Postgres-BDR with Google Cloud PlatformSungJae Yun
This document provides an overview of testing PostgreSQL-BDR with Google Cloud Platform. Key points:
- Google Cloud Platform was chosen as the test environment for its regions in Asia, Europe, and US. Nine virtual machines were created, with two in each region configured for PostgreSQL-BDR and one for performance testing.
- PostgreSQL-BDR was installed on the servers and a cluster was created by joining nodes in Asia, US, and Europe. Pgbench tests were run to measure performance of transactions on the replicated database.
- Pgbench results showed transaction rates increased from around 1000 TPS for a single client/server to over 6000 TPS when distributed across the BDR cluster nodes
The latest version of my PostgreSQL introduction for IL-TechTalks, a free service to introduce the Israeli hi-tech community to new and interesting technologies. In this talk, I describe the history and licensing of PostgreSQL, its built-in capabilities, and some of the new things that were added in the 9.1 and 9.2 releases which make it an attractive option for many applications.
PostgreSQL performance improvements in 9.5 and 9.6Tomas Vondra
The document summarizes performance improvements in PostgreSQL versions 9.5 and 9.6. Some key improvements discussed include optimizations to sorting, hash joins, BRIN indexes, parallel query processing, aggregate functions, checkpoints, and freezing. Performance tests on sorting, hash joins, and parallel queries show significant speedups from these changes, such as faster sorting times and better scalability with parallel queries.
Non-Relational Postgres / Bruce Momjian (EnterpriseDB)Ontico
Postgres has always had strong support for relational storage. However, there are many cases where relational storage is either inefficient or overly restrictive. This talk shows the many ways that Postgres has expanded to support non-relational storage, specifically the ability to store and index multiple values, even unrelated ones, in a single database field. Such storage allows for greater efficiency and access simplicity, and can also avoid the negatives of entity-attribute-value (eav) storage. The talk will cover many examples of multiple-value-per-field storage, including arrays, range types, geometry, full text search, xml, json, and records.
SCALE 15x Minimizing PostgreSQL Major Version Upgrade DowntimeJeff Frost
This document provides instructions for minimizing downtime when performing a major version upgrade of PostgreSQL using logical replication with Slony. It discusses various methods for performing the upgrade, including dump/restore, pg_upgrade, and logical replication with Slony. It then provides a step-by-step guide to setting up logical replication between two PostgreSQL nodes using Slony, including initializing the cluster and nodes, creating replication sets, subscribing nodes, and monitoring the initial synchronization process. The document demonstrates how Slony allows performing a graceful switchover and switchback between nodes when upgrading PostgreSQL versions.
This talk cover various advanced topics in the area of backups:
- incremental backups;
- archive management;
- backup validation;
- retention policies;
etc.
Based on these features, we'll compare various backup/recovery solutions for PostgreSQL.
This information will help you to choose the most appropriate tool for your system.
The document outlines the agenda for the 8th demand seminar held by EXEM, including presentations on PostgreSQL Vacuum and MySQL locks. The PostgreSQL presentation covers the details of Vacuum including its behavior during updates, deletes, and different Vacuum commands. The MySQL presentation covers different types of locks in MySQL including global read locks, table locks, and string locks.
This technical report discusses configuration of the Performance Schema in MySQL 5.6. It describes configuration tables for setting monitoring targets, consumers, instruments, and objects. It shows commands for checking default settings and updating configurations. Benchmarks with different Performance Schema settings show throughput decreased when instruments were enabled but wait events only configuration had less impact than fully enabling instruments.
The document provides information about Amazon Aurora including:
- An overview of Amazon Aurora describing its high performance, scalability, availability and security features compared to other databases.
- Details on Amazon Aurora's architecture which uses a multi-tenant storage layer and integrates with other AWS services for backups, replication and high availability across Availability Zones.
- Descriptions of new Aurora capabilities like Multi-Master which allows applications to read and write to multiple database instances for increased availability without downtime.
The document discusses configuring and monitoring buffer pools and memory settings for a DB2 database instance and partitions. It includes commands to:
- Show buffer pool information and alter a buffer pool size
- View tablespace to buffer pool mappings
- Check database and instance configuration parameters for memory settings
- List instances and reset the monitoring
- View buffer pool snapshots
Обзорный рассказ про новые возможности в мире PostgreSQL для митапа Big Data Minsk User Group 29 апреля 2016 г.: https://www.facebook.com/events/120784531655479/
This document summarizes the results of comparing standard Vacuum and Vacuum Full operations in PostgreSQL. Standard Vacuum deletes just deleted tuple identifiers, while Vacuum Full rewrites the entire table. The summary describes how inserting, deleting, and vacuuming data affects the table size and contents as seen in the data files.
MySQLinsanity! This document provides an overview of Stanley Huang's MySQL performance tuning experience and expertise. It begins with introductions and background on Stanley Huang. It then discusses the typical phases of MySQL performance tuning projects, including SQL tuning and RDBMS tuning. Specific tips are provided around topics like slow query logging, index usage, partitioning, and server configuration. The document concludes with an invitation for questions.
