events published via Kafka,
update model (application state) using
relational or NoSQL store
● Leverage ACLs to integrate with legacy
systems
○ Avoid pollution of new domain with
current technical implementation
● Focus on breaking down monolithic data
structures (i.e., “God objects”: Order,
Product, Trade) across bounded contexts
Initial Technical Design

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Domain Architecture / Boris

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Initial System Architecture

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Evolving to
Kafka Streams

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Non Functional Requirements
● Keep a log of NFRs: availability, recovery, etc.
● Allow the NFRs to organically drive out the need for Kafka
native architecture patterns: active-active and MTTR for
disaster recovery
● Pivot to focusing on the architecture and thinking
through unhappy path cases
Evolve to meet operational goals

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Re-look at example
Initial design shortcomings
● TPS limited by RDBMS table locks
● ACL logic complexity
● Disaster recovery operation
● Cross data center replication challenges

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Leverage same model with Kafka Streams Architecture
● If the domain model is implemented
properly, it can be reused wholesale
within Kafka Streams as an aggregate
rolled up from a given KStreams
processor topology
● The existing model then becomes the
materialized view derived from the event
streams
● The materialized view can then service
any existing queries with minimal
change

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Leverage Kafka Streams

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Evolved System Architecture

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Recap

21. Thank You
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