oracle modern cloud day · 2019-11-21 · exadata cloud autonomous database maa blueprints and best...

11/18/2019

1

Oracle Modern Cloud Day

Copyright © 2019 Oracle and/or its affiliates1Copyright © 2019, Oracle and/or its affiliates. All rights reserved. |

Oracle Modern Cloud Day

Copyright © 2019 Oracle and/or its affiliates1

1

여규채 상무Technology Solution EngineeringCloud Platform

Oracle Korea

Database 19c,Converged Database & MAA on Cloud


Safe harbor statement

The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions.

The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.

11/18/2019

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Program Agenda

1

2

3

4

Oracle 19c

Converged Database

MAA on Cloud

Demo - Data Guard Setup

Summary 5


Program Agenda

1

2

3

4

Oracle 19c

Converged Database

MAA on Cloud


Summary 5

11/18/2019

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Oracle 데이터관리목표

• DB DB DB DB 소프트웨어혁신선도 ---- Oracle DatabaseOracle DatabaseOracle DatabaseOracle Database

---- Best Database for all Workloads and PlatformBest Database for all Workloads and PlatformBest Database for all Workloads and PlatformBest Database for all Workloads and Platform

• DB DB DB DB 플랫폼혁신의선도 ---- Engineered Systems Engineered Systems Engineered Systems Engineered Systems

BestBestBestBest DatabaseDatabaseDatabaseDatabase Platform (onPlatform (onPlatform (onPlatform (on----premises, Cloud)premises, Cloud)premises, Cloud)premises, Cloud)

• DB CloudDB CloudDB CloudDB Cloud혁신의선도 ---- Autonomous Database Autonomous Database Autonomous Database Autonomous Database

---- Best Database Cloud (public Cloud, Cloud at CustomerBest Database Cloud (public Cloud, Cloud at CustomerBest Database Cloud (public Cloud, Cloud at CustomerBest Database Cloud (public Cloud, Cloud at Customer)


Long Term Support 버전인 19c

• 19c는 4년간의 Premier Support(2023/03), 3년간의 Extended Support(2026/03)

• 다음 Long Term Support 버전은 22c (4년간의 PS, 2 or 3년간의 ES)

200

9

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

202

1

202

2

202

3

2024

2025

2026

2027

EXTENDED*

18c

11.2.0.4 EXTENDED

EXTENDED12.1.0.2

12.2.0.1

19c

TODAY 2026 March

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19c의혁신적신기술

Sharding

Multitenant, OnlineClone and Relocate

Autonomous HealthFramework

New In-MemoryFeatures

Online Encryption

12.2.0.3

Sharded RAC

Multitenant SnapshotCarousel

Private Temporary Tables

Mem OptimizedRow Store

T1T2T3T4

Active Directory Integration

High speed dataingestion

Active Data Guard DMLRedirect

Automatic Indexing

Hybrid Partition Tables

JSON Improvements

SALESTABLE

Q1 Q2 Q3

12c 18c 19c

• 12.2, 18c, 19c 의모든혁신적기술을포함


지난 20년간의 Oracle DB 자동화기술발전여정

• Automatic Query Rewrite• Automatic Undo

Management

• Autonomous Health Framework • Automatic Diagnostic

Framework• Automatic Refresh of Clones

• Automatic SQL Tuning • Automatic Workload Capture/Replay• Automatic SQL Plan Management• Automatic Capture of SQL Monitor• Automatic Data Optimization

• Automatic Columnar Flash• Automatic IM population• Automatic Application

Continuity

9i9i10g10g

11g11g

12c12c

18c18c

• Automatic Indexes

• SQL Quarantine

• Real-Time Statistics 19c19c

• Automatic Memory Management

• Automatic Segment Space Mgmt

• Automatic Statistics Gathering• Automatic Storage

Management• Automatic Workload

Repository• Automatic Diagnostic Monitor

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• Oracle 19c의대표적인 ML 기반의최신 Autonomous 기능• Oracle DB에서발생되는문제점발생징후를실시간으로판단하고예측하여

장애를예방하고문제가발생시빠른근원원인분석을통한문제해결또는분석데이터를제공하는도구를제공

자동화신기능 1 : Autonomous Health Framework(AHF)


Cluster/DB 성능이슈예측분석 - AHF의일부기능

Cluster와 DB의성능이슈를 ML기반모델로선제적으로발견하여리포팅함

0. 첫 RAC DB Startup 시 Enable 됨

1. 메모리로부터 DB, OS 성능데이터를수집

2. Machine Learning모델을이용한분석진행

3. DB, O/S, N/W 문제점을미리예측하여발견

4. 원인분석

5. 문제점과분석내용을 EM에발송

OS Data

Node Prognostics Engine

Alert & Preventive Action

DB Data

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Cluster/DB 성능이슈예측분석 - 1 단계 : 데이터수집

