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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AWSの
15あるデータベース
を使いこなそう
アマゾン ウェブ サービス ジャパン 株式会社
シニア エバンジェリスト
亀田治伸
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データベース
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
万能のデータベース
は存在しない
“A one size fits all database
doesn't fit anyone”
Werner Vogels
CTO - Amazon.com
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
従来のエンタープライズ DB システム
アプリ
オンライン
トランザクション
ETLツール
分析
BIツールOLTP DB OLAP DB
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データベースの選択
• AWS では多様な
データベースの選択肢
• ワークロードに応じて
最適な選択が可能
Purpose built
The right tool for
the right job
https://www.allthingsdistributed.com/2018/06/purpose-built-databases-in-aws.html
適材適所の選択
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データの種類に応じて適切なデータストアを選択
サーバー
ローカル
ストレージ
サーバー
ローカル
ストレージ
共有
ストレージ
データベース
(RDBMS)
データベース
(NoSQL)
・ショッピングカート
・セッション情報
・ユーザ情報
・商品情報
・在庫情報
・商品画像データ
複数データストアの使い分けで効率を向上
“A one size fits all database
doesn't fit anyone”
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
A m a z o n
D y n a m o D B
キ ー
バ リ ュ ー イ ン メ モ リ グ ラ フリ レ ー シ ョ ナ ル
A m a z o n
R D S
A m a z o n
Q L D B
元 帳時 系 列
A m a z o n
T i m e s t r e a m
A m a z o n
A u r o r a
A m a z o n
D o c u m e n t D B
ド キ ュ メ ン ト
A m a z o n
N e p t u n e
A m a z o n
E l a s t i C a c h e
A m a z o n
R D S f o r
V M W a r e E l a s t i C a c h e
f o r R e d i s
E l a s t i C a c h e
f o r M e m c a c h e d
A m a z o n
R e d s h i f t
デ ー タ
ウ ェ ア ハ ウ ス 移 行
AWS Database Migration
Service
ワークロードに適した最適なデータベース選択
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データカテゴリ
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
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データカテゴリ
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
リレーショナル
キーバリュー
ドキュメント
インメモリー
グラフ
時系列
台帳
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データカテゴリ
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
リレーショナル
キーバリュー
ドキュメント
インメモリー
グラフ
時系列
台帳
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB
Amazon
Neptune
Amazon
RDS
Aurora CommercialCommunity
Amazon
Timestream
Amazon
QLDB
Amazon
ElastiCache
Amazon
DocumentDB
マネージドサービス
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
オンプレミス ミドルウェア on EC2 マネージドサービス
お客様がご担当する作業 AWSが提供するマネージド機能
電源、ネットワーク
ラック導入管理
サーバーメンテナンス
OSのパッチ
ミドルウェアのパッチ
バックアップ
スケーラビリティ
可用性
ミドルウェアの導入
OSの導入
アプリからの利用
電源、ネットワーク
ラック導入管理
サーバーメンテナンス
OSのパッチ
ミドルウェアのパッチ
バックアップ
スケーラビリティ
可用性
ミドルウェアの導入
OSの導入
アプリからの利用
電源、ネットワーク
ラック導入管理
サーバーメンテナンス
OSのパッチ
ミドルウェアのパッチ
バックアップ
スケーラビリティ
可用性
ミドルウェアの導入
OSの導入
アプリからの利用
マネージドサービスの特性
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
オンプレミスのサーバー 仮想サーバー
データベース
サービス
データベース構築の選択肢
AWS Cloud
Amazon EC2 Amazon RDS 等
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
リレーショナルデータベース
RDBMS
Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
リレーショナルデータ
• テーブル間でデータを分割
• 高度に構造化されたデータ
• キーを介して確立された
リレーションシップ(関係性)
• データの完全性と一貫性
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
リレーション
多 対 1
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Relational Database Service (Amazon RDS)
6つのデータベースエンジンから選択できるマネージリレーショナルデータベース
容易な管理 高可用性と永続性 高スケール 高速でセキュア
マネージドによる
運用自動化
データレプリケーション、
自動バックアップ、
スナップショット、
自動フェイルオーバー
コンピュートと
ストレージをスケール可能
SSDストレージのI/O保証、
