MapDB vs SQLite: What are the differences?
### Introduction
In this Markdown code, we will highlight the key differences between MapDB and SQLite, two popular database management systems.
1. **Storage Model**: MapDB utilizes an append-only storage model, where updates are written to the end of the file, while SQLite uses a B-tree structure for data storage, allowing for faster lookups and queries.
2. **Concurrency Support**: MapDB provides support for concurrent read and write operations through various concurrency models like lock striping, while SQLite has limited concurrency support with a single writer at a time.
3. **Memory Usage**: MapDB allows for off-heap memory storage, which can reduce memory overhead and provide better performance for large datasets compared to SQLite's in-memory database which may suffer from memory constraints.
4. **Performance**: MapDB excels in write-heavy workloads due to its append-only storage model and efficient flushing mechanism, while SQLite performs well in read-heavy scenarios with its optimized query processing and indexing.
5. **Durability**: MapDB offers configurable durability options such as memory-mapped files or disk-based storage, providing flexibility in balancing performance and data durability, whereas SQLite ensures durability by persisting data to disk after every transaction.
6. **Community and Ecosystem**: SQLite has a larger community and ecosystem with extensive support, documentation, and tools, making it a more popular choice for embedded and mobile applications, while MapDB has a smaller but dedicated community focused on performance and low-latency data processing.
In Summary, MapDB and SQLite differ in their storage models, concurrency support, memory usage, performance, durability options, and community ecosystems.
