A Java binding for LMDB providing a low-latency, transactional, sorted, embedded key-value store with an idiomatic Java API.
LmdbJava is a Java binding for the Lightning Memory Database (LMDB), providing an embedded key-value store with ACID transactions, sorted keys, and memory-mapped file access. It solves the need for high-performance, low-latency data storage in Java applications without requiring external database servers. The library wraps LMDB's C implementation while offering an idiomatic Java API with features like buffer agnosticism and zero-copy design.
Java developers building applications requiring embedded storage with high throughput and low latency, such as financial trading systems, real-time analytics platforms, or high-frequency data processing pipelines.
Developers choose LmdbJava for its exceptional performance across benchmarks, production-proven reliability with heavy workloads, and modern Java API that eliminates the complexity of direct JNI programming. Its embedded nature and zero installation requirements make deployment straightforward.
Lightning Memory Database (LMDB) for Java: a low latency, transactional, sorted, embedded, key-value store
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Benchmarks demonstrate it's extremely fast across various data sizes and access patterns, with allocation-free design minimizing latency for data-intensive workloads.
Provides ACID transactions with multi-version concurrency control (MVCC), ensuring data integrity for critical applications like financial trading systems.
JAR embeds pre-built LMDB libraries for Linux, macOS, and Windows, eliminating external dependencies and simplifying deployment.
Supports multiple buffer types like ByteBuffer and Netty ByteBuf, allowing seamless integration with various Java frameworks without serialization overhead.
As a key-value store, it lacks advanced query features like secondary indexes or SQL, requiring application-level logic for complex data retrieval.
Memory-mapped files can consume significant RAM for large datasets, potentially leading to performance issues if system memory is insufficient.
Relies on embedded C libraries, which may not support all Java environments or require manual configuration for unsupported platforms, adding setup complexity.