保存時と通信時の暗号化
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Aurora 基本アーキテクチャ
• SSDを利用したシームレスに
スケールするストレージ
• 10GBから64TBまでシームレスに自動で
スケールアップ
• 実際に使った分だけ課金
• 標準で高可用性を実現
• 3AZに6つのデータのコピーを作成
• 継続的に S3 へ増分バックアップ
• MySQL と Postgres 互換
SQL
Transactions
AZ 1 AZ 2 AZ 3
Caching
Amazon S3
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ディスク障害検知と修復
• 2つのコピーに障害が起こっても、読み書きに影響は無い
• 3つのコピーに障害が発生しても読み込みは可能
• 自動検知、修復
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
読み書き可能読み込み可能
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
リードレプリカ構成
Master Replica Replica Replica
Availability Zone 1
Aurora ストレージ
(共有ストレージボリューム)
プライマリイン
スタンス
リードレプリカ
リード
レプリカ
リード
レプリカ
Availability Zone 2 Availability Zone 3
リージョン
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Aurora Serverless
Master Replica Replica Replica
Availability Zone 1
Aurora ストレージ
(共有ストレージボリューム)
プライマリイン
スタンス
Availability Zone 2 Availability Zone 3
リージョン
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Non Relational – “ Not only SQL”
NoSQL:
 RDBMSではないデータベースの総称
 従来のRDBMSの課題を解決するために生まれた
 NoSQLは非常に多くの種類がある
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
RDBMS と NoSQL の主な特徴
リレーショナルデータベース NoSQL
ストレージに最適化 計算リソースに最適化
正規化/リレーショナル 非正規化
SQLを使用可能
各データベースによって
異なるクエリ方法
トランザクション処理 トランザクション処理は限定的
データの堅牢性/一貫性
データの堅牢性/一貫性
はデータベースによる
https://aws.amazon.com/jp/nosql/
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Key Value Store
Key-value
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
キーバリューストア (KVS)
• キーとバリュー(値)という単純な構造
• 超高速なパフォーマンス
• RDBMSに比べ読み書きが高速
Key1 Value1
Key2 Value2
Key3 Value3
1 対 1
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
選択指針
• スケーラビリティが求められる
• レスポンスタイム 数ミリ秒 が求められる
• シンプルなクエリ
• Amazon DynamoDB
• 規模に関係なく、数ミリ秒のレスポンス
• 1 日に 10 兆件以上のリクエスト処理可能
• 毎秒 2,000 万件を超えるリクエストをサポート
• マルチリージョンマルチマスター構成
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ドキュメントデータベース
Document
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ドキュメント指向データベース
• JSONやXML等の不定形なデータ構造に対応
• 複雑なデータモデリングを容易に表現可能
{
”id": ”tttak”,
“job”: “sa”,
”info": {
”skill": [ “youtuber”, ”video-shoping" ],
”database": ”oracle"
}
}
Key1 Object1
Key2 Object2
Key3 Object3
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
選択指針
• スキーマを決められないデータの格納
• 後から属性情報の変更を行いたい
• JSONやXML形式のをそのまま扱いたい
• 構造を意識したドキュメント思考の検索
• Amazon DocumentDB
• フルマネージドなMongoDB(3.6)互換
• 読み取り容量を数百万件/秒までスケール
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
インメモリーデータベース
In-memory
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
インメモリーデータベース
• KVS (キーバリューストア)
• 最大限メモリで処理
• 短い応答時間が期待できる
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
選択指針
• ミリ秒未満のレイテンシー求められる
• キャッシュ可能
• 障害時のデータ損失リスクを許容できる
• インメモリ処理のため障害によるデータ損失の可能性がある
• Amazon ElastiCache
• マイクロ秒の応答時間
• フルマネージドな運用管理
ElastiCac he
for Red is
ElastiCac he
for Memc ac hed
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
グラフデータベース
Graph
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
グラフ指向データベース
• データ間を相互に結びつけて
データ同士の関係をグラフという形で表す
• 複雑な関係性を表すのを得意とする
• SNSのフレンドの関連性等
多 対 多
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ユースケース
SNSニュースフィード リコメンデーション 不正検出
Friends
Use
Play
Like
Check in
Like
Connect
Read
Credit
card
Product
Email
address
Credit
card
Known
fraud
Uses
Paid
with
Uses
Paid
with
Paid with
Purchased
Approve
purchase?
