MariaDB vs MongoDB

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MariaDB
MariaDB

3.6K
2.6K
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MongoDB
MongoDB

16.6K
13.1K
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MariaDB vs MongoDB: What are the differences?

What is MariaDB? An enhanced, drop-in replacement for MySQL. 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 MongoDB? The database for giant ideas. 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.

MariaDB and MongoDB can be categorized as "Databases" tools.

"Drop-in mysql replacement", "Great performance" and "Open source" are the key factors why developers consider MariaDB; whereas "Document-oriented storage", "No sql" and "Ease of use" are the primary reasons why MongoDB is favored.

MariaDB and MongoDB are both open source tools. It seems that MongoDB with 16.3K GitHub stars and 4.1K forks on GitHub has more adoption than MariaDB with 2.82K GitHub stars and 864 GitHub forks.

According to the StackShare community, MongoDB has a broader approval, being mentioned in 2189 company stacks & 2218 developers stacks; compared to MariaDB, which is listed in 496 company stacks and 461 developer stacks.

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 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.
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    What are some alternatives to MariaDB and MongoDB?
    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.
    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.
    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.
    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 and MongoDB
    MongoDB
    MongoDB

    I starting using MongoDB because it was much easier to implement in production then hosted SQL, and found that a lot of the limitation you think of from a document store vs a relational database were overcome by connecting the application to a graphql API, making retrieval seamless. Mongos latest upgrades as well as Stitch and Mongo mobile make it a perfect fit especially if your application will be cross platform web and mobile.

    See more
    Antonio Sanchez
    Antonio Sanchez
    CEO at Kokoen GmbH · | 11 upvotes · 86.6K views
    atKokoen GmbHKokoen GmbH
    ExpressJS
    ExpressJS
    Node.js
    Node.js
    JavaScript
    JavaScript
    MongoDB
    MongoDB
    Go
    Go
    MySQL
    MySQL
    Laravel
    Laravel
    PHP
    PHP

    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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    Amazon ElastiCache
    Amazon ElastiCache
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    AWS Elastic Load Balancing (ELB)
    AWS Elastic Load Balancing (ELB)
    Memcached
    Memcached
    Redis
    Redis
    Python
    Python
    AWS Lambda
    AWS Lambda
    Amazon RDS
    Amazon RDS
    Microsoft SQL Server
    Microsoft SQL Server
    MariaDB
    MariaDB
    Amazon RDS for PostgreSQL
    Amazon RDS for PostgreSQL
    Rails
    Rails
    Ruby
    Ruby
    Heroku
    Heroku
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk

    We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

    We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

    In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

    Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

    See more
    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 24 upvotes · 282K views
    atFundsCornerFundsCorner
    Zappa
    Zappa
    AWS Lambda
    AWS Lambda
    SQLAlchemy
    SQLAlchemy
    Python
    Python
    Amazon SQS
    Amazon SQS
    Node.js
    Node.js
    MongoDB Stitch
    MongoDB Stitch
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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    Khauth György
    Khauth György
    CTO at SalesAutopilot Kft. · | 11 upvotes · 97.3K views
    atSalesAutopilot Kft.SalesAutopilot Kft.
    AWS CodePipeline
    AWS CodePipeline
    Jenkins
    Jenkins
    Docker
    Docker
    vuex
    vuex
    Vuetify
    Vuetify
    Vue.js
    Vue.js
    jQuery UI
    jQuery UI
    Redis
    Redis
    MongoDB
    MongoDB
    MySQL
    MySQL
    Amazon Route 53
    Amazon Route 53
    Amazon CloudFront
    Amazon CloudFront
    Amazon SNS
    Amazon SNS
    Amazon CloudWatch
    Amazon CloudWatch
    GitHub
    GitHub

    I'm the CTO of a marketing automation SaaS. Because of the continuously increasing load we moved to the AWSCloud. We are using more and more features of AWS: Amazon CloudWatch, Amazon SNS, Amazon CloudFront, Amazon Route 53 and so on.

