Tanel Poder - Troubleshooting Complex Oracle Performance Issues - Part 2Tanel Poder
This document summarizes a series of performance issues seen by the author in their work with Oracle Exadata systems. It describes random session hangs occurring across several minutes, with long transaction locks and I/O waits seen. Analysis of AWR reports and blocking trees revealed that many sessions were blocked waiting on I/O, though initial I/O metrics from the OS did not show issues. Further analysis using ASH activity breakdowns and OS tools like sar and vmstat found high apparent CPU usage in ASH that was not reflected in actual low CPU load on the system. This discrepancy was due to the way ASH attributes non-waiting time to CPU. The root cause remained unclear.
29回勉強会資料「PostgreSQLのリカバリ超入門」
See also http://www.interdb.jp/pgsql (Coming soon!)
初心者向け。PostgreSQLのWAL、CHECKPOINT、 オンラインバックアップの仕組み解説。
これを見たら、次は→ http://www.slideshare.net/satock/29shikumi-backup
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).
In 40 minutes the audience will learn a variety of ways to make postgresql database suddenly go out of memory on a box with half a terabyte of RAM.
Developer's and DBA's best practices for preventing this will also be discussed, as well as a bit of Postgres and Linux memory management internals.
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.
This document provides an overview and interpretation of the Automatic Workload Repository (AWR) report in Oracle database. Some key points:
- AWR collects snapshots of database metrics and performance data every 60 minutes by default and retains them for 7 days. This data is used by tools like ADDM for self-management and diagnosing issues.
- The top timed waits in the AWR report usually indicate where to focus tuning efforts. Common waits include I/O waits, buffer busy waits, and enqueue waits.
- Other useful AWR metrics include parse/execute ratios, wait event distributions, and top activities to identify bottlenecks like parsing overhead, locking issues, or inefficient SQL.
Tanel Poder - Performance stories from Exadata MigrationsTanel Poder
Tanel Poder has been involved in a number of Exadata migration projects since its introduction, mostly in the area of performance ensurance, troubleshooting and capacity planning.
These slides, originally presented at UKOUG in 2010, cover some of the most interesting challenges, surprises and lessons learnt from planning and executing large Oracle database migrations to Exadata v2 platform.
This material is not just repeating the marketing material or Oracle's official whitepapers.
The document discusses the Performance Schema in MySQL. It provides an overview of what the Performance Schema is and how it can be used to monitor events within a MySQL server. It also describes how to configure the Performance Schema by setting up actors, objects, instruments, consumers and threads to control what is monitored. Finally, it explains how to initialize the Performance Schema by truncating existing summary tables before collecting new performance data.
This document discusses PostgreSQL statistics and how to use them effectively. It provides an overview of various PostgreSQL statistics sources like views, functions and third-party tools. It then demonstrates how to analyze specific statistics like those for databases, tables, indexes, replication and query activity to identify anomalies, optimize performance and troubleshoot issues.
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).
In 40 minutes the audience will learn a variety of ways to make postgresql database suddenly go out of memory on a box with half a terabyte of RAM.
Developer's and DBA's best practices for preventing this will also be discussed, as well as a bit of Postgres and Linux memory management internals.
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.
This document provides an overview and interpretation of the Automatic Workload Repository (AWR) report in Oracle database. Some key points:
- AWR collects snapshots of database metrics and performance data every 60 minutes by default and retains them for 7 days. This data is used by tools like ADDM for self-management and diagnosing issues.
- The top timed waits in the AWR report usually indicate where to focus tuning efforts. Common waits include I/O waits, buffer busy waits, and enqueue waits.
- Other useful AWR metrics include parse/execute ratios, wait event distributions, and top activities to identify bottlenecks like parsing overhead, locking issues, or inefficient SQL.
Tanel Poder - Performance stories from Exadata MigrationsTanel Poder
Tanel Poder has been involved in a number of Exadata migration projects since its introduction, mostly in the area of performance ensurance, troubleshooting and capacity planning.
These slides, originally presented at UKOUG in 2010, cover some of the most interesting challenges, surprises and lessons learnt from planning and executing large Oracle database migrations to Exadata v2 platform.
This material is not just repeating the marketing material or Oracle's official whitepapers.
The document discusses the Performance Schema in MySQL. It provides an overview of what the Performance Schema is and how it can be used to monitor events within a MySQL server. It also describes how to configure the Performance Schema by setting up actors, objects, instruments, consumers and threads to control what is monitored. Finally, it explains how to initialize the Performance Schema by truncating existing summary tables before collecting new performance data.
This document discusses PostgreSQL statistics and how to use them effectively. It provides an overview of various PostgreSQL statistics sources like views, functions and third-party tools. It then demonstrates how to analyze specific statistics like those for databases, tables, indexes, replication and query activity to identify anomalies, optimize performance and troubleshoot issues.
This document provides an overview of pgCenter, an open source tool for monitoring and managing PostgreSQL databases. It summarizes pgCenter's main features, which include displaying statistics on databases, tables, indexes and functions; monitoring long running queries and statements; managing connections to multiple PostgreSQL instances; and performing administrative tasks like viewing logs, editing configuration files, and canceling queries. Use cases and examples of how pgCenter can help optimize PostgreSQL performance are also provided.
