This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both MongoDB and Redis. We update the post every year.
| Update History | Comment |
|---|---|
| 2025/04/23 | Initial version. |
Choosing the right database impacts performance, scalability, and development. MongoDB and Redis are both NoSQL but serve different purposes. MongoDB is a flexible document database using JSON-like structures. Redis is an ultra-fast in-memory store with rich data types.
This comparison covers their features, architectures, performance, and ideal use cases to help you decide which fits your needs.
MongoDB Overview
MongoDB is a document-oriented NoSQL database designed to store, retrieve, and manage document-oriented data. Developed by MongoDB Inc. (formerly 10gen), it was first released in 2009 and has since become one of the most popular NoSQL databases in the world.
Architecture and Data Model
MongoDB's architecture is built on several core foundations:
-
Document Model: Stores data as flexible JSON-like BSON documents (variable fields per document)
-
Distributed Design: Scales horizontally via sharding and replication
-
Unified Experience: Combines database, search, and analytics in one platform
Data is grouped in collections (schema-free alternatives to SQL tables), enabling easy data model evolution.
Key Features
MongoDB's modern feature set includes:
- Schema Flexibility: Varied document structures within collections
- Powerful Querying: Full CRUD, text search, aggregation, and geospatial support
- Optimized Performance: Multiple index types (single/compound/multi-key/geospatial/text)
- Elastic Scaling: Native sharding for horizontal growth
- Continuous Availability: Replica sets with auto-failover
- Data Integrity: Multi-document ACID transactions
Use Cases
- Content Management: Storing diverse content types with varying attributes
- Customer Data: Managing flexible customer profiles
- IoT Systems: Processing high-volume device data
- Mobile Apps: Enabling offline-first experiences with sync
- Real-time Analytics: Running complex aggregations on operational data
Redis Overview
Redis (Remote Dictionary Server) is an open-source, in-memory data structure store that can be used as a database, cache, message broker, and streaming engine. Created by Salvatore Sanfilippo in 2009, Redis is known for its exceptional performance and versatility.
Architecture and Data Model
Core Characteristics:
-
In-Memory First: RAM-based storage for microsecond latency (datasets must fit in memory)
-
Advanced Data Structures:
- Strings, hashes, lists, sets
- Sorted sets, bitmaps, hyperloglogs, geospatial
-
Single-Threaded: Event-loop model avoids concurrency issues
-
Persistence Options:
- Periodic snapshots of the dataset to disk
- Append-only files that log every write operation
Key Features
Redis delivers unique capabilities through:
- Sub-millisecond performance - In-memory operations enable ultra-low latency
- Rich data structures - Strings, hashes, lists, sets, sorted sets, and more
- Atomic operations - Guaranteed uninterrupted execution per command
- Pub/Sub messaging - Built-in publish-subscribe pattern support
- Server-side Lua scripting - Complex atomic operations via scripts
- Auto-expiring keys - TTL support for caching/session use cases
- Horizontal scaling - Redis Cluster for automatic sharding & HA
Use Cases
- Caching - Accelerate apps with microsecond data access
- Session Storage - Fast user sessions with auto-expiration
- Real-time Analytics - Process streams & maintain counters
- Leaderboards - Sorted sets for ranking systems
- Message Queues - Async task processing
- Rate Limiting - API request throttling
Detailed Comparison
Feature Comparison Table
| Feature | MongoDB | Redis |
|---|---|---|
| Type | Document-oriented NoSQL database | In-memory key-value data store |
| Data Model | Flexible JSON-like BSON documents | Rich data structures (strings, hashes, lists, sets, sorted sets, etc.) |
| Primary Storage | Disk-based with memory caching | In-memory with optional persistence |
| Performance | Good for complex queries on large datasets | Extremely fast for simple operations (sub-millisecond) |
| Scalability | Horizontal scaling through native sharding | Horizontal scaling through Redis Cluster |
| High Availability | Replica sets with automated failover | Redis Sentinel with monitoring and failover |
| Query Language | Rich query language with aggregation framework | Simple command-based interface |
| Indexing | Multiple index types (single field, compound, text, etc.) | Limited indexing (primarily through sorted sets) |
| Transactions | Multi-document ACID transactions | Basic transactions with MULTI/EXEC commands |
| Schema Flexibility | Dynamic schema, no fixed structure required | Schema-less with structured data types |
| Memory Usage | Optimized for datasets larger than available memory | Optimized for datasets that fit in memory |
| Consistency | Strong consistency with replica sets | Eventual consistency with replication |
| License | Server Side Public License (SSPL) | Redis Source Available License (RSALv2) or SSPL |
| Use Cases | Primary database, content management, user profiles, IoT | Caching, session storage, real-time analytics, message broker |
Data Model and Storage
MongoDB:
- Document-oriented with flexible schema
- JSON-like BSON format for rich data representation
- Primarily disk-based with memory caching
- Supports embedded documents and arrays for complex data structures
Redis:
- Key-value store with rich data structures
- Simple values or complex data structures associated with keys
- Primarily in-memory with optional persistence
- Specialized data types for specific use cases (lists, sets, sorted sets, etc.)
