MSA 기반의 최적의 데이터 베이스 With AWS

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
지속적인 성능과 확장을 보장하는
마이크로 서비스 패턴
데이터베이스 구현하기
AWS 데이터베이스 사업개발 담당
김용대
롯데 정보통신 이커머스팀 CSB
담당 매니저 주경호

2. 발표자료 바로 공개
발표자료는 발표 종료 후 해당
사이트에서 바로 보실 수 있습니다
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Database 시장 변화
마이크로 서비스 데이터베이스 이해
마이크로 서비스 데이터베이스 설계
마이크로 서비스 데이터베이스 구현
마이크로 서비스 데이터베이스 구현 사례 – Lotte B2
맺음말

4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Korea Database 시장의 변화
Lorem ipsum dolor sit amet,
consectetur adipiscing elit, sed do
eiusmod tempor incididunt ut labore
et dolore magna aliqua.
CUSTOMER
PERSPECTIVE
LEARNING/GROWTH
PERSPECTIVE
Lorem ipsum dolor sit amet,
consectetur adipiscing elit, sed do
eiusmod tempor incididunt ut labore
et dolore magna aliqua.
FINANCIAL
PERSPECTIVE
Database 시장은 아직 크지만
시장은 정체
클라우드 Native Database 증가
Database 오픈소스 증가
데이터베이스 진흥원 , 2018 데이터산업 백서

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon.com의 변화
https://twitter.com/ajassy/status/1060979175098437632?lang=ko

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Amazon.com의 多변화
#1 Wallet – Oracle --> DynamoDB
#2 Prime Video – Oracle -->DynamoDB
#3 Advertising Oracle –> PostgreSQL
#4 Buyer Fraud – Oracle-->PostgreSQL
#5 Items & Offers – Oracle --> DynamoDB
#6 FLASH – Oracle -->DynamoDB

8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
변화의 물결

9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
“어디에나 맞는 모놀리틱 데이터베이스” 는 지났다

11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
출발
A. Amazon Neptune
B. Amazon DynamoDB
C. Amazon Aurora PostgreSQL
D. Amazon RDS Oracle
고객 A 는 오라클 유지보수 금액에
계속 부담을 느끼고 있습니다
마케팅 및 인사관리에 사용되는
데이터베이스를 많은 재개발 없이
클라우드 로 전환하여 비용을
절감하고 싶습니다
어떻게 해야 할까요?

12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
도약
A. Amazon Neptune
B. Amazon DynamoDB
C. Amazon Aurora PostgreSQL
D. Amazon RDS Oracle
성공적인 마케팅 ,인사관리
클라우드 이전후 온라인 상점을
클라우드로 이전하기로 결정합니다
하지만 해당시스템은 지속적으로
사용자와 데이터가 늘어나면서
성능 및 저장공간 확보에 어려움이
예상됩니다
어떻게 해야 할까요?

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
발전
A. Amazon Neptune
B. Amazon DynamoDB
C. Amazon Aurora PostgreSQL
D. Amazon ElastiCache
성공적인 마케팅, 인사관리, 온라인
상점 (웹 및 모바일) 클라우드 이전후
프로그램은 훌륭하게 구동되지만
특정 페이지 와 특정 데이터는
지속적으로 빠른 응답을 요구합니다
어떻게 해야 할까요?

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데이터베이스 구축과 유지는 반복 작업
하드웨어 및 소프트웨어 설치
데이터베이스 구성 - 패치 - 백업
고가용성을 위한 클러스터 설정 및 데이터 복제
컴퓨팅 및 스토리지를 위한 용량 계획 및 확장

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프로젝트 팀의 생산성은 $ 이다
문제를 해결하도록 복잡한 앱을 작은 조각으로 나눕니다
각각의 조각이 잘 설계되고 통신하도록 개발 환경을 제공합니다
요구사항에 맞는 데이터베이스를 선택- 분산 환경에서 구축합니다

