`dt.time` objects upload with incorrect time or fail when using `bigquery.Client().load_table_from_dataframe`

[willbowditch]

Uploading datetime.time objects via bigquery.Client().load_table_from_dataframe fails or generates incorrect times.

Testing a single time (dt.time(12,0)) will upload, but the result in bigquery is incorrect (10:03:46.765952), but the example below fails with an out of expected range error. However, conversion to parquet and back works fine.

My guess is that the time stamp generated for parquet is in us but BigQuery expects ns?

Thanks.

Environment details

• OS type and version: OS X 10.15.7
• Python version: 3.6.10
• pip version: 20.2.2
• google-cloud-bigquery version: 2.3.1

Code example

import pandas as pd

df = pd.DataFrame(
 dict(
 start_time=[
 dt.time(0, 0),
 dt.time(0, 15),
 dt.time(0, 30),
 dt.time(0, 45),
 dt.time(1, 0),
 dt.time(1, 30),
 dt.time(10, 0),
 dt.time(10, 30),
 dt.time(11, 0),
 dt.time(11, 30),
 dt.time(12, 0),
 dt.time(12, 30),
 dt.time(12, 5),
 dt.time(13, 0),
 dt.time(13, 15),
 dt.time(13, 30),
 dt.time(14, 0),
 dt.time(14, 30),
 dt.time(14, 5),
 dt.time(15, 0),
 dt.time(15, 30),
 dt.time(16, 0),
 dt.time(16, 30),
 dt.time(17, 0),
 dt.time(17, 30),
 dt.time(18, 0),
 dt.time(18, 30),
 dt.time(19, 0),
 dt.time(19, 30),
 dt.time(2, 0),
 dt.time(2, 30),
 dt.time(20, 0),
 dt.time(20, 30),
 dt.time(21, 0),
 dt.time(21, 30),
 dt.time(22, 0),
 dt.time(22, 30),
 dt.time(23, 0),
 dt.time(23, 30),
 dt.time(3, 0),
 dt.time(3, 30),
 dt.time(3, 50),
 dt.time(4, 0),
 dt.time(4, 30),
 dt.time(5, 0),
 dt.time(5, 30),
 dt.time(6, 0),
 dt.time(6, 30),
 dt.time(7, 0),
 dt.time(7, 15),
 dt.time(7, 30),
 dt.time(7, 45),
 dt.time(8, 0),
 dt.time(8, 30),
 dt.time(9, 0),
 dt.time(9, 30),
 ]
 )
)

# Sense check that conversion to parquet and back works...
import pyarrow.parquet as pq
import pyarrow as pa

arrow_table = pa.Table.from_pandas(df)
arrow_table

pq.write_table(arrow_table, "test.parquet")
back_to_pandas = pq.read_table("test.parquet").to_pandas()
back_to_pandas["start_time"].apply(type)
back_to_pandas

from pandas.testing import assert_frame_equal
assert_frame_equal(back_to_pandas, df). # <-- this is fine

from google.cloud import bigquery
bq = bigquery.Client()
job = bq.load_table_from_dataframe(df, "wb_dev_us.time_test")
job.result() #<-- This errors

Stack trace

---------------------------------------------------------------------------
BadRequest Traceback (most recent call last)
~/projects/demand-model2/uk_demand_model/extract/mdd_extract.py in 
 466 bq = bigquery.Client()
 467 job = bq.load_table_from_dataframe(df, "wb_dev_us.time_test_2")
----> 468 job.result()

~/.local/share/virtualenvs/demand-model2-OBCa31Zf/lib/python3.6/site-packages/google/cloud/bigquery/job/base.py in result(self, retry, timeout)
 629 
 630 kwargs = {} if retry is DEFAULT_RETRY else {"retry": retry}
--> 631 return super(_AsyncJob, self).result(timeout=timeout, **kwargs)
 632 
 633 def cancelled(self):

~/.local/share/virtualenvs/demand-model2-OBCa31Zf/lib/python3.6/site-packages/google/api_core/future/polling.py in result(self, timeout, retry)
 132 # pylint: disable=raising-bad-type
 133 # Pylint doesn't recognize that this is valid in this case.
--> 134 raise self._exception
 135 
 136 return self._result

BadRequest: 400 Error while reading data, error message: Invalid time value 30600000000 for column 'start_time': generic::out_of_range: Cannot return an invalid time value of 30600000000 microseconds relative to the Unix epoch. The range of valid time values is [00:00:00, 23:59:59.999999]

[tswast]

Unfortunately, TIME is not a supported data type by the BigQuery backend Parquet support. https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-parquet#type_conversions

I see there is a public issue on the backend for this issue here: https://issuetracker.google.com/169230812

[tswast]

With #399, I believe time columns are supported when the data type is set to CSV in the job configuration.

[willbowditch]

Thanks @tswast, can confirm the above example now works with release 2.6.2

For anyone finding this issue in future, config to support loads with datetime.time columns is:

bq = bigquery.Client()
job_config = bigquery.LoadJobConfig(source_format=bigquery.SourceFormat.CSV)
job = bq.load_table_from_dataframe(df, "wb_dev_us.time_test", job_config=job_config)
job.result()