Lightweight locks (LWLocks) in PostgreSQL provide mutually exclusive access to shared memory structures. They support both shared and exclusive locking modes. The LWLocks framework uses wait queues, semaphores, and spinlocks to efficiently manage acquiring and releasing locks. Dynamic monitoring of LWLock events is possible through special builds that incorporate statistics collection.
Nine Circles of Inferno or Explaining the PostgreSQL VacuumAlexey Lesovsky
The document describes the nine circles of the PostgreSQL vacuum process. Circle I discusses the postmaster process, which initializes shared memory and launches the autovacuum launcher and worker processes. Circle II focuses on the autovacuum launcher, which manages worker processes and determines when to initiate vacuuming for different databases. Circle III returns to the postmaster process and how it launches autovacuum workers. Circle IV discusses what occurs within an autovacuum worker process after it is launched, including initializing, signaling the launcher, scanning relations, and updating databases. Circle V delves into processing a single database by an autovacuum worker.
This document discusses streaming replication in PostgreSQL. It covers how streaming replication works, including the write-ahead log and replication processes. It also discusses setting up replication between a primary and standby server, including configuring the servers and verifying replication is working properly. Monitoring replication is discussed along with views and functions for checking replication status. Maintenance tasks like adding or removing standbys and pausing replication are also mentioned.
This document provides an overview of troubleshooting streaming replication in PostgreSQL. It begins with introductions to write-ahead logging and replication internals. Common troubleshooting tools are then described, including built-in views and functions as well as third-party tools. Finally, specific troubleshooting cases are discussed such as replication lag, WAL bloat, recovery conflicts, and high CPU recovery usage. Throughout, examples are provided of how to detect and diagnose issues using the various tools.
PostgreSQL worst practices, version FOSDEM PGDay 2017 by Ilya KosmodemianskyPostgreSQL-Consulting
This talk is prepared as a bunch of slides, where each slide describes a really bad way people can screw up their PostgreSQL database and provides a weight - how frequently I saw that kind of problem. Right before the talk I will reshuffle the deck to draw ten random slides and explain you why such practices are bad and how to avoid running into them.
PostgreSQL Performance Problems: Monitoring and AlertingGrant Fritchey
PostgreSQL can be difficult to troubleshoot when the pressure is on without the right knowledge and tools. Knowing where to find the information you need to improve performance is central to your ability to act quickly and solve problems. In this training, we'll discuss the various query statistic views and log information that's available in PostgreSQL so that you can solve problems quickly. Along the way, we'll highlight a handful of open-source and paid tools that can help you track data over time and provide better alerting capabilities so that you know about problems before they become critical.
Monitoring Postgres at Scale | PGConf.ASIA 2018 | Lukas FittlCitus Data
Your PostgreSQL database is one of the most important pieces of your architecture. What should you really watch out for, send reports on and alert on? We’ll discuss how query performance statistics can be made accessible to application developers, critical entries one should monitor in the PostgreSQL log files, how to collect EXPLAIN plans at scale, how to watch over autovacuum and VACUUM operations, and how to flag issues based on schema statistics.
Monitoring Postgres at Scale | PostgresConf US 2018 | Lukas FittlCitus Data
Your PostgreSQL database is one of the most important pieces of your architecture - yet the level of introspection available in Postgres is often hard to work with. Its easy to get very detailed information, but what should you really watch out for, send reports on and alert on?
In this talk we'll discuss how query performance statistics can be made accessible to application developers, critical entries one should monitor in the PostgreSQL log files, how to collect EXPLAIN plans at scale, how to watch over autovacuum and VACUUM operations, and how to flag issues based on schema statistics.
We'll also talk a bit about monitoring multi-server setups, first going into high availability and read standbys, logical replication, and then reviewing how monitoring looks like for sharded databases like Citus.
The talk will primarily describe free/open-source tools and statistics views readily available from within Postgres.
This document discusses using PostgreSQL statistics to optimize performance. It describes various statistics sources like pg_stat_database, pg_stat_bgwriter, and pg_stat_replication that provide information on operations, caching, and replication lag. It also provides examples of using these sources to identify issues like long transactions, temporary file growth, and replication delays.
Deep dive into PostgreSQL internal statistics / Алексей Лесовский (PostgreSQL...Ontico
СУБД PostgreSQL — это огромный механизм, который состоит из множества подсистем, чья работа определяет производительность PostgreSQL. В процессе эксплуатации обеспечивается сбор статистики и информации о работе компонентов, что позволяет оценить эффективность PostgreSQL и принять меры для повышения производительности. Однако, этой информации очень много и представлена она в достаточно упрощенном виде. Обработка этой информации и ее интерпретация порой совсем нетривиальная задача, а зоопарк инструментов и утилит запросто поставит в тупик даже продвинутого DBA.
В докладе речь пойдет о подсистеме сбора статистики, о том какая информация доступна для оценки эффективности PostgreSQL, как её получить, не прибегая к зоопарку инструментов. Как интерпретировать и использовать полученную информацию, как найти узкие места, устранить их и повысить производительность PostgreSQL.
Webinar slides: An Introduction to Performance Monitoring for PostgreSQLSeveralnines
To operate PostgreSQL efficiently, you need to have insight into database performance and make sure it is at optimal levels.