The document summarizes the new features in PostgreSQL 11, including enhancements to table partitioning such as hash partitioning, default partitions, updating partition keys, automatically creating indexes on partitions, adding unique constraints to partitions, and enabling partition-wise joins and aggregates. It also provides examples of using various partitioning features and explains the benefits of partition-wise processing.
PGDay.Amsterdam 2018 - Stefan Fercot - Save your data with pgBackRestPGDay.Amsterdam
Ever heard of Point-in-time recovery? pgBackRest is an awsome tool to handle backups, restores and even helps you build streaming replication ! This talk will introduce the tool, its basic features and how to use it.
This document summarizes a presentation on Multi Version Concurrency Control (MVCC) in PostgreSQL. It begins with definitions and history of MVCC, describing how it allows transactions to read and write without blocking each other. It then discusses two approaches to MVCC - storing old versions in the main database (PostgreSQL) vs a separate area (Oracle). The rest of the document does a deep dive on how MVCC is implemented in PostgreSQL specifically, showing how tuple headers track transaction IDs and pointers to maintain multiple versions of rows.
Spencer Christensen
There are many aspects to managing an RDBMS. Some of these are handled by an experienced DBA, but there are a good many things that any sys admin should be able to take care of if they know what to look for.
This presentation will cover basics of managing Postgres, including creating database clusters, overview of configuration, and logging. We will also look at tools to help monitor Postgres and keep an eye on what is going on. Some of the tools we will review are:
* pgtop
* pg_top
* pgfouine
* check_postgres.pl.
Check_postgres.pl is a great tool that can plug into your Nagios or Cacti monitoring systems, giving you even better visibility into your databases.
This presentation covers all aspects of PostgreSQL administration, including installation, security, file structure, configuration, reporting, backup, daily maintenance, monitoring activity, disk space computations, and disaster recovery. It shows how to control host connectivity, configure the server, find the query being run by each session, and find the disk space used by each database.
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.
1. The document discusses Oracle RAC Cluster Synchronization Services (CSS) and how it handles split-brain situations when network failures occur.
2. CSS uses services like Group Management and Node Monitoring to manage cluster membership and detect node failures. It relies on voting disks to resolve split-brain situations when nodes cannot communicate over the network.
3. During a split-brain, the CSS reconfiguration process attempts to keep the largest surviving subcluster alive by exiling nodes from the smaller competing subclusters. It does this by checking network and disk heartbeats as well as voting information stored on voting disks.
This document summarizes full text search capabilities in PostgreSQL. It begins with an introduction and overview of common full text search solutions. It then discusses reasons to use full text search in PostgreSQL, including consistency and no need for additional software. The document covers basics of full text search in PostgreSQL like to_tsvector, to_tsquery, and indexes. It also covers fuzzy full text search using pg_trgm and functions like similarity. Other topics mentioned include ts_headline, ts_rank, and the RUM extension.
PostgreSQL is one of the most advanced relational databases. It offers superb replication capabilities. The most important features are: Streaming replication, Point-In-Time-Recovery, advanced monitoring, etc.
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. These stored functions currently only work on Linux-based systems.
10 Reasons to Start Your Analytics Project with PostgreSQLSatoshi Nagayasu
PostgreSQL provides several advantages for analytics projects:
1) It allows connecting to external data sources and performing analytics queries across different data stores using features like foreign data wrappers.
2) Features like materialized views, transactional DDLs, and rich SQL capabilities help build effective data warehouses and data marts for analytics.
3) Performance optimizations like table partitioning, BRIN indexes, and parallel queries enable PostgreSQL to handle large datasets and complex queries efficiently.
[Pgday.Seoul 2019] Citus를 이용한 분산 데이터베이스PgDay.Seoul
This document summarizes how to set up and use Citus, an open-source PostgreSQL-based distributed database. It explains how to install Citus, add worker nodes, create distributed tables, and use features like reference tables to perform distributed queries across the cluster.
PostgreSQL 8.4 introduced several new features including common table expressions, window functions, parallel restore, and performance improvements. Version 9.0 will focus on improving replication support through streaming replication and read-only hot standby servers. Overall, PostgreSQL continues to expand its feature set to better support modern SQL standards.
This talk cover various advanced topics in the area of backups:
- incremental backups;
- archive management;
- backup validation;
- retention policies;
etc.
Based on these features, we'll compare various backup/recovery solutions for PostgreSQL.
This information will help you to choose the most appropriate tool for your system.
The document outlines the agenda for the 8th demand seminar held by EXEM, including presentations on PostgreSQL Vacuum and MySQL locks. The PostgreSQL presentation covers the details of Vacuum including its behavior during updates, deletes, and different Vacuum commands. The MySQL presentation covers different types of locks in MySQL including global read locks, table locks, and string locks.
This technical report discusses configuration of the Performance Schema in MySQL 5.6. It describes configuration tables for setting monitoring targets, consumers, instruments, and objects. It shows commands for checking default settings and updating configurations. Benchmarks with different Performance Schema settings show throughput decreased when instruments were enabled but wait events only configuration had less impact than fully enabling instruments.
The document provides information about Amazon Aurora including:
- An overview of Amazon Aurora describing its high performance, scalability, availability and security features compared to other databases.
- Details on Amazon Aurora's architecture which uses a multi-tenant storage layer and integrates with other AWS services for backups, replication and high availability across Availability Zones.
