Oracle 12c Release 2 introduces new Oracle Autonomous Health Framework, a collaborative framework whose components work 24x7 to autonomously to ensure continuous availability and consistent performance of database systems while minimizing human effort!
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUGSandesh Rao
This session will focus on 15 troubleshooting tips and tricks for DBA’s covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUGSandesh Rao
This session will focus on 15 troubleshooting tips and tricks for DBA’s covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
ORACLE DBA Online Training by Keylabstraining is a well defined course and trained by our Well Qualified Oracle DBA Professionals all over the World. Our course content designed as per the current IT industry requirement.
Course Content : http://www.keylabstraining.com/oracle/oracle-dba-online-training-hyderabad-bangalore
For more information email us : info@keylabstraining.com
Introduction to Real Application Cluster
RAC - Savior of DBA
Oracle Clusterware (Platform on Platform)
RAC Startup sequence
RAC Architecture
RAC Components
Single Instance on RAC
Node Eviction
Important Log directories in RAC.
Tips to monitor and improve the RAC environment.
Expert performance tuning tips for Oracle RACSolarWinds
In Oracle RAC 12c here have been significant enhancements to scalability and high availability, with features such as Flex Clusters, Flex ASM, Application Continuity and Transaction Guard, to name just a few. Learn how to make the most of these features, including:
*Operational support enhancements to SRVCTL
*CRSCTL commands
*ADR support for Grid Infrastructure
*Enterprise Manager
*Other support tools such Orachk and TFA analyzer
Oracle Real Application Clusters 19c- Best Practices and Internals- EMEA Tour...Sandesh Rao
In this session, I will cover under-the-hood features that power Oracle Real Application Clusters (Oracle RAC) 19c specifically around Cache Fusion and Service management. Improvements in Oracle RAC helps in integration with features such as Multitenant and Data Guard. In fact, these features benefit immensely when used with Oracle RAC. Finally we will talk about changes to the broader Oracle RAC Family of Products stack and the algorithmic changes that helps quickly detect sick/dead nodes/instances and the reconfiguration improvements to ensure that the Oracle RAC Databases continue to function without any disruption
Oracle Data Guard ensures high availability, disaster recovery and data protection for enterprise data. This enable production Oracle databases to survive disasters and data corruptions. Oracle 18c and 19c offers many new features it will bring many advantages to organization.
Using Machine Learning to Debug Oracle RAC IssuesAnil Nair
This deck was used at UKOUG 2018 to explain how Oracle Real Application Clusters (RAC) uses Machine Learning to make the job of Database Administrators easier.
The Machine Learning behind the Autonomous Database ILOUG Feb 2020 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. Some of the other use cases is to use convolution filters...
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...Sandesh Rao
Oracle Autonomous Health Service is a new feature in Oracle Database 19c that supports centralized diagnostic monitoring of your on-premise Oracle RAC database clusters. In this session learn how to free up local cluster resources and improve monitoring efficiency with this centralized service-based solution. Practical tips and best practices for setup are covered along with a demonstration of its responsive browser-based dashboard and triage console. Specific Oracle RAC database, Oracle Automatic Storage Management, and cluster issues are rapidly triaged utilizing the built-in support for Trace File Analyzer and ORAchk/EXAchk collections, Cluster Health Advisor problem diagnosis, Hang Manager and Memory Guard events, and QoS management workload performance metrics.
Smart monitoring how does oracle rac manage resource, state ukoug19Anil Nair
An important requirement for HA and to provide scalability is to detect problems and resolve them quickly before the user sessions get affected. Oracle RAC along with its Family of Solutions work together cohesively to detect conditions such as "Un-responsive Instances", Network issues quickly and resolve them by either redirecting the work to other instances or redundant network paths
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
Oracle DataGuard Online Training in USA | INDIAXoom Trainings
Xoom Trainings providing Best Oracle DataGuard Online Training with complete tutorial by 10 years experienced professionals worldwide
For More online training Demo Please Reach the below link:
https://www.youtube.com/watch?v=2zXZPh4agwE
For More Information please follow the below link:
http://www.xoomtrainings.com/course/oracle-dataguard
For General Queries Email us at sales@xoomtrainings.com or +1-610-686-8077
Oracle RAC 12c Practical Performance Management and Tuning as presented during Oracle Open World 2013 with Michael Zoll.
This is part three of the Oracle RAC 12c "reindeer series" used for OOW13 Oracle RAC-related presentations.
This part concludes the main part of the "reindeer series" except for one bonus track "Oracle Multitenant meets Oracle RAC 12c" (available via SlidesShare, too).
Proposal for adding Events feature in LinkedIn ElevateAnkita Khandelwal
This proposal is to include Events feature within LinkedIn Elevate platform to make it easy for employees to find relevant company events, share them with other employees, and for event organizers to maximize their reach and connect with their audience.
ORACLE DBA Online Training by Keylabstraining is a well defined course and trained by our Well Qualified Oracle DBA Professionals all over the World. Our course content designed as per the current IT industry requirement.
Course Content : http://www.keylabstraining.com/oracle/oracle-dba-online-training-hyderabad-bangalore
For more information email us : info@keylabstraining.com
Introduction to Real Application Cluster
RAC - Savior of DBA
Oracle Clusterware (Platform on Platform)
RAC Startup sequence
RAC Architecture
RAC Components
Single Instance on RAC
Node Eviction
Important Log directories in RAC.
Tips to monitor and improve the RAC environment.
Expert performance tuning tips for Oracle RACSolarWinds
In Oracle RAC 12c here have been significant enhancements to scalability and high availability, with features such as Flex Clusters, Flex ASM, Application Continuity and Transaction Guard, to name just a few. Learn how to make the most of these features, including:
*Operational support enhancements to SRVCTL
*CRSCTL commands
*ADR support for Grid Infrastructure
*Enterprise Manager
*Other support tools such Orachk and TFA analyzer
Oracle Real Application Clusters 19c- Best Practices and Internals- EMEA Tour...Sandesh Rao
In this session, I will cover under-the-hood features that power Oracle Real Application Clusters (Oracle RAC) 19c specifically around Cache Fusion and Service management. Improvements in Oracle RAC helps in integration with features such as Multitenant and Data Guard. In fact, these features benefit immensely when used with Oracle RAC. Finally we will talk about changes to the broader Oracle RAC Family of Products stack and the algorithmic changes that helps quickly detect sick/dead nodes/instances and the reconfiguration improvements to ensure that the Oracle RAC Databases continue to function without any disruption
Oracle Data Guard ensures high availability, disaster recovery and data protection for enterprise data. This enable production Oracle databases to survive disasters and data corruptions. Oracle 18c and 19c offers many new features it will bring many advantages to organization.
Using Machine Learning to Debug Oracle RAC IssuesAnil Nair
This deck was used at UKOUG 2018 to explain how Oracle Real Application Clusters (RAC) uses Machine Learning to make the job of Database Administrators easier.
