uros-b commented on code in PR #56942:
URL: https://github.com/apache/spark/pull/56942#discussion_r3524933018
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/types/ops/TimestampNanosParquetOps.scala:
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@@ -167,3 +186,69 @@ private[ops] object TimestampNanosParquetOps {
case _ => false
})
}
+
+/**
+ * Vectorized (batch) updater for nanosecond-precision timestamps: reads an
INT64 epoch-nanos
+ * column and decomposes each value into the two-child column vector
(epochMicros: Long,
+ * nanosWithinMicro: Short). Sub-microsecond digits are truncated to the
requested precision.
+ * Mirrors the row-based `newConverter` path which calls
+ * `DateTimeUtils.epochNanosToTimestampNanos(value, precision)`.
Review Comment:
TimeVectorUpdater calls DateTimeUtils.truncateTimeToPrecision directly to
stay in lock-step with the row reader. TimestampNanosVectorUpdater inlines the
same math instead of calling DateTimeUtils.epochNanosToTimestampNanos.
The logic is equivalent today, but a shared helper would reduce drift risk
if truncation rules ever change. If object allocation is a concern, extracting
a zero-alloc (epochMicros, nanosWithinMicro) helper in DateTimeUtils would be
the cleanest fix.
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