Safe cloud native transformation approaches

안정적 클라우드
네이티브 전환 전략
© 2019 uEngine solutions
DevOps
MSA
DDD

Mission:
프론트엔드
데이터베이스
Serv
ice1
Serv
ice2
Serv
ice3
프론트엔드
데이터
베이스
Serv
ice1
데이터
베이스
Serv
ice2
데이터
베이스
Serv
ice3
모놀리씩 마이크로서비스

목표
• Scalable
• Resilient
• Loosely Coupled
• Independent Evolutionary
• Technical Diversity

해결책 – Strangler Pattern
** Strangler: Legacy 전환 전략과 Emerging Service 개발 전략을 분리, 서서히 기존 서비스를 신
규 아키텍처로 전환해가는 변화관리 전략
신규
• 컨테이터 기반
• 이벤트 드리븐
• 서비스 매시
• 기능 관점 단위
(마이크로 or 나노)
레가시
• 최소한의 수정
• 이벤트 후킹 (Change
Data Capturing)
• VM 기반 미니서비스
(매크로)
범위를넓혀감
범위를줄여감

Existing System
포탈
WAS
레가시1
(Java/
Tomcat)
IIS
레가시2
(.NET)

Lift and Shift - VM
포탈
WAS on VM
레가시1
(Java/
Tomcat)
IIS on VM
레가시2
(.NET)
IaaS

New Service, New Architecture
포탈
WAS on VM
레가시1
(Java/
Tomcat)
IIS on VM
레가시2
(.NET)
IaaS
API GW
Event Queue
CaaS
(Kubernetes)
container
New
Service
(Python)

Event Hook
포탈
WAS on VM
레가시1
(Java/
Tomcat)
IIS on VM
레가시2
(.NET)
IaaS
API GW
Event Queue
pub pub
hook
CDC
CaaS
(Kubernetes)
container
New
Service
(Python)
sub
HTML REST
e.g.
고객(테넌트)별
커스터마이징
요건

HATEOAS API와 UI 주도 Data
Aggregation

Decomposing Monolith – Shared Database but
Schema per service
프론트엔드
VM
레가
시1a
(Java/
Tomcat)
VM
레가
시2a
(.NET)
API GW
IaaS
CaaS (Kubernetes)
container
레가
시1b
(Java/
Spring)
container
레가
시2b
(Java/
Spring)
container
New
Service
(Python)
container
New
Service
(Go)
Event Queue

containercontainer
Materialized View or Schema per Service
프론트엔드
VM
레가
시1a
(Java/
Tomcat)
VM
레가
시2a
(.NET)
API GW
IaaS
CaaS (Kubernetes)
container
레가
시1b
(Java/
Spring)
container
레가
시2b
(Java/
Spring)
container
New
Service
(Python)
container
New
Service
(Go)
Event Queue

Replacing Legacy
프론트엔드
VM
레가
시1a
(Java/
Tomcat)
VM
레가
시2a
(.NET)
API GW
IaaS
CaaS (Kubernetes)
container
레가
시1b
(Java/
Spring)
container
레가
시2b
(Java/
Spring)
container
New
Service
(Python)
container
New
Service
(Go)
Event Queue
container
레가
시1a
(Java/
Spring)
CaaS
(Kubernetes)

마이그래이션 전략 Strangler
프론트엔드
VM
레가
시1a
(Java/
Tomcat)
VM
레가
시2a
(.NET)
API GW
IaaS
CaaS (Kubernetes)
container
레가
시1b
(Java/
Spring)
container
레가
시2b
(Java/
Spring)
container
New
Service
(Python)
container
New
Service
(Go)
Event Queue
container
레가
시1a
(Java/
Spring)
CaaS
(Kubernetes)

프론트엔드
VM
레가
시1a
(Java/
Tomcat)
VM
레가
시2a
(.NET)
API GW
IaaS
CaaS (Kubernetes)
container
레가
시1b
(Java/
Spring)
container
레가
시2b
(Java/
Spring)
container
New
Service
(Python)
container
New
Service
(Go)
Event Queue
container
레가
시1a
(Java/
Spring)
CaaS
(Kubernetes)
Tested!

프론트엔드
API GW
CaaS (Kubernetes)
container
레가
시1b
(Java/
Spring)
container
레가
시2b
(Java/
Spring)
container
New
Service
(Python)
container
New
Service
(Go)
container
레가
시1a
(Java/
Spring)
container
레가
시2a
(Java/
Spring)
IaaS
Event Queue

Legacy Issues
• Stateful Session Data
• Old-fashioned framework (JSP, SOAP, ORM …)
• Old-WAS (Heavy-weight EJBs)
• Join SQLs!

점진적 전환을 위한 기술들 -1
• API Gateway
• Blue/Green & Canary Deployment
• Dark Launch / Automated Test
개발자 확신, 문제 상황 대처

점진적 전환을 위한 기술들 -2
• Change Data Capturing
• Materialized View
데이터 성능, 코드 전환 없는 연동

MSA 구현 기술들
• 1세대 MSA (Spring Cloud / Netflix OSS) 코드수정
• 2세대 Service Mesh (Kubernetes / Istio) 코드수정X
• 3세대 Event Driven MSA (Serverless / Kafka) 간섭최소화

MSA 구현 기술들
비교 Spring Cloud /
Netflix OSS
Kubernetes / Istio Event Driven MSA
(Kafka)
제공기능 API GW, Service
Registry, Circuit
Breaker …
Covers all of Netflix
OSS + Canary Deploy,
Dark Launch …
PubSub, Materialized
View, Realtime
Streaming Processing
코드 변경 필요 (only Java) 불필요 (Polyglot) N/A (with CDC)
서비스 간 커플링 Loosely-Coupled Loosely-Coupled 가장 Loosely-coupled
데이터 어그리게이션
성능
느림
(Blocked, Request-
Response)
느림
(Blocked, Request-
Response)
빠름
(Non-blocking, Event
Sourcing)