The Machine Learning behind the Autonomous Database ILOUG Feb 2020 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. Some of the other use cases is to use convolution filters...
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...Sandesh Rao
Oracle Autonomous Health Service is a new feature in Oracle Database 19c that supports centralized diagnostic monitoring of your on-premise Oracle RAC database clusters. In this session learn how to free up local cluster resources and improve monitoring efficiency with this centralized service-based solution. Practical tips and best practices for setup are covered along with a demonstration of its responsive browser-based dashboard and triage console. Specific Oracle RAC database, Oracle Automatic Storage Management, and cluster issues are rapidly triaged utilizing the built-in support for Trace File Analyzer and ORAchk/EXAchk collections, Cluster Health Advisor problem diagnosis, Hang Manager and Memory Guard events, and QoS management workload performance metrics.
Smart monitoring how does oracle rac manage resource, state ukoug19Anil Nair
An important requirement for HA and to provide scalability is to detect problems and resolve them quickly before the user sessions get affected. Oracle RAC along with its Family of Solutions work together cohesively to detect conditions such as "Un-responsive Instances", Network issues quickly and resolve them by either redirecting the work to other instances or redundant network paths
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
Oracle DataGuard Online Training in USA | INDIAXoom Trainings
Xoom Trainings providing Best Oracle DataGuard Online Training with complete tutorial by 10 years experienced professionals worldwide
For More online training Demo Please Reach the below link:
https://www.youtube.com/watch?v=2zXZPh4agwE
For More Information please follow the below link:
http://www.xoomtrainings.com/course/oracle-dataguard
For General Queries Email us at sales@xoomtrainings.com or +1-610-686-8077
Oracle RAC 12c Practical Performance Management and Tuning as presented during Oracle Open World 2013 with Michael Zoll.
This is part three of the Oracle RAC 12c "reindeer series" used for OOW13 Oracle RAC-related presentations.
This part concludes the main part of the "reindeer series" except for one bonus track "Oracle Multitenant meets Oracle RAC 12c" (available via SlidesShare, too).
Proposal for adding Events feature in LinkedIn ElevateAnkita Khandelwal
This proposal is to include Events feature within LinkedIn Elevate platform to make it easy for employees to find relevant company events, share them with other employees, and for event organizers to maximize their reach and connect with their audience.
El periódico Diálogos es una publicación bimestral de distribución gratuita, realizada por los alumnos de la Escuela de Periodismo Carlos Septién García, con la coordinación del Departamento Editorial de la institución.
Is great content enough? Here are 7 ways to help your work get noticed by the curators of the Web. Presented at WebVisions Portland, 2012. Slidedeck designed by David Crandall.
Food safety along informal pork market chains in Vietnam: Experience from an ...ILRI
Presented by Fred Unger, Hung Nguyen-Viet, Lucy Lapar, Karen Marshall and Delia Grace at the Neglected Tropical Diseases (NTD) Asia 2016 conference, Khon Kaen, Thailand, 14–15 January 2016.
Food safety from a global perspective to a country perspective addressing cha...ILRI
Presented by Fred Unger, Hung Nguyen-Viet, Sinh Dang-Xuan, Phuc Pham Duc, Pham Van Hung, Lucila Lapar, Karen Marshall, Duong Van Nhiem and Delia Grace at the Global Health Institute scientific conference, Chiang Mai, Thailand, 19 February 2016.
Newsletter Proyecto GuidEU Febrero 2017golitran larc
GuidEU es un proyecto ERASMUS+ internacional que tiene como finalidad la creación de un sistema de orientación personalizado para estudiantes de ESO y Bachillerato con el objetivo de prevenir el abandono escolar y la desconexión con el mercado laboral.
This presentation is my research on dealing with stress, anxiety and depression while I was going through a rough patch in life despite everything being good on paper, I wasn't happy and smiling was a struggle. Depression is a evil which doesn't even sho itself and fights from the shadows. People will move out on you just because they can't see a reason or a visual wound. Not many will even understand what and why you are the way you are yet the battle is constantly raging inside. On this star wars day... I wish you the best. MAY THE FORCE BE WITH YOU!
Feel free to connect with me or add a comment if you want to share your thoughts on this subject!
Postscript - Life right now is still quite unsettled and paradoxically all of this happened while trying to settle it down with a purpose. I moved out of my structured super awesome job environment to achieve some personal goals that initially crashed me hard on the ground. In a lifetime of work, what would you do to be proud of? What would you do if you really want to make a difference? and what would you do to be able to say.. yes I did it! These questions needed answers. Mediocre isn't good, so I am giving this a thought and a perspective.
The Zen of High Performance Messaging with NATS (Strange Loop 2016)wallyqs
Video: https://www.youtube.com/watch?v=dYrYCt2dTkw
HTML5: https://wallyqs.github.io/stl-nats-talk/
NATS is an open source, high performant messaging system with a design oriented towards both being as simple and reliable as possible without at the same time trading off scalability. Originally written in Ruby, and then rewritten in Go, a NATS server can nowadays push over 11M messages per second.
In this talk, we will cover how following simplicity as the main design constraint as well as focusing on a limited built-in feature set, resulted in a system which is easy to operate and reason about, making up for an attractive choice for when building many types of distributed systems where low latency and high availability are very important.
How to Use Oracle RAC in a Cloud? - A Support QuestionMarkus Michalewicz
This presentation, which was first presented during Sangam16, discusses general and specific support rules for the Oracle Database and Oracle RAC with the purpose of enabling you to determine whether a given system is supported, certified or even recommended. This presentation was last updated on August 31st 2017 (minor update).
I naff presentation i did on Serverless, architecture, the state of development infrastructure options and a few other bits.
This presentation has been converted from Keynote to PowerPoint so may not display correctly.
1 ISACA JOURNAL VOLUME 1, 2012FeatureThe ability to r.docxhoney725342
1 ISACA JOURNAL VOLUME 1, 2012
Feature
The ability to restore databases from valid
backups is a vital part of ensuring business
continuity. Backup integrity and restorations
are an important piece of the IT Governance
Institute’s IT Control Objectives for Sarbanes-
Oxley, 2nd Edition. In many instances, IT auditors
merely confirm whether backups are being
performed either to disk or to tape, without
considering the integrity or viability of the
backup media.
This article covers the topics related to
data loss and the types of database backup
and recovery available. Best practices that can
assist an auditor in assessing the effectiveness
of database backup and recovery are also
provided. This article focuses on the technologies
and capabilities of the Oracle relational
database management system (RDBMS) and
Microsoft (MS) SQL Server because, together,
they cover approximately 40 percent of all
database installations. Figure 1 provides a short
comparison of Oracle and MS SQL Server.
One of the key responsibilities of a database
administrator (DBA) is to prepare for the
possibility of media, hardware and software
failure as well as to recover databases during a
disaster. Should any of these failures occur, the
major objective is to ensure that the database
is available to users within an acceptable time
period, while ensuring that there is no loss of
data. DBAs should evaluate their preparedness
to respond effectively to such situations by
answering the following questions:
• How confident is the DBA that the data on which
the company business depends are backed up
successfully and that the data can be recovered
from these backups within the permissible time
limits, per a service level agreement (SLA)
or recovery time objective, as specified in the
organization’s disaster recovery plan?
