Hey Li, If your input data is in UTC you don't need assume_timezone [1]. You would need it if your input was America/New_York local time and you wanted to convert to a zoned timestamp array where underlying data is in UTC and timezone is metadata only. Perhaps python tests are interesting for reference [2].
Available extraction kernels are listed here: [3]. Rok [1] https://arrow.apache.org/docs/python/generated/pyarrow.compute.assume_timezone.html [2] https://github.com/apache/arrow/blob/master/python/pyarrow/tests/test_compute.py#L1908-L1999 [3] https://arrow.apache.org/docs/cpp/compute.html#temporal-component-extraction On Thu, Feb 3, 2022 at 3:54 PM Li Jin <ice.xell...@gmail.com> wrote: > > Hello! > > I am new to the Arrow C++ compute engine and trying to figure out this time > zone conversion and time extraction: > > t.dt.tz_convert('America/New_York').dt.time == datetime.time(11, 30, 0) > > So I started looking at: > https://github.com/apache/arrow/blob/master/cpp/src/arrow/compute/kernels/scalar_temporal_unary.cc > > and found these these functions seem relevant: > assume_timezone > hour > minute > > So my thinking is trying to figure out a way to build plan that basically > does these steps: > (1) Assume timezone to New_York (input data is UTC) > (2) Extract hour value > (3) Extract minute value > (4) Filter on hour and minute value > > I wonder what is a good way to map these functions in > scalar_temporal_unary to an ExecPlan? (Looked under > https://github.com/apache/arrow/tree/master/cpp/src/arrow/compute/exec but > didn't see anything obvious) > > Thanks! > Li