pietro-agilelab commented on issue #8377:
URL: https://github.com/apache/iceberg/issues/8377#issuecomment-1734973961
Hi @sebastien-namy-cbp, after running some tests I came to the following
conclusion:
### Hadoop catalog
If you are using a catalog of type "hadoop", then you can't use the
`tableProperty("location", "s3://...")` property of the DataFrameWriterV2 API,
otherwise you'll get [an
error](https://github.com/apache/iceberg/issues/8377#issue-1862786604).
Similarly, creating a table with
`df.write.format("iceberg").mode("overwrite").save("/path/to/my/table")` raises
[an
error](https://github.com/apache/iceberg/issues/8377#issuecomment-1691790773)
too.
The only workaround I found is by using the DataFrameWriterV2 API: assuming
that you configured an Iceberg catalog called "local" with a warehouse path
`s3://my-bucket/warehouse`, then writing
```python
df.writeTo("local.db.my.table").using("iceberg").createOrReplace()
```
will create a table at location `s3://my-bucket/warehouse/local/db/my/table`.
### Hive catalog
If instead you are using a catalog of type "hive", then you can follow the
example of the
[documentation](https://iceberg.apache.org/docs/latest/spark-writes/#creating-tables),
thus writing:
```python
# creates table `iceberg.db.table` at location
`s3://my-bucket/path/to/location`
df.writeTo("iceberg.db.table")
.tableProperty("location", "s3://my-bucket/path/to/location")
.createOrReplace()
```
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