To implement in-memory caching with MongoDB, you would typically use the MongoDB's WiredTiger storage engine which supports an in-memory storage engine. The in-memory storage engine allows for all data to be stored in memory, providing a solution where read latency must be minimized.
Here is an example of running MongoDB with the in-memory storage engine using Docker:
Or if you are starting MongoDB from command line:
Remember, though, that because the in-memory storage engine does not persist data, when the mongod process terminates, all data is lost.
It’s also important to note that while MongoDB has this built-in support for in-memory storage, it doesn't replace other kinds of external caching systems like Redis. MongoDB's in-memory option is best used for specific use-cases where the persistence of the data isn't required (e.g., caching session data).
For cases where you need to cache frequently accessed DB results, an external caching layer such as Redis is often used in combination with MongoDB, keeping the frequently accessed data in fast, volatile storage for quick retrieval, and storing other data in MongoDB.
An example of using Node.js, MongoDB, and Redis together looks something like this:
So, while MongoDB can support in-memory operations, a multi-layered caching strategy including MongoDB for persistence storage and Redis for high-speed, in-memory caching yields optimal results in most scenarios.