    Our main Database is MySQL but for the hundreds of GB document data we use MongoDB more and more. We started to use Redis for cache and other time sensitive operations.

    On the front-end we use jQuery UI + Smarty but now we refactor our app to use Vue.js with Vuetify. Because our app is relatively complex we need to use vuex as well.

    On the development side we use GitHub as our main repo, Docker for local and server environment and Jenkins and AWS CodePipeline for Continuous Integration.

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    Jeyabalaji Subramanian
    Jeyabalaji Subramanian
    CTO at FundsCorner · | 12 upvotes · 21.4K views
    atFundsCornerFundsCorner
    MongoDB Atlas
    MongoDB Atlas
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL

    Database is at the heart of any technology stack. It is no wonder we spend a lot of time choosing the right database before we dive deep into product building.

    When we were faced with the question of what database to choose, we set the following criteria: The database must (1) Have a very high transaction throughput. We wanted to err on the side of "reads" but not on the "writes". (2) be flexible. I.e. be adaptive enough to take - in data variations. Since we are an early-stage start-up, not everything is set in stone. (3) Fast & easy to work with (4) Cloud Native. We did not want to spend our time in "ANY" infrastructure management.

    Based on the above, we picked PostgreSQL and MongoDB for evaluation. We tried a few iterations on hardening the data model with PostgreSQL, but realised that we can move much faster by loosely defining the schema (with just a few fundamental principles intact).

    Thus we switched to MongoDB. Before diving in, we validated a few core principles such as: (1) Transaction guarantee. Until 3.6, MongoDB supports Transaction guarantee at Document level. From 4.0 onwards, you can achieve transaction guarantee across multiple documents.

    (2) Primary Keys & Indexing: Like any RDBMS, MongoDB supports unique keys & indexes to ensure data integrity & search ability

    (3) Ability to join data across data sets: MongoDB offers a super-rich aggregate framework that enables one to filter and group data

    (4) Concurrency handling: MongoDB offers specific operations (such as findOneAndUpdate), which when coupled with Optimistic Locking, can be used to achieve concurrency.

    Above all, MongoDB offers a complete no-frills Cloud Database as a service - MongoDB Atlas. This kind of sealed the deal for us.

    Looking back, choosing MongoDB with MongoDB Atlas was one of the best decisions we took and it is serving us well. My only gripe is that there must be a way to scale-up or scale-down the Atlas configuration at different parts of the day with minimal downtime.

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    Joshua Dean Küpper
    Joshua Dean Küpper
    CEO at Scrayos UG (haftungsbeschränkt) · | 5 upvotes · 37.5K views
    atScrayos UG (haftungsbeschränkt)Scrayos UG (haftungsbeschränkt)
    Sentry
    Sentry
    GitLab
    GitLab
    PostgreSQL
    PostgreSQL
    MariaDB
    MariaDB

    We primarily use MariaDB but use PostgreSQL as a part of GitLab , Sentry and @Nextcloud , which (initially) forced us to use it anyways. While this isn't much of a decision – because we didn't have one (ha ha) – we learned to love the perks and advantages of PostgreSQL anyways. PostgreSQLs extension system makes it even more flexible than a lot of the other SQL-based DBs (that only offer stored procedures) and the additional JOIN options, the enhanced role management and the different authentication options came in really handy, when doing manual maintenance on the databases.

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    Ajit Parthan
    Ajit Parthan
    CTO at Shaw Academy · | 1 upvotes · 5K views
    atShaw AcademyShaw Academy
    MongoDB
    MongoDB
    MySQL
    MySQL
    #NosqlDatabaseAsAService

    Initial storage was traditional MySQL. The pace of changes during a startup mode made it very difficult to have a clean and consistent schema. Large portions ended up as unstructured data stuffed into CLOBs and BLOBs.

