HyukjinKwon commented on a change in pull request #32835:
URL: https://github.com/apache/spark/pull/32835#discussion_r648795252
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File path: python/docs/source/user_guide/pandas_on_spark/options.rst
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@@ -227,24 +227,24 @@ This is conceptually equivalent to the PySpark example as
below:
Available options
-----------------
-=============================== ==============
=====================================================
+=============================== ==============
===================================================================
Option Default Description
-=============================== ==============
=====================================================
-display.max_rows 1000 This sets the maximum number of
rows Koalas should
+=============================== ==============
===================================================================
+display.max_rows 1000 This sets the maximum number of
rows pandas APIs on Spark should
output when printing out
various output. For example,
this value determines the
number of rows to be shown
at the repr() in a dataframe.
Set `None` to unlimit
the input length. Default is
1000.
compute.max_rows 1000 'compute.max_rows' sets the
limit of the current
- Koalas DataFrame. Set `None` to
unlimit the input
+ pandas APIs on Spark DataFrame.
Set `None` to unlimit the input
length. When the limit is set,
it is executed by the
shortcut by collecting the data
into the driver, and
then using the pandas API. If
the limit is unset, the
operation is executed by
PySpark. Default is 1000.
compute.shortcut_limit 1000 'compute.shortcut_limit' sets
the limit for a
shortcut. It computes specified
number of rows and
use its schema. When the
dataframe length is larger
- than this limit, Koalas uses
PySpark to compute.
+ than this limit, pandas APIs on
Spark uses PySpark to compute.
Review comment:
```suggestion
than this limit, pandas APIs
on Spark use PySpark to compute.
```
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