Should we add a vector type to Cassandra designed to meet the needs of machine learning use cases, specifically feature and embedding vectors for training, inference, and vector search?
ML vectors are fixed-dimension (fixed-length) sequences of numeric types, with no nulls allowed, and with no need for random access. The ML industry overwhelmingly uses float32 vectors, to the point that the industry-leading special-purpose vector database ONLY supports that data type. This poll is to gauge consensus subsequent to the recent discussion thread at https://lists.apache.org/thread/0lj1nk9jbhkf1rlgqcvxqzfyntdjrnk0. Please rank the discussed options from most preferred option to least, e.g., A > B > C (A is my preference, followed by B, followed by C) or C > B = A (C is my preference, followed by B or A approximately equally.) (A) I am in favor of adding a vector type for floats; I do not believe we need to tie it to any particular implementation details. (B) I am okay with adding a vector type but I believe we must add array types that compose with all Cassandra types first, and make vectors a special case of arrays-without-null-elements. (C) I am not in favor of adding a built-in vector type. -- Jonathan Ellis co-founder, http://www.datastax.com @spyced
