[ML] Fix incorrect assumption about minimum ML node size #91694
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The ML autoscaling code was making an assumption that all ML nodes in Cloud will be at least 1GB. This is not correct. After allowing for logging and metrics collection it is possible for ML nodes to be smaller. This PR updates the assumption to 0.5GB.
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The ML autoscaling code was making an assumption that all ML nodes in Cloud will be at least 1GB. This is not correct. After allowing for logging and metrics collection it is possible for ML nodes to be smaller. This PR updates the assumption to 0.5GB.
💔 Backport failed
You can use sqren/backport to manually backport by running |
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The ML autoscaling code was making an assumption that all ML nodes in Cloud will be at least 1GB. This is not correct. After allowing for logging and metrics collection it is possible for ML nodes to be smaller. This PR updates the assumption to 0.5GB.
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On closer inspection the code changed quite radically in 8.3, so I think it would be best not to backport this change to 7.x. Doing so might aggravate some of the other ML autoscaling discrepancies that were fixed in 8.3. |
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…1696) The ML autoscaling code was making an assumption that all ML nodes in Cloud will be at least 1GB. This is not correct. After allowing for logging and metrics collection it is possible for ML nodes to be smaller. This PR updates the assumption to 0.5GB.
elasticsearchmachine
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Nov 18, 2022
…1697) The ML autoscaling code was making an assumption that all ML nodes in Cloud will be at least 1GB. This is not correct. After allowing for logging and metrics collection it is possible for ML nodes to be smaller. This PR updates the assumption to 0.5GB.
droberts195
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Nov 19, 2022
This change fixes a discrepancy that has existed for a long time but was revealed by elastic#91694. The ML automatic node/JVM sizing code contained a minimum node size but did not restrict the minimum JVM size to the size that would be chosen on that minimum node size. This could throw off calculations at small scale. Fixes elastic#91728
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Nov 21, 2022
This change fixes a discrepancy that has existed for a long time but was revealed by #91694. The ML automatic node/JVM sizing code contained a minimum node size but did not restrict the minimum JVM size to the size that would be chosen on that minimum node size. This could throw off calculations at small scale. Fixes #91728
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Nov 21, 2022
…ic#91732) This change fixes a discrepancy that has existed for a long time but was revealed by elastic#91694. The ML automatic node/JVM sizing code contained a minimum node size but did not restrict the minimum JVM size to the size that would be chosen on that minimum node size. This could throw off calculations at small scale. Fixes elastic#91728
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Nov 21, 2022
…ic#91732) This change fixes a discrepancy that has existed for a long time but was revealed by elastic#91694. The ML automatic node/JVM sizing code contained a minimum node size but did not restrict the minimum JVM size to the size that would be chosen on that minimum node size. This could throw off calculations at small scale. Fixes elastic#91728
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… (#91742) This change fixes a discrepancy that has existed for a long time but was revealed by #91694. The ML automatic node/JVM sizing code contained a minimum node size but did not restrict the minimum JVM size to the size that would be chosen on that minimum node size. This could throw off calculations at small scale. Fixes #91728
elasticsearchmachine
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Nov 21, 2022
… (#91741) This change fixes a discrepancy that has existed for a long time but was revealed by #91694. The ML automatic node/JVM sizing code contained a minimum node size but did not restrict the minimum JVM size to the size that would be chosen on that minimum node size. This could throw off calculations at small scale. Fixes #91728
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The ML autoscaling code was making an assumption that all ML nodes in Cloud will be at least 1GB. This is not correct. After allowing for logging and metrics collection it is possible for ML nodes to be smaller.
This PR updates the assumption to 0.5GB.