jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912416306
@Fokko Thank you! These 2 points of supporting hidden partitioning and
extracting metrics efficiently during writing are very insightful!
For using pyarrow.dataset.write_dataset(), its behavior removed the
partition columns in the written-out parquet files. **I think this is the deal
breaker for using write_dataset().** So either we extend
pyarrow.dataset.write_dataset() or fall back to the arrow API direction.
Some findings during chasing a solution of dataset.write_dataset():
1. pyarrow.dataset.partitioning() only supports static values in the column,
so we might need on Iceberg to add a transformed column. (this column will be
dropped into the directory when write_dataset() uses hive partitioning)
2. write_dataset() takes in a customized callable to collect file paths and
file metadata when files are written and when we create DataFile object this
metadata could be reused:
```
visited_paths = []
metadata_list = []
def file_visitor(written_file):
visited_paths.append(written_file.path)
metadata_list .append(written_file.metadata)
```
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