gengliangwang opened a new pull request #34681:
URL: https://github.com/apache/spark/pull/34681
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### What changes were proposed in this pull request?
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Under ANSI mode(spark.sql.ansi.enabled=true), the function invocation of
Spark SQL:
- In general, it follows the `Store assignment` rules as storing the input
values as the declared parameter type of the SQL functions
- Special rules apply for string literals and untyped NULL. A NULL can be
promoted to any other type, while a string literal can be promoted to any
simple data type.
### Why are the changes needed?
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Currently, the ANSI SQL mode resolves the function invocation with `Least
Common Type Resolution` based on`Type precedence list`. After a closer look at
the ANSI SQL standard, the "store assignment" syntax rules should be used for
resolving the type coercion between the input and parameters of SQL function,
while the `Type precedence list` is used for "Subject routine
determination"(SQL function overloads).
I have also done some data science among real-world SQL queries, the
following implicit function casts are not allowed as per `Least Common Type
Resolution` but they are commonly seen:
- Numeric/Date/Timestamp => String, e.g. tableau generated query
`CONCAT(DATE_ADD(%1, CAST(%2 AS INT)), SUBSTR(CAST(%1 AS TIMESTAMP), 11)) AS
TIMESTAMP)`
- Timestamp => Date, e.g `date_sub(now(), 7) < ...`
- Double => Long, e.g. `from_unixtime(updated/1000.0)`
The changes in this PR is ANSI compatible and it is good for the adoption of
ANSI SQL mode.
### Does this PR introduce _any_ user-facing change?
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Yes, Use store assignment rules for resolving function invocation under ANSI
mode.
### How was this patch tested?
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Unit tests
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