Tinder is the world’s largest, most popular dating app, with a staggering 75 million active monthly users and 100 million+ overall app downloads. Launched in 2012 as part of a hackathon in West Hollywood, California, Tinder quickly rose to fame and overtook every competitor quickly.
Within two years, Tinder was registering 1 billion daily ‘swipes. The number has now risen to almost 2 billion, and to date, Tinder has ‘matched’ 65 billion times!
Tinder: A Game-Changing Matchmaking Solution
If we say that Tinder has single-handedly revolutionized the concept of love and match-making, then it won’t be hyperbole. Millions of people are discovering their dates on Tinder and finding love daily.
Such is the popularity of Tinder: 6.4 million paid subscribers on this app now spend more money than Netflix, and in 2015, it became the fifth highest-grossing mobile app globally.
The popularity and usage of Tinder are unstoppable. One of the primary reasons behind this is the robust backend systems and easy-to-use interface that pulls in users and forms a habit for them.
In today’s blog, we will understand Tinder’s system design and architectural styles and discover what makes it unique: How can one mobile app support 75 million active monthly users logging in to the app 11 times a day?
How can a system be so robust and scalable that it has successfully matched 65 billion profiles in a flawless, seamless way?
Before we describe Tinder’s unique system design architecture, let’s first understand the jargon, terms, principles, architectural styles, and functionalities used within the ecosystem for greater clarity.
App Design & Main Features
The functioning of Tinder is super easy and super fast. Once you create an account, you will start seeing profiles of different users based on your preferences. The mobile app provides the feature to view content other users share, such as images, likes/dislikes, questions, and more.
For every user, Tinder shows these information details: current age, distance from your location, mutual friends (via Facebook/Instagram), mutual interests between the users, and target description. The process of matching starts here when you can do the following:
Swipe-Right: If you like a profile, you will swipe right.
Swipe-Left: If you don’t like a profile, you will swipe left.
Match: If both the profiles swipe right, it’s a match.
Double Opt-in Communication System: Communication between two users can only start once both have swiped right one another. Hence, a double opt-in system for communication ensures more safety and transparency.
Lately, Tinder has introduced features like Super Like & Boost, which are very interesting monetization programs to generate more revenues. Using object-oriented programming and unique software design patterns showcases excellence in every way and a promising future for the app.
Now, let’s talk more about the actual architecture, implementation details, and other important stuff that is set up behind the actual good-looking Tinder app.
Decoding The Complex Software Architecture Of Tinder
Now, let’s dive straight into the system architecture of Tinder and find out how it works. We will divide the entire understanding of system design into two parts:
The Recommendation Engine and the Matchmaking Process. Both comprise the unique service-oriented architecture and interesting software design patterns within the app.
In this blog, we will share how Tinder’s Recommendation Engine works, and in the 2nd part, we will understand the matchmaking process. But first, some information about software architecture and hosting data.
How Does Tinder’s Development Team Handle Database Hosting?
Tinder is entirely hosted on Amazon Web Services or AWS Cloud and has no web application, browser, or portal. Tinder is only available on Android (Google Play Store) and iOS (Apple App Store); hence, it is only accessible via mobile phones.
Tinder uses AWS Amplify to build, scale, and test its mobile applications and MongoDB for the database. Meanwhile, they use Redis for caching and in-memory databases.
The layered architecture was handled by their development teams well, focusing on the need for object-oriented programming in this specific case.
Understanding Tinder’s Recommendation Engine Software Architecture
The recommendation process is one of the most crucial functionalities of Tinder, and the entire user experience is based on this engine. The profiles a user sees on Tinder for potential matchmaking are based on the function and structure of this recommendation engine, which forms the entire foundation of using this app.
The recommendations from Tinder’s algorithm should be personalized and very targeted for specific users, which is how dating works in the physical world. If a video game enthusiast is looking for a date, there’s a high chance that he/she will look for a fellow video game lover.
Top Features Of Tinder’s Recommendation Engine (Software System)
The Recommendation Engine’s software architecture has the following abilities:
User’s Activity: Dating is all about being ‘active’ on the scene and letting your presence feel. This is why Tinder’s recommendation engine focuses extensively on the users’ active status and finds out how actively that person uses the application. The more active a user is, the better his/her positioning per the recommendation engine. Hence, the app utilizes an object-oriented design with all the functional requirements.
Collect Tags: Although Tinder offers three options to log in: Google, Facebook, and Phone Number, it’s Facebook that’s the most beneficial option for the user because of data collection. Tinder’s recommendation engine will recommend a person’s profile based on the data collected about him/her, and once logged in via Facebook, this becomes seamless.
Tinder’s recommendation engine will automatically collect information such as age, location, distance, gender preference, visited places, likes, dislikes, friend’s circle, and more via Collect Tags and use this information to recommend other profiles for the user. These individual components make the app even better and fill in the bigger picture of matchmaking.
Rating Of Users: Once the information is collected and the users’ activity status is monitored, Tinder allocates a rating for every profile, say between 1 and 10. And then groups the users under these ratings. If a profile has been rated 3, then the recommendation engine will suggest other profiles also rated 3 and ensure meaningful matches. The object-oriented design makes up for a unique case in the software industry.
User Actions: Tinder’s recommendation engine will find out about the users’ actions related to swipes. If the user is extensively swiping right, then it means that that user is not contributing to the purpose of the dating app, which is to rate and filter.
At the same time, if the user is only swiping left, that is also bad. Hence, the recommendation engine will rate profiles with the perfect balance between right and left swipes.
Maintaining the Balance: If a particular profile gets too many right swipes, that profile is hidden for some time to maintain the balance. Some term this as progressive taxation to make it fair to all.
If a profile gets fewer right swipes, that profile is automatically boosted for some time to induce fairness and balance. This ensures a dose of conceptual integrity in the app, all while following the unique design patterns.
Reply TAT: Tinder’s recommendation engine also finds out how soon a person replies to a matched user via Reply TAT or Turn Around Time. The sooner the person replies, the better because that will denote the user’s activeness. The application architecture works well with all the design patterns, ensuring maximum use and room for new features.
Technical Properties Of Tinder Recommendation Engine
Now, let’s find out the technical properties and performance of the Tinder recommendation engine that powers the Tinder app:
Fast Loading Via Low Latency: Time is crucial here. As soon as the user logs into the application, he/she is shown profiles of other users based on the recommendation engine’s data, which must happen quickly. For fast loading of the profiles, the recommendation engine has low latency.
Shard Database: The database of the Tinder recommendation engine is not centrally stored but shared globally across different AWS servers for faster responses and seamless storage. Some good architecture is implemented, ensuring the end user details are well-kept and secure.
Powerful Search Features: The Tinder recommendation engine supports a full-text search of the person’s profile to collect as much data as possible and make it easier for the users to locate the profiles they can match.
Data Transferring: An HTTP interface or web socket transfers data to the application. XML/JSON for creating a structured data format.
In the next part of this series, we will understand the ElasticSearch mechanism of Tinder that enables them to match the profiles based on the recommendation engine algorithm.
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We will carefully review your requirements, lay out a plan for the most common architectures, evaluate any unique software design principles we need to follow and develop a quality Tinder-themed dating app with robust functionalities and features.
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