Advanced Routing & Deployment
Strategy
• Ingress / Egress Gateway
• Canary Deploy
• 특정 유저의 신상, 지역, 권한, 접근 단말에 따른 다른 버전의 노출
• AB Testing / Shadow Deploy (Dark Launch)
• 신규 버전의 오류 노출 없는 실질적 테스트

Advanced Resilience
• Retry (Kubernetes X, Spring Cloud O)
• 서비스간 호출의 실패에 대한 재시도
• Circuit Breaking (Spring Cloud O) / Rate Limiting
• 인스턴스의 보호
• 전체 서비스 장애 차단
• Pool Ejection (Spring Cloud / Eureka)
• 죽은 인스턴스의 제외
• Circuit Breaking + Pool Ejection + Retry = High Resilience

Advanced Security
• TLS based Inter-Mi-services Communication
• By Auth (신규모듈)
• Whitelist and Blacklist

Advanced Observability
• Distributed Tracing and Measure
• 서비스간 호출의 내용 기록

After Istio (on K8S)
좋은점:
1. L7 레이어를 사용, 성능이 높음
2. Code 변경 없이 Cross-cutting 이슈를 다루어줌
3. Polyglot 다양한 언어에 무관하게 적용가능
4. Main 서비스의 재배포 없이 Sidecar 를 관리 가능함

Design Principle
• Pluggable / Extensible Adapters and Views
• Real-time Monitor (Push Service)
• Usage Prediction, Billing
• Multi-dimensional Analytic
• Event-driven Microservices
• Server-sent event
• Stream Processing
• Multiple Views / Polyglot Persistency

1 2 3 4 5 6 7 8 9
Service 1 Service 2 Legacy
Event Producers
…..
“Append only”
pub
Kafka
Loosely-coupled
Extensible and
pluggable
Event Sources
Cache (Redis) DB (RDB?)
Event Consumers
Index
(ElasticSear
ch or Solr)
sub
Loosely-coupled
Extensible and
pluggable
Views
Service 1
(Java)
Service 2
(Python)
Legacy
(Java)
App polling
containerSidecar 
Front-end (MVVM)
Load snapshot
Offset: 9 Offset: 8 Offset: 6
Cache data again
&
Set offset as DB’s offset
Read all again X
1. Web hoot or SSE
Read faster / frequently via REST directly (no backend) Text search
1. .
2. HTTP Polling
3. CDC or file polling
Pod
Container
…..Realtime
Analytic
(KSQL)
Billing /
Metering
Offset: 9

Event Formatting: Event Sourcing
1 2 3 4 5 6 7 8 9
Append the “Diff” only
Kafka
1: key: account1, value: { action: Deposit, amount: 100 }
4: key: account2, value: { action: Withdraw, amount: 50 }
5: key: account1, value: { action: Withdraw, amount: 50 }
….

1
Deposit
100 for
acc1
2
Deposit 50
for acc1
3
Deposit 50
for acc2
4
Withdraw
50 for acc2
5
Withdraw
50 for acc1
6
Deposit 50
for acc3
7
Deposit 50
for acc4
8
…
9
…
Account Deposit
Event Producers
Kafka
Cache (Redis)
DB (RDB?)
Event Consumers
Index
(ElasticSear
ch or Solr)
Front-end (MVVM)
…..Billing
(KSQL)
Offset: 9
Backend for
front …..SSE
Emitter
Offset: 9
Withdrawal

Kafka
Aggregate view in Database
id balanc
e
acct1 100
1
Deposit
100 for
acc1
2
Deposit 50
for acc1
3
Deposit 50
for acc2
4
Withdraw
50 for acc2
5
Withdraw
50 for acc1
6
Deposit 50
for acc3
7
Deposit 50
for acc4
8
…
9
…
id balanc
e
acct1 150
id balanc
e
acct1 150
acc2 50
id balanc
e
acct1 150
acc2 0
id balanc
e
acct1 100
acc2 0
id balanc
e
acct1 100
acc2 0
acc3 50
id balanc
e
acct1 100
acc2 0
acc3 50
acc4 50

Kafka
View in Front-end (Push service)
1 minlater 2 min later 3 min later 4 min later 5 min later 6 min later 7 min later
1
Deposit
100 for
acc1
2
Deposit 50
for acc1
3
Deposit 50
for acc2
4
Withdraw
50 for acc2
5
Withdraw
50 for acc1
6
Deposit 50
for acc3
7
Deposit 50
for acc4
8
Withdraw
50 for acc1
9
Deposit
150 for
acc1
Snack bar
pops up
Your Balance Is
100
You’ve
deposited 50
Undo
Your Balance Is
150
You’ve
deposited 50
Undo
Your Balance Is
100You’ve
withdrawed 50
Undo
Your Balance Is
200You’ve
Deposited 50
Undo
Your Balance Is
50You’ve
withdrawed 50
Undo

Kafka
View in Realtime Analytic (Early Warning)
1 second later 2 second later 3 second later 4 second later 5 second later 6 second later 7 second later
Warning: You
may go
bankrupt after
5 min !!!
** Using KSQL or Kstreams:
1
Deposit
100 for
acc1
2
Deposit 50
for acc1
3
Deposit 50
for acc2
4
Withdraw
50 for acc2
5
Withdraw
50 for acc1
6
Deposit 50
for acc3
7
Deposit 50
for acc4
8
Withdraw
50 for acc1
9
Deposit
150 for
acc1

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