Is it normal for 10,000 512-dimensional vectors to take more than 300ms for a single query? #32726
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I recently listened to a case study from your team, which mentioned that they have millions of vectors (dimension unknown), using the FLAT index, and the average query time for the top 1000 is only 41ms. Their machine configuration is a cluster of 8 machines with 16 cores and 64GB of memory. However, we only have around 10,000 512-dimensional vectors, also using the FLAT index, and the query takes more than 300ms (using the I have two questions:
Thank you! |
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Replies: 2 comments 3 replies
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Milvus maintains data consistency by timetick machinery, the root coordinator sends a timetick message to Pulsar for each 200ms, if all the other nodes(querynodes, datanodes) consume a timetick message, then we know the data before this time point is already ready for search. Consistency level affects the data visibility for search requests.
Change the consistency level to Bounder or Eventually then you will get fast query. In python, set the level like this:
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the reason is you are using Strong consistency level. |
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Milvus maintains data consistency by timetick machinery, the root coordinator sends a timetick message to Pulsar for each 200ms, if all the other nodes(querynodes, datanodes) consume a timetick message, then we know the data before this time point is already ready for search.
https://milvus.io/docs/time_sync.md#Time-Synchronization
Consistency level affects the data visibility for search requests.
https://milvus.io/docs/consistency.md#Consistency-levels