What's new in MariaDB TX 3.0

Oracle tables can be organized as heap tables or index organized tables. Heap tables store rows together in blocks without any particular order, while index organized tables order rows based on primary key values. Tables can be partitioned to improve manageability and performance for large volumes of data. Table clusters group related tables together in blocks to reduce disk I/O and access time for joined queries on those tables.

This document summarizes best practices for indexing in MySQL 5.6. It discusses the types of indexes, how indexes work, and how to optimize queries through proper index selection and design. Indexes can speed up queries by enabling fast data lookups, sorting, and avoiding full table scans. The document provides examples and guidelines for choosing effective primary keys, covering indexes, and multi-column indexes to maximize query performance.

The document provides contact information for Eric Nelson, a developer evangelist at Microsoft. It includes links to his blogs on MSDN which discuss .NET, Visual Basic, and UK developer events. It also lists his career history including his first computer experiences in the 1980s and his job at Microsoft since 1996.

The document provides biographical information about Eric Nelson, including details about his early career and interests. It discusses his first computer experiences in the 1980s, his first computer job programming in Fortran in 1986, and joining Microsoft in 1996 where he worked on ASP and SQL Server. It also mentions that he enjoys editing the UK MSDN Flash publication.

If you’re familiar with relational databases, designing your app to use a NoSQL database like DynamoDB may be new to you. In this webinar, we’ll walk you through common data design patterns for a variety of applications to help you learn how to design a schema, then store and retrieve the data with DynamoDB. We will discuss the benefits of using DynamoDB to develop mobile, web, IoT, and gaming apps. Learning Objectives: Learn schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others Who Should Attend: Architects, Developers, and SysOps interested in learning how to design NoSQL schemas to support mobile, web, IoT, AdTech, and gaming apps. Familiarity with DynamoDB is helpful

The document summarizes the scaling challenges faced by Fotolog, a large photo blogging community. It discusses how Fotolog grew to hosting hundreds of millions of photos and billions of comments. It describes Fotolog's technology stack including their use of MySQL, Memcached, 3Par storage and CDNs. It also outlines some of the MySQL scaling techniques used, such as sharding, replication, table partitioning and optimization.

These are the slides from my presentation at MySQL Conference and Expo 2007 held in Santa Clara, CA. The talk was focused on scaling InnoDB to meet Fotolog's unique challenges.

MySQL uses indexes to optimize queries and improve performance. Indexes are stored in b-trees to keep data sorted and allow fast searches, inserts and deletions. The selectivity of an index, or the ratio of unique values within a column, determines how effectively the index can reduce the result set size. Highly selective columns on frequently queried subsets of rows make the best candidates for indexes. MySQL can use indexes to optimize data lookups, sorting, avoiding full table scans, and certain aggregation functions.

The document discusses migrating job search data from MySQL to Elasticsearch to improve performance. The MySQL queries for job searches were slow, with some over 2 seconds. Testing showed Elasticsearch queries were faster at 0.5 seconds. The first phase of migration moved 160,000 job records to Elasticsearch. Performance tuning included adjusting thread pools, memory settings, and hardware. Monitoring and failure testing were conducted before migration. Latency for job searches improved greatly after migrating to Elasticsearch.

This document provides revision materials for an exam on database basics. It includes sections on database fundamentals, normalization, data validation, naming conventions, example questions, exam tips, and exam technique. The document covers key database concepts like entities, attributes, relationships, normalization forms, field data types, and validation rules. It also provides examples of database objects like tables, queries, forms, and reports. Overall, the document offers a comprehensive review of common database topics that may appear on the exam.

Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.

1. The document discusses 10 things people may not know about Oracle indexes. It begins by discussing how the common advice to rebuild an index if the percentage of deleted space is over 20% is flawed, as deleted space is usually just free space that will be reused over time without needing to rebuild the index. 2. It also discusses how bitmap indexes are commonly thought to only be suitable for low cardinality columns, but in reality can work for higher cardinality columns as well. An example shows populating a table with 10,000 distinct artist IDs without issues. 3. In general, the document suggests some commonly held beliefs about when to rebuild indexes and when bitmap indexes are suitable may not always be accurate, and

Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and use work load management. Learning Objectives: • Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities • Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently • Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features Who Should Attend: • Data Warehouse Developers, Big Data Architects, BI Managers, and Data Engineers

Deep dive into Clustered Columnstore structures with information on compression algorithms, compression types, locking and dictionaries, as well as the Batch Processing Mode.

