SlideShare a Scribd company logo
1 of 8
Download to read offline
System Capa Planning
2004 . 07
www.ex-em.com
연성석
System Capa Planing
• Typically 1 CPU per 200 concurrent users
• More medium speed CPUs are usually better
than fewer faster CPUs
• 10% of memory for operating system, 220 MB
for Oracle SGA and 3 MB per user process
• Disks should ideally be striped and mirrored
RAID0/RAID1 or striped with parity RAID5
System Capa Planning(cont.)
• Consider raw devices to take pressure
off your CPUs
• Consider SANs if budget permits –
SANs make heavy usage of disk cache
• Make sure that your network is
adequate – 256k for smaller site and
2Mbit+ for larger sites
System Capa Planning(cont.)
• 1GB RAM - OLTP by Oracle docs
- 200MB for operation system
- 0.8 * (1GB - 200MB) = 640MB for SGA
- 0.2 * (1GB - 200MB) = 160MB for PGA
System Capa Planning(cont.)
• Logical reads > 10,000(100MHz CPU)/sec
- usualy 100MHZ , 1 cpu당
- if 1GHZ 이면 100,000 blocks/sec 가능
• Physical reads > 100 disk/sec
• Hard, Soft Parses > 100, 300/sec
• SUN 의 filesystem 이 UFS이면 disk I/O 가 별로 안좋
다. 또한 async I/O 를 쓰면 더욱 안 좋음.
- 이때는 raw device 또는 QUICK I/O를 쓰면됨
(veritas 제품중 quick I/O 를 지원하는것도 있음)
성능 향상율은 약 30 ~ 50% 정도
System Capa Planning(cont.)
• Maximum Tolerated Oracle Kernal Event Latencies
Event
Max. tolerated latency
per event
Events-persecond
rate at
this latency
Single-block disk read (PIO) 10 ms 0.01 s 100
Single-block access from Oracle
buffer cache (LIO), 500 MHz CPU
20 μs 0.000 02 s 50,000
Single-block access from Oracle
buffer cache (LIO), 1 GHz CPU
10 μs 0.000 01 s 102,400
Single-block access from Oracle
buffer cache (LIO), 2 GHz CPU
5 μs 0.000 005 s 204,800
SQL*Net transmission via WAN 200 ms 0.2 s 5
SQL*Net transmission via LAN 15 ms 0.015 s 67
SQL*Net transmission via IPC 1 ms 0.001 s 1,000
Appendix - 시간 단위 환산
1일 day 86,400 s 24 h
1시간 h 3,600 s 60 min
1분 min 60 s
1초 s (sec)
1센티초 cs(csec) 10-2
1밀리초 ms (msec) 10-3 s 1 s / 1 000 = 0.001
1마이크로초 μs (μsec) 10-6 s 1 s / 1 000 000 = 0.000 001
1나노초 ns (nsec) 10-9 s 1 s / 1 000 000 000 = 0.000 000 001
1피코초 ps 10-12 s 1 s / 1 000 000 000 000 = 0.000 000 000 001
1펨토초 fs 10-15 s 1 s / 1 000 000 000 000 000 = 0.000 000 000 000 001
1아토초 as 10-18 s 1 s / 1 000 000 000 000 000 000 = 0.000 000 000 000 000 001
주요 Wait Event별 Root Cause
• db file sequintial/scattered reads
-> SQL by buffer gets/disk reads, file I/O stats
• CPU Time
-> Parse rates, Sorts, SQL executions, SQL by buffer gets/disk reads,
SMP processes(bugs)
• direct path read/write
-> sort/hash joins, parallel, sort/hash_area_size, file I/O stats
• buffer busy waits
-> buffer pool, buffer waits, file I/O stats, segment statistics

More Related Content

What's hot

«EC2 без страха»
«EC2 без страха»«EC2 без страха»
«EC2 без страха»Olga Lavrentieva
 
OOPs, OOMs, oh my! Containerizing JVM apps
OOPs, OOMs, oh my! Containerizing JVM appsOOPs, OOMs, oh my! Containerizing JVM apps
OOPs, OOMs, oh my! Containerizing JVM appsSematext Group, Inc.
 
Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013
Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013
Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013odnoklassniki.ru
 
Eduardo Silva - monkey http-server everywhere
Eduardo Silva - monkey http-server everywhereEduardo Silva - monkey http-server everywhere
Eduardo Silva - monkey http-server everywhereStarTech Conference
 
The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]
The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]
The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]Alluxio, Inc.
 
Some analysis of BlueStore and RocksDB
Some analysis of BlueStore and RocksDBSome analysis of BlueStore and RocksDB
Some analysis of BlueStore and RocksDBXiao Yan Li
 
Deep Visibility for Production Microservices
Deep Visibility for Production MicroservicesDeep Visibility for Production Microservices
Deep Visibility for Production MicroservicesPaul Bauer
 
System ctl file that is created at time of installation
System ctl file that is created at time of installationSystem ctl file that is created at time of installation
System ctl file that is created at time of installationAshishRanjan975320
 
Scaling IO-bound microservices
Scaling IO-bound microservicesScaling IO-bound microservices
Scaling IO-bound microservicesSalo Shp
 
Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...
Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...
Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...Ontico
 
Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)
Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)
Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)Ontico
 
Oracle cluster installation with grid and iscsi
Oracle cluster  installation with grid and iscsiOracle cluster  installation with grid and iscsi
Oracle cluster installation with grid and iscsiChanaka Lasantha
 
Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)
Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)
Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)Ontico
 
An Introduction to Priam
An Introduction to PriamAn Introduction to Priam
An Introduction to PriamJason Brown
 
Backing up thousands of containers
Backing up thousands of containersBacking up thousands of containers
Backing up thousands of containersMarian Marinov
 
Scalable Socket Server by Aryo
Scalable Socket Server by AryoScalable Socket Server by Aryo
Scalable Socket Server by AryoAgate Studio
 

What's hot (20)

T.Pollak y C.Yaconi - Prey
T.Pollak y C.Yaconi - PreyT.Pollak y C.Yaconi - Prey
T.Pollak y C.Yaconi - Prey
 
«EC2 без страха»
«EC2 без страха»«EC2 без страха»
«EC2 без страха»
 
OOPs, OOMs, oh my! Containerizing JVM apps
OOPs, OOMs, oh my! Containerizing JVM appsOOPs, OOMs, oh my! Containerizing JVM apps
OOPs, OOMs, oh my! Containerizing JVM apps
 
Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013
Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013
Being closer to Cassandra by Oleg Anastasyev. Talk at Cassandra Summit EU 2013
 
Puppet
PuppetPuppet
Puppet
 
Eduardo Silva - monkey http-server everywhere
Eduardo Silva - monkey http-server everywhereEduardo Silva - monkey http-server everywhere
Eduardo Silva - monkey http-server everywhere
 
The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]
The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]
The Practice of Alluxio in Near Real-Time Data Platform at VIPShop [Chinese]
 
Some analysis of BlueStore and RocksDB
Some analysis of BlueStore and RocksDBSome analysis of BlueStore and RocksDB
Some analysis of BlueStore and RocksDB
 
Deep Visibility for Production Microservices
Deep Visibility for Production MicroservicesDeep Visibility for Production Microservices
Deep Visibility for Production Microservices
 
Alluxio in MOMO
Alluxio in MOMOAlluxio in MOMO
Alluxio in MOMO
 
System ctl file that is created at time of installation
System ctl file that is created at time of installationSystem ctl file that is created at time of installation
System ctl file that is created at time of installation
 
Scaling IO-bound microservices
Scaling IO-bound microservicesScaling IO-bound microservices
Scaling IO-bound microservices
 
Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...
Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...
Tarantool как платформа для микросервисов / Антон Резников, Владимир Перепели...
 
Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)
Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)
Масштабируемая конфигурация Nginx, Игорь Сысоев (Nginx)
 
Haproxy - zastosowania
Haproxy - zastosowaniaHaproxy - zastosowania
Haproxy - zastosowania
 
Oracle cluster installation with grid and iscsi
Oracle cluster  installation with grid and iscsiOracle cluster  installation with grid and iscsi
Oracle cluster installation with grid and iscsi
 
Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)
Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)
Путь мониторинга 2.0 всё стало другим / Всеволод Поляков (Grammarly)
 
An Introduction to Priam
An Introduction to PriamAn Introduction to Priam
An Introduction to Priam
 
Backing up thousands of containers
Backing up thousands of containersBacking up thousands of containers
Backing up thousands of containers
 
Scalable Socket Server by Aryo
Scalable Socket Server by AryoScalable Socket Server by Aryo
Scalable Socket Server by Aryo
 

Viewers also liked (14)

Paperworld 2010 And Christmasworld 2010 : Messe Frankfurt
Paperworld 2010 And Christmasworld 2010 : Messe FrankfurtPaperworld 2010 And Christmasworld 2010 : Messe Frankfurt
Paperworld 2010 And Christmasworld 2010 : Messe Frankfurt
 
Otzyv semenov burmistrov
Otzyv semenov burmistrovOtzyv semenov burmistrov
Otzyv semenov burmistrov
 
resume (3) (2)
resume (3) (2)resume (3) (2)
resume (3) (2)
 
coco fresh
coco freshcoco fresh
coco fresh
 
Choice & Control in NDIS Literature Review August 2015
Choice & Control in NDIS Literature Review August 2015Choice & Control in NDIS Literature Review August 2015
Choice & Control in NDIS Literature Review August 2015
 
Etimología
EtimologíaEtimología
Etimología
 
Commercial Banking KYC, Trade-Based Money-Laundering, Sanctions & Fraud Controls
Commercial Banking KYC, Trade-Based Money-Laundering, Sanctions & Fraud ControlsCommercial Banking KYC, Trade-Based Money-Laundering, Sanctions & Fraud Controls
Commercial Banking KYC, Trade-Based Money-Laundering, Sanctions & Fraud Controls
 
Otzyv kvasnikova lukashenko
Otzyv kvasnikova lukashenkoOtzyv kvasnikova lukashenko
Otzyv kvasnikova lukashenko
 
Glosario
GlosarioGlosario
Glosario
 
Kovalenko androschuk
Kovalenko androschukKovalenko androschuk
Kovalenko androschuk
 
Vidguk golub
Vidguk golubVidguk golub
Vidguk golub
 
Introducción al desarrollo sustentable
Introducción al desarrollo sustentableIntroducción al desarrollo sustentable
Introducción al desarrollo sustentable
 
Diss nagi last
Diss nagi lastDiss nagi last
Diss nagi last
 
Análisis y evaluacion de puestos 3
Análisis y evaluacion de puestos 3Análisis y evaluacion de puestos 3
Análisis y evaluacion de puestos 3
 

Similar to System Capa Planning_DBA oracle edu

Nick Fisk - low latency Ceph
Nick Fisk - low latency CephNick Fisk - low latency Ceph
Nick Fisk - low latency CephShapeBlue
 
Best Practices with PostgreSQL on Solaris
Best Practices with PostgreSQL on SolarisBest Practices with PostgreSQL on Solaris
Best Practices with PostgreSQL on SolarisJignesh Shah
 
Exadata and OLTP
Exadata and OLTPExadata and OLTP
Exadata and OLTPEnkitec
 
Awrrpt 1 3004_3005
Awrrpt 1 3004_3005Awrrpt 1 3004_3005
Awrrpt 1 3004_3005Kam Chan
 
