The document provides an overview of using Automatic Workload Repository (AWR) for memory analysis in an Oracle database. It discusses various memory structures like the database buffer cache, shared pool, and process memory. It outlines signs of memory issues and describes analyzing the top waits, load profile, instance efficiency, SQL areas, and other AWR report sections to identify and address performance problems related to memory configuration and usage.
Any DBA from beginner to advanced level, who wants to fill in some gaps in his/her knowledge about Performance Tuning on an Oracle Database, will benefit from this workshop.
DB Time, Average Active Sessions, and ASH Math - Oracle performance fundamentalsJohn Beresniewicz
RMOUG 2020 abstract:
This session will cover core concepts for Oracle performance analysis first introduced in Oracle 10g and forming the backbone of many features in the Diagnostic and Tuning packs. The presentation will cover the theoretical basis and meaning of these concepts, as well as illustrate how they are fundamental to many user-facing features in both the database itself and Enterprise Manager.
Any DBA from beginner to advanced level, who wants to fill in some gaps in his/her knowledge about Performance Tuning on an Oracle Database, will benefit from this workshop.
DB Time, Average Active Sessions, and ASH Math - Oracle performance fundamentalsJohn Beresniewicz
RMOUG 2020 abstract:
This session will cover core concepts for Oracle performance analysis first introduced in Oracle 10g and forming the backbone of many features in the Diagnostic and Tuning packs. The presentation will cover the theoretical basis and meaning of these concepts, as well as illustrate how they are fundamental to many user-facing features in both the database itself and Enterprise Manager.
AWR Ambiguity: Performance reasoning when the numbers don't add upJohn Beresniewicz
A close look at an AWR report where DB Time is exceeded by the sum of DB CPU and foreground wait time. We recall core Oracle performance principles and instrumentation design on the way to untangling the confusion.
Your tuning arsenal: AWR, ADDM, ASH, Metrics and AdvisorsJohn Kanagaraj
Oracle Database 10g brought in a slew of tuning and performance related tools and indeed a new way of dealing with performance issues. Even though 10g has been around for a while, many DBAs haven’t really used many of the new features, mostly because they are not well known or understood. In this Expert session, we will look past the slick demos of the new tuning and performance related tools and go “under the hood”. Using this knowledge, we will bypass the GUI and look at the views and counters that matter and quickly understand what they are saying. Tools covered include AWR, ADDM, ASH, Metrics, Tuning Advisors and their related views. Much of information about Oracle Database 10g presented in this paper has been adapted from my book and I acknowledge that with gratitude to my publisher - SAMS (Pearson).
Session aims at introducing less familiar audience to the Oracle database statistics concept, why statistics are necessary and how the Oracle Cost-Based Optimizer uses them
Oracle Database Performance Tuning Advanced Features and Best Practices for DBAsZohar Elkayam
Oracle Week 2017 slides.
Agenda:
Basics: How and What To Tune?
Using the Automatic Workload Repository (AWR)
Using AWR-Based Tools: ASH, ADDM
Real-Time Database Operation Monitoring (12c)
Identifying Problem SQL Statements
Using SQL Performance Analyzer
Tuning Memory (SGA and PGA)
Parallel Execution and Compression
Oracle Database 12c Performance New Features
This is the presentation on ASH that I did with Graham Wood at RMOUG 2014 and that represents the final best effort to capture essential and advanced ASH content as started in a presentation Uri Shaft and I gave at a small conference in Denmark sometime in 2012 perhaps. The presentation is also available publicly through the RMOUG website, so I felt at liberty to post it myself here. If it disappears it would likely be because I have been asked to remove it by Oracle.
Troubleshooting Complex Performance issues - Oracle SEG$ contentionTanel Poder
From Tanel Poder's Troubleshooting Complex Performance Issues series - an example of Oracle SEG$ internal segment contention due to some direct path insert activity.
Stop the Chaos! Get Real Oracle Performance by Query Tuning Part 1SolarWinds
In this 2 part webinar series, Janis Griffin, Database Performance Evangelist, SolarWinds, first discusses how to quickly identify the performance disruptors in the database, find which queries to focus on, and show how to examine the execution plan for costly steps.