Time CPU ASM

IOPS

Network

% util

Network_

Packets

Dropped

Log

file

sync

Log file

parallel

write

GC CR

request

GC current

request

GC current

block 2-way

GC current

block busy

Enq: CF

-conte

ntion

…

15:16:00 0.90 4100 13% 0 2 ms 600 us 0 0 300 us 1.5 ms 0

• 150개이상의측정(Metric) 정보를다양한소스로부터실시간수집

OS, ASM , Network DB ( SH, AWR session, system and PDB statistics )


• 모든 Normal Operation에대한 Dynamic하게변경되는측정값을관측하여Reference Matrix 생성

0

5000

10000

15000

20000

25000

30000

35000

40000

10:00 2:00 6:00

51009025

4024

2350

4100

22050

10000

21000

4400

2500

4900

800

IOPS

user commits (/sec)

log file parallel write (usec)

log file sync (usec)

In-Memory Reference Matrix

(Part of “Normality” Model)

IOPS #### 2500 4900 800 ####

User Commits #### 10000 21000 4400 ####

Log File Parallel W

rite#### 2350 4100 22050 ####

Log File Sync #### 5100 9025 4024 ####

… … … … … …

Cluster/DB 성능이슈예측분석 - 2 단계 : 데이터수집분석

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• “Normality Model”에의해정상예측값(Predicted Values)이결정되고실시간으로수집되는관측값(Observed Values)과의차이(Residual Values)를지속적으로분석하고평가

Observed Values

(Part of a Data Point)

In-Memory Reference Matrix

(Part of “Normality” Model)

IOPS #### 2500 4800 800 ####

User Commits #### 10000 21050 4400 ####

Log File Parall

el Write#### 2350 4105 22050 ####

Log File Sync #### 5100 9020 4024 ####

… … … … … …

10500

20000

4050

10250

…

Residual Values**

(Part of a Data Point)

5600

-1000

-50

325

…

Observed -Predicted =

4900

21000

4100

9025

…

Predicted Values

(Using ESEE*)

Cluster/DB 성능이슈예측분석 - 2 단계 : 데이터수집분석

* ESEE: Expert Statue Estimation Engine

** we use an advanced form of “Sequential Probability Ratio Test (SPRT)” that support multiple distributions when evaluating the residuals.


• 문제적징후를발견하고근본원인분석

TimeTimeTimeTime CPUCPUCPUCPU ASMASMASMASMIOPSIOPSIOPSIOPS

NetworkNetworkNetworkNetwork% % % % utilutilutilutil

NetworNetworNetworNetworPacketsPacketsPacketsPacketsDroppedDroppedDroppedDropped

Log Log Log Log file file file file

syncsyncsyncsync

Log file pLog file pLog file pLog file parallelarallelarallelarallel wriwriwriwri

tetetete

GC CRGC CRGC CRGC CR rerererequestquestquestquest

GC current GC current GC current GC current requestrequestrequestrequest

GC current GC current GC current GC current block 2block 2block 2block 2----waywaywayway

GCGCGCGC current current current current blockblockblockblock busybusybusybusy

EnqEnqEnqEnq: C: C: C: CF F F F ----conconconcontentiontentiontentiontention

…………

15:16:00 0.90 4100 88% 105 2 ms 600 us 504 ms 513 ms 2 ms 5.9 ms 0

15:16:00OK OK HIGH

1

HIGH

2

OK OK HIGH

3

HIGH

3

HIGH

4

HIGH

4

OK

Fault Detection and ClassificationFault Detection and ClassificationFault Detection and ClassificationFault Detection and Classification

Diagnostic Inference

15:16:0015:16:0015:16:0015:16:00

CausesCausesCausesCauses1.1.1.1. Network Bandwidth UtilizationNetwork Bandwidth UtilizationNetwork Bandwidth UtilizationNetwork Bandwidth Utilization2.2.2.2. Network Packet LossNetwork Packet LossNetwork Packet LossNetwork Packet Loss3.3.3.3. Global Cache Requests IncompleteGlobal Cache Requests IncompleteGlobal Cache Requests IncompleteGlobal Cache Requests Incomplete4.4.4.4. Global Cache Message LatencyGlobal Cache Message LatencyGlobal Cache Message LatencyGlobal Cache Message Latency

Root CauseRoot CauseRoot CauseRoot Cause(Target of Corrective Action)(Target of Corrective Action)(Target of Corrective Action)(Target of Corrective Action)

