RoaringBitmap/RoaringBitmap
RoaringBitmap
RoaringBitmap is a compressed integer set library designed for memory-efficient storage and high-speed in-memory analytics. It provides a professional implementation of the Roaring Bitmap format to maintain compact representations of integer collections.
The project features a standardized binary serialization format that enables the exchange of compressed bitmap data across different programming languages and hardware architectures. This allows for consistent data portability and cross-language data exchange.
The library performs rapid logical set operations, including intersections, unions, and differences, using bitwise processing. It manages large-scale integer storage through adaptive compression strategies that automatically switch storage formats to maintain low memory overhead.
Features
- Bitmapped Integer Sets - Provides memory-efficient storage of integer sets using dense and sparse bitmaps.
- Compressed Bitwise Operations - Performs bitwise logical calculations directly on compressed data chunks without requiring full decompression.
- Adaptive Storage Containers - Implements adaptive container mapping that automatically selects optimal storage formats for sparse and dense integer datasets.
- In-Memory Set Analytics - Facilitates rapid intersections, unions, and differences across massive datasets for real-time analytics.
- Cross-Language Data Protocols - Enables sharing of compressed integer set data between different programming languages using a standardized binary format.
- Cross-Platform Binary Formats - Provides a standardized binary serialization format that ensures data portability across languages and architectures.
- Roaring Bitmaps - Provides a professional implementation of the Roaring Bitmap format for rapid bitwise set operations.
- Binary Serialization - Implements a standard specification to serialize bitmap data for cross-language reading and writing.
- Hybrid Compression Strategies - Dynamically switches between bitsets, sorted arrays, and run-length encoding based on data density.
- High-Speed Bitwise Processing - Processes large datasets at high speeds by performing bitwise AND, OR, and ANDNOT operations.
- Bitsets - Manages dense and sparse bitsets that automatically switch formats to maintain low memory overhead.
- Set Operations - Performs high-speed logical operations to calculate intersections, unions, and differences between integer sets.
- Bitmap Modification Strategies - Updates bitmap data by copying only affected internal containers to reduce memory overhead.
- Java Integer Collections - Offers memory-efficient collections for managing large-scale integer sets in Java.
- Hierarchical Indexing - Utilizes a two-level hierarchical index to achieve constant-time random access for integer lookups.
- Copy-on-Write Data Structures - Uses copy-on-write mutation to minimize memory overhead when converting immutable bitmaps to mutable ones.
- Data Structures and Memory - Compressed bitset implementation.
- Data Structures - High-performance compressed bitmap implementation.
Star history
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