TinyDB is primarily designed for simplicity and ease of use rather than performance with large datasets. While TinyDB can handle moderate-sized datasets effectively, its performance may degrade when dealing with very large datasets due to its in-memory storage model and lack of optimizations for handling large volumes of data.

Here are some factors to consider regarding TinyDB's efficiency with large datasets :

In-memory storage : By default, TinyDB stores data in memory, which may not be suitable for very large datasets. In-memory storage can lead to memory consumption issues and performance degradation as the dataset size increases.

JSON file storage : While TinyDB supports persisting data to a JSON file on disk, its performance with large datasets may still be limited. Reading and writing large JSON files can be slow, especially when the entire file needs to be loaded into memory for processing.

Single-threaded : TinyDB is single-threaded and does not support concurrent operations out of the box. As a result, it may not be able to fully utilize multi-core processors or parallel processing techniques to improve performance with large datasets.

No indexing or query optimization : TinyDB lacks indexing and query optimization features typically found in more robust database systems. This can lead to slower query performance, especially with complex queries or large datasets where full table scans are required.

Limited scalability : Due to its lightweight and simplistic design, TinyDB may not scale well to very large datasets or high-throughput applications. It is more suitable for small to medium-sized projects where simplicity and ease of use are prioritized over scalability and performance with large datasets.