Shak larry-jeder-perf-and-tuning-summit14-part2-final
Shak larry-jeder-perf-and-tuning-summit14-part2-finalShak larry-jeder-perf-and-tuning-summit14-part2-final
Shak larry-jeder-perf-and-tuning-summit14-part2-finalTommy Lee
 
Oow2007 performance
Oow2007 performanceOow2007 performance
Oow2007 performanceRicky Zhu
 
Deploying ssd in the data center 2014
Deploying ssd in the data center 2014Deploying ssd in the data center 2014
Deploying ssd in the data center 2014Howard Marks
 
Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)
Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)
Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)Anne Nicolas
 
Oracle Performance On Linux X86 systems
Oracle  Performance On Linux  X86 systems Oracle  Performance On Linux  X86 systems
Oracle Performance On Linux X86 systems Baruch Osoveskiy
 
Instaclustr introduction to managing cassandra
Instaclustr introduction to managing cassandraInstaclustr introduction to managing cassandra
Instaclustr introduction to managing cassandraInstaclustr
 
Sun Oracle Exadata V2 For OLTP And DWH
Sun Oracle Exadata V2 For OLTP And DWHSun Oracle Exadata V2 For OLTP And DWH
Sun Oracle Exadata V2 For OLTP And DWHMark Rabne
 
Your 1st Ceph cluster
Your 1st Ceph clusterYour 1st Ceph cluster
Your 1st Ceph clusterMirantis
 
Andy Parsons Pivotal June 2011
Andy Parsons Pivotal June 2011Andy Parsons Pivotal June 2011
Andy Parsons Pivotal June 2011Andy Parsons
 

Similar to System Capa Planning_DBA oracle edu (20)

Nick Fisk - low latency Ceph
Nick Fisk - low latency CephNick Fisk - low latency Ceph
Nick Fisk - low latency Ceph
 
Best Practices with PostgreSQL on Solaris
Best Practices with PostgreSQL on SolarisBest Practices with PostgreSQL on Solaris
Best Practices with PostgreSQL on Solaris
 
LUG 2014
LUG 2014LUG 2014
LUG 2014
 
Exadata and OLTP
Exadata and OLTPExadata and OLTP
Exadata and OLTP
 
NFS and Oracle
NFS and OracleNFS and Oracle
NFS and Oracle
 
ceph-barcelona-v-1.2
ceph-barcelona-v-1.2ceph-barcelona-v-1.2
ceph-barcelona-v-1.2
 
Ceph barcelona-v-1.2
Ceph barcelona-v-1.2Ceph barcelona-v-1.2
Ceph barcelona-v-1.2
 
Awrrpt 1 3004_3005
Awrrpt 1 3004_3005Awrrpt 1 3004_3005
Awrrpt 1 3004_3005
 
Shak larry-jeder-perf-and-tuning-summit14-part2-final
Shak larry-jeder-perf-and-tuning-summit14-part2-finalShak larry-jeder-perf-and-tuning-summit14-part2-final
Shak larry-jeder-perf-and-tuning-summit14-part2-final
 
Oow2007 performance
Oow2007 performanceOow2007 performance
Oow2007 performance
 
Deploying ssd in the data center 2014
Deploying ssd in the data center 2014Deploying ssd in the data center 2014
Deploying ssd in the data center 2014
 
Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)
Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)
Kernel Recipes 2016 - Understanding a Real-Time System (more than just a kernel)
 
Oracle Performance On Linux X86 systems
Oracle  Performance On Linux  X86 systems Oracle  Performance On Linux  X86 systems
Oracle Performance On Linux X86 systems
 
Tacc Infinite Memory Engine
Tacc Infinite Memory EngineTacc Infinite Memory Engine
Tacc Infinite Memory Engine
 
Instaclustr introduction to managing cassandra
Instaclustr introduction to managing cassandraInstaclustr introduction to managing cassandra
Instaclustr introduction to managing cassandra
 