Sport
Product
Purchased
Purchased
People
who also
follow sports
purchased…
Purchased
Knows
Knows
Do you
know…
Follows
Follows
Follows
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
サンプルデータ
ID Node Name Next Ptr
1 A NULL
2 B C
3 C A
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
サンプルデータ その2
ID Node Name Next Ptr
1 A B
2 B C
3 C A
4 B A
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ID Node Name Next Ptr Attr Num
1 A NULL NULL NULL
2 B C Like 1
3 C A Dislike 1
4 B A Like 2
5 B A Dislike 1
サンプルデータ その3
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ID Node Name Next Ptr Attr Num
1 A NULL NULL NULL
2 B C Like 1
3 C A Dislike 1
4 B A Like 2
5 B A Dislike 1
サンプルデータ その3
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
選択指針
• 関連を探索するクエリ (トラバーサル)
• 短いクエリが大量に来る要件がある
• Amazon Neptune
• 数十億のリレーションシップを扱える
• ミリ秒台のレイテンシー
• グラフに最適化された、専用のグラフデータベースエンジン
• SPARQLとGremlinに対応
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
時系列データベース
Time-series
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
時系列データ
• 時間が唯一の主軸
• 特定の間隔で記録され続ける
• 時間の経過に伴う変化を測定
• リアルタイムの意思決定、警告 等
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
選択指針
• 時系列データを扱うか
• 大量、粒度が小さい、すぐに分析したい
• 多数のソース (IoTデバイスなど) からの頻繁に送信されるか
• 一定の時間間隔で分析を実行したいか
• Amazon Timestream (Public Preview)
• RDB の 1/10 のコストで 1,000 倍のパフォーマンス
• 一日あたり数兆規模のイベントに対応
• 挿入とクエリを異なる処理階層で実行し、競合を解消
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS のデータベースサービス
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
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCacheDocumentDB
Non Relational
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
台帳データベース
Ledger
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Basics of Block Chain
ビザンチン耐性
イミュータブルトランザクション
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
通常のオンラインバンキング
1:N取引であり銀行が
TrustAnchor
TrustAnchorへの攻撃が
成功すれば
ハッキング可能
高いセキュリティが必要
メンテナンス、障害による
ダウンタイムが発生
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
BlockChain の P2Pネットワーク
攻撃者は複数のノードを一度に攻撃しデータを
書き換える必要がある
→不可能。高セキュリティ
DNSやCDN(のEdge)と同じように、すべての
ノードが一度に停止することはない
→ゼロダウンタイムの実現
ビザンチン耐性
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Block [Chain]
x x
x x
x x
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
台帳データベース
• データの変更履歴はイミュータブル
(変更や削除が不可能)
• 意図しない変更が発生していないことを
暗号技術で検証
C | H
J
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
選択指針
• 履歴の追跡と変更管理
• 完全で検証可能な変更履歴を長期間維持したい
• 管理者でも変更履歴を改ざんできないことを保証したい
• Amazon Quantum Ledger Database
• スケーラブルで完全
• 検証可能なトランザクション
• データの変更全てを追跡可能
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
データ構造
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Robert Dennison
History
Current
INSERT… UPDATE… DELETE… UPDATE… UPDATE… UPDATE…
SEQUENCE
NUMBER: 789
SEQUENCE
NUMBER: 790
SEQUENCE
NUMBER: 791
SEQUENCE
NUMBER: 793
SEQUENCE
NUMBER: 792
SEQUENCE
NUMBER: --
Journal
元帳
データ Amazon
Quantum
Ledger Database
ID Version Start Manufacturer Model Year VIN Owner
1 0 7/16/2012 Tesla Model S 2012 123456789 Traci Russell
1 1 8/03/2013 Tesla Model S 2012 123456789 Ronnie Nash
1 2 9/02/2016 Tesla Model S 2012 123456789 Robert Dennison
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
1
Tracy buys a car on Aug 3, 2013
Journal CurrentDMV Scenario
History
Immutability
ID Version Manufacturer Model Year VIN Owner Date of
Purchase
1 0 Tesla Model S 2012 123456789 Traci
Russell
8/3/2013