• Has the DBA taken measures to draft and test
the procedures to protect as well as recover the
databases from numerous types of failures?
The following is a checklist for database
backup and recovery procedures that are
explained throughout this article:
1. Develop a comprehensive backup plan.
2. Perform effective backup management.
3. Perform periodic databases restore testing.
4. Have backup and recovery SLAs drafted and
communicated to all stakeholders.
5. Have the disaster recovery plan (DRP)
database portion drafted and documented.
6. Keep your knowledge and know-how on
database and OS backup and recovery tools up
to date.
Comprehensive BaCkup plan
DBAs are responsible for making a
comprehensive backup plan for databases for
which they are accountable. The backup plan
should include all types of RDBMSs within the
enterprise and should cover the following areas:
• Decide what needs to be backed up. It is
imperative that the DBA be aware of database
and related OS and application components
that need to be backed up, whether via an
online backup or an offline cold backup.
The following are d ...
Oracle database performance diagnostics - before your beginHemant K Chitale
This is an article that I had written in 2011 for publication on OTN. It never did appear. So I am making it available here. It is not "slides" but is only 7 pages long. I hope you find it useful.
"It can always get worse!" – Lessons Learned in over 20 years working with Or...Markus Michalewicz
First presented during the DOAG 2022 Conference and Exhibition, this presentation discusses and reviews the most significant lessons learned in over 20 years of working with Oracle Maximum Availability Architecture. It explains why documentation is good, but automated checks are better, and why standardization can help increase the availability of nearly all systems, including database systems.
Analysis of Database Issues using AHF and Machine Learning v2 - SOUGSandesh Rao
Oracle Autonomous Health Framework (AHF) is Oracle’s Artificial Intelligence Operations platform for autonomous database health management. This session will focus on enhancements to current functionality and new features in 21c. We will discuss how to use the data which is derived from the Bayesian Net framework of AHF to conduct root cause analysis, telemetry and remediations for issues. You will learn to utilize these features to determine workload footprint, ongoing monitoring, early detection of anomalies and performance issues, their root causes and corrective actions, prevention of node or database failures, and targeted postmortem analysis enabling quick resolution.
Session Highlights:
• Insights into AHF enhancements to current functionality and new features in 21c
• Learn early detection of anomalies and performance issues, their root causes and corrective actions
• Targeted postmortem analysis enabling quick resolution
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
Oracle Autonomous Health Framework (AHF) White Paper
1. Oracle Database 12c Release 2
Oracle Autonomous Health Framework
O R A C L E W H I T E P A P E R | M A R C H 2 0 1 7
2. ORACLE AUTONOMOUS HEALTH FRAMEWORK
Table of Contents
Introduction 1
What Issues are Addressed by Oracle Autonomous Health Framework? 2
Availability Issues 2
Server Availability Issues 2
Database Availability Issues 2
Performance Issues 2
Database Server Performance Issues 3
Database Client-Caused Performance Issues 3
How Does Oracle Autonomous Health Framework Address These Issues? 3
Generates Diagnostic Metric View of Cluster and Databases 3
Cluster Health Monitor Architecture 3
Using Cluster Health Monitor to Collect Metrics 4
Establishes Baseline and Maintains Best Practice Configurations 6
Cluster Verification Utility Architecture 6
Using Cluster Verification Utility to Perform Health Checks 7
Maintains Compliance with Best Practices and Alerts Vulnerabilities to Known Issues 8
ORAchk Architecture 8
Using ORAchk to Maintain Compliance 8
Autonomously Monitors Performance and Manages Resources to Meet SLAs 10
Quality of Service Management Architecture 10
Using Quality of Service Management to Manage Resources and Maintain SLAs 11
3. ORACLE AUTONOMOUS HEALTH FRAMEWORK
Autonomously Preserves Database Availability and Performance During Hangs 15
Hang Manager Architecture 15
Using Hang Manager to Resolve Hangs 15
Autonomously Preserves Server Availability By Relieving Memory Stress 16
Memory Guard Architecture 16
Using Memory Guard to Relieve Memory Stress 17
Discovers Potential Cluster & Database Problems - Notifies with Corrective Actions 18
Cluster Health Advisor Architecture 18
Using Cluster Health Advisor for Prognosis of Potential Threats 19
Speeds Issue Diagnosis, Triage and Resolution 19
Trace File Analyzer Architecture 19
Using Trace File Analyzer to Collect Relevant Information for an Issue 20
Oracle Autonomous Health Framework in Oracle Cluster Domain 21
Conclusion 22
4. ORACLE AUTONOMOUS HEALTH FRAMEWORK 1
Introduction
Businesses today are becoming global. They have customers across the world using their applications and performing
transactions 24x7. These applications are powered by databases that provide relevant data to applications through
various database services. Therefore, in order to provide customers a continuous and consistent application
experience, businesses need to ensure that their underlying databases are running smoothly 24x7. This means that
databases not only need continuous availability, but also provide consistent performance. Therefore, any issues
affecting this availability and performance needs to be addressed and resolved quickly to bring these databases back
fully online.
Currently, these issues are resolved manually where human reaction time causes a delay in identification, diagnosis,
and resolution. This delay can prove to be costly by adversely affecting on-going business transactions and user
experience.
Oracle Autonomous Health Framework (AHF) presents the next generation of tools as components, which
autonomously work 24x7 to keep database systems healthy and running while minimizing human reaction time. These
components include both existing tools as components in ORAchk, Cluster Verification Utility, Trace File Analyzer,
Cluster Health Monitor, Quality of Service Management, Memory Guard, and new components in Cluster Health Advisor
and Hang Manager as shown in Figure 1.
Figure 1: Oracle Autonomous Health Framework with its components
Oracle Autonomous Health Framework components work together in daemon mode to address issues faced by
Database administrators and System administrators in areas of availability and performance.
5. ORACLE AUTONOMOUS HEALTH FRAMEWORK 2
What Issues are Addressed by Oracle Autonomous Health Framework?
Oracle Autonomous Health Framework addresses availability and performance issues in system administrator and database
administrator spaces. The responsibilities of system administrators include managing hardware resources - servers, OS, network,
storage, and Oracle Grid Infrastructure (GI) stack. They are operationally responsible for installation, patching, upgrades and resource
availability of these hardware resources. On the other hand, database administrators manage the database stack and the associated
services. They are operationally responsible for installation, patching, upgrades, resource allocations, and SLAs of these database
resources. Oracle AHF assists in fulfilling both these responsibilities by autonomously monitoring and managing the hardware
resources as well as the database stack.
While many of Oracle Autonomous Health Framework components can be used interactively during installation, patching, and
upgrading, their use within AHF is focused on operational runtime issues and either preventing their occurance or mitigating their
impact. These include the following availability and performance issues.