16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
개발 요구사항의 변화
Users: 1 million+
Data volume: TB–PB–EB
Locality: Global
Performance: Milliseconds–microseconds
Request rate: Millions
Access: Web, mobile, IoT, devices
Scale: Up-down, Out-in
Economics: Pay for what you use
Developer access: No assembly requiredSocial mediaRide hailing Media streaming Dating

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모든 개발 요건 을 충족하기 위해서는
데이터
모델
요건
파악
맞춤
선택

18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

19. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational 모델
• 데이터가 Row 형태로 테이블에 저장됨
• 데이터가 정규화됨
• 엄격한 스키마
• 키를 통해 관계 설정
• 고도의 데이터 정확도 및 일관성
• 복잡한 쿼리
Patient
* Patient ID
First Name
Last Name
Gender
DOB
* Doctor ID
Visit
* Visit ID
* Patient ID
* Hospital ID
Date
* Treatment ID
Medical Treatment
* Treatment ID
Procedure
How Performed
Adverse Outcome
Contraindication
Doctor
* Doctor ID
First Name
Last Name
Medical Specialty
* Hospital Affiliation
Hospital
* Hospital ID
Name
Address
Rating

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데이터 모델 + Use Case
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Amazon Aurora
Amazon RDS
Common
Use Cases
Common
Data
Models

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Key-value 모델
• 키-값 조합
• 키로 파티션
• 실패에 대한 내구성
• 높은 처리량, 낮은 지연 읽기 및 쓰기
• 지속적인 확장에도 일관된 성능
Table 1
…
…
Partitions
…
Highly partitionable data

22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Amazon
DynamoDB
Common
Use Cases
Common
Data
Models
Amazon Aurora
Amazon RDS
데이터 모델 + Use Case

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User profiles
{
id: 181276,
username: "sue1942",
name: {first: "Susan",
last: "Benoit"}
}
{
id: 181276,
username: "sue1942",
name: {first: "Susan",
last: "Benoit"}
}
{
id: 181276,
username: "sue1942",
name: {first: "Susan",
last: "Benoit"},
ExploidingSnails: {
hi_score: 3185400,
global_rank: 5139,
bonus_levels: true
},
promotions: ["new user","5%","snail lover"]
}
{
id: 181276,
username: "sue1942",
name: {first: "Susan",
last: "Benoit"},
ExploidingSnails: {
hi_score: 3185400,
global_rank: 5139,
bonus_levels: true
}
}
Document 모델

24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
Amazon
DocumentDB
Common
Use Cases
Common
Data
Models
Amazon Aurora
Amazon RDS
Amazon
DynamoDB
데이터 모델 + Use Case

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In-memory 모델
• 최종 저장목적인 아닌 메모리
캐싱
• 마이크로 세컨드 응답속도
• 메모리 데이터 구조를 처리하기
위한 간단한 명령 스트링, 해시,
목록, 세트 및 정렬 세트
Database
Memory
(buffer pool)
Disk
Query processor Get/Put APIs
Memory
Milliseconds to microseconds (10x faster)
Storage engine

26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Amazon
ElastiCache
Common
Use Cases
Common
Data
Models
Amazon
DynamoDB
Amazon Aurora
Amazon RDS
Amazon
DocumentDB
데이터 모델 + Use Case

27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Graph 모델

28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social networking,
recommendation
engine
Amazon
Neptune
Common
Use Cases
Common
Data
Models
Amazon Aurora
Amazon RDS
Amazon
DynamoDB
Amazon
DocumentDB
Amazon
ElastiCache
데이터 모델 + Use Case

29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Time series 모델
Application events
IoT sensor
readings
DevOps data
85
86
87
88
89
90
91
92
93
94
95
Humidity
% Water vapor
91.094.086.093.0
5:28:15 PM 5:28:30 PM 5:28:45 PM 5:29:05 PM

