davidrimprobable commented on issue #4363:
URL: https://github.com/apache/iceberg/issues/4363#issuecomment-1113519755
I assume based on the above that @krisvaz was able to get this to work on a
newer Spark - but are there any pointers for when you're already running a
Spark without the bug shown above?
I'm on Spark 3.2.0 and using
```python
df = session.createDataFrame([{'partition_a': 'A', 'partition_b': 'B',
'data': 'somestuff'}])
df.writeTo("my_catalog.db.test").using("iceberg").partitionedBy("partition_a",
"partition_b").create()
session.sql("describe my_catalog.db.test").show()
```
I receive
```
+---------------+---------+-------+
| col_name|data_type|comment|
+---------------+---------+-------+
| data| string| |
| partition_a| string| |
| partition_b| string| |
| | | |
| # Partitioning| | |
|Not partitioned| | |
+---------------+---------+-------+
```
This is S3A with catalog-impl of `org.apache.iceberg.jdbc.JdbcCatalog` to a
postgres database
Is this expected for this combination of options?
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