Availability Issues
Availability issues are runtime issues that can threaten availability of the software stack either through a software issue (DB, GI, O/S) or
underlying hardware resources (CPU, memory, network, storage). The specific availability issues addressed by Oracle Autononmous
Health Framework can be grouped into server and database issues.
Server Availability Issues
Server avaliabitiy issues can cause a server to be evicted from its cluster and shut down all database instances running there. Specific
issues addressed by Oracle Autonomous Health Framework are:
» Memory Stress caused by a node running out of free physical memory. This results in the O/S Swapper process running for extended
periods moving memory to and from disk and preventing time critical cluster processes from running thereby causing the node to be
evicted.
» Network issues, for example, network congestion on private interconnect caused by a change in configuration. This can result in
excessive latency in time-critical internode or storage I/O or dropped packets causing database instances to be non-responsive or
ultimately node eviction.
» Hardware issues that are not possible to anticipate. For example, network failures on private interconnect due to a network card
failure or cable pull. This will immediately result in an evicted node.
Database Availability Issues
Database availabilitty issues can cause a database or one of its instances to become unresponsive and thus unavailable. Specific
issues addressed by Oracle Autonomous Framework are:
» Runaway Queries or Hangs that can deny critical database resources in locks, latches, CPU to other sessions. This can result in a
database instance or the entire database being non-responsive to applications.
» Denial-of-Service attacks, rogue workloads or software bugs. These can cause a database or instance to be unresponsive.
» Software configuration or permission changes, for example, incorrect permissions on oracle.bin. This can also cause database
outages due to the inability to create sessions and can be very difficult to troubleshoot.
Performance Issues
Performance issues are runtime issues that threaten performance of the system as seen by database clients or applications either
through software issues (bugs, configuration, contention, etc.) or client issues (demand, query types, connection management, etc.).
The specific performance issues addressed by Oracle Autonomous Health Framework can be grouped into database server and client-
caused issues.
6. ORACLE AUTONOMOUS HEALTH FRAMEWORK 3
Database Server Performance Issues
Database server performance issues can result in a lower than optimum performance of database servers. Specific issues addressed
by Oracle Autonomous Health Framework are:
» Performance issues that can be caused by deviations from best practices in configuration.
» Issues that can be caused by bottlenecked resources such as insufficient storage disks, high block contention in global cache, poorly
constructed SQL, or a session that may be causing others to slow down waiting for it to release its resources or complete.
» Issues or bugs that are already known and can be fixed with upgrades, patches, or workarounds.
Database Client-Caused Performance Issues
Database clients can impact the performance of individual database instances or the entire database system. Specific issues
addressed by Oracle Autonomous Framework are:
» When a server hosts more databases instances than its resources and client load can handle, performance suffers due to waiting for
CPU, I/O, or memory. This misconfiguration or oversubscription of CPUs, I/O or memory can prevent critical or background
processes from running in a timely manner.
» Degraded performance due to misconfigured parameters in SGA versus PGA allocation, number of sessions/processes, CPU
counts, etc. based upon type of workload and level of concurrency required.
» Client demand exceeds server or database capacity.
Thus, Oracle Autonomous Health Framework addresses a wide variety of operational runtime issues in areas of availability and
performance for both hardware and software resources of the database system.
How Does Oracle Autonomous Health Framework Address These Issues?
Oracle Autonomous Health Framework components work 24x7 in daemon mode to address availability and performance issues, and
ensure high availability and consistent performance for the database system. They collaborate with each other to provide a framework
that:
» Continuously monitors database systems, collects OS metrics and generates diagnostic views of clusters and their hosted databases
» Establishes baseline and maintains best practice configurations
» Maintains compliance with best practices and alerts vulnerabilities to known issues
» Monitors performance and manages resources to meet SLAs
» Preserves database availability and performance by resolving hangs
» Preserves server availability by detecting and relieving memory stress
» Discovers potential cluster and database problems, and notifies with corrective actions to prevent the issues altogether
» Speeds issue diagnosis, triage and resolution for the problems that do occur
Generates Diagnostic Metric View of Cluster and Databases
Oracle Autonomous Health Framework continuously monitors and stores metrics associated with Clusterware and operating system
resources through its Cluster Health Monitor (CHM) component. CHM collects information in real-time that serves as a data feed for
other Oracle Autonomous Health Framework components. It also helps system admins to analyze issues and identify its cause. When
Grid Infrastructure (GI) is installed for RAC or RAC One Node database, Cluster Health Monitor is automatically enabled by default.
Cluster Health Monitor Architecture
CHM has two services to collect diagnostic metrics – System Monitor Service (osysmond) and Cluster Logger Service (ologgerd) as
shown in Figure 2. System monitor service is a real-time monitoring and operating system metric collection service that runs on each
cluster node and is managed as a High Availability Services (HAS) resource. The collected metrics are then forwarded to cluster logger
service that stores data in Oracle Grid Infrastructure Management Repository database.
7. ORACLE AUTONOMOUS HEALTH FRAMEWORK 4
Figure 2: Architecture of Cluster Health Monitor
In a cluster, there is one cluster logger service per 32 nodes. Additional logger services are spawned for every additional 32 nodes. If
logger service fails and is not able to come up after a fixed number of retries, all osysmond processes locally log and one respawns the
ologgerd process.
Using Cluster Health Monitor to Collect Metrics
Cluster Health Monitor helps analyze issues and identify their cause by collecting the historic metric data including CPU utilization,
memory utilization and total transfer rate as shown in Figure 3. This metric data from Cluster Health Monitor is available in graphical
display within Enterprise Manager Cloud Control. Complete cluster views of this data are accessible from the cluster target page.
8. ORACLE AUTONOMOUS HEALTH FRAMEWORK 5
Figure 3: Metrics collected by Cluster Health Monitor for multiple nodes in cluster as seen in Enterprise Manager
Cluster Health Monitor also provides the historical review capability to examine trends to diagnose cross cluster issues that occur for
example, over a weekend as shown in Figure 4.
Figure 4: Historical review of metrics collected by Cluster Health Monitor for multiple nodes in cluster as seen in Enterprise Manager
9. ORACLE AUTONOMOUS HEALTH FRAMEWORK 6
The metrics are broken down for further analysis as shown in Figure 5. For example, CPU utilization is broken down into CPU usage,
CPU system usage and CPU user usage. In addition, CPU utilization metric can be drilled down to see CPU system usage, CPU user
usage and CPU queue length.
Figure 5:CPU utilization metric broken down further into CPU usage, CPU system usage, and CPU user usage in Cluster Health Monitor
CHM by default monitors the top 127 processes to collect significant system metrics while keeping its resource consumption at
acceptable levels. These processes include critical cluster processes, for example, crsd.bin, ocssd.bin, gipcd.bin, etc. CHM also allows
user-specified critical processes to be monitored.
CHM supports plug-in collectors, for example, traceroute, netstat ping, etc. to provide enhanced network insight in 12.2. It listens to
CSS and GIPC events where CSS and GIPC are protocols that involve node-to-node communication. CSS maintains membership for
each node in the cluster. GIPC is used when blocks are moved between instances.