With that in mind, we dive into monitoring PostgreSQL for performance in this webinar replay.
PostgreSQL offers many metrics through various status overviews and commands, but which ones really matter to you? How do you trend and alert on them? What is the meaning behind the metrics? And what are some of the most common causes for performance problems in production?
We discuss this and more in ordinary, plain DBA language. We also have a look at some of the tools available for PostgreSQL monitoring and trending; and we’ll show you how to leverage ClusterControl’s PostgreSQL metrics, dashboards, custom alerting and other features to track and optimize the performance of your system.
AGENDA
- PostgreSQL architecture overview
- Performance problems in production
- Common causes
- Key PostgreSQL metrics and their meaning
- Tuning for performance
- Performance monitoring tools
- Impact of monitoring on performance
- How to use ClusterControl to identify performance issues
- Demo
SPEAKER
Sebastian Insausti, Support Engineer at Severalnines, has loved technology since his childhood, when he did his first computer course (Windows 3.11). And from that moment he was decided on what his profession would be. He has since built up experience with MySQL, PostgreSQL, HAProxy, WAF (ModSecurity), Linux (RedHat, CentOS, OL, Ubuntu server), Monitoring (Nagios), Networking and Virtualization (VMWare, Proxmox, Hyper-V, RHEV).
Prior to joining Severalnines, Sebastian worked as a consultant to state companies in security, database replication and high availability scenarios. He’s also a speaker and has given a few talks locally on InnoDB Cluster and MySQL Enterprise together with an Oracle team. Previous to that, he worked for a Mexican company as chief of sysadmin department as well as for a local ISP (Internet Service Provider), where he managed customers' servers and connectivity.
This webinar builds upon a related blog post by Sebastian: https://severalnines.com/blog/performance-cheat-sheet-postgresql.
How does PostgreSQL work with disks: a DBA's checklist in detail. PGConf.US 2015PostgreSQL-Consulting
This document discusses how PostgreSQL works with disks and provides recommendations for disk subsystem monitoring, hardware selection, and configuration tuning to optimize performance. It explains that PostgreSQL relies on disk I/O for reading pages, writing the write-ahead log (WAL), and checkpointing. It recommends monitoring disk utilization, IOPS, latency, and I/O wait. The document also provides tips for choosing hardware like SSDs or RAID configurations and configuring the operating system, file systems, and PostgreSQL to improve performance.
PGConf APAC 2018 - Monitoring PostgreSQL at ScalePGConf APAC
Speaker: Lukas Fittl
Your PostgreSQL database is one of the most important pieces of your architecture - yet the level of introspection available in Postgres is often hard to work with. Its easy to get very detailed information, but what should you really watch out for, send reports on and alert on?
In this talk we'll discuss how query performance statistics can be made accessible to application developers, critical entries one should monitor in the PostgreSQL log files, how to collect EXPLAIN plans at scale, how to watch over autovacuum and VACUUM operations, and how to flag issues based on schema statistics.
We'll also talk a bit about monitoring multi-server setups, first going into high availability and read standbys, logical replication, and then reviewing how monitoring looks like for sharded databases like Citus.
The talk will primarily describe free/open-source tools and statistics views readily available from within Postgres.
PgCenter is a tool for monitoring and troubleshooting PostgreSQL. It provides a graphical interface to view key performance metrics and statuses. Some of its main features include displaying server health, load, memory and disk usage, statement performance, replication status and more. It aims to help PostgreSQL administrators quickly check the health of their databases and identify potential problems.
Covered Database Maintenance & Performance and Concurrency :
1. PostgreSQL Tuning and Performance
2. Find and Tune Slow Running Queries
3. Collecting regular statistics from pg_stat* views
4. Finding out what makes SQL slow
5. Speeding up queries without rewriting them
6. Discovering why a query is not using an index
7. Forcing a query to use an index
8. EXPLAIN and SQL Execution
9. Workload Analysis
Introduction to PostgreSQL for System AdministratorsJignesh Shah
This document provides an introduction and overview of PostgreSQL for system administrators. It covers why to use open source databases like PostgreSQL, a quick start guide to installing and configuring PostgreSQL, PostgreSQL internals like file system layout and processes, and monitoring PostgreSQL performance using tools like iostat and top. The presentation is aimed at helping system administrators get started with managing PostgreSQL databases.
This document discusses using window functions in PostgreSQL to analyze sales data. It shows a table with movie titles, categories, and total sales. Window functions can be used to calculate things like running totals, ranks, and more to analyze the sales data across rows.
This document discusses how Solaris features like ZFS, Zones, and DTrace can be leveraged to improve PostgreSQL performance and capabilities. ZFS allows for cheap, space-efficient snapshots of databases that can be quickly cloned and used without locks. Zones enable point-in-time copies of entire databases to run independently with low overhead. DTrace provides powerful tracing of PostgreSQL queries, I/O, and system activity for troubleshooting performance issues. Tools from OmniTI Labs further exploit these Solaris features for PostgreSQL monitoring, testing, and disaster recovery. In summary, combining PostgreSQL with the advanced virtualization and instrumentation of Solaris can significantly enhance database capabilities and operations.