30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social networking,
recommendation
engine
Time-series
Collect, store,
and process
data
sequenced by
time
IoT applications,
event tracking
Amazon
Timestream
Common
Use Cases
Common
Data
Models
Amazon Aurora
Amazon RDS
Amazon
DynamoDB
Amazon
DocumentDB
Amazon
ElastiCache
Amazon
Neptune
데이터 모델 + Use Case

31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social networking,
recommendation
engine
Time-series
Collect, store,
and process
data
sequenced by
time
IoT applications,
event tracking
Ledger
Complete,
immutable, and
verifiable history
of all changes to
application data
Systems
of record, supply
chain, health care,
registrations,
financial
Amazon
QLDB
Common
Use Cases
Common
Data
Models
Amazon Aurora
Amazon RDS
Amazon
DynamoDB
Amazon
DocumentDB
Amazon
ElastiCache
Amazon
Neptune
Amazon
Timestream
데이터 모델 + Use Case

32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
User search history: Amazon DynamoDB
• Massive data volume
• Need quick lookups for personalized search
Session state: Amazon ElastiCache
• Authentication/Session Management is the primary use case,
along with User Feed and Messaging
Relational data: Amazon Aurora MySQL
• Referential integrity
• Primary transactional database

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Relational Data: Amazon Aurora PostgreSQL
• Migrating legacy commercial databases to Aurora
PostgreSQL for greater performance & lower cost
Key Value: Amazon DynamoDB
• GDPR opt-in data store across web properties for 100s of
millions of users
• Deployed across multiple AWS Regions globally
Real-Time Cache: Amazon ElastiCache
• Using Redis as cache layer in front of databases

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Mission Critical Data: Amazon DynamoDB
• Backup and restore of device data for 300 million users
• 70% cost savings fromCassandra through reduced ops
User Data in Relational Tables: Amazon Aurora
• Owner and warranty information in AmazonAurora
Social Graph: Amazon Neptune
• Creating an in app social network
Device State: Amazon ElastiCache
• Storing device state in Redis

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Massive Scale Transactional: Amazon DynamoDB
• Product Item Details
Real-Time Cache: Amazon ElastiCache
• Real time product list display
Reporting : Amazon Redshift
• BI reporting based on S3 Data Lake
Relational User Data : Amazon Aurora MySQL
• Admin, Orders, Product Catalog

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롯데정보통신

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롯데정보통신

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-
ME

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목차

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데이터 베이스_데이터 베이스 하면 생각 나는 그 이름

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데이터 베이스_관계형 데이터 베이스

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데이터 베이스_NoSQL

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_현 시점의 주류

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Rdb의 위험성_익숙함이 주는 고통

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Rdb의 위험성_익숙함이 주는 고통

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Rdb 부터로 자유_

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Rdb 부터로 자유_사고의 전환(오픈소스 데이터 베이스)

50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Rdb 부터로 자유_사고의 전환(오픈소스 데이터 베이스)

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Rdb 부터로 자유_사고의 전환(오픈소스 데이터 베이스)

52. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Multi Datastore_

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H/W/C 기준표 HOT WARM COLD
Volume MB-GB GB-TB PB
Item Size B-KB KB-MB KB-TB
Latency ms ms,sec min, hrs
Durability Low-High High Very High
Request rate Very High High Low
Retention Period Sort(~ under ?month) Long Very Long
Sample Data Source Transactional data
Operational data
Service log file
Click stream data
Flat files
System log file
Historical data
Marketing research data
Multi Datastore_

54. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Hot Data Warm Data Cold Data
Amazon
ElasticCache
Amazon
DynamoDB
Amazon
Aurora
(MySQL)
Amazon
Elasticsearch
Amazon
EMR /
Athena
/ RedShift
Amazon S3 Amazon
Glacier
Average
Latency
ms ms ms, sec ms, sec sec, min, hr ms, sec, min (~ size) hrs
Data
Volume
GB GB-TBs (no limit) GB-TB (64 TB
Max) (3 TB Refer)
GB-TB GB-PB MB-PB GB-PB
Item Size B-KB KB(400 KB Max) KB(64 KB) KB(1 MB Max) MB-GB KB-GB (5 TB Max) GB (40 TB Max)
Request
rate
High-Very High Very High (no limit) High High Low-Very High Low-Very High
(no limit)
Very Low
Durability Low-Moderate Very High Very High High High Very High Very High
Multi Datastore_

