Fokko commented on code in PR #8549:
URL: https://github.com/apache/iceberg/pull/8549#discussion_r1322658845
##########
python/pyiceberg/io/pyarrow.py:
##########
@@ -290,7 +290,7 @@ def to_input_file(self) -> PyArrowFile:
class PyArrowFileIO(FileIO):
def __init__(self, properties: Properties = EMPTY_DICT):
- self.get_fs: Callable[[str], FileSystem] = lru_cache(self._get_fs)
+ self.get_fs: Callable[[str], FileSystem] =
lru_cache(self._initialize_fs)
Review Comment:
I'm almost inclined to remove the `lru_cache` in general and make a `fs`
property. We could use a single FileIO to connect to both S3 and GCS, but I'm
not sure if that happens in practice, or if we should allow it. A FileIO is
bound to a table, so this would mean that a table is distributed across
different object stores 🤔
##########
python/pyiceberg/io/pyarrow.py:
##########
@@ -290,7 +290,7 @@ def to_input_file(self) -> PyArrowFile:
class PyArrowFileIO(FileIO):
def __init__(self, properties: Properties = EMPTY_DICT):
- self.get_fs: Callable[[str], FileSystem] = lru_cache(self._get_fs)
+ self.get_fs: Callable[[str], FileSystem] =
lru_cache(self._initialize_fs)
Review Comment:
I'm almost inclined to remove the `lru_cache` in general and make a `fs`
property. We could use a single FileIO to connect to both S3 and GCS, but I'm
not sure if that happens in practice, or if we should allow it. A FileIO is
bound to a table, so this would mean that a table is distributed across
different object stores 🤔
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