The document provides an overview of MariaDB ColumnStore, including its history, components, disk storage architecture, writing and querying data processes. It was presented by Andrew Hutchings, the lead software engineer for MariaDB ColumnStore, who has previous experience with MySQL, HP, and other companies. The presentation covers the technical use cases for ColumnStore, differences from row-oriented databases, and optimizations for ColumnStore.

The document discusses covering indexes and provides examples of how they can improve query performance. A covering index contains all the columns needed to satisfy a query, avoiding the need to access table data stored on disk. Case studies show how adding a covering composite index on (subscription, name) improved a query to retrieve names by subscription date and order by name from over 3 seconds to under 0.01 seconds. Covering indexes are very beneficial for I/O-bound workloads by reducing disk access.

This spring, the data warehouse team at Ancestry, flawlessly migrated and validated nearly half a trillion records from Actian Matrix to Amazon Redshift. During this session, the Ancestry team will describe how they orchestrated the entire migration in less than four months, the technical challenges they faced and overcame along the way, as well as share tips and tricks to break through common pitfalls of data warehouse migrations. They will also highlight how they tuned and optimized the Amazon Redshift environment, adopted Redshift Spectrum, and how they leverage their collaboration with Amazon to deliver a powerful customer experience.

MariaDB Paris Workshop 2023 - MaxScale 23.02.x

This document summarizes Newpharma's transition from a standalone database server to an enterprise MariaDB Galera cluster configuration between 2018-2023. It discusses the business needs that drove the change, including increased traffic and access to multiple data sources. Key benefits of the Galera cluster are highlighted like synchronous replication, read/write access from any node, and automatic node joining. Challenges of migrating like converting table types and splitting large transactions are also outlined. The transition has supported Newpharma's growth to over 100 million euro in turnover.

MariaDB Paris Workshop 2023 - Cloud

MariaDB Paris Workshop 2023 - MariaDB Enterprise

MariaDB is an open-source database that is highly tunable and modular. It allows for various storage engines, plugins, and configurations to optimize performance depending on usage. Key aspects that impact performance include memory allocation, disk access, query optimization, and architecture choices like replication, sharding, or using ColumnStore for analytics. Solutions like MyRocks, Spider, MaxScale can improve performance for transactional or large scale workloads by optimizing resources, adding high availability, and distributing load.

The document outlines requirements and criteria for a database solution involving two buildings 30km apart with a WAN link. The chosen solution was MariaDB with Galera cluster for high availability and synchronous replication across sites, along with Maxscale for read/write splitting and failover. Maxscale instances on each site allow for zero downtime database patching and upgrades per site, while the Galera cluster provides structure-independent synchronous replication between sites.

MariaDB Paris Workshop 2023 - novadys presentation

MariaDB Paris Workshop 2023 - DARVA presentation

MariaDB Enterprise Server 10.6 includes the following key features: - New JSON functions and data types like UUID and INET4. - Improved Oracle compatibility with function parameters. - Enhanced partitioning capabilities like converting partitions. - Optimistic ALTER TABLE for replicas to reduce downtime. - Online schema changes without locking tables for improved performance. - Security enhancements including password policies and privilege changes.

MariaDB SkySQL is a cloud database service that provides autonomous scaling, observability, and cloud backup capabilities. It offers multi-cloud and hybrid operations across AWS, Google Cloud, and on-premises databases. The service includes features like the Remote Observability Service (ROS) for monitoring across environments, and a Cloud Backup Service. It aims to provide a simple yet advanced service for scaling databases from small to extreme sizes with tools for automation, self-service, and unified operations.

Präsentation von MariaDB Tech und Business Update Hamburg 2023

The document discusses high availability solutions for MariaDB databases. It begins by defining high availability and concepts like Recovery Time Objective (RTO) and Recovery Point Objective (RPO). It then presents different MariaDB and MaxScale architectures that provide high availability, including single node, primary-replica, Galera cluster, and SkySQL solutions. Key aspects covered are automatic failover, load balancing, data filtering, and service level agreements.

