[spark] branch branch-3.3 updated: [MINOR][ML][DOCS] Fix sql data types link in the ml-pipeline page

[huaxingao]

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huaxingao pushed a commit to branch branch-3.3
in repository https://gitbox.apache.org/repos/asf/spark.git


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 <y...@apache.org>
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 <y...@apache.org>
    Signed-off-by: huaxingao <huaxin_...@apple.com>
    (cherry picked from commit de73753bb2e5fd947f237e731ff05aa9f2711677)
    Signed-off-by: huaxingao <huaxin_...@apple.com>
---
 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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