[spark] branch master updated: [SPARK-38382][DOC] Fix incorrect version infomation of migration guide doc

[we...@apache.org]

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The following commit(s) were added to refs/heads/master by this push:
     new 4883a80  [SPARK-38382][DOC] Fix incorrect version infomation of migration guide doc
4883a80 is described below

commit 4883a80eb6b4ca12d1c96151365aa3d5af28f1bd
Author: Angerszhuuuu <an...@gmail.com>
AuthorDate: Mon Mar 7 16:01:24 2022 +0800

    [SPARK-38382][DOC] Fix incorrect version infomation of migration guide doc
    
    ### What changes were proposed in this pull request?
    Unify the presentation of migration guide doc and fix some unclear describtion.
    
    ### Why are the changes needed?
    Make doc more clear
    
    ### Does this PR introduce _any_ user-facing change?
    No
    
    ### How was this patch tested?
    Not need
    
    Closes #35705 from AngersZhuuuu/SPARK-38382.
    
    Authored-by: Angerszhuuuu <an...@gmail.com>
    Signed-off-by: Wenchen Fan <we...@databricks.com>
---
 docs/sql-migration-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/sql-migration-guide.md b/docs/sql-migration-guide.md
index 9d7b1096..578586c 100644
--- a/docs/sql-migration-guide.md
+++ b/docs/sql-migration-guide.md
@@ -197,7 +197,7 @@ license: |
 
   - In Spark 3.0.2, `PARTITION(col=null)` is always parsed as a null literal in the partition spec. In Spark 3.0.1 or earlier, it is parsed as a string literal of its text representation, e.g., string "null", if the partition column is string type. To restore the legacy behavior, you can set `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` as true.
 
-  - In Spark 3.0.0, the output schema of `SHOW DATABASES` becomes `namespace: string`. In Spark version 2.4 and earlier, the schema was `databaseName: string`. Since Spark 3.0.2, you can restore the old schema by setting `spark.sql.legacy.keepCommandOutputSchema` to `true`.
+  - In Spark 3.0.2, the output schema of `SHOW DATABASES` becomes `namespace: string`. In Spark version 3.0.1 and earlier, the schema was `databaseName: string`. Since Spark 3.0.2, you can restore the old schema by setting `spark.sql.legacy.keepCommandOutputSchema` to `true`.
 
 ## Upgrading from Spark SQL 3.0 to 3.0.1
 
@@ -213,7 +213,7 @@ license: |
 
   - In Spark 2.4 and below, `Dataset.groupByKey` results to a grouped dataset with key attribute is wrongly named as "value", if the key is non-struct type, for example, int, string, array, etc. This is counterintuitive and makes the schema of aggregation queries unexpected. For example, the schema of `ds.groupByKey(...).count()` is `(value, count)`. Since Spark 3.0, we name the grouping attribute to "key". The old behavior is preserved under a newly added configuration `spark.sql.legacy [...]
 
-  - In Spark 3.0, the column metadata will always be propagated in the API `Column.name` and `Column.as`. In Spark version 2.4 and earlier, the metadata of `NamedExpression` is set as the `explicitMetadata` for the new column at the time the API is called, it won't change even if the underlying `NamedExpression` changes metadata. To restore the behavior before Spark 2.4, you can use the API `as(alias: String, metadata: Metadata)` with explicit metadata.
+  - In Spark 3.0, the column metadata will always be propagated in the API `Column.name` and `Column.as`. In Spark version 2.4 and earlier, the metadata of `NamedExpression` is set as the `explicitMetadata` for the new column at the time the API is called, it won't change even if the underlying `NamedExpression` changes metadata. To restore the behavior before Spark 3.0, you can use the API `as(alias: String, metadata: Metadata)` with explicit metadata.
 
 ### DDL Statements
 

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