MariaDB vs MySQL vs SQLite

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MariaDB
MariaDB

4.3K
3.2K
+ 1
472
MySQL
MySQL

28.7K
23.1K
+ 1
3.7K
SQLite
SQLite

4.1K
3.3K
+ 1
514
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What is MariaDB?

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

What is MySQL?

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

What is SQLite?

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.
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Why do developers choose MariaDB?
Why do developers choose MySQL?
Why do developers choose SQLite?

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    What are some alternatives to MariaDB, MySQL, and SQLite?
    PostgreSQL
    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
    Percona
    It delivers enterprise-class software, support, consulting and managed services for both MySQL and MongoDB across traditional and cloud-based platforms.
    Oracle
    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    See all alternatives
    Decisions about MariaDB, MySQL, and SQLite
    StackShare Editors
    StackShare Editors
    MySQL
    MySQL
    MariaDB
    MariaDB

    Airbnb’s web experience is powered by a Rails monolith, called Monorail, that talks to several different Java services. MySQL databases store business data and are partitioned by functionality, with messages and calendar management, for example, stored separately from the main booking flow in their own databases.

    As traffic to the site continued growing, though, “one notable resource issue with MySQL databases [was] the increasing number of database connections from application servers.”

    Airbnb uses AWS’s Relational Database Service (RDS) to power their MySQL instances, and “RDS uses the community edition of MySQL server, which employs a one-thread-per-connection model of connection management.” With Airbnb’s scale, this meant that their databases would hit the C10K problem, which states that “there is an upper bound in the number of connections that MySQL server can accept and serve without dramatically increasing the number of threads running, which severely degrades MySQL server performance.”

    When an RDS MySQL server hits resource limits, users will have trouble connecting to the site.

    MySQL does have dynamic thread pooling, but it’s only available in the enterprise edition; AWS MySQL RDS, though, doesn’t offer this feature, meaning Airbnb didn’t have access to dynamic thread pooling out-of-the-box.

    After surveying several options, the team chose MariaDB MaxScale, which is “a MySQL database proxy that supports intelligent query routing in between client applications and a set of backend MySQL servers.”

    Instead of using the MariaDB MaxScale off-the-shelf, however, they decided to fork it and implement their own version that would include connection pooling. Other MaxScale features, like request throttling and query blocklisting were implemented as well.

    To enable horizontal scaling of the web application, the team deployed a MaxScale database proxy service in between app servers and MySQL servers. Through the service discovery system SmartStack, applications now “discover and connect to the database proxy service instead of the MySQL database,” allowing horizontal scaling to meet capacity demands.

    Additionally, new Airbnb MaxScale proxy server instances can be launched to further enable horizontal scaling.

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    Gregory Koberger
    Gregory Koberger
    MongoDB
    MongoDB
    MySQL
    MySQL
    PostgreSQL
    PostgreSQL
    MongoDB Atlas
    MongoDB Atlas
    MongoLab
    MongoLab
    Compose
    Compose

    We went with MongoDB , almost by mistake. I had never used it before, but I knew I wanted the *EAN part of the MEAN stack, so why not go all in. I come from a background of SQL (first MySQL , then PostgreSQL ), so I definitely abused Mongo at first... by trying to turn it into something more relational than it should be. But hey, data is supposed to be relational, so there wasn't really any way to get around that.

    There's a lot I love about MongoDB, and a lot I hate. I still don't know if we made the right decision. We've been able to build much quicker, but we also have had some growing pains. We host our databases on MongoDB Atlas , and I can't say enough good things about it. We had tried MongoLab and Compose before it, and with MongoDB Atlas I finally feel like things are in a good place. I don't know if I'd use it for a one-off small project, but for a large product Atlas has given us a ton more control, stability and trust.

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    Antonio Sanchez
    Antonio Sanchez
    CEO at Kokoen GmbH · | 13 upvotes · 213.9K views
    atKokoen GmbHKokoen GmbH
    PHP
    PHP
    Laravel
    Laravel
    MySQL
    MySQL
    Go
    Go
    MongoDB
    MongoDB
    JavaScript
    JavaScript
    Node.js
    Node.js
    ExpressJS
    ExpressJS

    Back at the start of 2017, we decided to create a web-based tool for the SEO OnPage analysis of our clients' websites. We had over 2.000 websites to analyze, so we had to perform thousands of requests to get every single page from those websites, process the information and save the big amounts of data somewhere.

    Very soon we realized that the initial chosen script language and database, PHP, Laravel and MySQL, was not going to be able to cope efficiently with such a task.

    By that time, we were doing some experiments for other projects with a language we had recently get to know, Go , so we decided to get a try and code the crawler using it. It was fantastic, we could process much more data with way less CPU power and in less time. By using the concurrency abilites that the language has to offers, we could also do more Http requests in less time.

    Unfortunately, I have no comparison numbers to show about the performance differences between Go and PHP since the difference was so clear from the beginning and that we didn't feel the need to do further comparison tests nor document it. We just switched fully to Go.

    There was still a problem: despite the big amount of Data we were generating, MySQL was performing very well, but as we were adding more and more features to the software and with those features more and more different type of data to save, it was a nightmare for the database architects to structure everything correctly on the database, so it was clear what we had to do next: switch to a NoSQL database. So we switched to MongoDB, and it was also fantastic: we were expending almost zero time in thinking how to structure the Database and the performance also seemed to be better, but again, I have no comparison numbers to show due to the lack of time.

    We also decided to switch the website from PHP and Laravel to JavaScript and Node.js and ExpressJS since working with the JSON Data that we were saving now in the Database would be easier.

    As of now, we don't only use the tool intern but we also opened it for everyone to use for free: https://tool-seo.com

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    Tim Abbott
    Tim Abbott
    Founder at Zulip · | 23 upvotes · 238.7K views
    atZulipZulip
    PostgreSQL
    PostgreSQL
    MySQL
    MySQL
    Elasticsearch
    Elasticsearch

    We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

    We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

    And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

    I can't recommend it highly enough.

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    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · |