Hey Alex, great question—chat message persistence is one of those decisions that hinges on your app’s specific needs, growth plans, and the type of chat experience you’re building. Let’s break down your options, starting with MySQL (and other RDBMS) and then covering alternatives that might fit better depending on your use case:

MySQL (RDBMS): When It’s a Solid Choice #

MySQL isn’t just "old school"—it’s still a fantastic fit for many chat apps, especially if:

• You need strong ACID guarantees: If features like message retraction, read receipts, or thread-based conversations require atomic, consistent operations (e.g., marking a message as read and updating the unread count in one go), RDBMS’s transaction support is invaluable.
• Your message structure is mostly structured: Text messages, basic metadata (sender ID, recipient ID, timestamp, status) fit neatly into relational tables, making queries like "get all messages between User A and User B from the last week" straightforward.
• You’re in the early stages with manageable user volume: MySQL is easy to set up, has a massive ecosystem of tools for monitoring and maintenance, and handles millions of messages without breaking a sweat—you can always add sharding or read replicas later if you scale.

The main caveats with MySQL:

• Storing unstructured or semi-structured data (like rich media message metadata, custom message types, or dynamic content) feels clunky compared to NoSQL solutions.
• At extreme scale (hundreds of millions/billions of messages), sharding your database becomes necessary, adding operational complexity.

NoSQL Alternatives: For Flexibility and Scale #

If your chat app has specific needs that MySQL struggles with, these NoSQL options are worth considering:

MongoDB (Document-Oriented) #

• Perfect for mixed message types: If your app supports text, images, voice notes, or custom message formats (each with unique metadata), MongoDB’s flexible document schema lets you store all these in a single collection without forcing a rigid table structure.
• Easy horizontal scaling: As your user base grows, adding more MongoDB nodes to handle increased write/read load is simpler than sharding MySQL.
• Powerful querying: You can run complex queries on nested document fields (e.g., "find all image messages sent by User X in Group Y") without joining tables.

Cassandra (Distributed Columnar Storage) #

• Built for high throughput and availability: If you’re targeting massive user bases (think millions of concurrent users) and need to handle thousands of writes per second, Cassandra’s linear scalability and fault tolerance shine.
• Ideal for time-series queries: Chat messages are inherently time-ordered, and Cassandra’s data model (designed for partitioned, time-based queries) makes fetching historical conversations by user/channel and timestamp extremely fast.
• Tradeoff: Cassandra has limited transaction support, so it’s not the best fit if you need strict consistency for features like message retraction.

Redis (Caching + Short-Term Storage) #

• Use Redis as a complement to your main storage, not a replacement: Store the most recent 100-200 messages per chat thread here to serve fast, low-latency reads to users. This takes pressure off your primary database and improves the user experience for loading recent chats.

Hybrid Approach: The Best of Both Worlds #

Most production chat apps use a mix of storage solutions to balance performance, consistency, and scalability:

• Use Kafka as your real-time message broker (which you’re already doing) to buffer messages between WebSocket clients and your persistence layer.
• Write messages asynchronously from Kafka to your primary storage (MySQL/MongoDB/Cassandra) so real-time communication isn’t blocked by database writes.
• Add Redis as a cache for recent messages to speed up common read operations.

Final Recommendation #

• Start with MySQL if you’re building a standard chat app with structured messages, need strong consistency, and don’t anticipate explosive growth right away. It’s low-risk and easy to iterate with.
• Switch to MongoDB if you need flexible schemas for rich media or custom message types, or expect to scale quickly without the overhead of sharding.
• Go with Cassandra only if you’re targeting extreme scale (10M+ users) and prioritize high availability and write throughput over strict transactional consistency.

内容的提问来源于stack exchange，提问作者Alex Won