# ES Ingestion Performance Issue

**URL:** <https://discuss.elastic.co/t/es-ingestion-performance-issue/168446>\
**Category:** Elasticsearch\
**Created:** [February 14, 2019, 3:55pm UTC](https://discuss.elastic.co/t/es-ingestion-performance-issue/168446 "2019-02-14T15:55:25Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![samwzm](https://avatars.discourse-cdn.com/v4/letter/s/ac91a4/32.png) [@samwzm](https://discuss.elastic.co/u/samwzm)\
**Post date:** [February 14, 2019, 3:55pm UTC](https://discuss.elastic.co/t/es-ingestion-performance-issue/168446/1 "2019-02-14T15:55:25Z")

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Hi,

We have two environments. One environment has two ES hot data nodes, another has five ES hot data nodes. The nodes server type is similar, all other config also similar. However, we tried to ingest the same data set in those two environments. To my surprise, the performance with two hot data nodes is better than the performance with five hot data nodes! Is that normal? In another word, is it possible because more data nodes will cost more time to do the data allocation/copying? If not, what's the best practice/approaches to find the bottomneck?

Thanks!

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**Author:** ![spinscale](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/spinscale/32/25011_2.png) [@spinscale](https://discuss.elastic.co/u/spinscale)\
**Post date:** [February 15, 2019, 8:59am UTC](https://discuss.elastic.co/t/es-ingestion-performance-issue/168446/2 "2019-02-15T08:59:05Z")

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you havent mentioned anything about hardware, number of cores, memory, index configuration (same mapping, same number of shards and replicas?).

Please add some more information for other to be able to help you.

Also maybe add some raw numbers in here for other to get a feel for the issue.

Thanks!

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**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [March 15, 2019, 9:09am UTC](https://discuss.elastic.co/t/es-ingestion-performance-issue/168446/3 "2019-03-15T09:09:28Z")

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