Establishes Baseline and Maintains Best Practice Configurations
Configuration changes such as changes in a file or directory permissions during deployment lifecycle can cause a database outage. For
example, incorrect permissions on the oracle.bin file can prevent session processes from being created. Such issues are detected by
Oracle Autonomous Health Framework component, Cluster Verification Utility (CVU). When Oracle Grid Infrastructure (GI) is installed
for RAC or RAC One Node database, CVU is automatically enabled by default.
Cluster Verification Utility Architecture
Cluster Verification Utility daemon runs every 6 hours. to verify components including free disk space, memory, processes, and other
Clusterware and database components. For each of these components, as shown in Figure 6, the checks/verifications to be performed
are controlled through XML files. These files are processed to generate XML data which in turn generates a list of verification task Java
objects which are processed by Verification engine. Finally, verification results and summary are displayed. CVU generates a baseline
from the XML files, XML data about the pre-requisites and data on implicit Java tasks. This baseline is stored in a separate XML file to
be available for future comparisons.
10. ORACLE AUTONOMOUS HEALTH FRAMEWORK 7
Figure 6: Cluster Verification Utility Architecture
Using Cluster Verification Utility to Perform Health Checks
Cluster Verification Utility runs in daemon mode to maintain system health before and after any new installations, patches or upgrades.
It allows administrators to establish a baseline for a healthy system, and performs checks against this baseline for O/S, Grid
Infrastructure and Database compliance and best practices in the event of a configuration change. Users can access the results of CVU
checks through its generated report in text or HTML file format. Figure 7 displays an example HTML report. These reports can be saved
for later reference. CVU can be extended to include user-defined checks. Users can choose to run the CVU daemon for either the
entire cluster or specific databases.
Figure 7: Cluster Verification Utility report
11. ORACLE AUTONOMOUS HEALTH FRAMEWORK 8
Maintains Compliance with Best Practices and Alerts Vulnerabilities to Known Issues
DOS attacks, exploited vulnerabilities, software bugs, etc. can cause a database or instance to be unresponsive. Oracle Autonomous
Health Framework component ORAchk is a lightweight and non-intrusive health check for Oracle stack of software and hardware
components. It proactively scans database systems for known issues, analyzes them and recommends resolutions. When Oracle Grid
Infrastructure (GI) is installed for RAC or RAC One Node database, ORAchk is automatically enabled by default.
ORAchk Architecture
ORAchk works in three steps – Scheduling, Identification and Action. During scheduling, users set the frequency to run ORAchk’s data
collection for a cluster’s nodes and databases. Users then start the ORAchk daemon. During its identification step, as shown in Figure
8, the ORAchk daemon:
» Checks if version is out of date, if so either downloads or recommends download of latest version
» Discovers all Oracle RAC stack components (both hardware and software) for servers within same database cluster
» Executes health check scripts which compare node data against the baseline that ORAchk creates for healthy system
» Compare results of health checks to best practice and generate compliance results
These compliance results are then sent to Collection Manager, when configured, where users can view them. Finally, during the Action
step, ORAchk provides recommendations for resolving these issues within Collection Manager.
Figure 8: ORAchk Architecture
Using ORAchk to Maintain Compliance
ORAchk stores the results of the checks it performs in files called collections and in the user-specified database configured to run its
Apex-based application, Collection Manger. Collection Manager is sent the data by ORAchk and uses it to conveniently display health
of entire database system and can be extended to multiiple clusters as shown in Figure 9. Each bar on the cluster health chart denotes
health of a cluster. The green section of the bar indicates healthy cluster checks, yellow indicates warnings, while red section iindicates
problems on the cluster.
12. ORACLE AUTONOMOUS HEALTH FRAMEWORK 9
Figure 9: Collection Manager Dashboard
Collection Manager also allows users to dive deeper to assess health of individual clusters, to browse collections, to get details of
individual collections including checks performed, status of the checks, etc., to compare two different collections as shown in Figure 10,
to generate reports, and to add user-defined checks.
Figure 10: View of a collection in Collection Manager
13. ORACLE AUTONOMOUS HEALTH FRAMEWORK 10
Autonomously Monitors Performance and Manages Resources to Meet SLAs
Oracle Autonomous Health Framework component Quality of Service Management (QoSM) addresses database server performance
issues caused by bottlenecked resources. Quality of Service Management identifies these issues, generates notifications when they put
SLAs at risk, and provides recommendations to manage resources to resolve issues and meet SLAs. QoSM allocates server resources
where they are required the most based upon performance requirements in terms of performance objectives and business criticality
rankings, in order to manage workloads to their service level agreements (SLAs).
Today, multiple and varied workloads are now being handled by a single server, each with their own set of performance objectives in
terms of their response time. Some workloads may be highly critical from the business perspective and may need to be catered to more
quickly than other workloads and therefore have a very low response time as their performance objective. Quality of Service
Management provides a single dashboard to monitor and manage all workloads on the database system and helps to organize
workloads just-in-time, based on their ranking, performance objectives and other criteria and allocates resources to them accordingly in
order to optimize performance. When Grid Infrastructure (GI) is installed for RAC or RAC One Node database, Quality of Service
Management is automatically ready to be enabled on a database-by-database basis.
Quality of Service Management Architecture
Oracle Database QoS Management Server, as diagramed in Figure 11, retrieves database and OS metrics as well as topology from
data sources including Oracle RAC and RAC One Node databases, Oracle Clusterware and Cluster Health Monitor. QoSM displays the
results on a single dashboard in Enterprise Manager. These metrics include database request arrival rate, CPU use, CPU wait time, I/O
use, I/O wait time, Global Cache use and Global Cache wait times from each database instance. The data is correlated by Performance
Class every five seconds. Information about the current topology of cluster and health of servers is added to the data. The Policy and
Performance Management engine of Oracle Database QoS Management analyzes the data to determine overall performance and
resource profile of the system with regard to the current Performance Objectives established by the active Performance Policy.
The performance evaluation occurs once a minute and results in a recommendation and corresponding notification if any Performance
Class does not meet its objectives. The recommendation specifies the target workload represented as a Performance CLass, its
bottlenecked resource and if possible specific corrective actions. The recommendation also includes its projected impact on all
Performance Classes in the system.
Figure 11: Quality of Service Management Architecture
14. ORACLE AUTONOMOUS HEALTH FRAMEWORK 11
Using Quality of Service Management to Manage Resources and Maintain SLAs
Users can classify workloads through QoSM into different performance classes by setting parameters and creating policies to filter
workloads. QoSM uses these policies for autonomous resource management to trade-off resources between competing workloads to
maintain SLAs.
QoSM can be used in three phases or in combination: Measurement phase, Monitoring phase and Management phase. In
measurement phase, QoSM helps to analyze current performance of workloads in terms of average response time categorized into
resource usage time (blue bar) and resource wait time (grey bar) as shown in Figure 12. This helps to determine realistic performance
objectives (in terms of average response time) for workloads.
