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https://issues.apache.org/jira/browse/SPARK-30828?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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German Schiavon Matteo updated SPARK-30828:
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Description:
Actually when you call *_insertInto_* to add a dataFrame into an existing table
the only safety check is that the number of columns match, but the order
doesn't matter, and the message in case that the number of columns doesn't
match is not very helpful, specially when you have a lot of columns:
{code:java}
org.apache.spark.sql.AnalysisException: `default`.`table` requires that the
data to be inserted have the same number of columns as the target table: target
table has 2 column(s) but the inserted data has 1 column(s), including 0
partition column(s) having constant value(s).; {code}
I think a standard column check would be very helpful, just in almost other
cases with Spark:
{code:java}
"cannot resolve 'p2' given input columns: [id, p1];"
{code}
was:
Actually ****when you call _*insertInto*_ to add a dataFrame into an existing
table the only safety check is that the number of columns match, but the order
doesn't matter, and the message in case that the number of columns doesn't
match is not very helpful, specially when you have a lot of columns:
```org.apache.spark.sql.AnalysisException: `default`.`table` requires that the
data to be inserted have the same number of columns as the target table: target
table has 2 column(s) but the inserted data has 1 column(s), including 0
partition column(s) having constant value(s).; ```
I think a standard column check would be very helpful, just in almost other
cases with Spark:
``` cannot resolve 'p2' given input columns: [id, p1];" ```
> Improve insertInto behaviour
> ----------------------------
>
> Key: SPARK-30828
> URL: https://issues.apache.org/jira/browse/SPARK-30828
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core, SQL
> Affects Versions: 3.0.0
> Reporter: German Schiavon Matteo
> Priority: Minor
>
> Actually when you call *_insertInto_* to add a dataFrame into an existing
> table the only safety check is that the number of columns match, but the
> order doesn't matter, and the message in case that the number of columns
> doesn't match is not very helpful, specially when you have a lot of columns:
> {code:java}
> org.apache.spark.sql.AnalysisException: `default`.`table` requires that the
> data to be inserted have the same number of columns as the target table:
> target table has 2 column(s) but the inserted data has 1 column(s), including
> 0 partition column(s) having constant value(s).; {code}
> I think a standard column check would be very helpful, just in almost other
> cases with Spark:
>
> {code:java}
> "cannot resolve 'p2' given input columns: [id, p1];"
> {code}
>
>
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