This document discusses how Solaris features like ZFS, Zones, and DTrace can be leveraged to improve PostgreSQL performance and capabilities. ZFS allows for cheap, space-efficient snapshots of databases that can be quickly cloned and used without locks. Zones enable point-in-time copies of entire databases to run independently with low overhead. DTrace provides powerful instrumentation that allows deeply monitoring PostgreSQL queries, I/O, and system interactions. Tools from OmniTI Labs further exploit these Solaris features for PostgreSQL monitoring, testing, and disaster recovery. In summary, combining PostgreSQL with the advanced virtualization and tracing tools in Solaris can significantly enhance database capabilities and operations.
This document discusses advanced Postgres monitoring. It begins with an introduction of the speaker and an agenda for the discussion. It then covers selection criteria for monitoring solutions, compares open source and SAAS monitoring options, and provides examples of collecting specific Postgres metrics using CollectD. It also discusses alerting, handling monitoring changes, and being prepared to respond to incidents outside of normal hours.
Mark Wong
pg_proctab is a collection of PostgreSQL stored functions that provide access to the operating system process table using SQL. We'll show you which functions are available and where they collect the data, and give examples of their use to collect processor and I/O statistics on SQL queries.
pg_proctab: Accessing System Stats in PostgreSQLMark Wong
pg_proctab is a collection of PostgreSQL stored functions that provide access to the operating system process table using SQL. We'll show you which functions are available and where they collect the data, and give examples of their use to collect processor and I/O statistics on SQL queries.
Optimizing your app by understanding your Postgres | RailsConf 2019 | Samay S...Citus Data
I’m a Postgres person. Period. After talking to many Rails developers about their application performance, I realized many performance issues can be solved by understanding your database a bit better. So I thought I’d share the statistics Postgres captures for you and how you can use them to find slow queries, un-used indexes, or tables which are not getting vacuumed correctly. This talk will cover Postgres tools and tips for the above, including pgstatstatements, useful catalog tables, and recently added Postgres features such as CREATE STATISTICS.
As the popularity of PostgreSQL continues to soar, many companies are exploring ways of migrating their application database over. At Redgate Software, we recently added PostgreSQL as an optional data store for SQL Monitor, our flagship monitoring application, after nearly 18 years of being backed exclusively by SQL Server. Knowing that others will be taking this journey in the near future, we'd like to discuss what we learned. In this training, we'll discuss the planning that needs to take place before a migration begins, including datatype changes, PostgreSQL configuration modifications, and query differences. This will be a mix of slides and demo from our own learnings, as well as those of some clients we've helped along the way.
pg_proctab: Accessing System Stats in PostgreSQLMark Wong
pc_proctab is a collection of PostgreSQL stored functions that allow you to access the operating system process table using SQL. See examples on how to use these stored functions to collect processor and I/O statistics on SQL statements run against the database.
Отладка и устранение проблем в PostgreSQL Streaming Replication.Alexey Lesovsky
Потоковая репликация, которая появилась в 2010 году, стала одной из прорывных фич постгреса и в настоящее время практически ни одна инсталляция не обходится без использования потоковой репликации. Она надежна, легка в настройке, нетребовательна к ресурсам. Однако при всех своих положительных качествах, при её эксплуатации могут возникать различные проблемы и неприятные ситуации. Для диагностики и решения проблем, связанных с потоковой репликацией, есть множество инструментов, как встроенных в PostgreSQL, так и сторонних.
В этом докладе я сделаю обзор доступных инструментов и расскажу, как с помощью этих средств диагностировать различные типы проблем и как устранять их. Рассматривая методы решения, мы также рассмотрим проблемы, которые возникают при эксплуатации потоковой репликации.
Доклад будет полезен DBA и системным администраторам.
1. A PostgreSQL database outage occurred at GitLab on January 31st due to a combination of factors including an increase in load, replication lag, and the deletion of the database directory.
2. Lessons learned include monitoring replication, using tools like pg_basebackup properly, and having backups and disaster recovery processes in place.
3. Recommended preventative measures include setting sane configuration values, automated testing of backups, assigning an owner for data durability, and improving documentation.
This presentation discusses optimizing Linux systems for PostgreSQL databases. Linux is a good choice for databases due to its active development, features, stability, and community support. The presentation covers optimizing various system resources like CPU scheduling, memory, storage I/O, and power management to improve database performance. Specific topics include disabling transparent huge pages, tuning block I/O schedulers, and selecting appropriate scaling governors. The overall message is that Linux can be adapted for database workloads through testing and iterative changes.
This document provides an overview of pgCenter, a tool for managing and monitoring PostgreSQL databases. It describes pgCenter's interface which displays system metrics, PostgreSQL statistics and additional information. The interface shows values for items like CPU and memory usage, database connections, autovacuum operations, and query information. PgCenter provides a quick way to view real-time PostgreSQL and server performance metrics.
The document provides configuration instructions and guidelines for setting up streaming replication between a PostgreSQL master and standby server, including setting parameter values for wal_level, max_wal_senders, wal_keep_segments, creating a dedicated replication role, using pg_basebackup to initialize the standby, and various recovery target options to control the standby's behavior. It also discusses synchronous replication using replication slots and monitoring the replication process on both the master and standby servers.
Slides from Secon'2015 - Software Developers Conference. Penza, Russia.