- Descriptions of new Aurora capabilities like Multi-Master which allows applications to read and write to multiple database instances for increased availability without downtime.
The document discusses configuring and monitoring buffer pools and memory settings for a DB2 database instance and partitions. It includes commands to:
- Show buffer pool information and alter a buffer pool size
- View tablespace to buffer pool mappings
- Check database and instance configuration parameters for memory settings
- List instances and reset the monitoring
- View buffer pool snapshots
Обзорный рассказ про новые возможности в мире PostgreSQL для митапа Big Data Minsk User Group 29 апреля 2016 г.: https://www.facebook.com/events/120784531655479/
This document summarizes the results of comparing standard Vacuum and Vacuum Full operations in PostgreSQL. Standard Vacuum deletes just deleted tuple identifiers, while Vacuum Full rewrites the entire table. The summary describes how inserting, deleting, and vacuuming data affects the table size and contents as seen in the data files.
MySQLinsanity! This document provides an overview of Stanley Huang's MySQL performance tuning experience and expertise. It begins with introductions and background on Stanley Huang. It then discusses the typical phases of MySQL performance tuning projects, including SQL tuning and RDBMS tuning. Specific tips are provided around topics like slow query logging, index usage, partitioning, and server configuration. The document concludes with an invitation for questions.
The document summarizes the new features in PostgreSQL 11, including enhancements to table partitioning such as hash partitioning, default partitions, updating partition keys, automatically creating indexes on partitions, adding unique constraints to partitions, and enabling partition-wise joins and aggregates. It also provides examples of using various partitioning features and explains the benefits of partition-wise processing.
PGDay.Amsterdam 2018 - Stefan Fercot - Save your data with pgBackRestPGDay.Amsterdam
Ever heard of Point-in-time recovery? pgBackRest is an awsome tool to handle backups, restores and even helps you build streaming replication ! This talk will introduce the tool, its basic features and how to use it.
This document summarizes a presentation on Multi Version Concurrency Control (MVCC) in PostgreSQL. It begins with definitions and history of MVCC, describing how it allows transactions to read and write without blocking each other. It then discusses two approaches to MVCC - storing old versions in the main database (PostgreSQL) vs a separate area (Oracle). The rest of the document does a deep dive on how MVCC is implemented in PostgreSQL specifically, showing how tuple headers track transaction IDs and pointers to maintain multiple versions of rows.
Spencer Christensen
There are many aspects to managing an RDBMS. Some of these are handled by an experienced DBA, but there are a good many things that any sys admin should be able to take care of if they know what to look for.
This presentation will cover basics of managing Postgres, including creating database clusters, overview of configuration, and logging. We will also look at tools to help monitor Postgres and keep an eye on what is going on. Some of the tools we will review are:
* pgtop
* pg_top
* pgfouine
* check_postgres.pl.
Check_postgres.pl is a great tool that can plug into your Nagios or Cacti monitoring systems, giving you even better visibility into your databases.
This presentation covers all aspects of PostgreSQL administration, including installation, security, file structure, configuration, reporting, backup, daily maintenance, monitoring activity, disk space computations, and disaster recovery. It shows how to control host connectivity, configure the server, find the query being run by each session, and find the disk space used by each database.
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.
1. The document discusses Oracle RAC Cluster Synchronization Services (CSS) and how it handles split-brain situations when network failures occur.
2. CSS uses services like Group Management and Node Monitoring to manage cluster membership and detect node failures. It relies on voting disks to resolve split-brain situations when nodes cannot communicate over the network.
3. During a split-brain, the CSS reconfiguration process attempts to keep the largest surviving subcluster alive by exiling nodes from the smaller competing subclusters. It does this by checking network and disk heartbeats as well as voting information stored on voting disks.
This document summarizes full text search capabilities in PostgreSQL. It begins with an introduction and overview of common full text search solutions. It then discusses reasons to use full text search in PostgreSQL, including consistency and no need for additional software. The document covers basics of full text search in PostgreSQL like to_tsvector, to_tsquery, and indexes. It also covers fuzzy full text search using pg_trgm and functions like similarity. Other topics mentioned include ts_headline, ts_rank, and the RUM extension.
PostgreSQL is one of the most advanced relational databases. It offers superb replication capabilities. The most important features are: Streaming replication, Point-In-Time-Recovery, advanced monitoring, etc.
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. These stored functions currently only work on Linux-based systems.
10 Reasons to Start Your Analytics Project with PostgreSQLSatoshi Nagayasu
PostgreSQL provides several advantages for analytics projects:
1) It allows connecting to external data sources and performing analytics queries across different data stores using features like foreign data wrappers.
2) Features like materialized views, transactional DDLs, and rich SQL capabilities help build effective data warehouses and data marts for analytics.
3) Performance optimizations like table partitioning, BRIN indexes, and parallel queries enable PostgreSQL to handle large datasets and complex queries efficiently.
[Pgday.Seoul 2019] Citus를 이용한 분산 데이터베이스PgDay.Seoul
This document summarizes how to set up and use Citus, an open-source PostgreSQL-based distributed database. It explains how to install Citus, add worker nodes, create distributed tables, and use features like reference tables to perform distributed queries across the cluster.
