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[Data] Make object store tuning tips consistent with other pages (ray-project#56705)
<!-- Thank you for your contribution! Please review https://github.com/ray-project/ray/blob/master/CONTRIBUTING.rst before opening a pull request. --> <!-- Please add a reviewer to the assignee section when you create a PR. If you don't have the access to it, we will shortly find a reviewer and assign them to your PR. --> ## Why are these changes needed? Make the tips on Object store memory consistent with other pages in [Ray Data docs](https://docs.ray.io/en/latest/data/joining-data.html#configuring-joins) <!-- Please give a short summary of the change and the problem this solves. --> ## Related issue number <!-- For example: "Closes ray-project#1234" --> ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( --------- Signed-off-by: Praveen Gorthy <praveeng@anyscale.com> Signed-off-by: zac <zac@anyscale.com>
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doc/source/data/performance-tips.rst

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@@ -425,7 +425,7 @@ You can configure execution options with the global DataContext. The options are
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It's **not** recommended to modify the Ray Core object store memory limit, as this can reduce available memory for task execution. The one exception to this is if you are using machines with a very large amount of RAM (1 TB or more each); then it's recommended to set the object store to ~30-40%.
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Be mindful that by default Ray reserves only 30% of the memory for its Object Store. This is recommended to be set at least to ***50%*** for all Ray Data workloads.
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Locality with output (ML ingest use case)
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