The database is an essential element of any project. The database must be stable and provide good performance. If you plan to use PostgreSQL in your project, you will run into question the choice of operating system. Linux is one of the most popular operating system today. The combination of flexibility and stability makes Linux a good candidate as a platform for PostgreSQL. However, the default settings are suitable for a wide range of workloads. In this report, I will talk about what settings should pay attention and how they affect the performance of PostgreSQL. Which of these settings are more important, and what - no. How do the PostgreSQL more predictable and stable under normal circumstances or in cases of increasing load.
Rose Cultivation Practices by Kushal Lamichhane.pdfkushallamichhame
This includes the overall cultivation practices of Rose prepared by:
Kushal Lamichhane (AKL)
Instructor
Shree Gandhi Adarsha Secondary School
Kageshowri Manohara-09, Kathmandu, Nepal
Search Engine Optimization (SEO) for Website SuccessMuneeb Rana
Unlock the essentials of Search Engine Optimization (SEO) with this concise, visually driven PowerPoint. Inside you’ll find:
✅ Clear definitions and core concepts of SEO
✅ A breakdown of On‑Page, Off‑Page, and Technical SEO
✅ Actionable best‑practice checklists for keyword research, content optimization, and link building
✅ A quick‑start toolkit featuring Google Analytics, Search Console, Ahrefs, SEMrush, and Moz
✅ Real‑world case study demonstrating a 70 % organic‑traffic lift
✅ Common challenges, algorithm updates, and tips for long‑term success
Whether you’re a digital‑marketing student, small‑business owner, or PR professional, this deck will help you boost visibility, build credibility, and drive sustainable traffic. Download, share, and start optimizing today!
Different pricelists for different shops in odoo Point of Sale in Odoo 17Celine George
Price lists are a useful tool for managing the costs of your goods and services. This can assist you in working with other businesses effectively and maximizing your revenues. Additionally, you can provide your customers discounts by using price lists.
How to Manage Allocations in Odoo 18 Time OffCeline George
Allocations in Odoo 18 Time Off allow you to assign a specific amount of time off (leave) to an employee. These allocations can be used to track and manage leave entitlements for employees, such as vacation days, sick leave, etc.
Pests of Rice: Damage, Identification, Life history, and Management.pptxArshad Shaikh
Rice pests can significantly impact crop yield and quality. Major pests include the brown plant hopper (Nilaparvata lugens), which transmits viruses like rice ragged stunt and grassy stunt; the yellow stem borer (Scirpophaga incertulas), whose larvae bore into stems causing deadhearts and whiteheads; and leaf folders (Cnaphalocrocis medinalis), which feed on leaves reducing photosynthetic area. Other pests include rice weevils (Sitophilus oryzae) and gall midges (Orseolia oryzae). Effective management strategies are crucial to minimize losses.
How to Create a Rainbow Man Effect in Odoo 18Celine George
In Odoo 18, the Rainbow Man animation adds a playful and motivating touch to task completion. This cheerful effect appears after specific user actions, like marking a CRM opportunity as won. It’s designed to enhance user experience by making routine tasks more engaging.
Adam Grant: Transforming Work Culture Through Organizational PsychologyPrachi Shah
This presentation explores the groundbreaking work of Adam Grant, renowned organizational psychologist and bestselling author. It highlights his key theories on giving, motivation, leadership, and workplace dynamics that have revolutionized how organizations think about productivity, collaboration, and employee well-being. Ideal for students, HR professionals, and leadership enthusiasts, this deck includes insights from his major works like Give and Take, Originals, and Think Again, along with interactive elements for enhanced engagement.
Forestry Model Exit Exam_2025_Wollega University, Gimbi Campus.pdfChalaKelbessa
This is Forestry Exit Exam Model for 2025 from Department of Forestry at Wollega University, Gimbi Campus.
The exam contains forestry courses such as Dendrology, Forest Seed and Nursery Establishment, Plantation Establishment and Management, Silviculture, Forest Mensuration, Forest Biometry, Agroforestry, Biodiversity Conservation, Forest Business, Forest Fore, Forest Protection, Forest Management, Wood Processing and others that are related to Forestry.
THERAPEUTIC COMMUNICATION included definition, characteristics, nurse patient...parmarjuli1412
The document provides an overview of therapeutic communication, emphasizing its importance in nursing to address patient needs and establish effective relationships. THERAPEUTIC COMMUNICATION included some topics like introduction of COMMUNICATION, definition, types, process of communication, definition therapeutic communication, goal, techniques of therapeutic communication, non-therapeutic communication, few ways to improved therapeutic communication, characteristics of therapeutic communication, barrier of THERAPEUTIC RELATIONSHIP, introduction of interpersonal relationship, types of IPR, elements/ dynamics of IPR, introduction of therapeutic nurse patient relationship, definition, purpose, elements/characteristics , and phases of therapeutic communication, definition of Johari window, uses, what actually model represent and its areas, THERAPEUTIC IMPASSES and its management in 5th semester Bsc. nursing and 2nd GNM students
Analysis of Quantitative Data Parametric and non-parametric tests.pptxShrutidhara2
This presentation covers the following points--
Parametric Tests
• Testing the Significance of the Difference between Means
• Analysis of Variance (ANOVA) - One way and Two way
• Analysis of Co-variance (One-way)
Non-Parametric Tests:
• Chi-Square test
• Sign test
• Median test
• Sum of Rank test
• Mann-Whitney U-test
Moreover, it includes a comparison of parametric and non-parametric tests, a comparison of one-way ANOVA, two-way ANOVA, and one-way ANCOVA.
