stevomitric opened a new pull request, #56041:
URL: https://github.com/apache/spark/pull/56041
### What changes were proposed in this pull request?
Adds SQL parser support for parameterized nanosecond-precision timestamp
types introduced in SPARK-56876. The parser now accepts:
- TIMESTAMP_NTZ(p) -> TimestampNTZNanosType(p)
- TIMESTAMP_LTZ(p) -> TimestampLTZNanosType(p)
- TIMESTAMP(p) WITHOUT TIME ZONE (alias for TIMESTAMP_NTZ(p))
- TIMESTAMP(p) WITH LOCAL TIME ZONE (alias for TIMESTAMP_LTZ(p))
- TIMESTAMP(p) (resolves via spark.sql.timestampType session default)
with `p` from `[7, 9]`. Out-of-range precision throws
`INVALID_TIMESTAMP_PRECISION`; negative precision is rejected by the grammar as
`PARSE_SYNTAX_ERROR`.
The new syntax is gated behind a new internal preview flag
`spark.sql.timestampNanosTypes.enabled` (default false).
Unparameterized TIMESTAMP, TIMESTAMP_NTZ, TIMESTAMP_LTZ, and the
WITH/WITHOUT TIME ZONE variants continue to return the existing microsecond
types - no behavior change.
Part of SPIP SPARK-56822 (https://issues.apache.org/jira/browse/SPARK-56822).
### Why are the changes needed?
SPARK-56876 added TimestampNTZNanosType / TimestampLTZNanosType to the type
system but explicitly left out SQL/DDL integration - users cannot declare these
types in CREATE TABLE, CAST, or Column.cast(String) today. This PR is the
parser sub-task of the SPIP and wires those spellings through
DataTypeAstBuilder, behind a preview flag so the surface is opt-in until the
cast/runtime sub-tasks land.
### Does this PR introduce _any_ user-facing change?
Yes made the parser changes to allow timestamps with precision.
### How was this patch tested?
Extended DataTypeParserSuite
### Was this patch authored or co-authored using generative AI tooling?
Yes
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