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Apache Spark commented on SPARK-34246: -------------------------------------- User 'gengliangwang' has created a pull request for this issue: https://github.com/apache/spark/pull/31349 > New implicit cast syntax rules in ANSI mode > ------------------------------------------- > > Key: SPARK-34246 > URL: https://issues.apache.org/jira/browse/SPARK-34246 > Project: Spark > Issue Type: New Feature > Components: SQL > Affects Versions: 3.1.2 > Reporter: Gengliang Wang > Assignee: Gengliang Wang > Priority: Major > > Add new implicit cast syntax rules in ANSI mode. > In Spark ANSI mode, the type coercion rules are based on the type precedence > lists of the input data types. > As per the section "Type precedence list determination" of "ISO/IEC > 9075-2:2011 > Information technology — Database languages - SQL — Part 2: Foundation > (SQL/Foundation)", the type precedence lists of primitive > data types are as following: > * Byte: Byte, Short, Int, Long, Decimal, Float, Double > * Short: Short, Int, Long, Decimal, Float, Double > * Int: Int, Long, Decimal, Float, Double > * Long: Long, Decimal, Float, Double > * Decimal: Any wider Numeric type > * Float: Float, Double > * Double: Double > * String: String > * Date: Date, Timestamp > * Timestamp: Timestamp > * Binary: Binary > * Boolean: Boolean > * Interval: Interval > As for complex data types, Spark will determine the precedent list > recursively based on their sub-types. > With the definition of type precedent list, the general type coercion rules > are as following: > * Data type S is allowed to be implicitly cast as type T iff T is in the > precedence list of S > * Comparison is allowed iff the data type precedence list of both sides has > at least one common element. When evaluating the comparison, Spark casts both > sides as the tightest common data type of their precedent lists. > * There should be at least one common data type among all the children's > precedence lists for the following operators. The data type of the operator > is the tightest common precedent data type. > {code:java} > In > Except(odd) > Intersect > Greatest > Least > Union > If > CaseWhen > CreateArray > Array Concat > Sequence > MapConcat > CreateMap > {code} > * For complex types (struct, array, map), Spark recursively looks into the > element type and applies the rules above. If the element nullability is > converted from true to false, add runtime null check to the elements. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org