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
Part1 of SQL Tuning Workshop - Understanding the OptimizerMaria Colgan
Part 1 of a 5 part SQL Tuning workshop, This presentation covers the history of the Oracle Optimizer and explains the first thing the Optimizer does when it receives a SQL statements, which is to transform the SQL statement in order to open up additional access paths.
AWR Ambiguity: Performance reasoning when the numbers don't add upJohn Beresniewicz
A close look at an AWR report where DB Time is exceeded by the sum of DB CPU and foreground wait time. We recall core Oracle performance principles and instrumentation design on the way to untangling the confusion.
Your tuning arsenal: AWR, ADDM, ASH, Metrics and AdvisorsJohn Kanagaraj
Oracle Database 10g brought in a slew of tuning and performance related tools and indeed a new way of dealing with performance issues. Even though 10g has been around for a while, many DBAs haven’t really used many of the new features, mostly because they are not well known or understood. In this Expert session, we will look past the slick demos of the new tuning and performance related tools and go “under the hood”. Using this knowledge, we will bypass the GUI and look at the views and counters that matter and quickly understand what they are saying. Tools covered include AWR, ADDM, ASH, Metrics, Tuning Advisors and their related views. Much of information about Oracle Database 10g presented in this paper has been adapted from my book and I acknowledge that with gratitude to my publisher - SAMS (Pearson).
Session aims at introducing less familiar audience to the Oracle database statistics concept, why statistics are necessary and how the Oracle Cost-Based Optimizer uses them
Oracle Database Performance Tuning Advanced Features and Best Practices for DBAsZohar Elkayam
Oracle Week 2017 slides.
Agenda:
Basics: How and What To Tune?
Using the Automatic Workload Repository (AWR)
Using AWR-Based Tools: ASH, ADDM
Real-Time Database Operation Monitoring (12c)
Identifying Problem SQL Statements
Using SQL Performance Analyzer
Tuning Memory (SGA and PGA)
Parallel Execution and Compression
Oracle Database 12c Performance New Features
This is the presentation on ASH that I did with Graham Wood at RMOUG 2014 and that represents the final best effort to capture essential and advanced ASH content as started in a presentation Uri Shaft and I gave at a small conference in Denmark sometime in 2012 perhaps. The presentation is also available publicly through the RMOUG website, so I felt at liberty to post it myself here. If it disappears it would likely be because I have been asked to remove it by Oracle.
Troubleshooting Complex Performance issues - Oracle SEG$ contentionTanel Poder
From Tanel Poder's Troubleshooting Complex Performance Issues series - an example of Oracle SEG$ internal segment contention due to some direct path insert activity.
Stop the Chaos! Get Real Oracle Performance by Query Tuning Part 1SolarWinds
In this 2 part webinar series, Janis Griffin, Database Performance Evangelist, SolarWinds, first discusses how to quickly identify the performance disruptors in the database, find which queries to focus on, and show how to examine the execution plan for costly steps.
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
Part1 of SQL Tuning Workshop - Understanding the OptimizerMaria Colgan
Part 1 of a 5 part SQL Tuning workshop, This presentation covers the history of the Oracle Optimizer and explains the first thing the Optimizer does when it receives a SQL statements, which is to transform the SQL statement in order to open up additional access paths.
Cultural Intelligence: Bridging the Cultural Differences in the Emerging MarketsJIANGUANGLUNG DANGMEI
In the emerging markets, cross border management has become a big challenge among the organizations. Researchers
have suggested that a high IQ and emotional intelligence may not be sufficient to successfully handle the global situations,
interaction and complexity tasks for an organization due to diversity in cultures. As organizations rely on the emerging
markets for revenue growth and expansion, they need to familiarize with different cultures and need to communicate
well with other cultures. If these cultural differences are not well managed, misunderstanding and conflict may rise in
the business across the world and organizations could be at risk if management fails to deal with the cultural difference.
Fortunately, researchers have recognized that cultural intelligence is a critical factor to overcome the challenges of cultural
differences. The realities of contemporary organizations demonstrated that cultural intelligence has vital implications for
individuals and organizations in the globalization as cultural diversities require organizations to interact with people from
a variety of backgrounds. When the cultural diversity is handled properly by incorporating cultural intelligence in the
organizations, it will be a competitive advantage for the organizations. Organizations operating in the cross border business now need to incorporate cultural intelligence to overcome the challenges of cultural differences in the emerging markets.
http://petrafisher.com Donderdag 14 juni 2012 vond in De Balie Amsterdam "LinkedIn LIVE" plaats. Een event met training in het strategisch inzetten van LinkedIn, Tips, Gastsprekers en een uitgebreide netwerk borrel.