Sun Oracle Exadata V2 For OLTP And DWH
Sun Oracle Exadata V2 For OLTP And DWHSun Oracle Exadata V2 For OLTP And DWH
Sun Oracle Exadata V2 For OLTP And DWH
 
Your 1st Ceph cluster
Your 1st Ceph clusterYour 1st Ceph cluster
Your 1st Ceph cluster
 
IO Dubi Lebel
IO Dubi LebelIO Dubi Lebel
IO Dubi Lebel
 
Corralling Big Data at TACC
Corralling Big Data at TACCCorralling Big Data at TACC
Corralling Big Data at TACC
 
Andy Parsons Pivotal June 2011
Andy Parsons Pivotal June 2011Andy Parsons Pivotal June 2011
Andy Parsons Pivotal June 2011
 

More from 엑셈

WAS의 동작과 WEB, Servlet, JSP_Wh apm
WAS의 동작과 WEB, Servlet, JSP_Wh apmWAS의 동작과 WEB, Servlet, JSP_Wh apm
WAS의 동작과 WEB, Servlet, JSP_Wh apm엑셈
 
웹 서버의 기능 및 역할_Wh apm
웹 서버의 기능 및 역할_Wh apm웹 서버의 기능 및 역할_Wh apm
웹 서버의 기능 및 역할_Wh apm엑셈
 
TP-Monitor_Wh apm
TP-Monitor_Wh apmTP-Monitor_Wh apm
TP-Monitor_Wh apm엑셈
 
TCP 연결 과정_Wh apm
TCP 연결 과정_Wh apmTCP 연결 과정_Wh apm
TCP 연결 과정_Wh apm엑셈
 
스위치의 분류 및 역할_Wh apm
스위치의 분류 및 역할_Wh apm스위치의 분류 및 역할_Wh apm
스위치의 분류 및 역할_Wh apm엑셈
 
Runtime Data Areas_Wh apm
Runtime Data Areas_Wh apmRuntime Data Areas_Wh apm
Runtime Data Areas_Wh apm엑셈
 
네트워크 기반 통신 및 계층 구조_Wh apm
네트워크 기반 통신 및 계층 구조_Wh apm네트워크 기반 통신 및 계층 구조_Wh apm
네트워크 기반 통신 및 계층 구조_Wh apm엑셈
 
JVM Synchronization_Wh apm
JVM Synchronization_Wh apmJVM Synchronization_Wh apm
JVM Synchronization_Wh apm엑셈
 
IBM JVM GC_Wh apm
IBM JVM GC_Wh apmIBM JVM GC_Wh apm
IBM JVM GC_Wh apm엑셈
 
Hotspot JVM GC_Wh apm
Hotspot JVM GC_Wh apmHotspot JVM GC_Wh apm
Hotspot JVM GC_Wh apm엑셈
 
Class Loader_Wh apm
Class Loader_Wh apmClass Loader_Wh apm
Class Loader_Wh apm엑셈
 
All about JDBC Performance Tuning_Wh apm
All about JDBC Performance Tuning_Wh apmAll about JDBC Performance Tuning_Wh apm
All about JDBC Performance Tuning_Wh apm엑셈
 
WINDOW FUNCTION의 이해와 활용방법_Wh oracle
WINDOW FUNCTION의 이해와 활용방법_Wh oracleWINDOW FUNCTION의 이해와 활용방법_Wh oracle
WINDOW FUNCTION의 이해와 활용방법_Wh oracle엑셈
 
TABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracle
TABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracleTABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracle
TABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracle엑셈
 
SSD 개념 및 활용_Wh oracle
SSD 개념 및 활용_Wh oracleSSD 개념 및 활용_Wh oracle
SSD 개념 및 활용_Wh oracle엑셈
 
