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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.
Подробности инцидента не приводятся.，详情可参考新收录的资料
["SET b,(IY+o)", "FD CB o C6+8*b"],
，更多细节参见新收录的资料
В Кремле высказались об обсуждении возможного снятия части американских санкций по экспорту российской нефти в ходе вчерашнего разговора лидеров РФ и США. По словам пресс-секретаря президента РФ Дмитрия Пескова, которые приводит ТАСС, «сколь-либо детально эта тема не фигурировала».。关于这个话题，新收录的资料提供了深入分析
蔡磊多年来一直积极推动渐冻症相关研究和药物研发，希望推动这种疾病早日被攻克。