stevomitric opened a new pull request, #58550:
URL: https://github.com/apache/spark/pull/58550
### What changes were proposed in this pull request?
Let a nanosecond timestamp type read a Parquet INT64 TIMESTAMP(MICROS)
column, promoting each micros value to the internal (epochMicros,
nanosWithinMicro = 0) representation. Three touch points:
- Row-based: the extended newConverter now accepts a TIMESTAMP(MICROS)
column (LTZ applies the Julian→Gregorian rebase; NTZ needs none).
- Vectorized: new MicrosAsTimestampNanos(Rebase)Updater updaters,
dispatched for a nanos type over a MICROS file.
- Filter pushdown: only push down a micros/millis timestamp bound that lies
exactly on the microsecond grid, so a sub-µs bound never over-prunes row groups.
Scope: INT64 TIMESTAMP(MICROS) only; INT96 and TIMESTAMP_MILLIS sources
remain unsupported.
### Why are the changes needed?
It's the read side of widening a microsecond timestamp column (TIMESTAMP(6))
to nanosecond precision without rewriting existing files. Today the nanos
reader fails loudly on any non-TIMESTAMP(NANOS) column, so old micros files
would be unreadable after such a widening. The promotion is exact and
range-complete - micros to (micros, 0) involves no ×1000 encode, so it avoids
the INT64 epoch-nanos overflow range (~1677–2262).
### Does this PR introduce _any_ user-facing change?
Yes, behind the preview flag spark.sql.timestampNanosTypes.enabled.
Previously, reading a TIMESTAMP(MICROS) Parquet column under a nanosecond
timestamp type threw (SchemaColumnConvertNotSupportedException /
PARQUET_CONVERSION_FAILURE); now it reads the values correctly.
### How was this patch tested?
Added TimestampNanosParquetOpsSuite and updated ParquetTypeWideningSuite
ParquetFilterSuite.
### Was this patch authored or co-authored using generative AI tooling?
Co-authored-by: Claude Opus 4.8
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