Stewart Butler - OECD - How to design and deliver higher technical education ...EduSkills OECD
Stewart Butler, Labour Market Economist at the OECD presents at the webinar 'How to design and deliver higher technical education to develop in-demand skills' on 3 June 2025. You can check out the webinar recording via our website - https://oecdedutoday.com/webinars/ .
You can check out the Higher Technical Education in England report via this link 👉 - https://www.oecd.org/en/publications/higher-technical-education-in-england-united-kingdom_7c00dff7-en.html
You can check out the pathways to professions report here 👉 https://www.oecd.org/en/publications/pathways-to-professions_a81152f4-en.html
Completed Sunday 6/8. For Weekend 6/14 & 15th. (Fathers Day Weekend US.) These workshops are also timeless for future students TY. No admissions needed.
A 9th FREE WORKSHOP
Reiki - Yoga
“Intuition-II, The Chakras”
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Thank you for attending our workshops.
If you are new, do welcome.
Grad Students: I am planning a Reiki-Yoga Master Course (As a package). I’m Fusing both together.
This will include the foundation of each practice. Our Free Workshops can be used with any Reiki Yoga training package. Traditional Reiki does host rules and ethics. Its silent and within the JP Culture/Area/Training/Word of Mouth. It allows remote healing but there’s limits As practitioners and masters, we are not allowed to share certain secrets/tools. Some content is designed only for “Masters”. Some yoga are similar like the Kriya Yoga-Church (Vowed Lessons). We will review both Reiki and Yoga (Master tools) in the Course upcoming.
S9/This Week’s Focus:
* A continuation of Intuition-2 Development. We will review the Chakra System - Our temple. A misguided, misused situation lol. This will also serve Attunement later.
Thx for tuning in. Your time investment is valued. I do select topics related to our timeline and community. For those seeking upgrades or Reiki Levels. Stay tuned for our June packages. It’s for self employed/Practitioners/Coaches…
Review & Topics:
* Reiki Is Japanese Energy Healing used Globally.
* Yoga is over 5k years old from India. It hosts many styles, teacher versions, and it’s Mainstream now vs decades ago.
* Anything of the Holistic, Wellness Department can be fused together. My origins are Alternative, Complementary Medicine. In short, I call this ND. I am also a metaphysician. I learnt during the 90s New Age Era. I forget we just hit another wavy. It’s GenZ word of Mouth, their New Age Era. WHOA, History Repeats lol. We are fusing together.
* So, most of you have experienced your Spiritual Awakening. However; The journey wont be perfect. There will be some roller coaster events. The perks are: We are in a faster Spiritual Zone than the 90s. There’s more support and information available.
(See Presentation for all sections, THX AGAIN.)
*Order Hemiptera:*
Hemiptera, commonly known as true bugs, is a large and diverse order of insects that includes cicadas, aphids, leafhoppers, and shield bugs. Characterized by their piercing-sucking mouthparts, Hemiptera feed on plant sap, other insects, or small animals. Many species are significant pests, while others are beneficial predators.
*Order Neuroptera:*
Neuroptera, also known as net-winged insects, is an order of insects that includes lacewings, antlions, and owlflies. Characterized by their delicate, net-like wing venation and large, often prominent eyes, Neuroptera are predators that feed on other insects, playing an important role in biological control. Many species have aquatic larvae, adding to their ecological diversity.
How to Manage Maintenance Request in Odoo 18Celine George
Efficient maintenance management is crucial for keeping equipment and work centers running smoothly in any business. Odoo 18 provides a Maintenance module that helps track, schedule, and manage maintenance requests efficiently.
How to Create Time Off Request in Odoo 18 Time OffCeline George
Odoo 18 provides an efficient way to manage employee leave through the Time Off module. Employees can easily submit requests, and managers can approve or reject them based on company policies.
Coleoptera, commonly known as beetles, is the largest order of insects, comprising approximately 400,000 described species. Beetles can be found in almost every habitat on Earth, exhibiting a wide range of morphological, behavioral, and ecological diversity. They have a hardened exoskeleton, with the forewings modified into elytra that protect the hind wings. Beetles play important roles in ecosystems as decomposers, pollinators, and food sources for other animals, while some species are considered pests in agriculture and forestry.
This presentation was provided by Jennifer Gibson of Dryad, during the first session of our 2025 NISO training series "Secrets to Changing Behavior in Scholarly Communications." Session One was held June 5, 2025.
HOW YOU DOIN'?
Cool, cool, cool...
Because that's what she said after THE QUIZ CLUB OF PSGCAS' TV SHOW quiz.
Grab your popcorn and be seated.
QM: THARUN S A
BCom Accounting and Finance (2023-26)
THE QUIZ CLUB OF PSGCAS.
2. What is PostgreSQL activity statistics.
How to use statistics effectively.
