What is data matchmaking? Fast pairing at scale

You've felt it before: that frustrating lag when an app can't find you a match, a ride, or a delivery slot. High engagement benefits from real-time interactions, and real-time performance is what users expect. Users perceive systems as reacting instantaneously when visual response latency is 0.1 seconds or less. Because real-time performance is so critical to modern applications, Redis has become the data platform of choice for applications needing fast matchmaking.

This article covers what matchmaking is, the data requirements for building effective matchmaking systems, and how Redis capabilities support real-time matching at scale.

What is matchmaking?

Matchmaking is the process of connecting disparate groups of data based on common criteria. At its core, matchmaking is often modeled as an assignment problem—pairing (or grouping) entities under constraints like proximity, skill, capacity, and time windows.

Consumer apps

Matching engines power more of the stack than most people realize. Gaming platforms match players of similar skill level so matches feel competitive and fun. Ride-sharing apps connect drivers to riders based on proximity, route compatibility, and estimated pickup times. Food delivery services pair orders with drivers considering distance, capacity, and time windows. Logistics systems match time-sensitive inventory with optimal fulfillment centers.

Enterprise systems

Beyond consumer apps, matchmaking powers enterprise systems too. Job platforms match candidates to positions based on skills, experience, and cultural fit. Financial services match buy and sell orders in real-time trading systems. Healthcare platforms match patients to providers based on specialty, availability, and insurance networks. Cloud infrastructure matches workloads to compute resources based on requirements and cost constraints.

Scale & speed

Modern matchmaking operates at massive scale. Uber reported that their dispatch algorithms analyzed thousands of features in real time to generate more than 30 million match pair predictions per minute. DoorDash and other delivery platforms make real-time decisions at peak demand to optimize driver-order assignments.

These aren't batch jobs running overnight. They're real-time systems where delays matter: many competitive games aim for low tens of milliseconds of latency to regional servers, though exact targets vary by game and region. Casual games can typically tolerate more.

Data requirements for matchmaking

Matchmaking has specific performance, scalability, and availability requirements. Missing these benchmarks degrades the user experience and can affect retention over time.

Real-time database performance

For humans to perceive an experience as instantaneous, it needs to happen fast—but how fast depends on the feedback type. Research puts the threshold at 30-85ms for visual feedback, 20-70ms for audio, and 5-50ms for tactile.

In a client/server architecture, data travels through multiple networks and servers before reaching the user. High-performance managed databases are designed to target single-digit millisecond response times for simple operations, though actual latencies depend on workload, configuration, and network path. The database layer typically needs to contribute only a small fraction of the 100ms budget, leaving headroom for application processing, network transit, and client-side rendering.

In-memory databases like Redis store data in RAM, supporting single-digit millisecond response times for many operations, with sub-millisecond latencies achievable in some scenarios. In-memory caching layers can significantly improve performance, reducing latencies from tens of milliseconds to low milliseconds or microseconds at high throughput.

High concurrency

Requests spike during peak hours. Estimated time of arrival (ETA) and dispatch systems at companies like Uber and DoorDash operate at extremely high query rates and tight latency budgets during peak demand. Gaming platforms experience surge traffic during evening and weekend hours.

To maintain responsiveness, your database should handle high levels of concurrent read and write operations without conflicts. Matchmaking systems process constant updates to availability pools, status changes, and assignment decisions with tight latency targets.

Your database architecture should support horizontal scaling. When peak traffic arrives, distributing load across multiple nodes is often more practical than vertical scaling. Redis Cluster provides automatic sharding and failover, with most operations continuing even when nodes fail.

Global availability

A fast matchmaking service should also be reliably available whenever users are logged on. For globally distributed systems, each geographic region often benefits from its own processes to reduce network latency.

With an Active-Active Geo Distribution architecture, your data spreads across multiple regions. Data in Redis Active-Active deployments is locally readable and writable with eventual consistency across regions, and temporary inconsistencies may occur before replicas converge. Redis uses Conflict-Free Replicated Data Types (CRDTs) for conflict resolution across replicas.

CRDTs are data structures designed to be replicated across multiple nodes, with mathematical guarantees that all replicas converge to the same state without requiring coordination. Redis Cloud provides a 99.999% service level agreement (SLA) specifically for Active-Active Database deployments, helping apps stay available through certain failure scenarios depending on deployment and traffic routing.

Rich data model support

Matchmaking apps deal with multiple layers of external complexity. Dynamic user profiles, with all their interactions, preferences, and history, benefit from rich data modeling for fast reads and writes.

