Spark ANSI mode compatibility: diagnosing implicit cast and column ambiguity risks

Enabling `spark.sql.ansi.enabled` in Apache Spark 3.4 (and the experimental 3.3.x backport) enforces strict SQL standard compliance, rejecting implicit casts and ambiguous column references that legacy code often depends on. The compatibility boundary becomes uncertain when the Spark runtime is paired with a Hive metastore earlier than 3.1.0, because version mismatches may affect how the ANSI flag is propagated across catalog operations. Without an automatic migration path, teams must manually assess which queries and third-party libraries rely on non-standard SQL semantics before toggling the flag.

The core uncertainty is how to systematically identify queries that will fail under ANSI mode while accounting for metastore version differences. Practical verification would require testing against Spark 3.4+ with a Hive metastore 3.1.0 or later, but the exact interaction between the ANSI flag and older metastore schemas remains a deployment-specific risk that warrants review.

Which query patterns should be prioritized when auditing for implicit cast and column ambiguity failures under ANSI mode, and how can Hive metastore version mismatches be isolated as a variable during compatibility testing?