Category: Development

  • 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 […]

  • 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 […]

  • 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 […]

  • 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 […]

  • 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 […]

  • Making MariaDB the Natural Successor to MySQL

    At the MariaDB Foundation, clarity of purpose matters. In an ecosystem as foundational as open-source databases, confidence is built not through slogans, but through predictability, restraint, and long-term commitment.

    As I reflect upon what we learned in 2025 and how we can serve the database community in 2026, one thought stands out: MariaDB is the natural successor to MySQL, as Oracle loses its interest in the development of what has been the default Open Source Relational DBMS.

    The MariaDB Foundation […]

  • How deep can a bug be?

    Last year I filed a bug report MDEV-33603 on what a looked like a benign problem with an optimizer taking a different code path in a particular trivial looking test. Its benign looking nature lead to me not looking at it until last week. The “benign” bug as it turned out is a bug in an OpenSSL optimization on IBM POWER, which maybe not the lowest level of “How deep”, but its certainly a long way from the high level […]

  • MariaDB Cloud: A Semi-Technical Introduction

    MariaDB Cloud

    When co-founding MariaDB plc under the name SkySQL Corporation fifteen years ago (before MariaDB Foundation existed), we picked the name SkySQL with a bit of a reference to the cloud. Obviously, we didn’t then have a clear understanding of what type of a cloud offering MariaDB should have. I am glad to note that those days are finally and completely over, with MariaDB Cloud. I wish it hadn’t taken fifteen years!

    So now, I sat down for a proper chat […]