ID Version Manufacturer Model Year VIN Owner Date of Purchase
1 0 Tesla Model S 2012 123456789 Traci
Russell
9/10/2014
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
1
Tracy buys a car on Aug 3, 2013
2
Tracy sells car to
Ronnie on Sept 10, 2014
Journal CurrentDMV Scenario
Immutability
ID Version Manufacturer Model Year VIN Owner Date of
Purchase
1 0 Tesla Model S 2012 123456789 Traci
Russell
8/3/2013
1 1 Tesla Model S 2012 123456789 Ronnie
Nash
9/10/2014
ID Version Manufacturer Model Year VIN Owner Date of Purchase
1 1 Tesla Model S 2012 123456789 Ronnie
Nash
9/10/2014
History
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
1
Tracy buys a car on Aug 3, 2013
2
Tracy sells car to
Ronnie on Sept 10, 2014
Journal CurrentDMV Scenario
3
Ronnie’s car gets in an
accident and gets totaled
ID Version Manufacturer Model Year VIN Owner Date of Purchase
ID Version Manufacturer Model Year VIN Owner Date of
Purchase
1 0 Tesla Model S 2012 123456789 Traci
Russell
8/3/2013
1 1 Tesla Model S 2012 123456789 Ronnie
Nash
9/10/2014
1 2 Deleted
Immutability
History
DELETE
DATE: 09/02/2016
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
数学的なデータ結合性
Journal
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:08/03/2013
}
H (T1) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:09/10/2014
}
H(T2) DELETE cars
ID:1
Metadata: {
Date: 09/02/2016
}
H(T3)
H(T1) H(T1) + Update
= H(T2)
H(T2) + Update
= H(T3)
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
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Sapporo devfesta 2019/11/13

  • 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWSの 15あるデータベース を使いこなそう アマゾン ウェブ サービス ジャパン 株式会社 シニア エバンジェリスト 亀田治伸
  • 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データベース
  • 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 万能のデータベース は存在しない “A one size fits all database doesn't fit anyone” Werner Vogels CTO - Amazon.com
  • 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 従来のエンタープライズ DB システム アプリ オンライン トランザクション ETLツール 分析 BIツールOLTP DB OLAP DB
  • 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データベースの選択 • AWS では多様な データベースの選択肢 • ワークロードに応じて 最適な選択が可能 Purpose built The right tool for the right job https://www.allthingsdistributed.com/2018/06/purpose-built-databases-in-aws.html 適材適所の選択
  • 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データの種類に応じて適切なデータストアを選択 サーバー ローカル ストレージ サーバー ローカル ストレージ 共有 ストレージ データベース (RDBMS) データベース (NoSQL) ・ショッピングカート ・セッション情報 ・ユーザ情報 ・商品情報 ・在庫情報 ・商品画像データ 複数データストアの使い分けで効率を向上 “A one size fits all database doesn't fit anyone”
  • 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. A m a z o n D y n a m o D B キ ー バ リ ュ ー イ ン メ モ リ グ ラ フリ レ ー シ ョ ナ ル A m a z o n R D S A m a z o n Q L D B 元 帳時 系 列 A m a z o n T i m e s t r e a m A m a z o n A u r o r a A m a z o n D o c u m e n t D B ド キ ュ メ ン ト A m a z o n N e p t u n e A m a z o n E l a s t i C a c h e A m a z o n R D S f o r V M W a r e E l a s t i C a c h e f o r R e d i s E l a s t i C a c h e f o r M e m c a c h e d A m a z o n R e d s h i f t デ ー タ ウ ェ ア ハ ウ ス 移 行 AWS Database Migration Service ワークロードに適した最適なデータベース選択
  • 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データカテゴリ 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
  • 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データカテゴリ 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 リレーショナル キーバリュー ドキュメント インメモリー グラフ 時系列 台帳
