High-level architecture of a complete MariaDB deployment

SkySQL MariaDB 云数据组件 http://www.ossez.com/forum.php?mod=viewthread&tid=26725&fromuid=426 (出处: OSSEZ)

- MariaDB provides several high availability options including asynchronous replication, semi-synchronous replication, Galera synchronous replication, and MaxScale for load balancing and failover. - Asynchronous replication allows for read scaling but carries a risk of data loss during failover. Semi-synchronous replication reduces this risk by ensuring data is written to at least one slave before confirming to the client. - Galera synchronous multi-master replication ensures all nodes remain in sync with no data loss but can impact performance. MaxScale helps manage replication topology and perform automated failovers.

Uber uses a scalable real-time complex event processing system to analyze streaming data from its services. The system uses Apache Samza for distributed stream processing and WSO2 Siddhi for complex event processing. Key events are detected using Siddhi queries and then actions like notifications or indexing to databases are triggered. The system processes over 30 billion messages per day across many use cases. Maintaining scalability, fault tolerance, and low latency are ongoing challenges.

In Cassandra Lunch #88, CEO of Anant, Rahul Singh, will discuss how Cadence works on top of Cassandra to provide workflow management at scale and Cadence architecture in the context of SAGA Patterns Accompanying Blog: Coming Soon! Accompanying YouTube: https://youtu.be/YPPPM0F0xw0 Sign Up For Our Newsletter: http://eepurl.com/grdMkn Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/ Cassandra.Link: https://cassandra.link/ Follow Us and Reach Us At: Anant: https://www.anant.us/ Awesome Cassandra: https://github.com/Anant/awesome-cassandra Cassandra.Lunch: https://github.com/Anant/Cassandra.Lunch Email: solutions@anant.us LinkedIn: https://www.linkedin.com/company/anant/ Twitter: https://twitter.com/anantcorp Eventbrite: https://www.eventbrite.com/o/anant-1072927283 Facebook: https://www.facebook.com/AnantCorp/ Join The Anant Team: https://www.careers.anant.us

This document discusses using Pacemaker with MySQL for high availability (HA). It covers key concepts in HA including eliminating single points of failure. It then discusses various MySQL HA solutions like replication, DRBD, MySQL Cluster, and using Linux HA tools like Pacemaker. Pacemaker manages resources across nodes to ensure services are always running, and can monitor and migrate MySQL and other services in an HA cluster. The document provides configuration examples and best practices for setting up MySQL HA with Pacemaker.

MariaDB und mehr Presented by Ralf Gebhardt at the MariaDB Roadshow Germany: 4.7.2014 in Hamburg, 8.7.2014 in Berlin and 11.7.2014 in Frankfurt.

Galera Cluster vs. Continuent Tungsten Clusters Building a Geo-Scale, Multi-Region and Highly Available MySQL Cloud Back-End This second installment of our High Noon series of on-demand webinars is focused on Galera Cluster (including MariaDB Cluster & Percona XtraDB Cluster). It looks at some of the key characteristics of Galera Cluster and how it fares as a MySQL HA / DR / Geo-Scale solution, especially when compared to Continuent Tungsten Clustering. Watch this webinar to learn how to do better MySQL HA / DR / Geo-Scale. AGENDA - Goals for the High Noon Webinar Series - High Noon Series: Tungsten Clustering vs Others - Galera Cluster (aka MariaDB Cluster & Percona XtraDB Cluster) - Key Characteristics - Certification-based Replication - Galera Multi-Site Requirements - Limitations Using Galera Cluster - How to do better MySQL HA / DR / Geo-Scale? - Galera Cluster vs Tungsten Clustering - About Continuent & Its Solutions PRESENTER Matthew Lang - Customer Success Director – Americas, Continuent - has over 25 years of experience in database administration, database programming, and system architecture, including the creation of a database replication product that is still in use today. He has designed highly available, scaleable systems that have allowed startups to quickly become enterprise organizations, utilizing a variety of technologies including open source projects, virtualization and cloud.