Figure 12: Quality of Service Management dashboard in the measurement phase
Quality of Service Management also identifies bottlenecked resources that degrade performance of a workload. QoSM classifies
resource wait time for a workload into CPU, I/O, Global cache and Other wait time as shown in Figure 13 where the highest values
category of resource wait time is the bottlecked resource.
For example, high CPU contention would cause high CPU wait time, high block contention would cause high Global Cache wait time,
high I/O contention due to fewer disks would cause high I/O wait time and a SQL issue in latch or lock that could require an AWR report
analysis would cause high Other wait time.
15. ORACLE AUTONOMOUS HEALTH FRAMEWORK 12
Figure 13: Resource wait time breakdown by Quality of Service Management showing a high CPU contention in most of the workloads implying CPU as a
bottlenecked resource
As shown in Figure 14, Quality of Service Management also provides a historical view of workload performance in terms of resource
use time, resource wait time, demand, etc. for further analysis to identify causes of problems like fluctuations or sudden surge in the
workload performance.
Figure 14: Quality of Service Management display of the performance history of the workloads
16. ORACLE AUTONOMOUS HEALTH FRAMEWORK 13
By default, workloads are classified based on service names. However, in monitoring phase, users can set additional parameters to
classify workloads more granularly and set performance objectives and priority ranking for workloads through performance policy.
QoSM uses this policy to compare current workload performance with set performance objectives. If performance objectives are
violated, additional workload resource wait time is represented by red bar under the Resource Use vs Wait Time column as shown in
Figure 15. If performance objectives are met, extra headroom available is represented by green bar. QoSM displays workload
performance relative to its performance objective for last 5 mins under Performance Satisfaction Metric column. The red bar represents
the amount of time its response time exceeds a performance class exceeds its performance objective. QoSM also allows users to set
the threshold time within EMCC’s notification framework to receive warnings or alert notifications due to performance classes
continuously violating their objectives.
Figure 15: Quality of Service Management dashboard in the monitoring phase
In management phase, users can set a new policy to actively manage workloads. In this phase, the user defines server pool resource
parameters along with performance objectives and ranking for workloads. Based on this policy, QoSM recommends resource
reallocation to fulfill performance objectives for business critical workloads and optimize performance for other workloads as shown in
Figure 16. Note that QoSM manages reallocation of CPU resources only to manage to workload SLAs.
17. ORACLE AUTONOMOUS HEALTH FRAMEWORK 14
Figure 16: Quality of Service Management dashboard presenting recommendations in the Management phase
Through these three phases – measurement, monitoring and management, Quality of Service Management provides a continuous
workload health view through a single cluster-wide real-time dashboard. It also helps to identify bottleneck resources, analyse the
performance history of the workloads, and manage the resources with its targeted bottleneck resolution recommendations to meet
SLAs.
18. ORACLE AUTONOMOUS HEALTH FRAMEWORK 15
Autonomously Preserves Database Availability and Performance During Hangs
Database hangs occur when a chain of one or more sessions is blocked by another session and is not able to make any progress.
These can make databases unresponsive to applications by denying critical database resources in locks, latches, and CPU to other
sessions. Oracle Autonomous Health Framework component Hang Manager autonomously detects and resolves these hangs. Hang
Manager is enabled when RAC or RAC One Node databases are created.
Hang Manager Architecture
Figure 17: Hang Manager Architecture
Hang Manager autonomously runs as a DIA0 background process within Oracle databases as shown in Figure 17. Hang Manager has
three phases – Detect, Analyze and Verify. In its Detect phase, Hang Manager collects data on all the nodes from Cluster Health
Monitor. It detects sessions waiting for resources held by another session for some time and monitors them. Hang Manager then
analyzes these sessions in its Analyze phase to determine if they are part of potential hang. If so, Hang Manager waits to ensure that
sessions are truly hung. After a set time, Hang Manager verifies these sessions as hangs in its Verify phase and selects a final blocker
session as victim session. It applies hang resolution heurestics to victim session. In case the hang does not resolve, it terminates victim
session and if that fails, Hang Manager terminates the session process.
Using Hang Manager to Resolve Hangs
Hang Manager by default has its sensitivity parameter set to Normal and trace file size set to a default value. Users can change these
parameters if required. For example, for faster hang resolution the sensitivity parameter can be set to High.
While resolving hangs, Hang Manager also considers the active Quality of Service Management policy. For example, if a hang includes
a session associated with a highly ranked critical Performance Class in the QoSM policy, Hang Manager expedites the termination of
victim session to maintain performance objectives of the critical session.
Hang Manager detects and resolves hangs autonomously. However, it continuously logs all detections and resolutions in DB Alert Logs
as shown below.
19. ORACLE AUTONOMOUS HEALTH FRAMEWORK 16
2015-10-13T16:47:59.435039+17:00
Errors in file /oracle/log/diag/rdbms/hm6/hm6/trace/hm6_dia0_12433.trc (incident=7353):
ORA-32701: Possible hangs up to hang ID=1 detected
Incident details in: …/diag/rdbms/hm6/hm6/incident/incdir_7353/hm6_dia0_12433_i7353.trc
2015-10-13T16:47:59.506775+17:00
DIA0 requesting termination of session sid:40 with serial # 43179 (ospid:13031) on instance 2
due to a GLOBAL, HIGH confidence hang with ID=1.
Hang Resolution Reason: Automatic hang resolution was performed to free a
significant number of affected sessions.
DIA0: Examine the alert log on instance 2 for session termination status of hang with ID=1.
In the alert log on the instance local to the session (instance 2 in this case),
we see the following:
2015-10-13T16:47:59.538673+17:00
Errors in file …/diag/rdbms/hm6/hm62/trace/hm62_dia0_12656.trc (incident=5753):
ORA-32701: Possible hangs up to hang ID=1 detected
Incident details in: …/diag/rdbms/hm6/hm62/incident/incdir_5753/hm62_dia0_12656_i5753.trc
2015-10-13T16:48:04.222661+17:00
DIA0 terminating blocker (ospid: 13031 sid: 40 ser#: 43179) of hang with ID = 1
requested by master DIA0 process on instance 1
Hang Resolution Reason: Automatic hang resolution was performed to free a
significant number of affected sessions.
by terminating session sid:40 with serial # 43179 (ospid:13031)
The details of complete hang resolution are also available in dump trace files for later reference and troubleshooting.
Autonomously Preserves Server Availability By Relieving Memory Stress
Enterprise database servers can use all available free memory due to too many open sessions or runaway workloads causing node
eviction. This event where free memory falls below a safe threshold is called memory stress. Oracle Autonomous Health Framework
component Memory Guard autonomously monitors nodes for memory stress and relieves it in order to prevent node eviction and
maintain server availability. When Grid Infrastructure (GI) is installed for RAC or RAC One Node database, Memory Guard is
automatically enabled by default.
