How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Port of LevelDB to Java
The main features of dain/leveldb are: Data Structures and Memory, Databases and Storage.
Open-source alternatives to dain/leveldb include: aggregateknowledge/java-hll — Java library for the HyperLogLog algorithm. alibaba/druid — Druid is a database connection management and monitoring framework designed to maintain persistent, high-performance… alibaba/fastjson — Fastjson is a Java data binding framework and serialization library designed to convert objects to JSON strings and… alluxio/alluxio — Alluxio is a virtual distributed file system and data orchestration layer that serves as a high-performance caching… aloneguid/stowage — Bloat-free, no BS cloud storage SDK. addthis/stream-lib — Stream summarizer and cardinality estimator.
Java library for the HyperLogLog algorithm
Druid is a database connection management and monitoring framework designed to maintain persistent, high-performance links between applications and relational databases. It functions as a resource manager that automates the lifecycle of connection pools, reducing the overhead associated with repeatedly opening and closing network connections. The project distinguishes itself through an integrated query analysis engine that decomposes database statements into structured components. This capability enables real-time security auditing, syntax validation, and metadata extraction, allowing for the
Fastjson is a Java data binding framework and serialization library designed to convert objects to JSON strings and parse JSON data into typed objects. It functions as a JSON parser and stream processor capable of transforming JSON strings into data structures. The project emphasizes high performance JSON processing and memory management, specifically through the use of a pipeline to stream oversized JSON objects and texts to prevent memory exhaustion. It provides capabilities for JSON data serialization and deserialization workflows, including custom JSON data mapping and the ability to def
Stream summarizer and cardinality estimator.