PostgreSQL 8.4 introduced several new features including common table expressions, window functions, parallel restore, and performance improvements. Version 9.0 will focus on improving replication support through streaming replication and read-only hot standby servers. Overall, PostgreSQL continues to expand its feature set to better support modern SQL standards.
Стажировка 2016-07-27 02 Денис Нелюбин. PostgreSQL и jsonbSmartTools
The document discusses using PostgreSQL and JSONB data. It covers installing PostgreSQL, connecting to a database, configuring network access and authentication, creating a database and user, inserting and querying JSONB data using operators like ->> and ->, updating and deleting rows, and creating a functional index to query on fields within the JSONB data.
Build a Complex, Realtime Data Management App with Postgres 14!Jonathan Katz
Congratulations: you've been selected to build an application that will manage reservations for rooms!
On the surface, this sounds simple, but you are building a system for managing a high traffic reservation web page, so we know that a lot of people will be accessing the system. Therefore, we need to ensure that the system can handle all of the eager users that will be flooding the website checking to see what availability each room has.
Fortunately, PostgreSQL is prepared for this! And even better, we will be using Postgres 14 to make the problem even easier!
We will explore the following PostgreSQL features:
* Data types and their functionality, such as:
* Data/Time types
* Ranges / Multirnages
Indexes such as:
* GiST
* Common Table Expressions and Recursion (though multiranges will make things easier!)
* Set generating functions and LATERAL queries
* Functions and the PL/PGSQL
* Triggers
* Logical decoding and streaming
We will be writing our application primary with SQL, though we will sneak in a little bit of Python and using Kafka to demonstrate the power of logical decoding.
At the end of the presentation, we will have a working application, and you will be happy knowing that you provided a wonderful user experience for all users made possible by the innovation of PostgreSQL!
This document summarizes the key features and changes in PostgreSQL 9.0 beta release. It highlights major new features like replication, permissions, and anonymous code blocks. It also briefly outlines many other enhancements, including performance improvements, monitoring tools, JSON/XML output for EXPLAIN, and mobile app contest. The presentation aims to excite developers about trying the new beta version.
PostgreSQL is an open source object-relational database system that has been in development since 1982. It supports Linux, Windows, Mac OS X, and Solaris and can be installed using package managers or installers. PostgreSQL provides many features including procedural languages, functions, indexes, triggers, multi-version concurrency control, and point-in-time recovery. It also has various administration and development tools.
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.
Groovy is a dynamic language for the Java Virtual Machine that aims to provide a concise, readable syntax with features like closures, metaprogramming and domain-specific language support. Some key features include dynamic typing, operator overloading, builders for generating XML/Swing code and the ability to extend any class or category of objects with additional methods. Groovy aims to be fully interoperable with Java while allowing more compact representations of common patterns.
Testing multi outputformat based mapreduceAshok Agarwal
The document describes using a MultiOutputFormat in MapReduce to generate separate output files for each stock price based on input that contains stock price data. It includes code for a mapper that extracts the stock name and price from each input record and a reducer that writes these values to individual files for each stock name. Unit tests are also described to test the reducer by mocking the MultipleOutputs class and validating that the output files contain the expected stock price values.
PostgreSQL is a free and open-source relational database management system that provides high performance and reliability. It supports replication through various methods including log-based asynchronous master-slave replication, which the presenter recommends as a first option. The upcoming PostgreSQL 9.4 release includes improvements to replication such as logical decoding and replication slots. Future releases may add features like logical replication consumers and SQL MERGE statements. The presenter took questions at the end and provided additional resources on PostgreSQL replication.
This document provides a summary of PostgreSQL 9.1 features presented at the Postgres Open conference in September 2011. It discusses synchronous and asynchronous replication, per-column collations, writable common table expressions, serialized snapshot isolation, unlogged tables, extensions, and other features. Sessions at the conference focused on unlogged tables, accelerating local search, PostgreSQL in data management, and serializable transactions.
PostgreSQL is designed to be easily extensible. For this reason, extensions loaded into the database can function just like features that are built in. In this session, we will learn more about PostgreSQL extension framework, how are they built, look at some popular extensions, management of these extensions in your deployments.
This document summarizes new features in PostgreSQL 9, including common table expressions that allow defining in-place data structures using queries, improvements to hot standby and streaming replication for database synchronization, deferrable unique constraints, exclusion constraints for more complex uniqueness checks, and enhancements to PL/PgSQL like unnamed functions and named parameter calls.
CQL performance with Apache Cassandra 3.0 (Aaron Morton, The Last Pickle) | C...DataStax
The 3.0 storage engine re-write is the biggest and most exciting change to ever happen in Apache Cassandra. The new storage engine can efficiently store and read data from disk using the same concepts present in the CQL 3 language. This has delivered large space savings, and creates new performance characteristics.
In this talk Aaron Morton, Co Founder at The Last Pickle and Apache Cassandra Committer, will discuss the 3.0 storage engine, it's layout and performance characteristics.
About the Speaker
Aaron Morton CEO, The Last Pickle
Aaron Morton is the Co Founder & CEO at The Last Pickle (thelastpickle.com). A professional services company that works with clients to deliver and improve Apache Cassandra based solutions. He's based in New Zealand, is an Apache Cassandra Committer and a DataStax MVP for Apache Cassandra.
