(spark) branch master updated: [MINOR][DOCS] Explicitly mention RDD/SparkContext are unsupported in Spark Connect

[gu...@apache.org]

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The following commit(s) were added to refs/heads/master by this push:
     new 36327329779c [MINOR][DOCS] Explicitly mention RDD/SparkContext are unsupported in Spark Connect
36327329779c is described below

commit 36327329779c095932a4d5ec27558407f1402df1
Author: Hyukjin Kwon <gu...@apache.org>
AuthorDate: Wed Aug 14 16:01:00 2024 +0900

    [MINOR][DOCS] Explicitly mention RDD/SparkContext are unsupported in Spark Connect
    
    ### What changes were proposed in this pull request?
    
    This PR proposes to explicitly mention RDD/SparkContext are unsupported in Spark Connect instead of saying they are deprecated.
    
    ### Why are the changes needed?
    
    To guide users properly about supported API.
    
    ### Does this PR introduce _any_ user-facing change?
    
    Yes, it fixes the user-facing documentation.
    
    ### How was this patch tested?
    
    Documentation build
    
    ### Was this patch authored or co-authored using generative AI tooling?
    
    No.
    
    Closes #47752 from HyukjinKwon/minor-doc-connect.
    
    Authored-by: Hyukjin Kwon <gu...@apache.org>
    Signed-off-by: Hyukjin Kwon <gu...@apache.org>
---
 docs/spark-connect-overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/spark-connect-overview.md b/docs/spark-connect-overview.md
index b8e378b5e7ed..0d6a7dd826de 100644
--- a/docs/spark-connect-overview.md
+++ b/docs/spark-connect-overview.md
@@ -409,6 +409,6 @@ Majority of the Streaming API is supported, including
 [StreamingQueryListener](api/scala/org/apache/spark/sql/streaming/StreamingQueryListener.html).
 
 APIs such as [SparkContext](api/scala/org/apache/spark/SparkContext.html)
-and [RDD](api/scala/org/apache/spark/rdd/RDD.html) are deprecated in all Spark Connect versions.
+and [RDD](api/scala/org/apache/spark/rdd/RDD.html) are unsupported in Spark Connect.
 
 Support for more APIs is planned for upcoming Spark releases.


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