MapDB vs RocksDB: What are the differences?

Introduction: MapDB and RocksDB are both open-source key-value storage libraries that are designed for different use cases. While MapDB focuses on providing a simple and efficient solution for embedded databases, RocksDB is optimized for high-performance storage on flash-based storage devices.

1. Data Structure: MapDB uses a BTree-based data structure to organize and store key-value pairs. It provides support for concurrent access and efficient disk storage. On the other hand, RocksDB is built around a log-structured merge-tree (LSM-tree) data structure, which is optimized for write-intensive workloads. It uses a series of sorted string tables (SSTables) to store data on disk.

2. Storage Engine: MapDB supports a wide range of storage engine backends including in-memory storage, on-disk storage, and append-only storage. It also provides support for compression and encryption of data. In contrast, RocksDB is designed specifically for on-disk storage and provides various optimizations for this use case, such as block-based compression and parallelism.

3. File Formats: MapDB stores data using its own custom file format, which is highly compact and efficient. It uses a combination of binary and compressed formats to reduce the storage footprint. On the other hand, RocksDB uses a simpler file format called SSTable, which is based on LevelDB and is widely supported by various storage systems.

4. Concurrency Control: MapDB provides built-in support for concurrent access to the database, allowing multiple threads or processes to read and write simultaneously. It uses a combination of locks and optimistic concurrency control techniques to ensure data integrity. RocksDB also supports concurrent access but uses a different approach called optimistic locking, which reduces the need for expensive lock acquisitions.

5. Durability: MapDB provides durability guarantees by ensuring that data is flushed to disk periodically and after each transaction. It also supports various durability modes such as write-through and write-behind caching. RocksDB takes a similar approach but provides additional options for configuring the durability settings, making it more flexible in terms of trade-offs between performance and durability.

6. Supported Languages: MapDB is primarily written in Java and provides native language bindings for Java applications. It can be used with other JVM languages as well. On the other hand, RocksDB is written in C++ and provides native language bindings for C++, Java, Python, and other languages. This enables its usage in a wider range of applications and programming languages.

In Summary, MapDB and RocksDB differ in their data structures, storage engines, file formats, concurrency control mechanisms, durability options, and supported languages. These differences make each library suitable for specific use cases and performance requirements.