#172 added a warning. I think it is too expensive, and it may be better to just document the behavior instead.
I am reading a 7760500 row table written by pandas, which defaults to nanosecond resolution, and want to convert it to DateTime, I don't need sub-ms precision. The conversion worked out of the box, but took 75 seconds. Profiling showed that almost all time was in warntimestamp generating the log message. Without the log message it takes 0.05 seconds.
This shows it in a benchmark:
using Chairmarks, Arrow, Dates
# alternative to convert that doesn't have warntimestamp
function to_datetime(x::Arrow.Timestamp{U, nothing})::DateTime where {U}
x_since_epoch = Arrow.periodtype(U)(x.x)
ms_since_epoch = Dates.toms(x_since_epoch)
ut_instant = Dates.UTM(ms_since_epoch + Arrow.UNIX_EPOCH_DATETIME)
return DateTime(ut_instant)
end
const ts = Arrow.Timestamp{Arrow.Flatbuf.TimeUnit.NANOSECOND, nothing}(1764288000000000000)
@b convert(DateTime, ts) # 6.525 μs (119 allocs: 6.719 KiB)
@b to_datetime(ts) # 1.332 ns
I now avoid this with convert = false and using the to_datetime function above, but I think more people will run into this performance pitfall.

#172 added a warning. I think it is too expensive, and it may be better to just document the behavior instead.
I am reading a 7760500 row table written by pandas, which defaults to nanosecond resolution, and want to convert it to DateTime, I don't need sub-ms precision. The conversion worked out of the box, but took 75 seconds. Profiling showed that almost all time was in
warntimestampgenerating the log message. Without the log message it takes 0.05 seconds.This shows it in a benchmark:
I now avoid this with
convert = falseand using theto_datetimefunction above, but I think more people will run into this performance pitfall.