Slides presented at Percona Live Europe Open Source Database Conference 2019, Amsterdam, 2019-10-01. Imagine a world where all Wikipedia articles disappear due to a human error or software bug. Sounds unreal? According to some estimations, it would take an excess of hundreds of million person-hours to be written again. To prevent that scenario from ever happening, our SRE team at Wikimedia recently refactored the relational database recovery system. In this session, we will discuss how we backup 550TB of MariaDB data without impacting the 15 billion page views per month we get. We will cover what were our initial plans to replace the old infrastructure, how we achieved recovering 2TB databases in less than 30 minutes while maintaining per-table granularity, as well as the different types of backups we implemented. Lastly, we will talk about lessons learned, what went well, how our original plans changed and future work.

Hekaton is SQL Server's in-memory optimized database engine for online transaction processing (OLTP) workloads. It uses lock-free data structures, multi-version concurrency control, and compiled Transact-SQL queries to provide high performance and scalability. Transaction logging and continuous checkpointing ensure data durability. Hekaton tables and indexes are optimized for memory residency, with hash indexes and Bw-tree indexes to support efficient lookups and updates.

This document provides a summary of a presentation on becoming an accidental PostgreSQL database administrator (DBA). It covers topics like installation, configuration, connections, backups, monitoring, slow queries, and getting help. The presentation aims to help those suddenly tasked with DBA responsibilities to not panic and provides practical advice on managing a PostgreSQL database.

This document discusses strategies for building interactive streaming applications in Spark Streaming. It describes using Zookeeper as a dynamic configuration source to allow modifying a Spark Streaming application's behavior at runtime. The key points are: - Zookeeper can be used to track configuration changes and trigger Spark Streaming context restarts through its watch mechanism and Curator library. - This allows building interactive applications that can adapt to configuration updates without needing to restart the whole streaming job. - Examples are provided of using Curator caches like node and path caches to monitor Zookeeper for changes and restart Spark Streaming contexts in response.

Introducing Orchestrator: a MySQL replication topology management service, that greatly simplifies DBA's tasks and enhances visibility on your topologies.

Equnix Business Solutions (Equnix) is an IT Solution provider in Indonesia, providing comprehensive solution services especially on the infrastructure side for corporate business needs based on research and Open Source. Equnix has 3 (three) main services known as the Trilogy of Services: Support (Maintenance/Managed), World class level of Software Development, and Expert Consulting and Assessment for High Performance Transactions System. Equnix is customer oriented, not product or principal. Equal opportunity based on merit is our credo in managing HR development.

TubeMogul grew from few servers to over two thousands servers and handling over one trillion http requests a month, processed in less than 50ms each. To keep up with the fast growth, the SRE team had to implement an efficient Continuous Delivery infrastructure that allowed to do over 10,000 puppet deployment and 8,500 application deployment in 2014. In this presentation, we will cover the nuts and bolts of the TubeMogul operations engineering team and how they overcome challenges.

MariaDB 10 and Beyond - the Future of Open Source Databases by Ivan Zoratti. Presented 24.6.2014 at the MariaDB Roadshow in Maarssen, Utrecht, The Netherlands.

Linkedin has multiple data-centers hosting tens of thousands of servers across them. A large percentage of these servers host our data infrastructure - our distributed data store called Espresso is sizeable amongst them. The fleet of servers contain various hardware components including, but not limited to, SSDs; and hardware has a tendency of failing from time to time. In case of hardware failures the servers need to undergo maintenance which can take a significant amount of time based on type of failure. This creates reduced capacity for that duration and throws an interesting problem of maintaining capacity in the face of multiple failures. This talk covers how LinkedIn uses Camunda wrapped around with several components to achieve hands-off capacity management via multiple workflows, with asynchronous pauses and synchronisation among them. It will also highlight how we achieved seamless integrations with various platforms and components within Linkedin's Infrastructure, and a few best practices that helped us achieve the final state.

The Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real time data needs for various Uber products. This platform has been in production for almost a year and it has proven to be very flexible to solve many use cases. In this talk, we will share in detail the design and architecture of the platform, and how we employ Samza, Kafka, and Siddhi at scale. This slides was presented at Stream Processing Meetup @ LinkedIn on June 15 2016.

Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you. A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services. But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts. We will discuss these topics: - How to gather information about a business; - Understanding dictionaries and how to identify business entities; - Dimensions and facts; - Setting a table granularity; - Types of facts; - Types of dimensions; - Snowflakes and how to avoid them; - Expanding existing dimensions and facts.

MindsDB enormously simplifies the process of making machine learning based predictions. Intead of developing a model and prepare data, you can connect MindsDB to an external data source (such as MySQL, PostgreSQL, other databases, or APIs) and run SQL queries about the future. Any AI engine (predictive algorithm) can be used.

MariaDB best practices for user and permissions management, secrets storage, SSL, encryption at rest, and more. Includes an overview of MariaDB most advanced security features. Webinar organised in December 2023.

From my webinar "A first look at MariaDB 11.x features and ideas on how to use them", from November 2023. I talked about the features introduced in MariaDB versions 11.0 to 11.3, not yet production-ready. For some features, I provided ideas about how they can be used.

This document discusses improvements that could be made to MariaDB stored procedures. It begins by explaining why stored procedures are useful from a user and community perspective. It then outlines currently limitations with MariaDB stored procedures, such as them being too slow and missing features that make development easier. The document proposes several specific improvements, such as supporting external programming languages like Python, adding more flexible input/output features, and implementing optimizations like inline functions and deterministic function caching. It concludes by suggesting adding support for array types and polymorphic types to MariaDB stored procedures.

MariaDB Temporal Tables are useful to track how data change over time, and to handle data that refer to specific time periods. In this webinar I showed: * Which problems Temporal Tables solve * How to create Temporal Tables * How to turn regular tables into Temporal Tables * Best practices * Examples of what can be done with Temporal Tables

- MySQL 5.7 is no longer supported and will not receive any bugfixes or security updates after October 2023. Users need to upgrade to either MySQL 8.0 or MariaDB 10.11. - MySQL is developed by Oracle while MariaDB has its own independent foundation. MariaDB aims to be compatible with MySQL but also has unique features like storage engines. - Both MySQL 8.0 and MariaDB 10.11 are good options to upgrade to. Users should consider each product's unique features and governance model as well as test which one works better for their applications and use cases.

Webinar: MariaDB 10.11 key features overview for DBAs Orgnised by Vettabase 27 April 2023 Amongst other topics: - Long ALTER TABLES now don’t cause replicas to lag - InnoDB configuration is now more dynamic, and certain important variables can be modified without a restart - Populating an empty table is now much faster - New data types: UUID, INET4, INET6 - SFORMAT() function, NATURAL_KEY_SORT() function

After MariaDB 10.6 LTS was made available last year, three Short Term Support versions were released. While they shouldn’t be used in production, they allow us to test the features that will be included in the next LTS version. I follow the development of MariaDB through their JIRA, I test the new features, and I regularly review each new major version on the Vettabase website. In this talk I will summarise the most relevant features, show how to use them, and discuss how we can leverage them for real-world cases.

MariaDB is one of the most widely used relational databases. It is compatible with MySQL for most practical purposes, and it is appreciated by developers communities all over the world. Over the years, MariaDB has developed many features that are extremely useful for developers, saving a lot of development time and enabling its use in situations where it wouldn't be practical otherwise. In this talk, we'll briefly discuss some of those features and why they are so useful. We'll talk about: * Querying remote or heterogeneous data sources in SQL; * Using temporal tables to analyse how data changes over time; * Using JSON in a relational database; * Miscellaneous tips and tricks.

Best practices for writing Ansible roles to administrate a real world MariaDB Galera Cluster, including monitoring, backups,, cron jobs, and so on.

How to create Vagrant development machines with MariaDB running on them. Best practices to follow to achieve production parity and code testability.

This document summarizes a talk on automating database infrastructures using MariaDB, MySQL and Ansible. It discusses Ansible concepts like inventories, modules, playbooks, roles, plays, variables and facts. It provides code examples of using Ansible to automate the deployment and configuration of MariaDB and MySQL database servers through plays, roles, variables and templates. It also discusses best practices for making tasks idempotent, using conditional tasks, tags and validating variables.