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Sapporo devfesta 2019/11/13

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Sapporo devfesta 2019/11/13

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Sapporo devfesta 2019/11/13

  1. 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. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. データベース
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 23. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  24. 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. 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. 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. 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. 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. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Key Value Store Key-value
  30. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. グラフデータベース Graph
  42. 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. 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. 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. 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. 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. 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. 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. 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. 50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 時系列データベース Time-series
  51. 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. 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. 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. 54. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 台帳データベース Ledger
  55. 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. 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. 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. 58. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Block [Chain] x x x x x x
  59. 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. 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. 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. 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. 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. 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. 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. 66. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Thank you !

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