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I’ll give you an example of what this looks like, which I went through myself: a couple years ago I was working at PlanetScale and we shipped a MySQL extension for vector similarity search. We had some very specific goals for the implementation; it was very different from everything else out there because it was fully transactional, and the vector data was stored on disk, managed by MySQL’s buffer pools. This is in contrast to simpler approaches such as pgvector, that use HNSW and require the similarity graph to fit in memory. It was a very different product, with very different trade-offs. And it was immensely alluring to take an EC2 instance with 32GB of RAM and throw in 64GB of vector data into our database. Then do the same with a Postgres instance and pgvector. It’s the exact same machine, exact same dataset! It’s doing the same queries! But PlanetScale is doing tens of thousands per second and pgvector takes more than 3 seconds to finish a single query because the HNSW graph keeps being paged back and forth from disk.
。PDF资料对此有专业解读
这种增长本身，是制造成本降低与用户共创生态形成的合力。而无论哪一方，都离不开拓竹的催化与培育。
"I just felt like it was my responsibility to speak on behalf of the community." His father and his uncles, who were immigrants to the US, did not feel the same level of security, he adds.
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