Category Archives: AI & Vector
laravel-mariadb-vector is an open-source project by Erik Ros, bringing MariaDB’s native vector search to Laravel’s Eloquent ORM. In his guest post, Erik shares how it works, and his insights about picking an embedding model.
I maintain laravel-mariadb-vector, a small open source package that brings MariaDB’s native vector search to Laravel’s Eloquent ORM. It’s my first open source project, it has over 100 installs, no marketing budget, and it exists because I needed it.
This post is a quick introduction and an experiment with 2,942 job titles in English and Dutch that shows why the embedding model you pick and how you use it matters far more than you might expect.
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Continue reading “MariaDB Vector in Laravel: insights on choosing an embedding model”
Who says developing MariaDB plugins is hard? I was able to produce one in under 3 minutes!
I of course did it by asking Grok nicely:
The produced result is actually very decent:
/*
errorwatch.c – MariaDB Audit API plugin
Records statement execution errors into a dedicated log file.
This plugin subscribes to the GENERAL audit class and logs
events of type MYSQL_AUDIT_GENERAL_ERROR (and STATUS events
that carry a non-zero error code). It writes structured
entries to /var/log/mariadb/errorwatch.log (or /tmp/ fallback).
To build (inside MariaDB source tree):
1. Copy this file and CMakeLists.txt to plugin/errorwatch/
2. …
Continue reading “Vibe-coding an Audit Plugin in Under 3 Minutes”
Earlier this month, I had a half-hour chat with Kellyn Gorman, a Database and AI Advocate and Engineer at Redgate. The UK software company is known for database DevOps and database management tools most databases – and since 2024 as the owner of DB-Engines popularity Ranking of database management systems.
The chat was an intellectual pleasure, to say the least. Kellyn is outstandingly well informed on databases, with a background starting in Oracle, spanning most databases as a DBA and industry analyst, and by now using MariaDB for about fifteen years, almost since its inception.
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Continue reading “Database Trends: What is changing in the database world (besides AI)”
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.
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 linear on the number of dimension — computing the distance between vectors of 1536 dimensions takes twice as long compared to vectors of 768 dimensions.
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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.
Not so greedy!
HWNS, like many, if not most, graph based vector search algorithms is greedy. Think of it this way, when it needs to find just one nearest vector (ef=1), it will walk the graph always choosing the node that will take it the closest to the target at this particular step.
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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 account for 80–90% of search time. And this operation time grows linearly with the vector length.
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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 could’ve written. I will only show the basic concepts beyond HNSW, concepts that are crucial for the rest of the post.
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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 example, image transformations and filters to construct vectors. While useful for certain purposes, they are not the main use case for the MariaDB Vector and providing these results would be misleading and distracting from what matters to users.
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Continue reading “Big Vector Search Benchmark: 10 databases comparison”