    Moving to MongoDB definitely made this part much easier.

    Accessing data for analysis is a little bit of a challenge - especially for people coming from the world of SQL Workbench. But with tools like Exploratory this is becoming less of a problem.

    #NosqlDatabaseAsAService

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    Tim Nolet
    Tim Nolet
    Founder, Engineer & Dishwasher at Checkly · | 8 upvotes · 61.3K views
    atChecklyHQChecklyHQ
    Amazon DynamoDB
    Amazon DynamoDB
    MongoDB
    MongoDB
    Node.js
    Node.js
    Heroku
    Heroku
    PostgreSQL
    PostgreSQL

    PostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

    When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

    In a nutshell:cPostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

    When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

    In a nutshell:

    • We use Postgres on Heroku.
    • We use a "one database, on schema" approach for partitioning customer data.
    • We use an accounts, memberships and users table to create a many-to-many relation between users and accounts.
    • We completely decouple prices, payments and the exact ingredients for a customer's plan.

    All the details including a database schema diagram are in the linked blog post.

    See more
    Łukasz Korecki
    Łukasz Korecki
    CTO & Co-founder at EnjoyHQ · | 12 upvotes · 38.8K views
    atEnjoyHQEnjoyHQ
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB
    RethinkDB
    RethinkDB

    We initially chose RethinkDB because of the schema-less document store features, and better durability resilience/story than MongoDB In the end, it didn't work out quite as we expected: there's plenty of scalability issues, it's near impossible to run analytical workloads and small community makes working with Rethink a challenge. We're in process of migrating all our workloads to PostgreSQL and hopefully, we will be able to decommission our RethinkDB deployment soon.

    See more
    Mauro Bennici
    Mauro Bennici
    CTO at You Are My GUide · | 7 upvotes · 10.4K views
    atYou Are My GUideYou Are My GUide
    MongoDB
    MongoDB
    TimescaleDB
    TimescaleDB
    PostgreSQL
    PostgreSQL

    PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.

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    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 22 upvotes · 163.4K views
    atCircleCICircleCI
    Amazon S3
    Amazon S3
    GitHub
    GitHub
    Redis
    Redis
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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    Martin Johannesson
    Martin Johannesson
    Senior Software Developer at IT Minds · | 10 upvotes · 15.2K views
    atIT MindsIT Minds
    AMP
    AMP
    PWA
    PWA
    React
    React
    MongoDB
    MongoDB
    Next.js
    Next.js
    GraphQL
    GraphQL
    Apollo
    Apollo
    PostgreSQL
    PostgreSQL
    TypeORM
    TypeORM
    Node.js
    Node.js
    TypeScript
    TypeScript
    #Serverless
    #Backend
    #B2B

    At IT Minds we create customized internal or #B2B web and mobile apps. I have a go to stack that I pitch to our customers consisting of 3 core areas. 1) A data core #backend . 2) A micro #serverless #backend. 3) A user client #frontend.

    For the Data Core I create a backend using TypeScript Node.js and with TypeORM connecting to a PostgreSQL Exposing an action based api with Apollo GraphQL

    For the micro serverless backend, which purpose is verification for authentication, autorization, logins and the likes. It is created with Next.js api pages. Using MongoDB to store essential information, caching etc.

    Finally the frontend is built with React using Next.js , TypeScript and @Apollo. We create the frontend as a PWA and have a AMP landing page by default.

    See more
    MongoDB
    MongoDB
    MySQL
    MySQL
    .NET Core
    .NET Core
    C#
    C#

    Hi! I needed to choose a full stack of tools for a web drop shipping site without the payment process for a family startup proyect. It will feed from several web services (JSON), I'm looking forward a 4,200 articles tops. For web use only and for a few clients at the beginning.