  • 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データカテゴリ 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 リレーショナル キーバリュー ドキュメント インメモリー グラフ 時系列 台帳
  • 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB Amazon Neptune Amazon RDS Aurora CommercialCommunity Amazon Timestream Amazon QLDB Amazon ElastiCache Amazon DocumentDB マネージドサービス
  • 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. オンプレミス ミドルウェア on EC2 マネージドサービス お客様がご担当する作業 AWSが提供するマネージド機能 電源、ネットワーク ラック導入管理 サーバーメンテナンス OSのパッチ ミドルウェアのパッチ バックアップ スケーラビリティ 可用性 ミドルウェアの導入 OSの導入 アプリからの利用 電源、ネットワーク ラック導入管理 サーバーメンテナンス OSのパッチ ミドルウェアのパッチ バックアップ スケーラビリティ 可用性 ミドルウェアの導入 OSの導入 アプリからの利用 電源、ネットワーク ラック導入管理 サーバーメンテナンス OSのパッチ ミドルウェアのパッチ バックアップ スケーラビリティ 可用性 ミドルウェアの導入 OSの導入 アプリからの利用 マネージドサービスの特性
  • 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. オンプレミスのサーバー 仮想サーバー データベース サービス データベース構築の選択肢 AWS Cloud Amazon EC2 Amazon RDS 等
  • 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB
  • 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB
  • 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. リレーショナルデータベース RDBMS Relational
  • 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. リレーショナルデータ • テーブル間でデータを分割 • 高度に構造化されたデータ • キーを介して確立された リレーションシップ(関係性) • データの完全性と一貫性 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 リレーション 多 対 1
  • 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Relational Database Service (Amazon RDS) 6つのデータベースエンジンから選択できるマネージリレーショナルデータベース 容易な管理 高可用性と永続性 高スケール 高速でセキュア マネージドによる 運用自動化 データレプリケーション、 自動バックアップ、 スナップショット、 自動フェイルオーバー コンピュートと ストレージをスケール可能 SSDストレージのI/O保証、 保存時と通信時の暗号化
  • 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Aurora 基本アーキテクチャ • SSDを利用したシームレスに スケールするストレージ • 10GBから64TBまでシームレスに自動で スケールアップ • 実際に使った分だけ課金 • 標準で高可用性を実現 • 3AZに6つのデータのコピーを作成 • 継続的に S3 へ増分バックアップ • MySQL と Postgres 互換 SQL Transactions AZ 1 AZ 2 AZ 3 Caching Amazon S3
  • 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ディスク障害検知と修復 • 2つのコピーに障害が起こっても、読み書きに影響は無い • 3つのコピーに障害が発生しても読み込みは可能 • 自動検知、修復 SQL Transaction AZ 1 AZ 2 AZ 3 Caching SQL Transaction AZ 1 AZ 2 AZ 3 Caching 読み書き可能読み込み可能
  • 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. リードレプリカ構成 Master Replica Replica Replica Availability Zone 1 Aurora ストレージ (共有ストレージボリューム) プライマリイン スタンス リードレプリカ リード レプリカ リード レプリカ Availability Zone 2 Availability Zone 3 リージョン
  • 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Aurora Serverless Master Replica Replica Replica Availability Zone 1 Aurora ストレージ (共有ストレージボリューム) プライマリイン スタンス Availability Zone 2 Availability Zone 3 リージョン
  • 23. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Non Relational – “ Not only SQL” NoSQL:  RDBMSではないデータベースの総称  従来のRDBMSの課題を解決するために生まれた  NoSQLは非常に多くの種類がある
  • 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. RDBMS と NoSQL の主な特徴 リレーショナルデータベース NoSQL ストレージに最適化 計算リソースに最適化 正規化/リレーショナル 非正規化 SQLを使用可能 各データベースによって 異なるクエリ方法 トランザクション処理 トランザクション処理は限定的 データの堅牢性/一貫性 データの堅牢性/一貫性 はデータベースによる https://aws.amazon.com/jp/nosql/
  • 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Key Value Store Key-value
  • 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. キーバリューストア (KVS) • キーとバリュー(値)という単純な構造 • 超高速なパフォーマンス • RDBMSに比べ読み書きが高速 Key1 Value1 Key2 Value2 Key3 Value3 1 対 1
