3 ms·
Other day i was trying out Azure SQL for storing vector DB. After a POC i was able to get results on sub 1 second to get the results with 2 core serverless inst
by sabareesh 3y ago
Other day i was trying out Azure SQL for storing vector DB. After a POC i was able to get results on sub 1 second to get the results with 2 core serverless instance. It begs me to reconsider does dedicated Vector db really worth it ?
Here is the article on how to store and query vector data https://devblogs.microsoft.com/azure-sql/vector-similarity-search-with-azure-sql-database-and-openai/ https://devblogs.microsoft.com/azure-sql/vector-similarity-s...
- marcinzm 3y agoThe article says you had 500ms latency while searching through only 25k records. Presumably at 1 query at a time? That's probably 2 orders of magnitude slower than a vector database.
- beoberha 3y agoAsk yourself the same question about an OLAP system. Once you’re at a large enough scale, it makes sense to use tools custom designed for your access patterns. I work on Azure SQL, but it’s pretty clear that this is a cool demo. It may scale for some usages, but I wouldn’t build a production system on it.
- sabareesh 3y agoWell tried it with ~ 34 million records , which is good enough sample for our usecase
- marcinzm 3y agoAt what number of queries per second?
- bob1029 3y ago> Azure SQL > large enough scale You mean something like the Hyperscale service tier? This is what we are building our new products on top of.
- beoberha 3y agoHyperscale is awesome. My point is more for vector search. Once you’re at a large enough scale, the simple algorithms for similarity search break down. You’d need something that implements HNSW and designed their database indexes around it.
- weberer 3y ago>After a POC i was able to get results on sub 1 second to get the results with 2 core serverless instance Is that good or bad?
- sabareesh 3y ago~ 34 million embeddings , Seems this is a good enough sample for our use case.