Memory Guard Architecture
Memory Guard as shown in Figure 18 runs as an MBean daemon in a J2EE container managed by Cluster Ready Services (CRS).
Memory Guard is hosted on the qosmserver singleton resource that runs on any cluster node for high availability. Cluster Health
Monitor sends a metrics stream to Memory Guard providing real-time memory resources information for cluster nodes including amount
of available memory and amount of memory currently in use. Memory Guard also collects cluster topology from Oracle Clusterware. It
uses cluster topology and memory metrics to identify database nodes that have memory stress.
Memory Guard then stops database services managed by Oracle Clusterware on the stressed node transactionally. It relieves memory
stress without affecting already running sessions and their associated transactions. After completion, memory used by these processes
starts freeing up and adding to pool of available memory on the node. When Memory Guard detects that amount of available memory is
more than threshold, it restarts services on the affected node.
20. ORACLE AUTONOMOUS HEALTH FRAMEWORK 17
Figure 18: Memory Guard Architecture
While a service is stopped on a stressed node, new connections for that service are redirected by the listener to other nodes providing
the same service for non-singleton database instances. However, for policy-managed databases, the last instance of a service is never
stopped in order to maintain availability.
Using Memory Guard to Relieve Memory Stress
Memory Guard autonomously detects and monitors Oracle Real Application Clusters (Oracle RAC) or Oracle RAC One Node
databases when they are open. Memory Guard sends alert notifications when it detects memory stress on a database node. Memory
Guard alerts can be found in audit logs under $ORACLE_BASE/crsdata/node name/qos/logs/dbwlm/auditing.
Memory Guard log file when the services are stopped due to memory stress is as shown below:
<MESSAGE>
<HEADER>
<TSTZ_ORIGINATING>2016-07-28T16:11:03.701Z</TSTZ_ORIGINATING>
<COMPONENT_ID>wlm</COMPONENT_ID>
<MSG_TYPE TYPE="NOTIFICATION"></MSG_TYPE>
<MSG_LEVEL>1</MSG_LEVEL>
<HOST_ID>hostABC</HOST_ID>
<HOST_NWADDR>11.111.1.111</HOST_NWADDR>
<MODULE_ID>gomlogger</MODULE_ID>
<THREAD_ID>26</THREAD_ID>
<USER_ID>userABC</USER_ID>
<SUPPL_ATTRS>
<ATTR NAME="DBWLM_OPERATION_USER_ID">userABC</ATTR>
<ATTR NAME="DBWLM_THREAD_NAME">MPA Task Thread 1469722257648</ATTR>
</SUPPL_ATTRS>
</HEADER>
<PAYLOAD>
<MSG_TEXT>Server Pool Generic has violation risk level RED.</MSG_TEXT>
</PAYLOAD>
</MESSAGE>
<MESSAGE>
<HEADER>
<TSTZ_ORIGINATING>2016-07-28T16:11:03.701Z</TSTZ_ORIGINATING>
<COMPONENT_ID>wlm</COMPONENT_ID>
<MSG_TYPE TYPE="NOTIFICATION"></MSG_TYPE>
<MSG_LEVEL>1</MSG_LEVEL>
<HOST_ID>hostABC</HOST_ID>
<HOST_NWADDR>11.111.1.111</HOST_NWADDR>
<MODULE_ID>gomlogger</MODULE_ID>
<THREAD_ID>26</THREAD_ID>
<USER_ID>userABC</USER_ID>
<SUPPL_ATTRS>
<ATTR NAME="DBWLM_OPERATION_USER_ID">userABC</ATTR>
<ATTR NAME="DBWLM_THREAD_NAME">MPA Task Thread 1469722257648</ATTR>
</SUPPL_ATTRS>
</HEADER>
<PAYLOAD>
MSG_TEXT>Server userABC-hostABC-0 has violation risk level RED. New connection requests will no longer be accepted.</MSG_TEXT>
</PAYLOAD>
</MESSAGE>
21. ORACLE AUTONOMOUS HEALTH FRAMEWORK 18
Memory Guard log file when the services were restarted after relieving the memory stress is as shown below:
<MESSAGE>
…
<MSG_TEXT>Memory pressure in Server Pool Generic has returned to normal.</MSG_TEXT>
…
<MSG_TEXT>Memory pressure in server userABC-hostABC-0 has returned to normal. New connection requests are now accepted.</MSG_TEXT>
…
</MESSAGE>
Discovers Potential Cluster & Database Problems - Notifies with Corrective Actions
Oracle Autonomous Health Framework component Cluster Health Advisor (CHA) provides system and database administrators with
early warning of pending performance issues, and root causes and corrective actions for Oracle RAC databases and cluster nodes.
Oracle Cluster Health Advisor then performs anomaly detection for each input based on the difference between observed and expected
values. If sufficient inputs associated with a specific problem are abnormal, then Oracle Cluster Health Advisor raises a warning and
generates an immediate targeted diagnosis and corrective action.
Oracle Cluster Health Advisor stores the analysis results, along with diagnosis information, corrective action, and metric evidence for
later triage, in the Grid Infrastructure Management Repository (GIMR). Oracle Cluster Health Advisor also sends warning messages to
Enterprise Manager Cloud Control using the Oracle Clusterware event notification protocol.
When Grid Infrastructure (GI) is installed for RAC or RAC One Node database, Cluster Health Advisor is automatically enabled by
default.
Cluster Health Advisor Architecture
As shown in Figure 19, Oracle Cluster Health Advisor runs as a highly available cluster resource, ochad, on each node in the cluster.
Each Oracle Cluster Health Advisor daemon (ochad) monitors the operating system on the cluster node and optionally, each Oracle
Real Application Clusters (Oracle RAC) database instance on the node.
Figure 19: Flow diagram for Cluster Health Advisor architecture
22. ORACLE AUTONOMOUS HEALTH FRAMEWORK 19
The ochad daemon receives OS metric data from the Cluster Health Monitor and gets Oracle RAC database instance metrics from a
memory-mapped file. The daemon does not require a connection to each database instance. This data, along with the selected model,
is used in the Health Prognostics Engine of Oracle Cluster Health Advisor for both the node and each monitored database instance in
order to analyze their health multiple times a minute.
The results of this analysis alcdong with any diagnosis and corrective action are stored in Grid Infrastructure Management repository
(GIMR) along with its metric evidence for later triage. CHA accesses stored data through Oracle Enterprise Manager Cloud Control
(EMCC) or cluster terminal through CHACTL.
Using Cluster Health Advisor for Prognosis of Potential Threats
By default, Cluster Health Advisor models are designed to be conservative to prevent false warning notifications. However, default
configuration may not be sensitive enough for critical production systems. Therefore, Cluster Health Advisor provides an onsite model
calibration capability to use actual production workload data to form the basis of its default setting and increase accuracy and sensitivity
of node and database models. Since workloads may vary on specific cluster nodes and Oracle RAC databases, Cluster Health Advisor
also provides the capability to create, store, and activate multiple models with their own specific calibration data. This functionality is
also managed by CHACTL. Sample problems detected by CHA along with their corrective actions using CHACTL query diagnosis are
as shown:
Problem: DB Control File IO Performance
Description: CHA has detected that reads or writes to the control files are slower than expected.
