Famous Architectures: Designing of Modern Dating Apps

Introduction

In the digital age, dating apps like Tinder, Bumble, and Hinge have revolutionized how people meet and connect. These platforms support millions of users and demand architectures that are not only scalable and reliable but also deliver smooth, responsive user experiences. In this article, we'll explore the core system design principles and architecture patterns required to build a modern dating app at scale.

Defining the Requirements

Before diving into the architecture, it’s essential to outline both functional and non-functional requirements—key features and performance standards expected from any contemporary dating platform.

Non-Functional Requirements

• The system must support a high volume of active users spread across different geographic regions.

• Swiping interactions must be consistent and reliable; mutual interest should trigger a match notification in real-time.

• Profiles that a user has already swiped on should not appear again.

• The potential matches feed should load quickly to maintain a responsive user experience.

Functional Requirements

• Users can create profiles, including personal details and match preferences (e.g., maximum search radius).

• The app displays a swipeable stack of nearby potential matches based on the user’s location and preferences.

• Swiping right indicates interest, while swiping left passes on the profile.

• If two users swipe right on each other, the system generates a match and notifies both parties.

High-Level Design

Entities

We should begin by identifying the essential entities. Establishing these core elements right away will not only guide our thought process but also create a strong foundation for defining the API.

For most dating apps the primary entities are:

1. User: This represents both a user using the app and a profile that might be shown to the user. We typically omit the "user" concept when listing entities, but because users are swiping on other users, we'll include it here.

2. Swipe: Expression of "yes" or "no" on a user profile; belongs to a user (swiping_user) and is about another user (target_user).

3. Match: A connection between 2 users as a result of them both swiping "yes" on each other.

Basic API

Now let's talk about the first endpoints responsible for interacting with the above mentioned entities. The first one will be the endpoint to create a profile for a user. This would include images, bio, prompts, interest...etc. In my definition I will focus on their most common matches preferences.

HTTP Verb: POST 

Path: /profile

Body:

{
  "name": “John Doe”
  "age": 33,
  "preference": {
  	"age_min": 28,
  	"age_max": 40,
  	"distance": 10,
  	"interestedIn": "female" | "male" | "both"
	}
}

Now that the user's profile is available, the next step is to create an endpoint that provides a "feed" of user profiles for swiping, ensuring a "stack" of profiles is ready for the user. This endpoint is related to the Discovery Engine that I will talk about later.

WebSocket Connection

Path: /profilefeed

QueryParams: age_min=20&age_max=40&distance=10

Response: User[]

Note: The previous endpoint can be accessed multiple times to obtain additional recommendations if the current list is depleted.

The last endpoint and not least is the endpoint responsible for swiping. It’s here where began all the process that leads two users to a possible match between them.

HTTP Verb: POST

Path: /swipe/{userId}

Body: 

{
  hasLiked: true | false
}

In this endpoint “userId” is the Id of the user in the stack at the moment of swipe to left or the right. With each of these requests, the requester user information will be passed in the headers (either via session token or JWT). This is a common pattern for APIs and is a good way to ensure that the user is authenticated and authorized to perform the action while preserving security. For example, a JWT could be:

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiIxMjM0NTYiLCJuYW1lIjoiSm9obiBEb2UiLCJpYXQiOjE2MTUxNTU2MDAsImV4cCI6MTYxNTE1OTIwMH0.FakeSignatureForExample

And the payload after been decoded in the backend will provide to us the necessary information to identify the requester user for each of the previous endpoints.

{
  "userId": "123456",
  "name": "John Doe",
  "iat": 1615155600,
  "exp": 1615159200
}

Designing each Functional Requirement

Now let's analyze and design how to implement each functional requirement.

Users can set up a profile where they include personal details and specify preferences.

After the user signs into the platform, the app needs to allow users to tell their information(i.e name, age,..) and preferences. To accomplish that the design needs to be capable of handling a big amount of users creating their profiles and also consider that a user created in Canada, North America should be recommended to other users in such a region, but not to users created in Burundi, East Africa.