SQL Profile을 이용한 SQL Plan 변경_Wh oracle
SQL Profile을 이용한 SQL Plan 변경_Wh oracleSQL Profile을 이용한 SQL Plan 변경_Wh oracle
SQL Profile을 이용한 SQL Plan 변경_Wh oracle엑셈
 
SQL PlAN MANAGEMENT 활용_Wh oracle
SQL PlAN MANAGEMENT 활용_Wh oracleSQL PlAN MANAGEMENT 활용_Wh oracle
SQL PlAN MANAGEMENT 활용_Wh oracle엑셈
 
SQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracle
SQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracleSQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracle
SQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracle엑셈
 
SPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracle
SPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracleSPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracle
SPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracle엑셈
 
Result Cache 동작원리 및 활용방안_Wh oracle
Result Cache 동작원리 및 활용방안_Wh oracleResult Cache 동작원리 및 활용방안_Wh oracle
Result Cache 동작원리 및 활용방안_Wh oracle엑셈
 

More from 엑셈 (20)

WAS의 동작과 WEB, Servlet, JSP_Wh apm
WAS의 동작과 WEB, Servlet, JSP_Wh apmWAS의 동작과 WEB, Servlet, JSP_Wh apm
WAS의 동작과 WEB, Servlet, JSP_Wh apm
 
웹 서버의 기능 및 역할_Wh apm
웹 서버의 기능 및 역할_Wh apm웹 서버의 기능 및 역할_Wh apm
웹 서버의 기능 및 역할_Wh apm
 
TP-Monitor_Wh apm
TP-Monitor_Wh apmTP-Monitor_Wh apm
TP-Monitor_Wh apm
 
TCP 연결 과정_Wh apm
TCP 연결 과정_Wh apmTCP 연결 과정_Wh apm
TCP 연결 과정_Wh apm
 
스위치의 분류 및 역할_Wh apm
스위치의 분류 및 역할_Wh apm스위치의 분류 및 역할_Wh apm
스위치의 분류 및 역할_Wh apm
 
Runtime Data Areas_Wh apm
Runtime Data Areas_Wh apmRuntime Data Areas_Wh apm
Runtime Data Areas_Wh apm
 
네트워크 기반 통신 및 계층 구조_Wh apm
네트워크 기반 통신 및 계층 구조_Wh apm네트워크 기반 통신 및 계층 구조_Wh apm
네트워크 기반 통신 및 계층 구조_Wh apm
 
JVM Synchronization_Wh apm
JVM Synchronization_Wh apmJVM Synchronization_Wh apm
JVM Synchronization_Wh apm
 
IBM JVM GC_Wh apm
IBM JVM GC_Wh apmIBM JVM GC_Wh apm
IBM JVM GC_Wh apm
 
Hotspot JVM GC_Wh apm
Hotspot JVM GC_Wh apmHotspot JVM GC_Wh apm
Hotspot JVM GC_Wh apm
 
Class Loader_Wh apm
Class Loader_Wh apmClass Loader_Wh apm
Class Loader_Wh apm
 
All about JDBC Performance Tuning_Wh apm
All about JDBC Performance Tuning_Wh apmAll about JDBC Performance Tuning_Wh apm
All about JDBC Performance Tuning_Wh apm
 
WINDOW FUNCTION의 이해와 활용방법_Wh oracle
WINDOW FUNCTION의 이해와 활용방법_Wh oracleWINDOW FUNCTION의 이해와 활용방법_Wh oracle
WINDOW FUNCTION의 이해와 활용방법_Wh oracle
 
TABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracle
TABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracleTABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracle
TABLE ACCESS 패턴을 이용한 SQL 튜닝_Wh oracle
 
SSD 개념 및 활용_Wh oracle
SSD 개념 및 활용_Wh oracleSSD 개념 및 활용_Wh oracle
SSD 개념 및 활용_Wh oracle
 
