read_sql from SQL Server to Arrow truncates datetime

[t-alex-fritz]

I'm working with a MS SQL Server 2019. When reading a datetime field, read_sql correctly captures it in a pandas dataframe. When I convert that dataframe to an arrow table, datetimes are also retained correctly. However, loading directly to an arrow table truncates the datetime fields to midnight. I'd like to remove the pandas dependency and load directly to an arrow table. Is there a way to do this without truncating the datetime?

Example Code:

import connectorx as cx
import pyarrow as pa
con_string = 'mssql://user:pwd@hostname.de%5CG:1439/database'
print('------- Pandas Table -------')
query = 'SELECT top 5 Datum FROM Termine WHERE Datum>getdate()'
pandas_table = cx.read_sql(con_string, query, return_type='pandas')
print(pandas_table)
print('------- Arrow table from pandas -------')
arrow_table_from_pandas = pa.Table.from_pandas(pandas_table)
print(arrow_table_from_pandas)
print('------- Arrow Table -------')
arrow_table = cx.read_sql(con_string, query, return_type='arrow')
print(arrow_table)

Example Output:

------- Pandas Table -------
                Datum
0 2022-02-06 07:30:00
1 2022-02-07 00:00:00
2 2022-02-07 00:00:00
3 2022-02-07 07:00:00
4 2022-02-07 07:30:00
------- Arrow table from pandas -------
pyarrow.Table
Datum: timestamp[ns]
----
Datum: [[2022-02-06 07:30:00.000000000,2022-02-07 00:00:00.000000000,2022-02-07 00:00:00.000000000,2022-02-07 07:00:00.000000000,2022-02-07 07:30:00.000000000]]
------- Arrow Table -------
pyarrow.Table
Datum: date64[ms]
----
Datum: [[2022-02-06,2022-02-07,2022-02-07,2022-02-07,2022-02-07]]

[wangxiaoying]

Hi @t-alex-fritz , there is some issue when we dealing with datetime for arrow and we are currently shifting from arrow to arrow2. Can you set the return_type="arrow2" to see whether it solves the problem?

[t-alex-fritz]

Hi @wangxiaoying, thank you! This throws a ValueError: arrow2

[wangxiaoying]

@t-alex-fritz , can you update to the latest alpha version 0.2.4a6?

[t-alex-fritz]

On 0.2.4a6 I do get non-truncated timestamp[ns] - that worked! However, pyarrow then fails to write the table to parquet whenever datetimes are involved that aren't rounded.

import connectorx as cx
import pyarrow.parquet as pq
con_string = 'mssql://user:pwd@hostname.de%5CG:1439/database'
query = 'SELECT top 5 ModificationDate FROM Termine WHERE Datum>getdate()'
arrow_table = cx.read_sql(con_string, query, return_type='arrow2')
print(arrow_table)
pq.write_table(arrow_table, 'output.parquet')

pyarrow.Table
ModificationDate: timestamp[ns]
----
ModificationDate: [[2021-04-14 00:13:02.216666666,2021-07-22 10:11:13.310000000,2021-08-24 15:59:44.463333333,2021-05-12 00:04:40.973333333,2021-08-24 16:14:56.170000000]]
Traceback (most recent call last):
  File "...", line 7, in <module>
    pq.write_table(arrow_table, 'output.parquet')
  File "...", line 2092, in write_table
    writer.write_table(table, row_group_size=row_group_size)
  File "...", line 754, in write_table
    self.writer.write_table(table, row_group_size=row_group_size)
  File "pyarrow/_parquet.pyx", line 1506, in pyarrow._parquet.ParquetWriter.write_table
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Casting from timestamp[ns] to timestamp[us] would lose data: 1618359182216666666

[wangxiaoying]

@t-alex-fritz looks like it is the issue of type conversion of pyarrow. Can you check whether this allow_truncated_timestamps answers your question? https://stackoverflow.com/questions/53893554/transfer-and-write-parquet-with-python-and-pandas-got-timestamp-error

[t-alex-fritz]

Oh, you are right, my bad! That last part was a pyarrow thing and works now after setting coerce_timestamps and allow_truncated_timestamps. Thanks a lot @wangxiaoying. And great project! Will be using this a lot.