This document summarizes new features in MariaDB Enterprise Server. Key points include: - MariaDB Enterprise Server is geared toward enterprise customers and focuses on stability, robustness, and predictability. - It has a longer release cycle than Community Server, with new versions every 2 years and long maintenance cycles. New features from Community Server are backported. - Recent additions include analytics functions, JSON support, bi-temporal modeling, schema changes, database compatibility features, and security enhancements. - The upcoming 23.x release will include new JSON functions, data types like UUID and INET4, Oracle compatibility features, partitioning improvements, and Galera enhancements.

Ansell Guardian® faced challenges with their previous database replication solution as their data and usage grew globally. They evaluated MariaDB/Galera and implemented it to replace their legacy solution. The implementation was smooth using automation scripts. MariaDB/Galera provided increased performance, faster deployment times, and more reliable data synchronization across their 3 data centers compared to their previous solution. It helped resolve a critical data divergence issue and improved the user experience. They plan to further enhance their database infrastructure using MaxScale in the future.

SkySQL is the first and only database-as-a-service (DBaaS) to perform workload analysis with advanced deep learning models, identifying and classifying discrete workload patterns so DBAs can better understand database workloads, identify anomalies and predict changes. In this session, we’ll explain the concepts behind workload analysis and show how it can be used in the real world (and with sample real-world data) to improve database performance and efficiency by identifying key metrics and changes to cyclical patterns.

SkySQL uses best-of-breed software, and when it comes to metrics and monitoring that means Prometheus and Grafana. SkySQL Monitor is built on both, and provides customers with interactive dashboards for both real-time and historic metrics monitoring. In addition, it meets the same high availability and security requirements as other SkySQL components, ensuring metrics are always available and always secure. In this session, we’ll explain how SkySQL Monitor works, walk through its dashboards and show how to monitor key metrics for performance and replication.

Not too long ago, a reactive variant of the JDBC driver was released, known as Reactive Relational Database Connectivity (R2DBC for short). While R2DBC started as an experiment to enable integration of SQL databases into systems that use reactive programming models, it now specifies a full-fledged service-provider interface that can be used to retrieve data from a target data source. In this session, we’ll take a look at the new MariaDB R2DBC connector and examine the advantages of fully reactive, non-blocking development with MariaDB. And, of course, we’ll dive in and get a first-hand look at what it’s like to use the new connector with some live coding!

The capabilities and features of MariaDB Platform continue to expand, resulting in larger and more sophisticated production deployments – and the need for better tools. To provide DBAs with comprehensive, consolidating tooling, we created MariaDB Enterprise Tools: an easy-to-use, modular command-line interface for interacting with any part of MariaDB Platform. In this session, we will provide a preview of the MariaDB Enterprise Client, walk through current and planned modules and discuss future plans for MariaDB Enterprise Tools – including SkySQL modules and the ability to create custom modules.

For MariaDB Enterprise Server 10.5, the default transactional storage engine, InnoDB, has been significantly rewritten to improve the performance of writes and backups. Next, we removed a number of parameters to reduce unnecessary complexity, not only in terms of configuration but of the code itself. And finally, we improved crash recovery thanks to better consistency checks and we reduced memory consumption and file I/O thanks to an all new log record format. In this session, we’ll walk through all of the improvements to InnoDB, and dive deep into the implementation to explain how these improvements help everything from configuration and performance to reliability and recovery.

SkySQL implements a groundbreaking, state-of-the-art architecture based on Kubernetes and ServiceNow, and with a strong emphasis on cloud security – using compartmentalization and indirect access to secure and protect customer databases. In this session, we’ll walk through the architecture of SkySQL and discuss how MariaDB leverages an advanced Kubernetes operator and powerful ServiceNow configuration/workflow management to deploy and manage databases on cloud infrastructure.

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red hat

Aurora PostgreSQL에서 가장 일반적인 performance use case 들에 대해 Aurora PostreSQL의 모니터링 Tool들을 통해 어떤게 문제를 식별하고 분석하는지 그리고 이 문제를 해결해나가는 절차와 방법에 대한 Deep Dive입니다.

❻❸❼⓿❽❻❷⓿⓿❼ SATTA MATKA DPBOSS KALYAN FAST RESULTS CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY

Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados. QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo. Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.