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https://issues.apache.org/jira/browse/SPARK-24780?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
holdenk updated SPARK-24780:
----------------------------
Description:
If we join a dataframe with another dataframe which has the same column name of
the conditions (e.g. shared lineage on one of the conditions) even though the
join condition may be written with the full name, the columns returned don't
have the dataframe alias and as such will create a cross-join.
For example this currently works even if both posts_by_sampled_authors &
mailing_list_posts_in_reply_to contain both in_reply_to and message_id fields.
{code:java}
posts_with_replies = posts_by_sampled_authors.join(
mailing_list_posts_in_reply_to,
[F.col("mailing_list_posts_in_reply_to.in_reply_to") ==
F.col("posts_by_sampled_authors.message_id")],
"inner"){code}
But a similarly written expression:
{code:java}
posts_with_replies = posts_by_sampled_authors.join(
mailing_list_posts_in_reply_to,
[mailing_list_posts_in_reply_to.in_reply_to ==
posts_by_sampled_authors.message_id],
"inner"){code}
will fail.
I'm not super sure whats going on inside of the resolution that's causing it to
get confused.
was:
If we join a dataframe with another dataframe which has the same column name of
the conditions (e.g. shared lineage on one of the conditions) even though the
join condition may be written with the full name, the columns returned don't
have the dataframe alias and as such will create a cross-join.
For example this currently works even if both posts_by_sampled_authors &
mailing_list_posts_in_reply_to contain both in_reply_to and message_id fields.
{code:java}
posts_with_replies = posts_by_sampled_authors.join(
mailing_list_posts_in_reply_to,
[F.col("mailing_list_posts_in_reply_to.in_reply_to") ==
F.col("posts_by_sampled_authors.message_id")],
"inner"){code}
But a similarly written expression:
{code:java}
posts_with_replies = posts_by_sampled_authors.join(
mailing_list_posts_in_reply_to,
[mailing_list_posts_in_reply_to.in_reply_to ==
posts_by_sampled_authors.message_id],
"inner"){code}
will fail.
We could fix this by changing it so that dataframe.column in PySpark returns
the fully qualified column reference if the dataframe has an alias.
> DataFrame.column_name should take into account DataFrame alias for future
> joins
> -------------------------------------------------------------------------------
>
> Key: SPARK-24780
> URL: https://issues.apache.org/jira/browse/SPARK-24780
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 2.4.0
> Reporter: holdenk
> Priority: Minor
>
> If we join a dataframe with another dataframe which has the same column name
> of the conditions (e.g. shared lineage on one of the conditions) even though
> the join condition may be written with the full name, the columns returned
> don't have the dataframe alias and as such will create a cross-join.
> For example this currently works even if both posts_by_sampled_authors &
> mailing_list_posts_in_reply_to contain both in_reply_to and message_id fields.
>
> {code:java}
> posts_with_replies = posts_by_sampled_authors.join(
> mailing_list_posts_in_reply_to,
> [F.col("mailing_list_posts_in_reply_to.in_reply_to") ==
> F.col("posts_by_sampled_authors.message_id")],
> "inner"){code}
>
> But a similarly written expression:
> {code:java}
> posts_with_replies = posts_by_sampled_authors.join(
> mailing_list_posts_in_reply_to,
> [mailing_list_posts_in_reply_to.in_reply_to ==
> posts_by_sampled_authors.message_id],
> "inner"){code}
> will fail.
>
> I'm not super sure whats going on inside of the resolution that's causing it
> to get confused.
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