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https://issues.apache.org/jira/browse/SPARK-57462?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Max Gekk updated SPARK-57462:
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Affects Version/s: 4.3.0
(was: 5.0.0)
> Add PySpark support for nanosecond-precision timestamp types
> ------------------------------------------------------------
>
> Key: SPARK-57462
> URL: https://issues.apache.org/jira/browse/SPARK-57462
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 4.3.0
> Reporter: Max Gekk
> Priority: Major
>
> Umbrella: SPARK-56822 (Timestamps with nanosecond precision).
> Expose the nanosecond-capable timestamp types TIMESTAMP_NTZ(p) and
> TIMESTAMP_LTZ(p) (p in 7-9) in PySpark, to reach parity with the microsecond
> TimestampType / TimestampNTZType. Today python/pyspark/sql/types.py defines
> only the microsecond singletons; there are no nanosecond type classes
> anywhere under python/.
> Scope:
> - Add TimestampNTZNanosType and TimestampLTZNanosType (parameterized by
> precision) to python/pyspark/sql/types.py, including toInternal /
> fromInternal handling for the (epoch micros, nanos-within-micro)
> representation and JSON/DDL (de)serialization.
> - Mirror in the Spark Connect Python path
> (python/pyspark/sql/connect/types.py) and the generated proto stubs
> (types_pb2.pyi) - depends on the Connect proto work (SPARK-57160/57161).
> - Decide type inference for datetime.datetime (keep inferring microsecond
> TimestampType by default; nanos only via an explicit schema).
> - Arrow / pandas conversion depends on the Arrow mapping (SPARK-57159).
> - Tests: python/pyspark/sql/tests for createDataFrame / collect roundtrip
> with explicit nanos schema, DDL parsing, and equality/repr.
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