NOTE: Some dating apps allow users to change regions, but that feature is not part of the Functional Requirements for this article.

In such case a diagram describing the architecture for the profile creation looks like this:

When the app in the Phone starts the profile creation, this first communicates with an API Gateway that works together with a Load Balancer to distribute the requests. This request is forwarded to the Profile Service, and the user preferences are persisted in a Fast database like Cassandra or DynamoDB. After the preferences are persisted, it’s necessary to persist the user information to make him/her discoverable to others. This part of the user creation part, is related to the Discovery Engine System, which will be discussed later. To achieve this, the profile information(name, age,location) to be used by the Discovery Engine System is enqueued together with all the thousands of users created at the same time, and a Queue Worker Service is responsible for fetching and processing each message on demand. This service sends the information to an ElasticSearch/ES Service that, using the user location and a geometry library like S2 Geometry, identifies efficiently into which Elasticsearch(ES Server) must persist this profile information.

Data Clustering

Lets have a close and quick look into the ES Servers. In the previous section I mentioned user location and geometry library. Based in the following hypothetical scenario:

Each cell in the image corresponds to a server, so if a user is in the center  of the circle and the circle represents the distance, then server 1, server 2 and server 3 fall inside the search criteria. So, each ES Server will be used to get recommendations of users. However, ES Server 4, 5,..etc wont be used to query the recommendations. With this way, the system can distribute the load of operation related to profile creations and further user discovery.

Users can view a stack of potential matches within max distance of their current location.

In the previous section I mentioned the Discovery Engine System. This is the part in the whole system responsible for providing suggested profiles to the user. Let's go deep and analyze its functionality.

When the user requests a stack of potential matches or recommendations, a new Websocket connection is established between the phone and the Discovery Engine. And inside of it, a request to /profilefeed. 

The Discovery Engine System, using the user latitude and longitude, search distance and the S2 library, identifies which servers will be used during the search of recommendations. After this, the ES Service is capable of performing a parallel search on the servers resulting from the previous analysis. When the result of all the parallel search has finished, then it can be returned to the user. Ultimately, the websocket connection to the Discovery Engine is closed.

Users can swipe right or left on profiles.

The system will receive a big volume of swipe operations every day. And if this functionality fails profiles can have proper matches. To achieve this requirement will be introduced two new components:

1. Swipe Service: Persist swipes and checks for future matches.

2. Swipe Database: Store swipes data for users.

Using a separated database for the swipe, allow us to partition the data by user_id1_user_id2. If we use Redis for the database, then the entries in the database can have the following format.

Key: "swipes:123:456"

Value: {
    "123_swipe": "right", // or null
    "456_swipe": "left"   // or null
}

And with this, it is fast to identify in the future a possible match.

A Match notification is received if they mutually swipe to right

When user “123” swipes right on user “456”, a key like swipes:123:456 is stored in Redis to record the action. This data remains untouched unless the reverse happens—user “456” also swipes right on user “123”. At that point, the matchmaker service checks Redis for a corresponding key (e.g., swipes:123:456) to determine if a mutual interest exists. If a match is confirmed, a message is pushed to a match queue. A separate notification system then picks up this message and notifies both users—either via WebSockets or long polling—letting them know it’s a match.

Conclusions

Building a modern dating app demands more than just a sleek interface — it requires a tightly engineered system capable of handling global scale, real-time interactions, and intelligent matching. The architecture must support millions of users with seamless swiping, geospatially-aware recommendations, and instant match notifications — all while ensuring consistency, avoiding duplicates, and maintaining low latency. Technologies like Redis for swipe tracking, Elasticsearch for location-based search, and WebSocket for real-time feedback create a responsive, scalable foundation.

Ultimately, the success of a dating platform lies in its ability to connect users meaningfully — and that’s only possible with a design that’s both technically sound and user-centric. From secure authentication to distributed data handling and intelligent discovery, the architecture must anticipate growth, adapt to global users, and deliver a frictionless experience. In short, the right system doesn’t just power a dating app — it enables real connection, at scale.