PostgreSQL is an open-source relational database management system that runs on Linux, Unix, Windows and Mac OS. It supports SQL queries, transactions, foreign keys, triggers and views. To install PostgreSQL, download the installer package for your platform and run through the installation process, which sets up the database files and creates a default database and user account. The psql command-line tool can then be used to interact with and administer the PostgreSQL database using SQL commands.
This document provides an overview of basic usage of the Apache Spark framework for data analysis. It describes what Spark is, how to install it, and how to use it from Scala, Python, and R. It also explains the key concepts of RDDs (Resilient Distributed Datasets), transformations, and actions. Transformations like filter, map, join, and reduce return new RDDs, while actions like collect, count, and first return results to the driver program. The document provides examples of common transformations and actions in Spark.
Les tests unitaires se sont pas limités au code des applications, des tests peuvent également être effectués sur les données et les schémas des bases de données.
Conférence donnée lors du meetup PostgreSQL le 22 juin 2016 à Nantes
John Melesky - Federating Queries Using Postgres FDW @ Postgres OpenPostgresOpen
This document discusses federating queries across PostgreSQL databases using foreign data wrappers (FDWs). It begins by introducing the author and their background. It then covers using FDWs to partition tables across multiple nodes for queries, the benefits over traditional views, and demonstrates counting rows across nodes. It notes limitations like network overhead, lack of keys/constraints, and single-threaded execution. Finally, it discusses strategies like using many small nodes, node-level partitioning, distributed processing, and multi-headed setups to optimize federated querying.
[pgday.Seoul 2022] PostgreSQL with Google CloudPgDay.Seoul
Google Cloud offers several fully managed database services for PostgreSQL workloads, including Cloud SQL and AlloyDB.
Cloud SQL provides a fully managed relational database service for PostgreSQL, MySQL, and SQL Server. It offers 99.999% availability, unlimited scaling, and automatic failure recovery.
AlloyDB is a new database engine compatible with PostgreSQL that provides up to 4x faster transactions and 100x faster analytics queries than standard PostgreSQL. It features independent scaling of storage and computing resources.
Google Cloud aims to be the best home for PostgreSQL workloads by providing compatibility with open source PostgreSQL and enterprise-grade features, performance, reliability, and support across its database services.
[Pgday.Seoul 2021] 2. Porting Oracle UDF and OptimizationPgDay.Seoul
The document discusses porting functions from Oracle to PostgreSQL and optimizing performance, including different function types in PostgreSQL like SQL functions and PL/pgSQL functions, as well as volatility categories. It also provides examples of test data created for use in examples and covers strategies for analyzing inefficient Oracle functions and improving them to leverage the PostgreSQL optimizer.
[Pgday.Seoul 2018] PostgreSQL 성능을 위해 개발된 라이브러리 OS 소개 apposhaPgDay.Seoul
This document introduces AppOS, an operating system specialized for database performance. It discusses how AppOS improves on Linux by being more optimized for database workloads through techniques like specialized caching, I/O scheduling based on database priorities, and atomic writes. It also explains how AppOS is portable, high performing, and extensible to support different databases through its modular design. Future plans include improving cache management, parallel query optimization, and cooperative CPU scheduling.
[Pgday.Seoul 2018] 이기종 DB에서 PostgreSQL로의 Migration을 위한 DB2PGPgDay.Seoul
This document discusses DB2PG, a tool for migrating data between different database management systems. It began as an internal project in 2016 and has expanded its supported migration paths over time. It can now migrate schemas, tables, data types and more between Oracle, SQL Server, DB2, MySQL and other databases. The tool uses Java and supports multi-threaded imports for faster migration. Configuration files allow customizing the data type mappings and queries used during migration. The tool is open source and available on GitHub under the GPL v3 license.
[Pgday.Seoul 2018] AWS Cloud 환경에서 PostgreSQL 구축하기PgDay.Seoul
The document discusses setting up PostgreSQL in an AWS cloud environment. It provides information on using PostgreSQL with AWS services like RDS, Aurora, EC2 and EBS. It compares IaaS vs PaaS deployment options and discusses features of AWS databases like backups, read replicas, high availability and security. The document also summarizes benefits of Aurora over traditional databases like faster crash recovery, continuous backups and independent scaling of database layers.
[Pgday.Seoul 2017] 6. GIN vs GiST 인덱스 이야기 - 박진우PgDay.Seoul
B-tree is ideal for unique values while GIN is ideal for indexes with many duplicates. GIST can index most data types and is useful for operations like containment and overlap. A comparison found that GIN indexes have faster search times but slower update times than GiST indexes, and GIN indexes are larger in size and take longer to build. In summary, the best index type depends on the data characteristics and query operations.
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxAnoop Ashok
In today's fast-paced retail environment, efficiency is key. Every minute counts, and every penny matters. One tool that can significantly boost your store's efficiency is a well-executed planogram. These visual merchandising blueprints not only enhance store layouts but also save time and money in the process.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB
Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
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Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
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Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
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Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
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AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
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#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
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This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
2. 강연자 소개
u 이름: 이지호, 호: 서치(슬기 서, 기다릴 치), 영문 중간 이름: persy
u 소속: 한국방송통신대학교 대학원 정보과학과, (주)위비즈넷, 사단법인 E.S.C
u 98년 알짜레드햇 6.0에서 PostgreSQL 6.4를 보고 아!!! 이거다!!
