Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21647#discussion_r198364542
--- Diff: docs/sql-programming-guide.md ---
@@ -2017,6 +2017,7 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
- Literal values used in SQL operations are converted to DECIMAL with
the exact precision and scale needed by them.
- The configuration `spark.sql.decimalOperations.allowPrecisionLoss`
has been introduced. It defaults to `true`, which means the new behavior
described here; if set to `false`, Spark uses previous rules, ie. it doesn't
adjust the needed scale to represent the values and it returns NULL if an exact
representation of the value is not possible.
- In PySpark, `df.replace` does not allow to omit `value` when
`to_replace` is not a dictionary. Previously, `value` could be omitted in the
other cases and had `None` by default, which is counterintuitive and
error-prone.
+ - Un-aliased subquery is supported by Spark SQL for a long time. Its
semantic was not well defined and had confusing behaviors. Since Spark 2.3, we
invalid a weird use case: `SELECT v.i from (SELECT i FROM v)`. Now this query
will throw analysis exception because users should not be able to use the
qualifier inside a subquery. See
[SPARK-20690](https://issues.apache.org/jira/browse/SPARK-20690) and
[SPARK-21335](https://issues.apache.org/jira/browse/SPARK-21335) for details.
--- End diff --
Also consider:
Now this query will throw analysis exception because users should not be
able to use the qualifier inside a subquery.
->
The cases throw an analysis exception because users should not be able to
use the qualifier inside a subquery.
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