How to solve problems with statistics.
http://goo.gl/uDuSvs
Agenda
5. $ ps hf -u postgres -o cmd
/usr/pgsql-9.5/bin/postgres -D /var/lib/pgsql/9.5/data
_ postgres: logger process
_ postgres: checkpointer process
_ postgres: writer process
_ postgres: wal writer process
_ postgres: autovacuum launcher process
_ postgres: stats collector process
_ postgres: postgres pgbench [local] idle in transaction
_ postgres: postgres pgbench [local] idle
_ postgres: postgres pgbench [local] UPDATE
_ postgres: postgres pgbench [local] UPDATE waiting
_ postgres: postgres pgbench [local] UPDATE
Black box
6. Write Ahead Log
Shared
Buffers
Buffers IO Autovacuum Workers
Autovacuum Launcher
Background Workers
Indexes IO
Query Execution
Query Planning
Client Backends Postmaster
Relations IO
Logger Process Stats Collector
Logical
Replication
WAL Sender
Process
Archiver
Process
Background
Writer
Checkpointer
Process
Network Storage
Recovery Process
WAL Receiver Process
Tables/Indexes Data Files
Where PostgreSQL spends its time
7. Too much information (more than 100 counters in 9.5).
Statistics are provided as an online counters.
No history (but reset functions are available).
No native handy stat tools in PostgreSQL.
A lot of 3rd party tools and programs.
Problems
8. Too much information (more than 100 counters in 9.5).
Statistics are provided as an online counters.
No history (but reset functions are available).
No native handy stat tools in PostgreSQL.
A lot of 3rd party tools and programs.
Important to use stats directly from PostgreSQL.
Basic SQL skills are required.
Problems
9. Counters in shared memory.
Functions.
Builtin views.
Official extensions in contribs package.
Unofficial extensions.
Statistics sources
13. $ select * from pg_stat_database;
...
blks_read | 7978770895
blks_hit | 9683551077519
...
$ select
sum(blks_hit)*100/sum(blks_hit+blks_read) as hit_ratio
from pg_stat_database;
More is better, and not less than 90%
Cache hit ratio
21. $ select * from pg_stat_replication;
...
sent_location | 1691/EEE65900
write_location | 1691/EEE65900
flush_location | 1691/EEE65900
replay_location | 1691/EEE658D0
...
1692/EEE65900 — location in transaction log (WAL)
All values are equal = ideal
Replication lag
22. Lag causes:
Networking
Storage
CPU
How many bytes written in WAL
$ select
pg_xlog_location_diff(pg_current_xlog_location(),'0/00000000');
Replication lag in bytes
$ select
client_addr,
pg_xlog_location_diff(pg_current_xlog_location(), replay_location)
from pg_stat_replication;
Replication lag in seconds
$ select
extract(epoch from now() - pg_last_xact_replay_timestamp());
Replication lag
23. $ select
client_addr as client,
pg_size_pretty(pg_xlog_location_diff(pg_current_xlog_location(),sent_location)) as pending,
pg_size_pretty(pg_xlog_location_diff(sent_location,write_location)) as write,
pg_size_pretty(pg_xlog_location_diff(write_location,flush_location)) as flush,
pg_size_pretty(pg_xlog_location_diff(flush_location,replay_location)) as replay,
pg _size_pretty(pg_xlog_location_diff(pg_current_xlog_location(),replay_location)) as total
from pg_stat_replication;
client | pending | network | written | flushed | total
-----------+----------+----------+---------+------------+------------
127.0.0.1 | 0 bytes | 0 bytes | 0 bytes | 48 bytes | 48 bytes
10.1.0.8 | 12 GB | 30 MB | 0 bytes | 156 kB | 12 GB
10.2.0.6 | 0 bytes | 48 bytes | 0 bytes | 551 MB | 552 MB
Replication lag
28. $ select
s.relname,
pg_size_pretty(pg_relation_size(relid)),
coalesce(n_tup_ins,0) + 2 * coalesce(n_tup_upd,0) -
coalesce(n_tup_hot_upd,0) + coalesce(n_tup_del,0) AS total_writes,
(coalesce(n_tup_hot_upd,0)::float * 100 / (case when n_tup_upd > 0
then n_tup_upd else 1 end)::float)::numeric(10,2) AS hot_rate,
(select v[1] FROM regexp_matches(reloptions::text,E'fillfactor=(d+)') as
r(v) limit 1) AS fillfactor
from pg_stat_all_tables s
join pg_class c ON c.oid=relid
order by total_writes desc limit 50;
What is Heap-Only Tuples?
HOT does not cause index update.
HOT is only for non-indexed columns.
Big n_tup_hot_upd = good.
How to increase n_tup_hot_upd?
Write activity
32. $ select * from pg_stat_all_indexes where idx_scan = 0;
-[ RECORD 1 ]-+------------------------------------------
relid | 98242
indexrelid | 55732253
schemaname | public
relname | products
indexrelname | products_special2_idx
idx_scan | 0
idx_tup_read | 0
idx_tup_fetch | 0
pg_stat_all_indexes
33. $ select * from pg_stat_all_indexes where idx_scan = 0;
...
indexrelname | products_special2_idx
idx_scan | 0 0 = bad
...
Unused indexes are bad.
Uses storage.
Slow down UPDATE, DELETE, INSERT operations.