Network Bandwidth UtilizationNetwork Bandwidth UtilizationNetwork Bandwidth UtilizationNetwork Bandwidth UtilizationDiagnosticDiagnosticDiagnosticDiagnosticInferenceInferenceInferenceInferenceEngine *Engine *Engine *Engine *

Cluster/DB 성능이슈예측분석 - 3~4 단계 : 문제점발견및원인분석

* based upon “Probabilistic Bayesian Belief Network” we determine that the underlying root cause is high Network Bandwidth Utilization

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Cluster/DB 성능이슈예측분석 - 5 단계 : Notification (EM)


Autonomous HealthFramwork - Cloud Platform

MachinesSmart Collectors

SRs

Expert Input

Feedback & Improvement

SRs

ModelGeneration

Model

KnowledgeExtraction

Applied Machine Learning

Cloud Ops

Object Store

Admin UI in Control PlaneOracle Support

Bug DB

SE UI in Support

Tenant (CNS)

Cleansing, metadata creation & clustering

5 Model generation with expert scrubbing

6

Deployed as part of cloud image, running from the start

1 Proactive regular health checking, real-time fault detection, automatic incident analysis, diagnostic collection & masking of sensitive data

2

Use real-time health dashboards for anomaly detection, root cause analysis & push of proactive, preventative & corrective actions. Auto SR creation.

3

Auto SR analysis, diagnosis assistance via automatic anomaly detection, collaboration and one click bug creation

4

MessageBroker

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Capture

Identify

VerifyDecide

Monitor

• Automatic Indexing은일반적으로사용되는수동(manual) 튜닝기법과동일한방법론이이용됨

• Index는자동으로생성되기전에후보인덱스가결정되고테스트되고평가됨

• Automatic Indexing 과정은 100% 자동으로실행됨

• 이러한영리하고복잡하게여겨지는Automatic Indexing 과정은투명하게리포트를통해 기록됨

자동화신기능 2 : Automatic Indexing


Automatic Indexing 테스트예• 튜닝전문가가관리해온복잡한 Netsuite 워크로드를 ATP에서동일하게수행하여비교

• Netsuite Workload: 17,542 SQL statements, 1,852 tables, 8,151 indexes

- Index를만들고개선하기위해수년간에걸핀노력이필요했음

• ATP로 Import하여수행하기전에모든 Index와통계정보를삭제

Achieved identical performance to expert manual tuning

Stays tuned as workload changes

ElapsedTime(sec)

Expert Autonomous

11551172

IndexesUsed

Expert

Autonomous

1733

4663

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Program Agenda

1

2

3

4

Oracle 19c

Converged Database

MAA on Cloud


Summary 5


과거부터시장트렌드기능은 Oracle DB가수용• 단일목적 DB는여러번시장에나옴

• 기술트렌드가바뀌거나 Converged DB의기능수용으로사라져감

1970s

Relational

DB

1980s

Object DBs

Converged

DB

XML DBs

Converged

DB

JSON DBs

Converged

DB

1990s 2000s 2010s 20??

Cobol

ISAM

Next DB

Solution Complexity

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Oracle Converged Database Enables Simplicity and Data Synergy

개발자가원하는것은…

• 유행하는최신개발도구와 API사용

• 모든종류의데이터에 SQL 질의사용

• 모든종류의데이터에 Transaction 실행

Structured and UnstructuredTraditional Next Generation

MicroserviceIoT, Key-Value


Oracle Converged Database Delivers Union of Best Capabilities

운영자와분석가가가원하는것은…

• 일관된질의가가능한데이터

• 신뢰성

• 확장성

• 보안성

Security Management Distributed DevelopmentAvailabilityScalabilityPlatforms

No Risk or Compromise

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Next | Blockchain tables in Oracle DB

Multi-model Enhancements• Native Native Native Native BlockchainBlockchainBlockchainBlockchain tables tables tables tables

Better Performance • JSON binary representationJSON binary representationJSON binary representationJSON binary representation

2020


IT의복잡성(Complexity) 문제해결과투자보호

• Autonomous DB와 Machine Learning 은지적이지만노동집약적인기업 IT의복잡하고느리고힘든운영및관리문제를해소하기위함

• 모든필요한기능을갖춘 Converged DB는 IT 거버넌스와시스템통합(Systems Integration)의복잡성해소하고투자를보호하기위함

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Program Agenda

1

2

3

4

Oracle 19c

Converged Database

MAA on Cloud


Summary 5


MAA Evolution: On-Premises to Cloud

On-Premises

On-Premises Exadata and Recovery Appliance

Database / Exadata Cloud

Autonomous Database

MAA Blueprints and Best Practices

On-Premise와동일한 MAA를 OCI사의VM과 Engineered Systems에적용(config practices, exachk, lowest brownouts, HA QoS, data protection)

