Github user mgaido91 commented on a diff in the pull request: https://github.com/apache/spark/pull/23042#discussion_r234155688 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala --- @@ -138,6 +138,11 @@ object TypeCoercion { case (DateType, TimestampType) => if (conf.compareDateTimestampInTimestamp) Some(TimestampType) else Some(StringType) + // to support a popular use case of tables using Decimal(X, 0) for long IDs instead of strings + // see SPARK-26070 for more details + case (n: DecimalType, s: StringType) if n.scale == 0 => Some(DecimalType(n.precision, n.scale)) --- End diff -- @cloud-fan I think we have seen many issues on this. I don't think there is a standard for them, every RDBMS has different rules. The worst thing about the current rules IMHO is that they are not even coherent in Spark (see #19635 for instance). The option I'd prefer is to follow Postgres behavior, ie. no implicit cast at all. When there is a type mismatch the user has to choose how to cast the things. It is a bit more effort on user side, but it is the safest option IMHO. What do you think?
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