3. PostgreSQL은 어떤 데이터베이스에요?
u PostgreSQL은 1986년 캘리포니아 버클리에서 시작해 1988년 만들어진 BSD 기반의 오픈소
스 데이터베이스
u 이후 여러번의 지속적인 개선을 통해 1996년 PostgreSQL 발표.
u PostgreSQL이 제공하는 특징
u 연산자, 복합 자료형, 집계 함수, 자료형 변환자, 확장 함수 등 제공
u 테이블 생성 및 조회시 상속 기능 사용 가능
u 함수(프로시저) 사용 가능(pl/pgsql이 공식이나 python, java, php, perl, tcl도 가능)
u 이름은 어떻게 부르나요? -> 여러분이 부르고 싶은대로 부르세요.
4. PostgreSQL 커뮤니티
u 한국 PostgreSQL 사용자 모임은
u http://www.facebook.com/groups/postgres.kr
u 2016년 10월 10일 기준 1,030명에 이르는 회원수를 자랑합니다.
u 모임은?
u 매달 넷째주 일요일에 애 안봐도 되고, 애인 안 만나도 되고, 다른 할 일도 없는 분들이
모여 정기 기술 세미나를 오후 2시 ~ 오후 5시까지 하고,
u 주말의 필수품목인 ‘알콜’ Drinking을 하지 않는 정말 건전한 모임을 합니다.
6. Parallel execution of sequential scans,
joins and aggregates
u max_parallel_workers_per_gather (integer) - 병렬 쿼리를 실행할 작업자 수. 기본값 0
u max_worker_processes (integer) - 백그라운드 프로세스의 최대 수. 기본값: 시스템 코어 수
u parallel_setup_cost (floating point) - Sets the planner's estimate of the cost of launching
parallel worker processes. The default is 1000.
u parallel_tuple_cost (floating point) - Sets the planner's estimate of the cost of transferring
one tuple from a parallel worker process to another process. The default is 0.1.
u min_parallel_relation_size (integer) - Sets the minimum size of relations to be considered
for parallel scan. The default is 8 megabytes (8MB).
7. Parallel execution of sequential scans,
joins and aggregates
=# EXPLAIN SELECT count(*) FROM partest;
QUERY PLAN
-----------------------------------------------------------------------
Aggregate (cost=37059.38..37059.39 rows=1 width=8)
-> Seq Scan on partest (cost=0.00..31417.50 rows=2256750 width=0)
8. Parallel execution of sequential scans,
joins and aggregates
template1=# set max_parallel_workers_per_gather = 1;
template1=# EXPLAIN SELECT count(*) FROM partest;
QUERY PLAN
-----------------------------------------------------------------------
Finalize Aggregate (cost=24556.00..24556.01 rows=1 width=8)
-> Gather (cost=24555.88..24555.99 rows=1 width=8)
Workers Planned: 1
-> Partial Aggregate (cost=23555.88..23555.89 rows=1 width=8)
-> Parallel Seq Scan on partest (cost=0.00..20614.71 rows=1176471 width=0)
9. Parallel execution of sequential scans,
joins and aggregates
template1=# set max_parallel_workers_per_gather = 3;
template1=# EXPLAIN SELECT count(*) FROM partest;
QUERY PLAN
-----------------------------------------------------------------------
Finalize Aggregate (cost=20266.88..20266.89 rows=1 width=8)
-> Gather (cost=20266.67..20266.88 rows=1 width=8)
Workers Planned: 2
-> Partial Aggregate (cost=19266.67..19266.68 rows=1 width=8)
-> Parallel Seq Scan on partest (cost=0.00..17183.33 rows=833333 width=0)
10. Parallel execution of sequential scans,
joins and aggregates
template1=# set max_parallel_workers_per_gather = 5;
template1=# explain analyze select min(col1) from partest;
QUERY PLAN
-----------------------------------------------------------------------
Finalize Aggregate (cost=127493.90..127493.91 rows=1 width=8) …
-> Gather (cost=127493.37..127493.88 rows=5 width=8) …
Workers Planned: 5
Workers Launched: 5
-> Partial Aggregate (cost=126493.37..126493.38 rows=1 width=8) …
-> Parallel Seq Scan on partest (cost=0.00..121493.30 rows=…) …
Planning time: 0.110 ms
Execution time: 1422.139 ms
11. Parallel execution of sequential scans,
joins and aggregates
template1=# set max_parallel_workers_per_gather = 6;
template1=# explain analyze select min(col1) from partest;
QUERY PLAN
-----------------------------------------------------------------------
Finalize Aggregate (cost=127493.90..127493.91 rows=1 width=8) …
-> Gather (cost=127493.37..127493.88 rows=5 width=8) …
Workers Planned: 6
Workers Launched: 6
-> Partial Aggregate (cost=126493.37..126493.38 rows=1 width=8) …
-> Parallel Seq Scan on partest (cost=0.00..121493.30 rows=…) …
Planning time: 0.110 ms
Execution time: 1422.139 ms
12. Autovacuum no longer performs
repetitive scanning of old data
u PostgreSQL 9.6 부터 성능 향상을 위해 영구보관용 데이터에 대해서
auto vacuum 중지
u PostgreSQL 9.6 이전까지 모든 힙 페이지 스캔
u 9.6은 마지막 동결 이후 수정된 페이지만 스캔
u 이 결정 덕분에 쓰기가 드문 테이블 성능 향상 혜택
14. Synchronous replication now allows multiple
standby servers for increased reliability
synchronous_commit = off
15. Synchronous replication now allows multiple
