Comments (2)
It's hard to say without knowing your setup.
But some general tips
- Use the suggested configuration from the readme (make sure speculation and dynamic allocation are off etc)
- use inner product with a normalizer instead of cosine distance
- increasing the number of partitions will speed up indexing at the expense of slowing down querying
- increasing the number of replicas (setNumReplicas) will speed up querying at the expense of resource usage, you use if you set it to one, you will need to double the number of executors you use when querying
A good way to work out why things are slow is to look at the logs and look for lines that contain the word partition
from hnswlib.
Thanks for the tips!
from hnswlib.
Related Issues (20)
- Not able to load persisted index HOT 6
- excludeSelf parameter in hnswlib class not working HOT 5
- Repeatability HOT 1
- Why is there no float-64 support in hnswlib-core-jdk17 HOT 1
- Small number of queries succeeds, whereas larger number fails HOT 4
- benchmark / performance HOT 1
- PySpark OOM advice HOT 12
- Unable to update index HOT 4
- Unable to load the HnswSimilarity model HOT 1
- scala 2.12 port running very slowly HOT 7
- Error: Cannot up cast `partition` from bigint to int. HOT 1
- Brute Force vs hnsw Problem HOT 3
- Big K - output empty result HOT 3
- Load does not work HOT 1
- not all index partitions are persisted (data loss) on k8s HOT 28
- cannot calculate resize correctly leading to SizeLimitExceededException HOT 2
- support for num_threads parameter while building the index HOT 1
- Question about maxLevel
- Performance much worse than hnswlib HOT 5
- If there is no near similar items. What is the output? HOT 1
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from hnswlib.