Troubleshooting Complex Oracle Performance Problems with Tanel PoderTanel Poder
Troubleshooting Complex Oracle Performance Problems hacking session & presentation by Tanel Poder.
This presentation is about a complex performance issue where the initial symptoms pointed somewhere else than the root cause. Only when systematically following through the troubleshooting drilldown method, we get to the root cause of the problem. This session aims to help you understand (and reason about) the Oracle’s multi-process & multi-layer system behavior, preparing you for independent troubleshooting of such complex performance issues in the future.
Video recordings of this presentation are in my YouTube channel:
1) Hacking Session: https://www.youtube.com/watch?v=INQewGJMdCI
2) Presentation: https://www.youtube.com/watch?v=aaHZ8A8Ygdg
Tanel's blog and training information: https://blog.tanelpoder.com/seminar
(DAT402) Amazon RDS PostgreSQL:Lessons Learned & New FeaturesAmazon Web Services
Learn the specifics of Amazon RDS for PostgreSQL’s capabilities and extensions that make it powerful. This session begins with a brief overview of the RDS PostgreSQL service, how it provides High Availability & Durability and will then deep dive into the new features that we have released since re:Invent 2014, including major version upgrade and newly added PostgreSQL extensions to RDS PostgreSQL. During the session, we will also discuss lessons learned running a large fleet of PostgreSQL instances, including specific recommendations. In addition we will present benchmarking results looking at differences between the 9.3, 9.4 and 9.5 releases.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
10. AWR Report Header WORKLOAD REPOSITORY report for DB Name DB Id Instance Inst Num Startup Time Release RAC ------------ ----------- ------------ -------- --------------- ----------- --- AULTDB 4030696936 aultdb1 1 04-Aug-08 10:16 11.1.0.6.0 YES Host Name Platform CPUs Cores Sockets Memory(GB) ---------------- -------------------------------- ---- ----- ------- ---------- aultlinux3 Linux IA (32-bit) 2 1 1 2.97 Snap Id Snap Time Sessions Curs/Sess --------- ------------------- -------- --------- Begin Snap: 91 04-Aug-08 12:00:15 41 1.2 End Snap: 92 04-Aug-08 13:00:28 47 1.1 Elapsed: 60.22 (mins) DB Time: 139.52 (mins) Cache Sizes Begin End ~~~~~~~~~~~ ---------- ---------- Buffer Cache: 1,312M 1,312M Std Block Size: 8K Shared Pool Size: 224M 224M Log Buffer: 10,604K
11.
12. Load Profile Section Load Profile Per Second Per Transaction Per Exec Per Call ~~~~~~~~~~~~ --------------- --------------- ---------- ---------- DB Time(s): 2.3 7.1 0.63 1.05 DB CPU(s): 0.3 0.9 0.07 0.13 Redo size: 800.5 2,461.8 Logical reads: 6,307.6 19,396.7 Block changes: 3.6 10.9 Physical reads: 2,704.9 8,317.8 Physical writes: 86.9 267.3 User calls: 2.2 6.8 Parses: 2.0 6.1 Hard parses: 0.0 0.1 W/A MB processed: 932,965.4 2,868,990.9 Logons: 0.1 0.2 Executes: 3.7 11.3 Rollbacks: 0.1 0.3 Transactions: 0.3
13.
14. Load Profile Section Instance Efficiency Percentages (Target 100%) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Buffer Nowait %: 100.00 Redo NoWait %: 99.97 Buffer Hit %: 96.09 In-memory Sort %: 100.00 Library Hit %: 98.17 Soft Parse %: 97.88 Execute to Parse %: 45.80 Latch Hit %: 99.95 Parse CPU to Parse Elapsd %: 0.00 % Non-Parse CPU: 99.77 Shared Pool Statistics Begin End ------ ------ Memory Usage %: 81.53 85.39 % SQL with executions>1: 79.29 79.48 % Memory for SQL w/exec>1: 76.73 78.19
15.
