JVector: Cutting-Edge Vector Search in Java

An airhacks.fm conversation with Jonathan Ellis (@spyced) about:
discussion of JVector, a Java-based vector search engine, Apache
Kudu as an alternative to Cassandra for wide-column databases,
FoundationDB - is a NoSQL database, explanation of vectors and
embeddings in machine learning, different embedding models and
their dimensions, the Hamming distance, binary quantization and
product quantization for vector compression, DiskANN algorithm for
efficient vector search on disk, optimistic concurrency control in
JVector, challenges in implementing academic papers, the Neon
database, JVector's performance characteristics and typical
database sizes, advantages of astra DB over Cassandra, separation
of compute and storage in cloud databases, Vector's use of Panama
and SIMD instructions, the potential for contributions to the
JVector project, Upstash uses of JVector for their vector search
service, the cutting-edge nature of JVector in the Java ecosystem,
the logarithmic performance of JVector for index construction and
search, typical search latencies in the 30-50 millisecond range,
the young and rapidly evolving field of vector search, the
self-contained nature of the JVector codebase

Jonathan Ellis on twitter: @spyced