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Top Social Media Sites and Their Databases

Databases for Social Media Platforms

by Snehal Naik
top social media platforms and their databases
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Introduction

Around 62.3% of the total global population actively uses social media sites Thus, making it crucial to have an optimal database system to effectively manage the overwhelming amount of data generated on these platforms. The need to offer users a better experience has led top social media sites and apps to leverage the power of databases to manage their Big Data. In this blog, we’ll look at the top social media sites and their databases. 

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Globally Popular Social Media Platforms (Jan 2024)

Popular Social Media Platforms 2024

                                                                                              Source: Datareportal 

Research reveals that the younger generation is more likely to use Instagram than other social media platforms, at least in the Western markets. However, it is important to understand that these preferences might vary based on age, gender, and country. For instance, some social media platforms, such as Twitter, Facebook, and TikTok are banned in some countries.

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Why Do People Use Social Media?

Before we explore the tech stack of social media sites and apps, it’s important to understand why people use social media. It gives an insight into the best databases one can choose to manage social media Big Data. Facebook, YouTube Instagram, TikTok, and Snapchat are among the popular social media sites consumed by users right now. Most people use social media to keep in touch with friends and family and to tackle boredom.

Here are the key highlights from a global overview of people and their social media usage:  

                                                                                                  Via Datareportal 

  • Users aged 35 and above use social media primarily to stay connected with friends and family and the least to watch live streams or make new contacts.  
  • Youngsters between 16 and 24 years of age use social media the most to fill up spare time and the least to make new contacts. 
  • For information on trending topics, 30% of the 25 to 34 years age group use social media. 
  • Users between the ages of 16 and 24 use social media to avoid missing out.  
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How Do Social Media Sites Manage Data?

Over the past two decades, popular social media platforms have seen explosive growth in terms of the content and data generated. The astonishing proportion of data calls for the need to have a secure place where all this data can be sorted, stored, and retrieved for user insights, product development, and data security, preferably without the fear of data loss. Social media platforms use database technologies and distributed systems to manage their data.

Social media data analytics is how data is stored in a structured way from social media platforms. These databases store user profiles, posts, comments, likes, shares, and more to help visualize, analyze, and report the data.  

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Databases Used by Top Social Media Platforms

Here are the top social media apps used worldwide as of January 2024, and the databases used to manage them.

Social Media AppDatabases Leveraged
Facebook
  • MySQL
  • MyRocks
  • Apache Hadoop
  • Apache Cassandra
  • Apache Thrift
  • HBase
  • Hive
  • PrestoDB
YouTube
  • Bigtable
  • MySQL
Instagram
  • PostgreSQL
  • Cassandra
  • Redis
  • Hive
  • Apache Hadoop
TikTok
  • PostgreSQL
  • Cassandra
  • Redis
  • ByteGraph
Twitter (X)
  • MySQL
  • Manhattan
  • Hadoop
  • Memcache
  • Redis
  • FlockDB
  • MetricsDB
  • Blobstore
Snapchat
  • Google Cloud
  • AWS
  • MySQL
  • MariaDB
  • PostgreSQL
  • NoSQL Server
Pinterest
  • MySQL
  • Redis
  • HBase
LinkedIn
  • Apache Cassandra
  • Apache Kafka
  • Apache Hadoop
  • Apache Samza
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Facebook

Facebook is the most popular social networking site with around 3 billion monthly active users as of Q2 2023. The social media platform has all types of content — images, text, videos, and Stories. Millions of small businesses also use Facebook to promote their brands. In addition, it also has more than 7 million active advertisers.  

Databases Programming Languages Leveraged by Facebook  

  • The primary database that Facebook uses is MySQL.  
  • The default MySQL storage engine is InnoDB.  
  • The user-facing database engine was migrated from InnoDB to MyRocks to deal with space issues.  
  • For its Big Data needs, Facebook uses Apache Hadoop, HBase, Hive, Apache Thrift, and PrestoDB.  
  • For the inbox search, it uses Apache Cassandra. 
  • Beringei and Gorilla are storage engines used for infrastructure monitoring.  
  • LogDevice, a distributed data store is used for storing logs. 
  • PHP, Java, and C++ are among the programming languages used in Facebook.  
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YouTube

More than 150,0000 new videos are uploaded to YouTube every minute. It is a video-sharing platform but is also used as a search engine after Google. Whether you want to share your daily life or travel journeys through vlogs, promote your business, share news or educational content, or watch movies and music videos, YouTube has every type of content uploaded.  

Database and Programming Languages Leveraged by YouTube   

  • Powered by Vitess, MySQL is the primary database used.  
  • YouTube uses Bigtable to store and query data.  
  • JavaScript programming language is used for the user interface. 
  • The backend microservices are written using Python, C, C++, Java with Guice framework, and Go.
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Instagram

This visual social networking platform took the world by storm when it was launched in October 2010. Instagram is a popular photo and videosharing social networking service owned by Meta Platforms. On average, people spend 29.7 minutes a day on Instagram. Instagram is also used by millions of big and small businesses to showcase their products. The platform is widely used to share photos, videos, Stories, Reels, and live videos. 