SQL Profile을 이용한 SQL Plan 변경_Wh oracle
SQL Profile을 이용한 SQL Plan 변경_Wh oracleSQL Profile을 이용한 SQL Plan 변경_Wh oracle
SQL Profile을 이용한 SQL Plan 변경_Wh oracle
 
SQL PlAN MANAGEMENT 활용_Wh oracle
SQL PlAN MANAGEMENT 활용_Wh oracleSQL PlAN MANAGEMENT 활용_Wh oracle
SQL PlAN MANAGEMENT 활용_Wh oracle
 
SQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracle
SQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracleSQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracle
SQL 튜닝에 Dictionary View 활용하기 Part2_Wh oracle
 
SPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracle
SPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracleSPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracle
SPA(SQL Performance Analyze)를 이용한 통계 정보 수집_Wh oracle
 
Result Cache 동작원리 및 활용방안_Wh oracle
Result Cache 동작원리 및 활용방안_Wh oracleResult Cache 동작원리 및 활용방안_Wh oracle
Result Cache 동작원리 및 활용방안_Wh oracle
 

Recently uploaded

ICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdfICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdfVanessa Camilleri
 
Textual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHSTextual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHSMae Pangan
 
4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptx4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptxmary850239
 
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxQ4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxlancelewisportillo
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management SystemChristalin Nelson
 
Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Seán Kennedy
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17Celine George
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Celine George
 
TEACHER REFLECTION FORM (NEW SET........).docx
TEACHER REFLECTION FORM (NEW SET........).docxTEACHER REFLECTION FORM (NEW SET........).docx
TEACHER REFLECTION FORM (NEW SET........).docxruthvilladarez
 
4.18.24 Movement Legacies, Reflection, and Review.pptx
4.18.24 Movement Legacies, Reflection, and Review.pptx4.18.24 Movement Legacies, Reflection, and Review.pptx
4.18.24 Movement Legacies, Reflection, and Review.pptxmary850239
 
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...Postal Advocate Inc.
 
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONTHEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONHumphrey A Beña
 
Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4JOYLYNSAMANIEGO
 
Presentation Activity 2. Unit 3 transv.pptx
Presentation Activity 2. Unit 3 transv.pptxPresentation Activity 2. Unit 3 transv.pptx
Presentation Activity 2. Unit 3 transv.pptxRosabel UA
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)lakshayb543
 
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Celine George
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPCeline George
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Mark Reed
 

Recently uploaded (20)

ICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdfICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdf
 
Textual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHSTextual Evidence in Reading and Writing of SHS
Textual Evidence in Reading and Writing of SHS
 
4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptx4.16.24 21st Century Movements for Black Lives.pptx
4.16.24 21st Century Movements for Black Lives.pptx
 
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptxQ4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
Q4-PPT-Music9_Lesson-1-Romantic-Opera.pptx
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management System
 
Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...Student Profile Sample - We help schools to connect the data they have, with ...
Student Profile Sample - We help schools to connect the data they have, with ...
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
 
TEACHER REFLECTION FORM (NEW SET........).docx
TEACHER REFLECTION FORM (NEW SET........).docxTEACHER REFLECTION FORM (NEW SET........).docx
TEACHER REFLECTION FORM (NEW SET........).docx
 
4.18.24 Movement Legacies, Reflection, and Review.pptx
4.18.24 Movement Legacies, Reflection, and Review.pptx4.18.24 Movement Legacies, Reflection, and Review.pptx
4.18.24 Movement Legacies, Reflection, and Review.pptx
 
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
 
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONTHEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
 
Paradigm shift in nursing research by RS MEHTA
Paradigm shift in nursing research by RS MEHTAParadigm shift in nursing research by RS MEHTA
Paradigm shift in nursing research by RS MEHTA
 
Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4Daily Lesson Plan in Mathematics Quarter 4
Daily Lesson Plan in Mathematics Quarter 4
 