Redis supports core data types that are often used in matchmaking applications: lists, sets, sorted sets, and hashes. For user profiles and multi-criteria matching, Redis provides the Redis Query Engine with advanced search capabilities and secondary indexing.

Flexible, customizable query engine

Matchmaking requirements vary across industries and use cases. Gaming platforms need skill brackets and latency constraints. Ride-sharing needs route optimization and driver ratings. Delivery services need capacity tracking and time windows.

The Redis Query Engine supports this diversity through configurable indexes and query composition. You define which fields to index: numeric ranges for ratings, geospatial coordinates for location, tags for categories, and tune them as needed.

This flexibility matters because users rarely know exactly what they're searching for. A rider wants "a good driver nearby soon," not a specific driver ID. A shopper wants "delivery in the next hour," not a specific time slot. Traditional exact-match queries often fall short of these fuzzy requirements. When the first search returns no results, users often leave rather than refine their query. With the Redis Query Engine, you can design queries and scoring rules that surface the best available options instead of strict exact matches. This application-defined approximate matching helps keep users engaged.

Unlike rigid query structures, you can combine filters dynamically using advanced query expressions: finding drivers within a certain distance who have high ratings, meet payment criteria, and are currently available. The query engine evaluates these multi-criteria constraints efficiently, supporting the flexibility that real-world systems require.

Rich query language & search

Matchmaking systems benefit from flexible querying—not just exact matches, but approximate results that satisfy user intent. This flexibility is built on rules: geo-proximity, tag matching, range differences, and personalized preferences.

The Redis Query Engine powers full-text search as well as complex structured queries. Let's look at some search commands in action.

Find the closest driver & fulfillment center

Redis geospatial commands support proximity searches within a radius or bounding box. The GEOSEARCH command can return distances from the center point, sort results from nearest to farthest, and limit results to the top N matches.

Find users on the closest server

When matching players to game servers, proximity matters for latency. Combine geospatial filtering with tag-based server attributes:

This query finds active servers within 100km of San Francisco that support battle royale mode. The query engine processes geospatial coordinates alongside categorical filters in a single operation, filtering results by distance within a radius.

Find inventory within delivery windows

This range query capability works well for logistics and fulfillment systems. You can find available inventory within a radius that can be delivered within a specified time window, combining location, availability status, and time constraints in a single query.

Match players with similar play style

Tag fields in the query engine provide exact match search capabilities with fast filtering, useful for game modes, categories, preferences, or other categorical attributes.

Find the optimal driver for delivery orders

Modern delivery platforms use various algorithms and batching techniques to improve driver-order matching efficiency. DoorDash's system evaluates batch combinations in real time, scoring each based on route efficiency and time constraints. Uber's H3 is a geospatial indexing system that supports optimization tasks like ride pricing and dispatch, using a hexagonal grid for spatial analysis within their marketplaces.

Why Redis for matchmaking?

Modern applications benefit from real-time responsiveness, particularly during peak demand periods, and managing this consistently often calls for specific architectural solutions. Effective matchmaking can directly impact user engagement across domains: research suggests optimized matchmaking increases player retention in competitive gaming, while routing efficiency in order fulfillment improves customer satisfaction through faster deliveries.

The data requirements are demanding but clear: database performance fast enough to keep end-to-end interactions within human "instantaneous" perception thresholds (around 100ms or less for many interactions), high concurrency to handle peak traffic spikes, global availability through geo-distributed architecture, rich data models for complex user profiles, and flexible query engines that return approximate results when exact matches aren't available. Getting these requirements right helps keep users engaged. Getting them wrong risks losing them.

Redis supports single-digit millisecond performance for many core operations, high concurrency handling, global availability through Active-Active Geo Distribution with CRDTs, and flexible multi-criteria queries through geospatial commands, sorted sets, and the query engine. Whether you're building a gaming platform that needs to match millions of players by skill level, a ride-sharing app connecting drivers and riders in real time, or a logistics system routing orders to fulfillment centers, Redis can provide the foundation for matchmaking that feels instantaneous.

Companies like Niantic (Pokémon GO) and MrQ use Redis to power their real-time gaming experiences. Niantic relies on Redis for location-based matchmaking that connects millions of players to nearby game events and other trainers. MrQ uses Redis to handle the high-concurrency demands of live multiplayer gaming, where match timing and player synchronization are essential to the experience.

If you're building matchmaking systems that need to scale to millions of users with single-digit millisecond response times, talk to our team about how Redis can support your architecture.