Database and Programming Languages Leveraged by Instagram    

  • Django Python runs the server-side code for Instagram. 
  • Instagram mainly uses PostgreSQL and Cassandra to store user data, photos, tags, meta-tags, etc. 
  • Redis is used as an in-memory database to store the activity feed, sessions, and other app’s real-time data. 
  • Memcached is used for caching throughout the service. 
  • The asynchronous tasks such as sending notifications are handled by RabbitMQ and Celery. 
  • Hive, a data warehousing software built on top of Apache Hadoop, is used for data archiving.
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TikTok

TikTok is a short-form video hosting service owned by Chinese Internet company ByteDance. Known as Douyin in China, TikTok is one of the fastestgrowing apps in the world, with over 1 billion monthly active users. Users share videos between 15 seconds and 10 minutes long on TikTok. You can find videos of almost all interests — lip syncs, dance challenges, make-up tutorials, DIYs, etc.  

Database and Programming Languages Leveraged by TikTok     

  • TikTok uses relational databases like PostgreSQL and NoSQL databases like Cassandra and Redis 
  • The platform also leverages ByteGraph, a distributed graph database, to handle its graph data.  
  • The machine learning software used to analyze user behavior are TensorFlow and PyTorch. 
  • TikTok’s mobile apps are developed using Java, Kotlin, Swift, Objective-C, etc.
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X (Twitter)

As of September 2023, X, formerly Twitter, had 550 million monthly active users. X is widely used for breaking news and real-time updates in just 280 characters. Users also share text messages, images, and videos as ‘tweets.’ Additionally, it also allows video and audio calling, direct messages, bookmarks, and reposts. 

Database and Programming Languages Leveraged by X (Twitter)      

  • MySQL and Manhattan are the primary data stores for storing user data. 
  • X uses Hadoop for social graph analysis, recommendations, API analytics, and more. 
  • For caching, it uses Memcached and Redis. 
  • FlockDB helps store social graphs. 
  • MetricsDB is used to store platform data metrics 
  • Blobstore is used to store images, videos & large binary objects.
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Snapchat

This American instant messaging app is a fun way of sharing pictures and messages. Interestingly, the shared content is available only for a short time before it becomes inaccessible to the recipients. Snapchat popularized the vertical video format before it started trending on Instagram and TikTok. Snapchat is popular among those below the age of 16.

Database and Programming Languages Leveraged by Snapchat       

  • The Snapchat platform was developed on Google Cloud in 2011.  
  • Snap migrated to an AWS microservices architecture to improve scalability, optimize availability, minimize latency, and reduce costs. 
  • C / C++, Swift, Objective-C, Python, R, SQL, C#, Typescript, JavaScript, and Java are some programming languages Snapchat uses.  
  • Snapchat uses many databases including MySQL, MariaDB, PostgreSQL, Oracle, SQL Server, and NoSQL
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Pinterest

Pinterest is the world’s online pinning platform where users discover and save information. Just as you would pin a favorite quote or a photo to a physical soft board at work or in your room, you can ‘pin’ recipes, DIY videos, inspirational quotes, doodles, and home, beauty, and fashion tips on your profile. 

Database and Programming Languages Leveraged by Pinterest        

  • User engagement trends of Pinterest are analyzed using SQL.  
  • The metadata corresponding to a Pin’s image URL, title, and description are stored in the MySQL database. 
  • Redis is an in-memory database that helps achieve low latency and high throughput. 
  • Pinterest is known to have used HBase for backend storage.  
  • Most of the source code of Pinterest is written in Python but it also uses Java, Python, JavaScript, C++, and Go.
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LinkedIn

Today, LinkedIn is no longer a job search engine. It has become a professional networking platform used by people to build their personal brand. The number of LinkedIn users worldwide is expected to reach 793 million by 2025. LinkedIn has professional profiles, job postings, company pages, and more. Many organizations and business leaders use LinkedIn as a platform to establish thought leadership. LinkedIn allows users to find top jobs, author and share posts, use DMs, and conduct polls, among other things. 

Database and Programming Languages Leveraged by LinkedIn         

  • LinkedIn was built using Apache Cassandra, a distributed NoSQL database. 
  • It was written in Java, JavaScript, and Scala. 
  • Apache Kafka allows the platform with real-time data feeds.  
  • LinkedIn uses Espresso for real-time indexing and searching.  
  • For distributed key-value storage, it built Voldemort, its own distributed data store. 
  • Apache Hadoop and Apache Samza are used for storage and analytics.  
  • LinkedIn uses NLP techniques for improved search results and relevant content.  
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How to Choose the Right Database for Social Media Apps?

There is no one-size-fits-all approach when choosing a database for social media apps. It is all about selecting the right combination of databases that offer cutting-edge data management capabilities, robust scalability, and optimal performance. The right approach is to consider the data model requirements, for instance, graph databases for relationship-heavy networks or document databases for flexible content storage. Social media apps also need databases to support additional features like transactions, indexing, and querying. As a database developer, your knowledge of databases can help social media apps efficiently handle massive volumes of data while facilitating seamless interactions. If you are a developer or a database specialist looking for jobs that match your skill sets, sign up with Olibr to know more. 

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Frequently Asked Questions

NoSQL databases such as MongoDB and Cassandra offer the scalability to manage data in the form of images and videos.  

SQL databases can store images but if images are heavier than 1 and 2 mb, it can overload the database and affect app performance. A good solution is to store images in project dir or file system and store images path in the database. 

NoSQL databases such as MongoDB, Cassandra, Redis, and Neo4j can handle large volumes of data, whether unstructured or semi-structured.  

Big Data is too fast and traditional structures of RDMS might not be the best option as it would not seamlessly process large amounts of data with minimal latency. 

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