# Scaling vertically?

**URL:** <https://discuss.elastic.co/t/scaling-vertically/121360>\
**Category:** Logstash\
**Created:** [February 25, 2018, 4:57am UTC](https://discuss.elastic.co/t/scaling-vertically/121360 "2018-02-25T04:57:40Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![wakinov](https://avatars.discourse-cdn.com/v4/letter/w/49beb7/32.png) [@wakinov](https://discuss.elastic.co/u/wakinov)\
**Post date:** [February 25, 2018, 4:57am UTC](https://discuss.elastic.co/t/scaling-vertically/121360/1 "2018-02-25T04:57:40Z")

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Version 2,4 in case it matters.

The documentation really only mentions scaling horizontally. I'm in a position where I can get access to very "large" machines with 12-cores and 64GB+ of RAM. I'm just wondering if there are any general guidelines for scaling vertically, If there are any architectural limitations to Logstash that will make having very "big" instances of Logstash not as ideal as having many "smaller" instances.

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**Author:** ![rcowart](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/rcowart/32/88091_2.png) [@rcowart](https://discuss.elastic.co/u/rcowart)\
**Post date:** [February 25, 2018, 11:07am UTC](https://discuss.elastic.co/t/scaling-vertically/121360/2 "2018-02-25T11:07:15Z")

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Ultimately the best thing to do will be to do your own benchmarking. However, all of my testing leads me to the conclusion that Logstash scales poorly vertically. Given a machine with 12-cores I would expect you to have greater overall throughput running three instances of Logstash allocating 4 cores for each.

Often when sizing an Elastic Stack deployment there is a lot of focus put on the number of Elasticsearch nodes required. However the collection/ingest layer requires far more attention and planning to scale well.

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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 25, 2018, 11:07am UTC](https://discuss.elastic.co/t/scaling-vertically/121360/3 "2018-03-25T11:07:58Z")

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Closed on March 25, 2018, 11:07am UTC

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