  • 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 選択指針 • スケーラビリティが求められる • レスポンスタイム 数ミリ秒 が求められる • シンプルなクエリ • Amazon DynamoDB • 規模に関係なく、数ミリ秒のレスポンス • 1 日に 10 兆件以上のリクエスト処理可能 • 毎秒 2,000 万件を超えるリクエストをサポート • マルチリージョンマルチマスター構成
  • 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ドキュメントデータベース Document
  • 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ドキュメント指向データベース • JSONやXML等の不定形なデータ構造に対応 • 複雑なデータモデリングを容易に表現可能 { ”id": ”tttak”, “job”: “sa”, ”info": { ”skill": [ “youtuber”, ”video-shoping" ], ”database": ”oracle" } } Key1 Object1 Key2 Object2 Key3 Object3
  • 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 選択指針 • スキーマを決められないデータの格納 • 後から属性情報の変更を行いたい • JSONやXML形式のをそのまま扱いたい • 構造を意識したドキュメント思考の検索 • Amazon DocumentDB • フルマネージドなMongoDB(3.6)互換 • 読み取り容量を数百万件/秒までスケール
  • 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. インメモリーデータベース In-memory
  • 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. インメモリーデータベース • KVS (キーバリューストア) • 最大限メモリで処理 • 短い応答時間が期待できる
  • 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 選択指針 • ミリ秒未満のレイテンシー求められる • キャッシュ可能 • 障害時のデータ損失リスクを許容できる • インメモリ処理のため障害によるデータ損失の可能性がある • Amazon ElastiCache • マイクロ秒の応答時間 • フルマネージドな運用管理 ElastiCac he for Red is ElastiCac he for Memc ac hed
  • 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. グラフデータベース Graph
  • 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. グラフ指向データベース • データ間を相互に結びつけて データ同士の関係をグラフという形で表す • 複雑な関係性を表すのを得意とする • SNSのフレンドの関連性等 多 対 多
  • 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ユースケース SNSニュースフィード リコメンデーション 不正検出 Friends Use Play Like Check in Like Connect Read Credit card Product Email address Credit card Known fraud Uses Paid with Uses Paid with Paid with Purchased Approve purchase? Sport Product Purchased Purchased People who also follow sports purchased… Purchased Knows Knows Do you know… Follows Follows Follows
  • 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. サンプルデータ ID Node Name Next Ptr 1 A NULL 2 B C 3 C A
  • 45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. サンプルデータ その2 ID Node Name Next Ptr 1 A B 2 B C 3 C A 4 B A
  • 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ID Node Name Next Ptr Attr Num 1 A NULL NULL NULL 2 B C Like 1 3 C A Dislike 1 4 B A Like 2 5 B A Dislike 1 サンプルデータ その3
  • 47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ID Node Name Next Ptr Attr Num 1 A NULL NULL NULL 2 B C Like 1 3 C A Dislike 1 4 B A Like 2 5 B A Dislike 1 サンプルデータ その3
  • 48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 選択指針 • 関連を探索するクエリ (トラバーサル) • 短いクエリが大量に来る要件がある • Amazon Neptune • 数十億のリレーションシップを扱える • ミリ秒台のレイテンシー • グラフに最適化された、専用のグラフデータベースエンジン • SPARQLとGremlinに対応
  • 49. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 時系列データベース Time-series
  • 51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 時系列データ • 時間が唯一の主軸 • 特定の間隔で記録され続ける • 時間の経過に伴う変化を測定 • リアルタイムの意思決定、警告 等
  • 52. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 選択指針 • 時系列データを扱うか • 大量、粒度が小さい、すぐに分析したい • 多数のソース (IoTデバイスなど) からの頻繁に送信されるか • 一定の時間間隔で分析を実行したいか • Amazon Timestream (Public Preview) • RDB の 1/10 のコストで 1,000 倍のパフォーマンス • 一日あたり数兆規模のイベントに対応 • 挿入とクエリを異なる処理階層で実行し、競合を解消
  • 53. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS のデータベースサービス 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 DynamoDB NeptuneAmazon RDS Aurora CommercialCommunity Timestream QLDBElastiCacheDocumentDB Non Relational
  • 54. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 台帳データベース Ledger