standby servers for increased reliability
synchronous_commit = local
16. Synchronous replication now allows multiple
standby servers for increased reliability
synchronous_commit = on (default)
17. Synchronous replication now allows multiple
standby servers for increased reliability
synchronous_commit = remote_write
18. Synchronous replication now allows multiple
standby servers for increased reliability
synchronous_commit = remote_apply
19. Synchronous replication now allows multiple
standby servers for increased reliability
u synchronous_standby_names =
‘standby_name [,
…]’
u synchronous_standby_names =
‘num (standby_name [,
…])’
u ex)
synchronous_standby_names =
‘2(*)’
u synchronous_standby_names is
empty
u If
this parameter
is
empty as
shown it
changes behaviour of
setting synchronous_commit to on, remote_write or remote_apply to
behave same
as local (ie,
the COMMIT will only wait for
flushing to
local disk).
20. postgres_fdw now supports remote
joins, sorts, UPDATEs, and DELETEs
FDW
Foreign
Server
Foreign
Server
Foreign
Server
SQL 쿼리
Join, Sort, Updates, Deletes
21. Allow Limiting of Snapshot Age
old_snapshot_threshold 값은 서버를 시작할때만 설정 가능(DEFAULT: -1, 0, 1min - 60d)
Session 1 Session 2
show old_snapshot_threshold;;
create table snaptest(x int);;
insert into snaptest values (1);;
begin work;;
set transaction isolation level serializable;;
select * from snaptest;;
update snaptest set x = 2;;
select pg_sleep(100);;
vacuum snaptest;;
select * from snaptest;;
ERROR:snapshot too old;;
22. Allow Sessions with Long-Idle
Transactions To Be Terminated
u 오래 기다리고 있는 세션에 대해서 연결을 기다리지 않고 세션 자동 종료. default: 0
=# SET idle_in_transaction_session_timeout = ’2s’;
=# BEGIN WORK;
-- sit idle for 3 seconds
=# SELECT 1;
FATAL: terminating connection due to idle-in-transaction timeout
server closed the connection unexpectedly
23. CREATE
EXTENSION
CASCADE
Support
u PostgreSQL 9.6 이전에 EXTENSION 설치 시 종속성 부터 설치
=# create extension cube;
=# create extension earthdistance;
u PostgreSQL 9.6 부터는 EXTENSION의 control 파일에 종속성 정의
=# create extension earthdistance cascade;
NOTICE: installing required extension "cube"
CREATE EXTENSION
25. SUPPORT
CROSSTABVIEW
IN
PSQL
u PostgreSQL 9.6 이전엔 crosstab 쿼리는 tablefunc 모듈 사용.
=# create table test2 as
select
('{a,b,c,d,e,f}'::text[])[1 + floor(random() * 6)] as type_1,
('{m,n,o,p,q,r}'::text[])[1 + floor(random() * 6)] as type_2,
random() * 1000 as some_float
from generate_series(1,20000);
26. SUPPORT
CROSSTABVIEW
IN
PSQL
u =# select type_1, type_2, count(*)
from test2 group by type_1, type_2 crosstabview
type_1 | q | n | o | r | m | p
--------+-----+-----+-----+-----+-----+-----
f | 519 | 569 | 515 | 593 | 519 | 560
e | 605 | 539 | 547 | 555 | 587 | 577
c | 574 | 590 | 534 | 549 | 541 | 556
d | 569 | 487 | 569 | 521 | 523 | 553
b | 543 | 594 | 547 | 573 | 548 | 590
a | 586 | 576 | 568 | 555 | 559 | 510
(6 rows)
type_1
|
type_2
|
some_float
-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐
1
|
b
|
m
|409.59995565936
2
|
e
|
n
|453.834847547114
3
|
f
|
o
|113.634209148586
4
|
b
|
n
|332.8404747881
5
|
d
|
n
|832.656755112112
6
|
b
|
o
|525.860982947052
7
|
b
|
r
|873.10529500246
27. Generic
WAL
facility
u PostgreSQL 9.6은 외부 개발자가 CUSTOM EXTENSION 개발을 쉽게 할
수 있도록 일반화된 WAL 인터페이스 제공
u GenericXLogStart
u GenericXLogRegisterBuffer
u GenericXLogFinish
u GenericXLogAbort
28. Allow COPY to copy the output of
an INSERT/UPDATE/DELETE ... RETURNING query
=# COPY (INSERT INTO tab_data VALUES (1, 'aa'), (2, 'bb') RETURNING a, b) TO stdout;
1 aa
2 bb
=# COPY (UPDATE tab_data SET b = 'cc' WHERE a = 1 RETURNING *) TO stdout;
1 cc
=# COPY (DELETE FROM tab_data WHERE a = 2 RETURNING *) TO stdout;
2 bb
29. Prompt variable to show PId of backend
u psql 프롬프트에 Postmaster PID 표시
u 주의) pg_bouncer, pg_pool 등 사용시 PID 예측 불가
=# set PROMPT1 '%n@%/ : %p $ '
depesz@depesz : 26853 $
u prompt에 사용 가능한 모든 옵션:
u https://www.postgresql.org/docs/current/static/app-psql.html#APP-PSQL-PROMPTING
30. Monitoring Features
u Add System Catalog
u Vacuum progress reporting
u pg_config System View
u System view to monitor WAL receiver status
u Modified System Catalog
u pg_stat_activity wait-type reporting
u Add System Function
u pg_control Values Exposed
u Notification queue monitoring
