Tag: MariaDB

In the previous parts of this series we’ve seen how MariaDB stores vector indexes in a table and how to implement HNSW for a good performance. But MariaDB is not implementing HNSW, it calls its vector search algorithm mHNWS, a modified HNSW. Let’s see how exactly it was modified.

MathJax = { output: { linebreaks: { inline: false } } }

Not so greedy!

HWNS, like many, if not most, graph based vector search algorithms is greedy. Think of it […]

In the first post of this series, I’ve described how the vector index is stored in a table and how it achieves full transactional behavior and ACID properties compatible with the storage engine of the table the user created. But while the table provides persistent storage of the index, it’s in-memory part that gives it the performance. This is how it works.

Distance calculations

This is the most performance sensitive part of the HNSW. According to various estimates, distance calculations […]

You might have seen that MariaDB Vector is fast. And is getting faster. But why? How does it achieve that? And why it is said to use mHNSW (modified HNSW) algorithm? What did it modify in the conventional HNSW that all other databases are using? Let’s take it apart and analyze piece by piece.

Introduction into HNSW

This post is not a full description of HNSW, there are many HNSW descriptions online and they are good, better than what I […]