16. Top 5 Waits Section With possible cache starvation Top 5 Timed Foreground Events ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Avg wait % DB Event Waits Time(s) (ms) time Wait Class ------------------------------ ------------ ----------- ------ ------ ---------- db file sequential read 465,020 3,969 9 47.4 User I/O DB CPU 995 11.9 db file parallel read 2,251 322 143 3.8 User I/O db file scattered read 15,268 153 10 1.8 User I/O gc current block 2-way 108,739 116 1 1.4 Cluster
17. Top 5 Waits Section With Shared Pool Issues Top 5 Timed Events Avg %Total ~~~~~~~~~~~~~~~~~~ wait Call Event Waits Time (s) (ms) Time Wait Class ------------------------------ ------------ ----------- ------ ------ ---------- CPU time 435,461 41.1 PX Deq Credit: send blkd 124,829,330 138,223 1 13.0 Other library cache pin 20,347 57,692 2835 5.4 Concurrenc library cache lock 19,226 56,078 2917 5.3 Concurrenc db file sequential read 16,798,329 42,215 3 4.0 User I/O ------------------------------------------------------------- Top 5 Timed Events Avg %Total ~~~~~~~~~~~~~~~~~~ wait Call Event Waits Time (s) (ms) Time Wait Class ------------------------------ ------------ ----------- ------ ------ ---------- CPU time 24,956 29.3 latch: library cache 1,757,331 9,886 6 11.6 Concurrenc db file sequential read 759,605 6,146 8 7.2 User I/O cursor: pin S 2,103,389 4,988 2 5.9 Other log file sync 250,039 2,387 10 2.8 Commit -------------------------------------------------------------
18.
19.
20.
21.
22.
23.
24. Classes Wait Class DB/Inst: Snaps: 84084-84108 -> s - second -> cs - centisecond - 100th of a second -> ms - millisecond - 1000th of a second -> us - microsecond - 1000000th of a second -> ordered by wait time desc, waits desc Avg %Time Total Wait wait Waits Wait Class Waits -outs Time (s) (ms) /txn -------------------- ---------------- ------ ---------------- ------- --------- Other 153,619,985 16.5 192,921 1 102.3 Concurrency 2,536,362 26.9 128,816 51 1.7 User I/O 30,594,385 .0 124,207 4 20.4 System I/O 5,104,873 .0 17,633 3 3.4 Application 65,645 5.0 6,508 99 0.0 Commit 267,317 .0 4,234 16 0.2 Configuration 553,825 69.5 858 2 0.4 Network 13,513,847 .0 274 0 9.0 Administrative 30 70.0 0 10 0.0 -------------------------------------------------------------
30. System Statistics user I/O wait time 12,422,069 575.6 8.3 user calls 8,038,839 372.5 5.4 user commits 1,439,821 66.7 1.0 user rollbacks 61,684 2.9 0.0 workarea executions - multipass 0 0.0 0.0 workarea executions - onepass 5,293 0.3 0.0 workarea executions - optimal 7,113,060 329.6 4.7
31. Instance Activity Statistics Instance Activity Stats - Absolute -> Statistics with absolute values (should not be diffed) Statistic Begin Value End Value -------------------------------- --------------- --------------- session cursor cache count 28,024,069 28,789,659 opened cursors current 2,921 6,982 workarea memory allocated 289,532 2,531,741 logons current 144 287 --------------------------------------------------
35. Buffer Pool Advisory Section Buffer Pool Advisory -> Only rows with estimated physical reads >0 are displayed -> ordered by Block Size, Buffers For Estimate Est Phys Size for Size Buffers for Read Estimated P Est (M) Factor Estimate Factor Physical Reads --- -------- ------ ---------------- ------ ------------------ D 5,344 .1 335,670 1.9 15,767,325,073 D 10,688 .2 671,340 1.4 11,371,357,960 … D 106,880 2.0 6,713,400 1.0 7,964,367,701 K 512 .1 32,160 102.8 3,507,100,178 K 1,024 .2 64,320 7.8 264,615,629 K 1,536 .3 96,480 1.4 49,384,590 … K 10,240 2.0 643,200 1.0 32,639,643 ----------------------------------------------
36.
37.
38.
39.