    I'm considering C# with .NET Core 3.0 as is the one language I'm starting to learn. For the Database I haven´t made my mind yet, but could be MySQL or MongoDB any advice is welcome as I'm getting back to programming after year away from this awesome world. Thanks

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    Nicolas Apx
    Nicolas Apx
    CEO - FullStack Javascript at Apx Development Limited · | 14 upvotes · 17.4K views
    atAPX DevelopmentAPX Development
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB
    Node.js
    Node.js
    Python
    Python

    I am planning on building a micro-service eCommerce back-end to be easy to reuse in any project as we need. I would like to use both Python and Node.js and MongoDB & PostgreSQL , in your opinion which one would best suited for the following services:

    • Users-service
    • Products-service
    • Auth-service
    • Inventory-service
    • Order-service
    • Payment-service
    • Sku-service
    • And more not yet defined....

    Thanks

    Nicolas

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    Interest over time
    Reviews of MariaDB and MongoDB
    Review ofMariaDBMariaDB

    MySQL was founded by Allan Larsson, Michael Widenius and David Axmark in the year 1995, 19 years ago. It was released under the name of co-founder Michael Widenius daughter, ‘My‘. This project was released under GNU General Public License as well as under certain Proprietary License. MySQL was owned by MySQL AB firm until it went into the hands of Oracle Corporation. It is written in Programming Language – C and C++ and is available for Windows, Linux, Solaris, MacOS and FreeBSD.

    In the year 2009, Michael Widenius started working on MarisDB as a fork of MySQL. In the year 2012 the bricks of nonprofit MariaDB Foundation was laid. It was named after the founder’s daughter Maria.

    MariaDB is a fork of MySQL Relational Database Management System which again is released under GNU General Public License. It is written in Programming Language – C, C++, Perl and Bash and is available for Systems Linux, Windows, Solaris, MacOS and FreeBSD.

    How developers use MariaDB and MongoDB
    Avatar of Tarun Singh
    Tarun Singh uses MongoDBMongoDB

    Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.

    Avatar of Trello
    Trello uses MongoDBMongoDB

    MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.

    Avatar of Foursquare
    Foursquare uses MongoDBMongoDB

    Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.

    Avatar of Scrayos UG (haftungsbeschränkt)
    Scrayos UG (haftungsbeschränkt) uses MariaDBMariaDB

    Aside from Redis, we use MariaDB to store long-term information like user-data and big-data like regeneration-information for our open-world servers. We extensively use the relational aspects of MariaDB in joins, nested queries and unions.

    Avatar of AngeloR
    AngeloR uses MongoDBMongoDB

    We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by agenda. If it works out well we might look to see where it could become a primary document storage engine for us.

    Avatar of Matt Welke
    Matt Welke uses MongoDBMongoDB

    Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.

    Avatar of Seungkwon Park
    Seungkwon Park uses MariaDBMariaDB

    mysql보다 mariaDB가 join면에서 우수하다는 문서를 읽었습니다. 이 부분은 저의 블로그에서도 다뤘고 저의 word press 블로그는 mysql 대신 mariaDB 를 사용합니다.

    특히 limit 기능이 pagenation 처리를 할 때 너무 직관적이고 편해서 mariaDB, mysql을 사랑합니다.

    Avatar of Ana Phi Sancho
    Ana Phi Sancho uses MariaDBMariaDB

    Introduced in computer science course.managing relational database management systems, database analytics, and for data processing

    Avatar of nrise
    nrise uses MariaDBMariaDB

    수 백만개가 넘는 태그 키워드의 자동완성을 위해서 별도의 데이터베이스를 구축하였습니다. MariaDB 는 MySQL 을 포크한 프로젝트입니다. MySQL 과의 강력한 호환성을 지니며, 큰 튜닝 없이 강력한 성능을 보장합니다.

    Avatar of Dolls Kill
    Dolls Kill uses MariaDBMariaDB

    MariaDB has allowed us to easily scale out our DB cluster. Also has better replication tools than MySQL

    How much does MariaDB cost?
    How much does MongoDB cost?
    Pricing unavailable