# Dense vector Euclidean Distance comparison function speed **URL:** \ **Category:** Elasticsearch\ **Created:** [February 12, 2020, 1:36pm UTC](https://discuss.elastic.co/t/dense-vector-euclidean-distance-comparison-function-speed/218992 "2020-02-12T13:36:41Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![ndtreviv](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/ndtreviv/32/22494_2.png) [@ndtreviv](https://discuss.elastic.co/u/ndtreviv)\ **Post date:** [February 12, 2020, 1:36pm UTC](https://discuss.elastic.co/t/dense-vector-euclidean-distance-comparison-function-speed/218992/1 "2020-02-12T13:36:42Z")
Are there any benchmarks on the speed of this function (release in 7.4, I believe)? I have some data that represents a high-dimension vectors that I'd like to consider it for. Thanks for any help! ---
**Author:** ![mayya](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/mayya/32/83147_2.png) [@mayya](https://discuss.elastic.co/u/mayya)\ **Post date:** [February 13, 2020, 3:37pm UTC](https://discuss.elastic.co/t/dense-vector-euclidean-distance-comparison-function-speed/218992/2 "2020-02-13T15:37:35Z")
In this [comment](https://github.com/elastic/elasticsearch/pull/51243#issuecomment-576454016) look under "Bruteforce benchmarks" section. We have used a single shard for those bechmarks. By having N shards, you can speed up computations N times. ---
**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\ **Post date:** [March 12, 2020, 3:37pm UTC](https://discuss.elastic.co/t/dense-vector-euclidean-distance-comparison-function-speed/218992/3 "2020-03-12T15:37:36Z")
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