Cause: The Cluster Health Advisor (CHA) detected that reads or writes to the control files were
slow because of an increase in disk IO.
The slow control file reads and writes may have an impact on checkpoint and Log Writer (LGWR)
performance.
Action: Separate the control files from other database files and move them to faster disks or Solid
State Devices.
Problem: DB CPU Utilization
Description: CHA detected larger than expected CPU utilization for this database.
Cause: The Cluster Health Advisor (CHA) detected an increase in database CPU utilization
because of an increase in the database workload.
Action: Identify the CPU intensive queries by using the Automatic Diagnostic and Defect Manager
(ADDM) and follow the recommendations given there. Limit the number of CPU intensive queries or
relocate sessions to less busy machines. Add CPUs if the CPU capacity is insufficent to support
the load without a performance degradation or effects on other databases.
When OCHAD detects an Oracle RAC or Oracle RAC One Node database instance running, it autonomously starts monitoring cluster
nodes. However, to monitor Oracle RAC database instances, Oracle Grid Infrastructure user is required to use CHACTL to explicitly
turn on monitoring for each database.
Speeds Issue Diagnosis, Triage and Resolution
While Oracle Autonomous Health Framework components - ORAchk, Cluster Verification Utility, Quality of Service
Management and Cluster Health Advisor autonomously identify issues and recommend solutions, some issues require Oracle
Support Services. For example, network failures on the private interconnect which can immediately result in evicted nodes
should a cable be pulled or a NIC fail. While such issues cannot be detected early, Oracle Autonomous Health Framework
component Trace File Analyzer (TFA) helps to collect and extract relavant information in a timely manner across multiple
nodes involved for issue analysis by Oracle Support Services. This is esspecially useful when data is frequently lost or
overwritten as diagnostic collections may not happen until some time after the issue occurred. TFA’s daemon mode is
enabled by default when Grid Infrastructure (GI) is installed for RAC or RAC One Node database.
Trace File Analyzer Architecture
As shown in Figure 20, when running in daemon mode, TFA Collector monitors Oracle logs for events symptomatic of a significant
problem as step 1. In step 2, based on the event type detected, TFA then starts an automatic diagnostic collection. The data collected
23. ORACLE AUTONOMOUS HEALTH FRAMEWORK 20
depends on event detected. TFA coordinates collection cluster-wide, trims the logs around relevant time periods, and then packs all
collection results into a single package on one node.
Figure 20: Trace File Analyzer Architecture
TFA does not do a collection for every event detected. When an event is first identified, it triggers a start point for a collection and then
waits for five minutes before starting diagnostic gathering in order to capture any other relevant events.
» If events are still occurring after 5 minutes, then TFA waits to complete diagnostic collection for up to a further five minutes for 30
seconds with no events occurring.
» If events are still occurring 10 minutes after first detection, then TFA forces a diagnostic collection and generates a new collection
start point for the next event.
Once the collection is complete, TFA sends email notification that includes the details of where the collection results are, to the relevant
recipients as step 3. And finally, in step 4, the recipients can then upload the collections to Oracle Support Services for further help.
Using Trace File Analyzer to Collect Relevant Information for an Issue
TFA collects data from nodes based on parameters set, for example, collecting data related to a specific incident time or time range,
complete files that have relevant data, or just a time slice of data from those files. Exactly what is collected is dependent on the string
and in which alert it is found but potentially trimmed versions of all O/S, CRS, ASM and RDBMS logs can be collected for each string.
This type of operation could be called many times a second if not controlled so there can never be a collection generated at less than 5
minute intervals due to Trace File Analyzer implementation of flood control. Furthermore, if Trace File Analyzer repository reaches its
repository max size then no auto collection takes place. After data is collected, Trace File Analyzer, then, generates reports based on
that data as shown in Figure 21.
24. ORACLE AUTONOMOUS HEALTH FRAMEWORK 21
Figure 21: Sample Trace File Analyzer files for an issue
Thus, TFA collects only the relevant information regarding an issue based on time specified or event occurrence that triggered the
issue. This information is packaged in a compact manner which can be easily sent to Oracle Support Services to resolve the issue
quickly.
Oracle Autonomous Health Framework in Oracle Cluster Domain
Oracle AHF generates and stores a lot of diagnostic data while diagnosing and resolving availability and performance issues in the
database system. A 4 node cluster on an average generates 6-7GB of diagnostic data for retention of 3 days. This would create
overhead by consuming local resources. Furthermore, Oracle AHF components interact and use data generated by each other. This
becomes convenient if the entire data is stored at one place instead of in local repositories of each component.
Oracle Cluster Domain supports four types of clusters:
» A Standalone Cluster (formerly Flex Cluster )
» Two types of Member (formerly “Client”) Clusters:
» Application Member Cluster
» Database Member Cluster
» Domain Services Cluster
25. ORACLE AUTONOMOUS HEALTH FRAMEWORK 22
A member cluster, here, is a cluster that is managed in Cluster Domain, in which all clusters are registered with a common
Management Repository Service. It can use different services that are offered as Cluster Domain Services through Oracle Domain
Services Cluster (DSC). The components of Oracle AHF are also provided as services to all the member clusters of Oracle Cluster
domain through the centralized Domain Services Cluster (DSC) as shown in Figure 22. For example, ORAchk component is provided
as ORAchk collection service, Rapid Home Provisioning component is provided as Rapid Home Provisioning Service.
Figure 22: Oracle Cluster Domain
Oracle AHF is therefore supported in Oracle Cluster Domain where the overhead of storing diagnostic data of Oracle AHF is offloaded
to the centralized infrastructure repository – Grid Infrastructure Management Repository (GIMR). GIMR is available to all Oracle RAC
users for free. Thus, the centralization of the Oracle AHF in DSC makes it easy to manage, easily accessible to all the member clusters
and also helps to reduce the local footprint of Oracle AHF.
Conclusion
With globalization of businesses, database systems need to be available and perform consistently at all times in order that customers
may perform transactions 24x7. Any daily operational issues that threaten availability and performance of such database systems,
therefore, need to be addressed quickly.
Oracle Autonomous Health Framework is a solution that helps to prevent and resolve these issues. Its components work together to
identify situations which are potential threats to the database system and provides corrective actions to resolve them. For issues that do
occur, Oracle AHF helps to resolve them quickly with minimal effort by identifying the issue, diagnosing its cause and providing
resolutions. For issues that require Oracle Support Service (OSS), Oracle AHF also collects relevant information required by OSS for
quickly resolving the issue. Oracle AHF; therefore, provides a solution at every step – prevent issues before they occurs, resolve issues
when they occur and expedites resolution of issues that require OSS assistance. This makes Oracle AHF a complete solution to
maintain availability and mange performance of Oracle database systems.