In-Memory Database
In-Memory Databases rely primarily on main memory (RAM) for data storage, rather than disk drives. This eliminates the seek time required to access disks, providing microsecond-level latency.
Core Business Values
In-Memory Databases deliver real-time responsiveness by eliminating disk I/O, enabling user experiences that feel essentially “instant,” such as updating a live gaming leaderboard or processing an auction bid. They provide exceptionally high throughput, capable of handling millions of requests per second on a single node, which is ideal for absorbing bursty traffic spikes. Their architecture often favors simplicity, frequently deployed as a caching layer to protect slower, more complex backend databases from being overwhelmed by read traffic.
Typical Problems Solved
The most common use case is Caching, where they store frequently accessed database queries or API responses (using tools like Redis or Memcached) to speed up web applications. They are also the standard solution for Gaming Leaderboards, which require sorting and updating millions of player scores in real-time. Additionally, they are used for Real-time Analytics, calculating aggregations on live data streams in the fleeting moments before that data is written to slower persistent storage.
Potential Values for Artificial Intelligence
In Artificial Intelligence workflows, In-Memory Databases are crucial for Inference Caching. By storing the results of expensive, GPU-intensive model inferences, the system can serve repeated questions instantly from memory, saving time and compute cost. They also manage the Session Context for LLMs, storing the “short-term memory” or conversation history for a chatbot, ensuring it can maintain context and coherence throughout a dialogue.