SAI is a highly scalable, globally distributed index for Cassandra databases. With SAI, column-level indexes can be added leading to unparalleled I/O throughput for searches across different data types, including vectors. SAI also enables lightning-fast data retrieval through zero-copy streaming of indices, resulting in unparalleled efficiency.

Vector Search is a powerful technique for searching relevant content or discovering connections by comparing similarities in large document collections, particularly useful for AI applications. It uses storage-attached indexing and dense indexing techniques to enhance data exploration and analysis.

This unifies compaction approaches, including leveled, tiered, and time-windowed strategies. The strategy leads to a major reduction in SSTable sizes. Smaller SSTables mean better read and write performance, reduced storage requirements, and improved overall efficiency.

Utilizes trie data structures to enhance the efficiency of both reads and writes, optimizing storage space and access speed.

Expands CQL with additional mathematical functions like ‘abs’, ‘exp’, ‘log’, ‘log10’, and round and aggregation scalar CQL functions like ‘count’, ‘max’, ’min’, ‘sum’, ‘avg’ at a collection level enhancing support for complex analytics.

Strengthens security by allowing sensitive data to be masked from unauthorized access dynamically, ensuring data privacy and compliance.

Cassandra 5.0 introduces numerous stability and testing improvements.