Extra work for VACUUM.
Unused indexes
34. $ select * from pg_stat_all_indexes where idx_scan = 0;
...
indexrelname | products_special2_idx
idx_scan | 0 0 = bad
...
Unused indexes are bad.
Uses storage.
Slow down UPDATE, DELETE, INSERT operations.
Extra work for VACUUM.
https://goo.gl/0qXDjl
http://goo.gl/5QxTm4
Unused indexes
38. $ select * from pg_stat_activity;
...
backend_start | 2015-10-14 15:18:03.01039+00
xact_start | 2015-10-14 15:21:15.336325+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
...
Long queries and xacts
39. $ select * from pg_stat_activity;
...
backend_start | 2015-10-14 15:18:03.01039+00
xact_start | 2015-10-14 15:21:15.336325+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
...
$ select
client_addr, usename, datname,
clock_timestamp() - xact_start as xact_age,
clock_timestamp() - query_start as query_age,
query
from pg_stat_activity order by xact_start, query_start;
Long queries and xacts
40. $ select * from pg_stat_activity;
...
backend_start | 2015-10-14 15:18:03.01039+00
xact_start | 2015-10-14 15:21:15.336325+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
...
$ select
client_addr, usename, datname,
clock_timestamp() - xact_start as xact_age,
clock_timestamp() - query_start as query_age,
query
from pg_stat_activity order by xact_start, query_start;
clock_timestamp() for calculating query or transaction age.
Long queries: remember, terminate, optimize.
Long queries and xacts
41. $ select * from pg_stat_activity where state in
('idle in transaction', 'idle in transaction (aborted)';
...
xact_start | 2015-10-14 15:21:21.128192+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
state | idle in transaction
...
Bad xacts
42. $ select * from pg_stat_activity where state in
('idle in transaction', 'idle in transaction (aborted)';
...
xact_start | 2015-10-14 15:21:21.128192+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
state | idle in transaction
...
idle in transaction, idle in transaction (aborted) = bad
Warning value: > 5
clock_timestamp() for calculate xact age.
Bad xacts: remember, terminate, optimize app.
Bad xacts
43. $ select * from pg_stat_activity where waiting;
...
xact_start | 2015-10-14 15:21:21.128192+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
waiting | t
...
Waiting clients
44. $ select * from pg_stat_activity where waiting;
...
xact_start | 2015-10-14 15:21:21.128192+00
query_start | 2015-10-14 15:21:30.336325+00
state_change | 2015-10-14 15:21:30.33635+00
waiting | t
...
waiting = true = bad.
clock_timestamp() for calculating query or xact age.
Enable log_lock_waits GUC, examine server logs.
Use pg_locks for searching blocking query or xact.
Waiting queries: remember, terminate, optimize app.
Waiting clients
47. $ select * from pg_stat_statements;
...
query | SELECT "id" FROM run_plan_xact(?)
calls | 11165832
total_time | 11743325.6880088
rows | 11165832
blk_read_time | 495425.535999976
blk_write_time | 0
Statements average time in ms
$ select (sum(total_time) / sum(calls))::numeric(6,3)
from pg_stat_statements;
The most writing (to shared_buffers) queries
$ select query, shared_blks_dirtied
from pg_stat_statements
where shared_blks_dirtied > 0 order by 2 desc;
pg_stat_statements
48. query total time: 15:43:07 (14.9%, CPU: 18.2%, IO: 9.0%)
сalls: 476 (0.00%) rows: 476,000
avg_time: 118881.54ms (IO: 21.2%)
user: app_user db: ustats
query: SELECT
filepath, type, deviceuid
FROM imvevents
WHERE
state = ?::eventstate
AND servertime BETWEEN $1 AND $2
ORDER BY servertime DESC LIMIT $3 OFFSET $4
https://goo.gl/6025wZ
Query reports
49. query total time: 15:43:07 (14.9%, CPU: 18.2%, IO: 9.0%)
сalls: 476 (0.00%) rows: 476,000
avg_time: 118881.54ms (IO: 21.2%)
user: app_user db: ustats
query: SELECT
filepath, type, deviceuid
FROM imvevents
WHERE
state = ?::eventstate
AND servertime BETWEEN $1 AND $2
ORDER BY servertime DESC LIMIT $3 OFFSET $4
Use sum() for calculating totals.
Calculate queries «contribution» in totals.
Resource usage (CPU, IO).
Query reports
50. pg_statio_all_tables, pg_statio_all_indexes.
pg_stat_user_functions.
Size functions - df *size*
pgstattuple (in official contribs package)
● Bloat estimation for tables and indexes.
● Estimation time depends on table (or index) size.
pg_buffercache (in official contribs package)
● Shared buffers inspection.
● Heavy performance impact (buffers lock).
Behind this talk
51. pgfincore (3rd party module)
● Low-level operations with tables using mincore().
● OS page cache inspection.
pg_stat_kcache (3rd party module)
● Using getrusage() before and after query.
● CPU usage and real filesystem operations stats.
● Requires pg_stat_statements and postgresql >= 9.4.
● No performance impact.
Behind this talk
52. ● The ability to use statistics is useful.
● Statistics are not difficult.
● Statistics help to answer the questions.
● Do experiments.
Resume