Whenever adding other tables, be sure to either append to the array ( $var = 'value') or include the user table in the new array definition. Note that each of the subsections here assume that you are also sharing the user table. ![]() If a table contains any data specific to one wiki, sharing may cause problems. You can share tables other than the user table, but be careful when doing so. To prevent this from happening until all wikis are upgraded, set $wgPasswordDefault to 'B' on all wikis, and remove it on all wikis when they have been upgraded to ensure a stronger encryption is used. In MediaWiki 1.24 the default password type for MediaWiki has been changed from MD5 to PBKDF2, and passwords hashes will automatically be updated as users log in. When upgrading from the command line, running the update.php script, you need to use the -doshared parameter for the script to upgrade the shared tables. Otherwise, the shared tables are not touched at all (neither tables with $wgSharedPrefix, nor those with $wgDBprefix), which may lead to a failed upgrade. ![]() $wgCookieDomain = '.' Upgrading Īs of MediaWiki 1.21, when upgrading MediaWiki from the web installer, $wgSharedTables must be temporarily removed from LocalSettings.php during upgrade. Granting permissions can be performed using MySQL commands similar to the following: If you use different MySQL users for each wiki, you will need to grant additional permissions to the shared wiki user as specified in that wiki's $wgDBuser setting.įor those who use shared hosting, note that this is possible at some but not all providers. The MySQL user of the shared wiki must have at least SELECT and UPDATE permissions for the main wiki user tables. Before any users get created or edits are made. Suggestion: For the reason mentioned above, it is advisable to share tables (especially user tables) right after creating a new wiki. Unfortunately, there is also a bug that breaks login for new users on wikis with shared user tables but separate actor tables. This ends up causing confusion and data corruption in many places, in more or less subtle ways. If you have two wikis, and you create different users on both, and then start sharing the actor table later, things do break because the user id that was referenced in the local actor table is not the same user id being referenced in the shared actor table. The simplest setup: A shared user table Ī shared user table can be used to have multiple wikis that have shared user registrations, so that users need only sign up to one wiki. Shared databases are configured with 3 main global Configuration variables in your LocalSettings.php:ĭepending on your needs and environment, you may not need to use all of these. This does not cover "merging" user tables nor a Wikimedia-style set up (with a global user table and local user tables) using CentralAuth. This guide assumes that you are either starting your wikis from scratch, or are transitioning from one wiki to multiple. Other database engines may not support shared databases in this way. Support for PostgreSQL was added in MediaWiki 1.19. Support for SQLite was added in MediaWiki 1.17. Note that this is designed around MySQL databases. Most of the information here should work with a plain installation of MediaWiki (no extensions). Support for various data types, enhanced vector search with attribute filtering, UDF support, configurable consistency level, time travel, and more.This page provides a brief overview of using shared databases in MediaWiki. Milvus vector database adopts a systemic approach to cloud-nativity, separating compute from storage and allowing you to scale both up and out. The distributed and high-throughput nature of Milvus makes it a natural fit for serving large scale vector data. With extensive isolation of individual system components, Milvus is highly resilient and reliable. Milvus vector database has been battle-tested by over a thousand enterprise users in a variety of use cases. Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed. ![]() Simple and intuitive SDKs are also available for a variety of different languages. With Milvus vector database, you can create a large scale similarity search service in less than a minute. Fuel your machine learning deployment Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
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