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Multi Datastore_

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OLTP
HOT WARM
Redis
DynamoDB
RDB(Aurora)
ElasticSearch
DW/BI
Analyze DSS Batch
비 정형
ElasticSearch RedShift Athena
EMR
Multi Datastore_

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Multi Datastore_

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Multi Datastore_

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Multi Datastore_

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Multi Datastore_

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Multi Datastore

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Multi Datastore

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Multi Datastore

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Multi Datastore

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Multi Datastore

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Multi Datastore

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Multi Datastore

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Check point

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Check point

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Join us

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데이터베이스 선택 가이드
Situation Solution
기존 애플리케이션 Use your existing engine on RDS
• MySQL Amazon Aurora, RDS for MySQL
• PostgreSQL Amazon Aurora, RDS for PostgreSQL
• MariaDB Amazon Aurora, RDS for MariaDB
• Oracle Use SCT to determine complexity Amazon Aurora, RDS for Oracle
• SQL Server Use SCT to determine complexity Amazon Aurora, RDS for SQL Server
신규 애플리케이션 • If you can avoid relational features DynamoDB
• If you need relational features Amazon Aurora
인메모리 저장 및 캐싱 • Amazon ElastiCache
시계열 데이터 • Amazon Timestream
모든 애플리케이션 변경사항을 추적/검증
중앙 집중 관리
• Amazon Quantum Ledger Database (QLDB)
중앙 집중 관리가 불필요한 경우 • Amazon Managed Blockchain
Data Warehouse & BI • Amazon Redshift, Amazon Redshift Spectrum, and Amazon QuickSight
S3의 데이터에 대한 Adhoc 분석 • Amazon Athena and Amazon QuickSight
Apache Spark, Hadoop, HBase (needle in a haystack
type queries)
• Amazon EMR
Log 분석, 운영 모니터링, & 검색 • Amazon Elasticsearch Service and Amazon Kinesis

73. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Next Step
Database 세션
1. 엔터프라이즈의 효과적인 클라우드 도입을 위한
전략 및 적용 사례
( 엔터프라이즈 , 04/17 1:40 PM – 2:20 PM )
1. AWS 신규 데이터베이스 서비스 분석
( AWS 기술트랙 4, 04/18 11:10-12:00 )
2. 갤럭시 규모의 인공지능 서비스를 위한 AWS
데이터베이스 아키텍처
( AWS 기술트랙 5, 04/18 17:20 – 18:10)
관련 리소스
AWS Databases https://aws.amazon.com/databases
Amazon RDS https://aws.amazon.com/rds
Amazon Aurora https://aws.amazon.com/rds/aurora
Amazon DynamoDB https://aws.amazon.com/dynamodb
Amazon ElastiCache https://aws.amazon.com/databases
Amazon DocumentDB (with MongoDB compatibility)
https://aws.amazon.com/ko/documentdb
Amazon Database Migration Service
https://aws.amazon.com/ko/dms
Amazon Neptune https://aws.amazon.com/ko/neptune
Amazon Quantum Ledger Database (QLDB)
https://aws.amazon.com/ko/qldb
Amazon Timestream https://aws.amazon.com/ko/timestream/
Amazon Redshift https://aws.amazon.com/ko/redshift

74. 여러분의 피드백을 기다립니다!
#AWSSummit 해시태그로
소셜미디어에 여러분의
행사소감을 올려주세요.
AWS Summit Seoul 2019
모바일 앱과 QR코드를 통해
강연평가 및 설문조사에
참여하시고 재미있는
기념품을 받아가세요.
내년 Summit을 만들 여러분의
소중한 의견 부탁 드립니다.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

75. 감사합니다!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.