Tag: vector search
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MariaDB Vector: How it works. Part IV
This is the last post in the “MariaDB Vector: How it works” series. The first three were about storage, in-memory representation, HNSW modifications. Everything that was done in MariaDB 11.8. This post talks about new feature in MariaDB 12.3: optimized distance calculation.
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As I mentioned earlier, distance calculation is the most time consuming part of the vector search, taking 80–90% of the total search time. Also it is […]
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MariaDB Vector: How it works. Part III
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.
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Not so greedy!
HWNS, like many, if not most, graph based vector search algorithms is greedy. Think of it […]
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MariaDB Vector: How it works. Part II
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 […]
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MariaDB Vector: How it works
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 […]
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Big Vector Search Benchmark: 10 databases comparison
I have benchmarked MariaDB Vector before, but it was a while ago. Users kept asking about Milvus. New pgvector alternatives were gaining popularity. And I simply wanted to see if MariaDB got any better. This benchmark round includes more databases, larger dataset, and no irrelevant datasets that only add noise but don’t really help today in 2026.
Dataset
Now is the AI time. Vector search is used for embeddings generated by LLMs. Most ann-benchmarks datasets are pre-AI and use, for […]
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Mirror, Mirror on DB-Engines: The MariaDB Story
DB-Engines has become one of the most visible indicators of database popularity, capturing how the world talks about MariaDB. This post explores what the rankings truly measure and how visibility, community engagement, and education shape MariaDB’s position. It highlights recent innovations such as native vector search and explains how MariaDB’s governance model safeguards long-term openness. As universities adopt MariaDB and developers name it more explicitly in their profiles, the global conversation continues to shift. DB-Engines becomes not just a ranking,…