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The following commit(s) were added to refs/heads/branch-3.3 by this push:
new 2a31bf572bf [MINOR][ML][DOCS] Fix sql data types link in the
ml-pipeline page
2a31bf572bf is described below
commit 2a31bf572bf386bbae2a8c6941ea43722068e0c6
Author: Kent Yao <[email protected]>
AuthorDate: Mon May 23 07:45:50 2022 -0700
[MINOR][ML][DOCS] Fix sql data types link in the ml-pipeline page
### What changes were proposed in this pull request?
<img width="939" alt="image"
src="https://user-images.githubusercontent.com/8326978/169767919-6c48554c-87ff-4d40-a47d-ec4da0c993f7.png";>
[Spark SQL datatype
reference](https://spark.apache.org/docs/latest/sql-reference.html#data-types)
- `https://spark.apache.org/docs/latest/sql-reference.html#data-types` is
invalid and it shall be [Spark SQL datatype
reference](https://spark.apache.org/docs/latest/sql-ref-datatypes.html) -
`https://spark.apache.org/docs/latest/sql-ref-datatypes.html`
https://spark.apache.org/docs/latest/ml-pipeline.html#dataframe
### Why are the changes needed?
doc fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
`bundle exec jekyll serve`
Closes #36633 from yaooqinn/minor.
Authored-by: Kent Yao <[email protected]>
Signed-off-by: huaxingao <[email protected]>
(cherry picked from commit de73753bb2e5fd947f237e731ff05aa9f2711677)
Signed-off-by: huaxingao <[email protected]>
---
docs/ml-pipeline.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/ml-pipeline.md b/docs/ml-pipeline.md
index 105b1273311..5f9c94781ba 100644
--- a/docs/ml-pipeline.md
+++ b/docs/ml-pipeline.md
@@ -72,7 +72,7 @@ E.g., a learning algorithm is an `Estimator` which trains on
a `DataFrame` and p
Machine learning can be applied to a wide variety of data types, such as
vectors, text, images, and structured data.
This API adopts the `DataFrame` from Spark SQL in order to support a variety
of data types.
-`DataFrame` supports many basic and structured types; see the [Spark SQL
datatype reference](sql-reference.html#data-types) for a list of supported
types.
+`DataFrame` supports many basic and structured types; see the [Spark SQL
datatype reference](sql-ref-datatypes.html) for a list of supported types.
In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML
[`Vector`](mllib-data-types.html#local-vector) types.
A `DataFrame` can be created either implicitly or explicitly from a regular
`RDD`. See the code examples below and the [Spark SQL programming
guide](sql-programming-guide.html) for examples.
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