  • 55. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Basics of Block Chain ビザンチン耐性 イミュータブルトランザクション
  • 56. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 通常のオンラインバンキング 1:N取引であり銀行が TrustAnchor TrustAnchorへの攻撃が 成功すれば ハッキング可能 高いセキュリティが必要 メンテナンス、障害による ダウンタイムが発生
  • 57. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. BlockChain の P2Pネットワーク 攻撃者は複数のノードを一度に攻撃しデータを 書き換える必要がある →不可能。高セキュリティ DNSやCDN(のEdge)と同じように、すべての ノードが一度に停止することはない →ゼロダウンタイムの実現 ビザンチン耐性
  • 58. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Block [Chain] x x x x x x
  • 59. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 台帳データベース • データの変更履歴はイミュータブル (変更や削除が不可能) • 意図しない変更が発生していないことを 暗号技術で検証 C | H J
  • 60. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 選択指針 • 履歴の追跡と変更管理 • 完全で検証可能な変更履歴を長期間維持したい • 管理者でも変更履歴を改ざんできないことを保証したい • Amazon Quantum Ledger Database • スケーラブルで完全 • 検証可能なトランザクション • データの変更全てを追跡可能
  • 61. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データ構造 ID Manufacturer Model Year VIN Owner 1 Tesla Model S 2012 123456789 Robert Dennison History Current INSERT… UPDATE… DELETE… UPDATE… UPDATE… UPDATE… SEQUENCE NUMBER: 789 SEQUENCE NUMBER: 790 SEQUENCE NUMBER: 791 SEQUENCE NUMBER: 793 SEQUENCE NUMBER: 792 SEQUENCE NUMBER: -- Journal 元帳 データ Amazon Quantum Ledger Database ID Version Start Manufacturer Model Year VIN Owner 1 0 7/16/2012 Tesla Model S 2012 123456789 Traci Russell 1 1 8/03/2013 Tesla Model S 2012 123456789 Ronnie Nash 1 2 9/02/2016 Tesla Model S 2012 123456789 Robert Dennison
  • 62. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 1 Tracy buys a car on Aug 3, 2013 Journal CurrentDMV Scenario History Immutability ID Version Manufacturer Model Year VIN Owner Date of Purchase 1 0 Tesla Model S 2012 123456789 Traci Russell 8/3/2013 ID Version Manufacturer Model Year VIN Owner Date of Purchase 1 0 Tesla Model S 2012 123456789 Traci Russell 9/10/2014
  • 63. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 1 Tracy buys a car on Aug 3, 2013 2 Tracy sells car to Ronnie on Sept 10, 2014 Journal CurrentDMV Scenario Immutability ID Version Manufacturer Model Year VIN Owner Date of Purchase 1 0 Tesla Model S 2012 123456789 Traci Russell 8/3/2013 1 1 Tesla Model S 2012 123456789 Ronnie Nash 9/10/2014 ID Version Manufacturer Model Year VIN Owner Date of Purchase 1 1 Tesla Model S 2012 123456789 Ronnie Nash 9/10/2014 History
  • 64. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 1 Tracy buys a car on Aug 3, 2013 2 Tracy sells car to Ronnie on Sept 10, 2014 Journal CurrentDMV Scenario 3 Ronnie’s car gets in an accident and gets totaled ID Version Manufacturer Model Year VIN Owner Date of Purchase ID Version Manufacturer Model Year VIN Owner Date of Purchase 1 0 Tesla Model S 2012 123456789 Traci Russell 8/3/2013 1 1 Tesla Model S 2012 123456789 Ronnie Nash 9/10/2014 1 2 Deleted Immutability History DELETE DATE: 09/02/2016
  • 65. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 数学的なデータ結合性 Journal INSERT cars ID:1 Manufacturer: Tesla Model: Model S Year: 2012 VIN: 123456789 Owner: Traci Russell Metadata: { Date:08/03/2013 } H (T1) UPDATE cars ID:1 Owner: Ronnie Nash Metadata: { Date:09/10/2014 } H(T2) DELETE cars ID:1 Metadata: { Date: 09/02/2016 } H(T3) H(T1) H(T1) + Update = H(T2) H(T2) + Update = H(T3)
  • 66. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Thank you !