The fundamental difference in data models influences how you structure your data and the types of operations you can efficiently perform. MongoDB's document model is well-suited for complex, hierarchical data with varying attributes, while Redis's specialized data structures excel at specific operations like counting, ranking, or queueing.
Performance Characteristics
MongoDB Performance:
- Optimized for complex queries across large datasets
- Efficient for operations requiring joins and aggregations
- Good performance for write-heavy workloads with appropriate indexing
- Handles datasets larger than available memory
Redis Performance:
- Exceptional speed for simple operations (sub-millisecond latency)
- Optimized for high throughput scenarios
- Extremely efficient for read-heavy workloads
- Performance degrades when dataset exceeds available memory
Scalability and High Availability
Both databases offer robust solutions for scaling and ensuring high availability, but with different approaches:
MongoDB:
- Horizontal scaling through native sharding
- Automatic balancing of data across shards
- Replica sets for high availability and data redundancy
- Supports geographically distributed clusters
Redis:
- Redis Cluster for horizontal scaling through hash slot-based sharding
- Redis Sentinel for monitoring and automatic failover
- Master-replica architecture for read scalability
- Cluster mode supports automatic re-sharding
MongoDB's sharding is more mature and offers more sophisticated options for distributing data, while Redis Cluster provides simpler but effective sharding for in-memory datasets.
Use Case Suitability
MongoDB excels at:
- Serving as a primary database for applications
- Content management systems
- Applications requiring complex queries and data relationships
Redis excels at:
- Caching and session storage
- Real-time analytics and leaderboards
- Message brokers and task queues
Feature Comparison
The feature radar chart provides a visual representation of how MongoDB and Redis compare across various dimensions:
- Query Flexibility: MongoDB offers more comprehensive query capabilities
- Performance: Redis delivers superior raw performance for simple operations
- Data Model Flexibility: MongoDB provides greater flexibility in data modeling
- Durability: MongoDB has stronger durability guarantees
- Memory Efficiency: MongoDB is more efficient for large datasets
- Developer Experience: Both offer good developer experiences but for different use cases
When to Choose MongoDB
MongoDB is the better choice when:
- A primary operational database - Full ACID compliance and durability
- Complex data structures - Native handling of nested documents and arrays
- Advanced querying - Rich filtering, sorting, and aggregation pipelines
- Large-scale persistence - Efficient disk-based storage beyond RAM limits
- Strong consistency - Replica sets with configurable consistency levels
- Schema evolution - No-migration flexibility for changing data models
When to Choose Redis
Redis is the better choice when:
- Sub-millisecond responses - Critical for ultra-low latency applications
- High-performance caching - Industry-standard cache implementation
- Specialized data structures - Optimized sets, sorted sets, hyperloglogs
- Memory-sized datasets - Full in-memory operation for maximum speed
- Real-time systems - Leaderboards, counters, rate limiting
- Pub/sub messaging - Built-in publish-subscribe functionality
Using MongoDB and Redis Together
Modern systems often combine both databases for optimal performance:
-
Caching Layer
- MongoDB: Primary source of truth
- Redis: Hot data cache → 10-100x faster reads
-
Real-Time + Persistence
- MongoDB: Full dataset storage
- Redis: Leaderboards/feeds with sorted sets
-
Session Optimization
- MongoDB: User profiles (complex queries)
- Redis: Session data (TTL auto-expiry)
-
Event-Driven Systems
- Redis: Pub/sub message broker
- MongoDB: Audit/event logging
This combined approach leverages the strengths of both databases while mitigating their respective limitations.
Conclusion
MongoDB excels as a flexible document database for complex queries and persistent storage, while Redis delivers blazing-fast in-memory operations for caching and real-time systems. Many modern architectures combine both - using MongoDB as the primary data store and Redis for performance-critical layers. Choose MongoDB for rich data structures and queries, Redis for sub-millisecond operations, or both for optimal performance and flexibility.
