Vamsi-klu commented on code in PR #58039:
URL: https://github.com/apache/spark/pull/58039#discussion_r3803159439
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python/pyspark/sql/pandas/types.py:
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@@ -75,6 +75,8 @@
# Should keep in line with org.apache.spark.sql.util.ArrowUtils.metadataKey
metadata_key = b"SPARK::metadata::json"
+# Keep in line with org.apache.spark.sql.util.ArrowUtils.timePrecisionKey
+time_precision_key = b"SPARK::time::precision"
Review Comment:
Yes, those paths all go through the same SPARK::time::precision field
metadata. I added e2e tests for them:
- createDataFrame / collect / toArrow / toPandas:
`test_time_precision_e2e_createDataFrame_collect` (Connect via ArrowParityTests)
- Python UDF (Arrow and pickled): `test_time_precision_python_udf`
- scalar arrow_udf: `test_arrow_udf_time_precision`
- mapInArrow: `test_time_precision_map_in_arrow`
For p in {0, 3, 6, 9} the schema stays TIME(p) and collect matches. Untagged
time64 still falls back to TIME(6).
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