31. Monitoring Features -
pg_stat_activity wait-type reporting
u PostgreSQL 9.6에서 pg_stat_activity catalog 컬럼 추가
u wait_event_type - The type of event for which the backend is waiting
u wait_event - Wait event name if backend is currently waiting
u The value of wait_event_type column
• LWLockNamed - Waiting by a particular lightweight lock
• LWLockTranche - Waiting by the lightweight lock for the group
• Lock - Waiting by weight lock (LOCK TABLE statement etc)
• BufferPin - PIN waiting for the buffer
u wait_event
• https://www.postgresql.org/docs/9.6/static/monitoring-stats.html
32. Monitoring Features -
Vacuum progress reporting
u This catalog provides information about the state of the progress of vacuum processing.
This catalog is readable by the general superuser.
Column
Name Data
Type Description
pid integer
Process
ID
of
backend
datid oid OID
of
database
datname name Connected
database
name
relid oid OID
of
the
table
being
vacuumed
phase text Current
processing
phase
of
vacuum
heap_blks_total bigint Total
number
of
heap
blocks
in
the
table
heap_blks_scanned bigint Number
of
heap
blocks
scanned
heap_blks_vacuumed bigint Number
of
heap
blocks
vacuumed
index_vacuum_count bigint Number
of
completed
index
vacuum
cycles
max_dead_tuples bigint Number
of
dead
tuples
that
we
can
store
before
needing
to
perform
an
index
vacuum
cycle
num_dead_tuples bigint Number
of
dead
tuples
collected
since
the
last
index
vacuum
cycle
34. Monitoring Features -
pg_config System View
u pg_config를 시스템 유저 권한으로 SQL로 조회
u Column: name, setting
postgres=#
SELECT
*
FROM
pg_config
;
name
|
setting
-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐+-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐
BINDIR
|
/usr/local/pgsql/bin
DOCDIR
|
/usr/local/pgsql/share/doc
HTMLDIR
|
/usr/local/pgsql/share/doc
INCLUDEDIR
|
/usr/local/pgsql/include
PKGINCLUDEDIR
|
/usr/local/pgsql/include
35. Monitoring Features -
pg_control Values Exposed
Name Return
Type Description
pg_control_checkpoint() record
Returns
information
about
current
checkpoint
state.
pg_control_system() record
Returns
information
about
current
control
file
s
tate.
pg_control_init() record
Returns
information
about
cluster
initialization
state.
pg_control_recovery() record Returns
information
about
recovery
state.
They also show information about write-ahead logging and checkpoint processing. This
information is cluster-wide, and not specific to any one database
36. Monitoring Features -
System view to monitor WAL receiver status
This catalog provides information about the state of a slave instance's WAL receiver
process in replication environment. This catalog is readable by the general user.
pid integer Process ID of the WAL receiver process
status text Activity status of the WAL receiver process
receive_start_lsn pg_lsn
First transaction log position used when WAL
receiver is started
receive_start_tli integer
First timeline number used when WAL receiver is
started
received_lsn pg_lsn
Last transaction log position already received and
flushed to disk, the initial value
received_tli integer
Timeline number of last transaction log position
received and flushed to disk, the initial value
last_msg_send_time timestamp with time zone
Send time of last message received from origin WAL
sender
37. Monitoring Features -
System view to monitor WAL receiver status (…)
This catalog provides information about the state of a slave instance's WAL receiver
process in replication environment. This catalog is readable by the general user.
pid integer Process ID of the WAL receiver process
last_msg_receipt_time timestamp with time zone
Receipt time of last message received from origin
WAL sender
latest_end_lsn pg_lsn
Last transaction log position reported to origin WAL
sender
latest_end_time timestamp with time zone
Time of last transaction log position reported to
origin WAL sender
slot_name text Replication slot name used by this WAL receiver
conninfo text
Connection string used by this WAL receiver, with
security-sensitive fields obfuscated.
38. Monitoring Features -
Notification queue monitoring
u PostgreSQL 큐는 8GB까지의 제한 존재
u DBA 관리자가 큐에 남은 작업이 있는지 확인할 필요
=# select pg_notification_queue_usage();
u 이 함수가 반환하는 실수 양에 따라 남아있는 작업량 판단 가능