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docs: remove experimental label from optimize and warn about delete_unverified (#3128)

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## Summary
- Removes the "Experimental API" section from `optimize` method
documentation across Rust, Python, and TypeScript
- Adds a warning to `delete_unverified` documentation in all bindings:
this should only be set to true if you can guarantee no other process is
working on the dataset, otherwise it could be corrupted
- Fixes a typo ("shoudl" → "should")

Closes #3125


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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Weston Pace
2026-03-11 23:37:42 -07:00
committed by GitHub
parent fc1867da83
commit 216c1b5f77
4 changed files with 43 additions and 63 deletions
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63
python/python/lancedb/table.py
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@@ -1506,22 +1506,17 @@ class Table(ABC):
in-progress operation (e.g. appending new data) and these files will not
be deleted unless they are at least 7 days old. If delete_unverified is True
then these files will be deleted regardless of their age.
.. warning::
This should only be set to True if you can guarantee that no other
process is currently working on this dataset. Otherwise the dataset
could be put into a corrupted state.
retrain: bool, default False
This parameter is no longer used and is deprecated.
Experimental API
----------------
The optimization process is undergoing active development and may change.
Our goal with these changes is to improve the performance of optimization and
reduce the complexity.
That being said, it is essential today to run optimize if you want the best
performance. It should be stable and safe to use in production, but it our
hope that the API may be simplified (or not even need to be called) in the
future.
The frequency an application shoudl call optimize is based on the frequency of
The frequency an application should call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
you have added or modified 100,000 or more records or run more than 20 data
@@ -3047,22 +3042,17 @@ class LanceTable(Table):
in-progress operation (e.g. appending new data) and these files will not
be deleted unless they are at least 7 days old. If delete_unverified is True
then these files will be deleted regardless of their age.
.. warning::
This should only be set to True if you can guarantee that no other
process is currently working on this dataset. Otherwise the dataset
could be put into a corrupted state.
retrain: bool, default False
This parameter is no longer used and is deprecated.
Experimental API
----------------
The optimization process is undergoing active development and may change.
Our goal with these changes is to improve the performance of optimization and
reduce the complexity.
That being said, it is essential today to run optimize if you want the best
performance. It should be stable and safe to use in production, but it our
hope that the API may be simplified (or not even need to be called) in the
future.
The frequency an application shoudl call optimize is based on the frequency of
The frequency an application should call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
you have added or modified 100,000 or more records or run more than 20 data
@@ -4630,22 +4620,17 @@ class AsyncTable:
in-progress operation (e.g. appending new data) and these files will not
be deleted unless they are at least 7 days old. If delete_unverified is True
then these files will be deleted regardless of their age.
.. warning::
This should only be set to True if you can guarantee that no other
process is currently working on this dataset. Otherwise the dataset
could be put into a corrupted state.
retrain: bool, default False
This parameter is no longer used and is deprecated.
Experimental API
----------------
The optimization process is undergoing active development and may change.
Our goal with these changes is to improve the performance of optimization and
reduce the complexity.
That being said, it is essential today to run optimize if you want the best
performance. It should be stable and safe to use in production, but it our
hope that the API may be simplified (or not even need to be called) in the
future.
The frequency an application shoudl call optimize is based on the frequency of
The frequency an application should call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
you have added or modified 100,000 or more records or run more than 20 data