Matthieusalor opened a new issue, #45062:
URL: https://github.com/apache/arrow/issues/45062
### Describe the bug, including details regarding any error messages,
version, and platform.
When using coerce_timestamps=us, it seems that the parquet metadata are
correclty being set as datetime[us] however, the stored binary arrow schema
seems to still be a datetime[ns].
```python
import pandas as pd
import polars as pl
from pyarrow.parquet import ParquetFile, ParquetWriter
df = pd.DataFrame({"date": [pd.Timestamp.now()]})
df.to_parquet("us.parquet", coerce_timestamps="us",
allow_truncated_timestamps=True, index=False)
pqf = ParquetFile("us.parquet")
writer = ParquetWriter("us_pyarrow.parquet",
schema=pqf.schema.to_arrow_schema())
writer.write_table(pqf.read())
writer.close()
pl.read_parquet("us.parquet") # Gives datetime[ns]
pl.read_parquet("us_pyarrow.parquet") # Gives datetime[us]
ParquetFile("us.parquet").metadata.schema.to_arrow_schema() # gives
datetime[us]
ParquetFile("us_pyarrow.parquet").metadata.schema.to_arrow_schema() # gives
datetime[us]
```
Polars is probably leveraging the binary arrow schema stored in the
metadata.
Running the following prevent the mismatch when using polars and we indeed
get datetime[us]
```python
df.to_parquet("us.parquet", coerce_timestamps="us",
allow_truncated_timestamps=True, index=False, store_schema=False)
```
The issue is that store_schema is not supported in the `write_to_dataset`
function as this parameter is not available in `ParquetFileWriteOptions` but
only in the `write_table` function and `ParquetWriter` class.
Therefore, running the following doesn't work.
```python
df.to_parquet("us.parquet", coerce_timestamps="us",
allow_truncated_timestamps=True, index=False, store_schema=False,
partition_cols=[])
```
I guess the stored binary arrow schema should be stored with datetime[us]
instead of datetime[ns] when using `coerce_timestamps` parameter but the
`store_schema` parameter should also be made available to the
`write_to_dataset` function
### Component(s)
Python
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