Fokko commented on code in PR #1427:
URL: https://github.com/apache/iceberg-python/pull/1427#discussion_r1886483368
##########
pyiceberg/table/__init__.py:
##########
@@ -1423,6 +1451,66 @@ def plan_files(self) -> Iterable[FileScanTask]:
for data_entry in data_entries
]
+ def _target_split_size(self) -> int:
+ table_value = property_as_int(
+ self.table_metadata.properties, TableProperties.READ_SPLIT_SIZE,
TableProperties.READ_SPLIT_SIZE_DEFAULT
+ )
+ return property_as_int(self.options, TableProperties.READ_SPLIT_SIZE,
table_value) # type: ignore
+
+ def _loop_back(self) -> int:
+ table_value = property_as_int(
+ self.table_metadata.properties,
TableProperties.READ_SPLIT_LOOKBACK, TableProperties.READ_SPLIT_LOOKBACK_DEFAULT
+ )
+ return property_as_int(self.options,
TableProperties.READ_SPLIT_LOOKBACK, table_value) # type: ignore
+
+ def _split_open_file_cost(self) -> int:
+ table_value = property_as_int(
+ self.table_metadata.properties,
+ TableProperties.READ_SPLIT_OPEN_FILE_COST,
+ TableProperties.READ_SPLIT_OPEN_FILE_COST_DEFAULT,
+ )
+ return property_as_int(self.options,
TableProperties.READ_SPLIT_OPEN_FILE_COST, table_value) # type: ignore
+
+ def plan_task(self) -> Iterable[CombinedFileScanTask]:
Review Comment:
I have the same concern as @corleyma. For example, Java splits on row-groups
to make full use of the parallelism of Spark, Trino, Hive etc. On a local
machine, it makes more sense to just plow through the file itself, preferably
using a native reader like PyArrow or Iceberg-Rust in the future.
There are a lot of details around this API, for example, a task might point
to a row-group that doesn't contain any relevant information, and we don't know
until we open the file itself.
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