jkleinkauff opened a new issue, #1032:
URL: https://github.com/apache/iceberg-python/issues/1032
### Question
Hey, thanks for this very convenient library.
This is not a bug, just want to better understand something.
I have a question regarding the performance - ie time to query the table (?)
- for such methods.
```python
if __name__ == "__main__":
catalog = SqlCatalog(
"default",
**{
"uri":
f"postgresql+psycopg2://postgres:Password1@localhost/postgres",
},
)
table = catalog.load_table("bronze.curitiba_starts_june")
df = table.scan(limit=100)
pa_table = df.to_arrow()
````
The code above will run ok. My question is regarding the last command,
to_arrow() transformation takes around 50s (+-) to execute. I believe this is
mostly because of the network itself?
The execution time will stay roughly the same with different row limit (10,
100, 1000).
Querying the same table in motherduck - using iceberg_scan - is faster:
<img width="836" alt="image"
src="https://github.com/user-attachments/assets/21a05d45-ebcd-4323-ba31-2689d2d12fe7";>
When running the same query locally - without motherduck - the execution
time will be similar to what pyiceberg takes, actually it will be a little bit
slower. That's why I think this is mostly like a network "issue". Can you help
be understand what's happening? Thank you!
#### Table Data
The table has two parquet files (110mb, 127mb)
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