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The Good and the Bad of Redis In-Memory Database - AltexSoft
### Memory-intensive and not ideal for large datasets While Redis’s approach to storing data in RAM contributes to its speed, this has its consequences. RAM is significantly more expensive than disk storage, which means that using Redis for large datasets can become expensive, especially when scaling up. Companies that store and process terabytes of data must make significant investments when working with Redis. This is why it’s rarely used as a standalone solution and is often paired with other databases to balance performance and cost. ### Manual memory management Redis does not automatically manage memory like relational databases do. Developers must manually configure eviction policies to decide what happens when memory is full. This disadvantage is addressed once you move to Redis Cloud since it’s a fully-managed Redis service.
Related Pain Points2件
Redis memory constraints limit dataset size and increase costs
7As an in-memory store, Redis requires all data to reside in RAM, limiting total dataset size by available memory. Large datasets consume significant memory overhead per instance, creating cost and performance pressure when data grows beyond infrastructure limits.
Manual memory eviction policy configuration required
5Redis does not automatically manage memory like relational databases. Developers must manually configure eviction policies to handle out-of-memory scenarios, adding operational complexity and risk of data loss.