Presentation Activity 2. Unit 3 transv.pptx
Presentation Activity 2. Unit 3 transv.pptxPresentation Activity 2. Unit 3 transv.pptx
Presentation Activity 2. Unit 3 transv.pptx
 
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptxFINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
 
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERP
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)
 

System Capa Planning_DBA oracle edu

  • 1. System Capa Planning 2004 . 07 www.ex-em.com 연성석
  • 2. System Capa Planing • Typically 1 CPU per 200 concurrent users • More medium speed CPUs are usually better than fewer faster CPUs • 10% of memory for operating system, 220 MB for Oracle SGA and 3 MB per user process • Disks should ideally be striped and mirrored RAID0/RAID1 or striped with parity RAID5
  • 3. System Capa Planning(cont.) • Consider raw devices to take pressure off your CPUs • Consider SANs if budget permits – SANs make heavy usage of disk cache • Make sure that your network is adequate – 256k for smaller site and 2Mbit+ for larger sites
  • 4. System Capa Planning(cont.) • 1GB RAM - OLTP by Oracle docs - 200MB for operation system - 0.8 * (1GB - 200MB) = 640MB for SGA - 0.2 * (1GB - 200MB) = 160MB for PGA
  • 5. System Capa Planning(cont.) • Logical reads > 10,000(100MHz CPU)/sec - usualy 100MHZ , 1 cpu당 - if 1GHZ 이면 100,000 blocks/sec 가능 • Physical reads > 100 disk/sec • Hard, Soft Parses > 100, 300/sec • SUN 의 filesystem 이 UFS이면 disk I/O 가 별로 안좋 다. 또한 async I/O 를 쓰면 더욱 안 좋음. - 이때는 raw device 또는 QUICK I/O를 쓰면됨 (veritas 제품중 quick I/O 를 지원하는것도 있음) 성능 향상율은 약 30 ~ 50% 정도
  • 6. System Capa Planning(cont.) • Maximum Tolerated Oracle Kernal Event Latencies Event Max. tolerated latency per event Events-persecond rate at this latency Single-block disk read (PIO) 10 ms 0.01 s 100 Single-block access from Oracle buffer cache (LIO), 500 MHz CPU 20 μs 0.000 02 s 50,000 Single-block access from Oracle buffer cache (LIO), 1 GHz CPU 10 μs 0.000 01 s 102,400 Single-block access from Oracle buffer cache (LIO), 2 GHz CPU 5 μs 0.000 005 s 204,800 SQL*Net transmission via WAN 200 ms 0.2 s 5 SQL*Net transmission via LAN 15 ms 0.015 s 67 SQL*Net transmission via IPC 1 ms 0.001 s 1,000
  • 7. Appendix - 시간 단위 환산 1일 day 86,400 s 24 h 1시간 h 3,600 s 60 min 1분 min 60 s 1초 s (sec) 1센티초 cs(csec) 10-2 1밀리초 ms (msec) 10-3 s 1 s / 1 000 = 0.001 1마이크로초 μs (μsec) 10-6 s 1 s / 1 000 000 = 0.000 001 1나노초 ns (nsec) 10-9 s 1 s / 1 000 000 000 = 0.000 000 001 1피코초 ps 10-12 s 1 s / 1 000 000 000 000 = 0.000 000 000 001 1펨토초 fs 10-15 s 1 s / 1 000 000 000 000 000 = 0.000 000 000 000 001 1아토초 as 10-18 s 1 s / 1 000 000 000 000 000 000 = 0.000 000 000 000 000 001
  • 8. 주요 Wait Event별 Root Cause • db file sequintial/scattered reads -> SQL by buffer gets/disk reads, file I/O stats • CPU Time -> Parse rates, Sorts, SQL executions, SQL by buffer gets/disk reads, SMP processes(bugs) • direct path read/write -> sort/hash joins, parallel, sort/hash_area_size, file I/O stats • buffer busy waits -> buffer pool, buffer waits, file I/O stats, segment statistics