40. PGA Analysis PGA Aggr Summary DB/Inst: Snaps: 84084-84108 -> PGA cache hit % - percentage of W/A (WorkArea) data processed only in-memory PGA Cache Hit % W/A MB Processed Extra W/A MB Read/Written --------------- ------------------ -------------------------- 82.1 4,495,435 979,073 -------------------------------------------------------------
41. PGA Analysis PGA Aggr Target Stats DB/Inst: Snaps: 84084-84108 -> B: Begin snap E: End snap (rows dentified with B or E contain data which is absolute i.e. not diffed over the interval) -> Auto PGA Target - actual workarea memory target -> W/A PGA Used - amount of memory used for all Workareas (manual + auto) -> %PGA W/A Mem - percentage of PGA memory allocated to workareas -> %Auto W/A Mem - percentage of workarea memory controlled by Auto Mem Mgmt -> %Man W/A Mem - percentage of workarea memory under manual control %PGA %Auto %Man PGA Aggr Auto PGA PGA Mem W/A PGA W/A W/A W/A Global Mem Target(M) Target(M) Alloc(M) Used(M) Mem Mem Mem Bound(K) - ---------- ---------- ---------- ---------- ------ ------ ------ ---------- B 5,120 4,320 1,680.5 193.5 11.5 99.7 .3 524,280 E 5,120 4,202 4,400.5 2,219.2 50.4 99.9 .1 524,280 -------------------------------------------------------------
47. Shared Pool Advisor Shared Pool Advisory DB/Inst: Snap: 84108 -> SP: Shared Pool Est LC: Estimated Library Cache Factr: Factor -> Note there is often a 1:Many correlation between a single logical object in the Library Cache, and the physical number of memory objects associated with it. Therefore comparing the number of Lib Cache objects (e.g. in v$librarycache), with the number of Lib Cache Memory Objects is invalid. Est LC Est LC Est LC Est LC Shared SP Est LC Time Time Load Load Est LC Pool Size Size Est LC Saved Saved Time Time Mem Size(M) Factr (M) Mem Obj (s) Factr (s) Factr Obj Hits ---------- ----- -------- ------------ ------- ------ ------- ------ ----------- 2,160 .4 619 76,837 ####### 1.0 ####### 2.4 88,538,740 2,736 .5 1,188 95,749 ####### 1.0 ####### 2.1 88,936,333 3,312 .6 1,761 110,785 ####### 1.0 ####### 1.8 89,297,339 3,888 .7 2,333 125,755 ####### 1.0 ####### 1.6 89,610,155 … 8,496 1.5 6,916 238,592 ####### 1.0 ####### .5 91,076,008 9,072 1.6 7,489 252,248 ####### 1.0 ####### .4 91,187,541 9,648 1.7 8,061 264,748 ####### 1.0 ####### .3 91,291,114 10,224 1.8 8,632 274,837 ####### 1.0 ####### .3 91,388,340 10,800 1.9 9,201 284,723 ####### 1.0 ####### .2 91,480,577 11,376 2.0 9,774 293,073 ####### 1.0 ####### .2 91,569,191 -------------------------------------------------------------
48.
49.
50.
51. Streams Pool Advisor Streams Pool Advisory DB/Inst: Snap: 84108 Size for Size Est Spill Est Spill Est Unspill Est Unspill Est (MB) Factor Count Time (s) Count Time (s) ---------- --------- ----------- ----------- ----------- ----------- 16 0.5 0 0 0 0 32 1.0 0 0 0 0 48 1.5 0 0 0 0 64 2.0 0 0 0 0 80 2.5 0 0 0 0 … 240 7.5 0 0 0 0 256 8.0 0 0 0 0 272 8.5 0 0 0 0 288 9.0 0 0 0 0 304 9.5 0 0 0 0 320 10.0 0 0 0 0 -------------------------------------------------------------
52.
53. Java Pool Advisor Java Pool Advisory DB/Inst: Snap: 37 Est LC Est LC Est LC Est LC Java JP Est LC Time Time Load Load Est LC Pool Size Size Est LC Saved Saved Time Time Mem Size(M) Factr (M) Mem Obj (s) Factr (s) Factr Obj Hits ---------- ----- -------- ---------- ------- ------ ------- ------ ----------- 64 .5 10 168 10 1.0 11,974 1.0 389 128 1.0 12 201 10 1.0 11,974 1.0 465 192 1.5 12 201 10 1.0 11,974 1.0 465 256 2.0 12 201 10 1.0 11,974 1.0 465 -------------------------------------------------------------