MAA Config와 Life Cycle Operations 추가, Admin ownership을 Oracle로

이관하고 MAA SLAs 제시

26

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클라우드상에서 MAA 구성안

• 간편한 UI / CLI / REST interfaces로 MAA 구성

• 생성되는 DB는선택된 MAA 최적파라미터로설정되어배포됨

• 클라우드또는고객 D/C에서 MAA 구현- Oracle Cloud Infrastructure

- Cloud at Customer

Primary

AD

#1

StandbyA

D #

2GOLD (DR)

GOLD (DR) A

D #

1A

D #

2

PLATINUM (HA)

PLATINUM (HA)

GG replication

Primary

FSFO

FSFO

Standby

BRONZE

BRONZE

Single Instance

DB Backup Service RAC

SILVER (HA)

SILVER (HA)

DB Backup Service

27


Outage MatrixOutage MatrixOutage MatrixOutage MatrixUnplanned Outage RTO/RPO*

Recoverable node or instance failure Seconds

Disasters including corruptions and site failures

Seconds~ Minutes. RPO zero or seconds

Planned Maintenance

Most common software/hardware updates

Zero

Major database upgrade Seconds~Minutes

Primary Region Secondary Region

Cloud backup

Remote Standby

PrimaryLocal

Standby

Cloud backup

AD2 AD1

Mission Critical

Silver +• Active Data Guard

• Comprehensive Data Protection

MAA Architecture:

• Minimum one standby either across AD or region.

• ExaCC/ExaCS primary in one data center(or AD) replicated to a standby ExaCC/ExaCS in another data center

• Local backups on both sites

GOLD

* RPO=0 unless explicitly specified

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Cloud Infrastructure

Backup/Restore Options RAC ADGDB Replication across ADs/Regions

OCI (BM)Backup to OCI Object Storage (manual/automatic)Automatic backup copies acrossAvailability Domains (ADs)

✓

Across ADsAcross Regions via VCN peering

OCI (VM)(with Single or RAC)

✓* ✓

Exadata on OCI(X6/X7,X8)

✓ ✓

ExaCC(X6, X7, X8)

Backup to NFS, Local Object Storage, ZDLRA, Cloud Object Storage

✓ ✓

Across ADs and regions where available

MAA Achitecture 구현현황 on OCI

*In OCI, RAC VM node are now in separate fault domains. Existing Autonomous DB solutions are in OCI.

29


Autonomous DB - High Availability 정책

• RAC Database in a single Availability Domain(AD)

- 야간백업수행(across Ads)

• 가장일반적인 H/W Failures, S/W crashes로부터보호. 분기마다 Software Updates

• SLA 99.95% - 한달간 22 분이하의 Downtime

• 테스트와개발등의 non-mission critical 시스템에적합

* SLA excludes AD or Regional Failures, data corruptions and certain planned maintenance tasks like major upgradesDB Backup

Service

Region #1

Database Backups

Primary Database

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Autonomous DB - Extreme Availability 정책 (출시예정)

• RAC Database(HA), Active Data Guard(DR)Active Data Guard(DR)Active Data Guard(DR)Active Data Guard(DR), and Backup

• H/W Failures, S/W Crashes, Corruptions으로부터보호하고, Patching, Upgrades, Site Disaster 상황에서서비스유지

• SLA 99.99 % for Serverless, SLA 99.995 for Dedicated

- SAL 99.99% : 한달간 4분 24초이하 Downtime

- 어떠한이슈가발생하더라도업무영향이 30초이하가되도록목표

• Mission Critical 운영 DB에적합

Primary Database

Region #1, AD #1 Region #1, AD #2

Backup

Standby Database

Active

Data

Guard

31


Program Agenda

1

2

3

4

Oracle 19c

Converged Database

MAA on Cloud


Summary 5

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Program Agenda

1

2

3

4

Oracle 19c

Converged Database

MAA on Cloud


Summary 5


Summary

• Oracle Database 19c는혁신적인신기능으로최상의 DB를Cloud와 On-Premise 환경에서제공합니다.

• Oracle DB는모든 Workload를가장잘실행하며개발자, 운영자, 분석가가필요한기능과요건을포함하는 Converged 제품입니다.

• On-Premise환경에서검증되고널리사용되고있는오라클최고고가용아키텍처인 MAA는 Oracle Cloud 환경에서도동일하게제공됩니다.

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Thank you

Master Principal Sales ConsultantTechnology Solution Engineering

Qche Yeo


oracle modern cloud day · 2019-11-21 · exadata cloud autonomous database maa blueprints and best...

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