smaheshwar-pltr commented on code in PR #2031:
URL: https://github.com/apache/iceberg-python/pull/2031#discussion_r2102715753
##########
pyiceberg/table/__init__.py:
##########
@@ -1536,10 +1595,181 @@ def __init__(
self.row_filter = _parse_row_filter(row_filter)
self.selected_fields = selected_fields
self.case_sensitive = case_sensitive
- self.snapshot_id = snapshot_id
self.options = options
self.limit = limit
+ @abstractmethod
+ def projection(self) -> Schema: ...
+
+ @abstractmethod
+ def plan_files(self) -> Iterable[ScanTask]: ...
+
+ @abstractmethod
+ def to_arrow(self) -> pa.Table: ...
+
+ @abstractmethod
+ def count(self) -> int: ...
+
+ def select(self: S, *field_names: str) -> S:
+ if "*" in self.selected_fields:
+ return self.update(selected_fields=field_names)
+ return
self.update(selected_fields=tuple(set(self.selected_fields).intersection(set(field_names))))
+
+ def filter(self: S, expr: Union[str, BooleanExpression]) -> S:
+ return self.update(row_filter=And(self.row_filter,
_parse_row_filter(expr)))
+
+ def with_case_sensitive(self: S, case_sensitive: bool = True) -> S:
+ return self.update(case_sensitive=case_sensitive)
+
+ def update(self: S, **overrides: Any) -> S:
+ """Create a copy of this table scan with updated fields."""
+ return type(self)(**{**self.__dict__, **overrides})
+
+ def to_pandas(self, **kwargs: Any) -> pd.DataFrame:
+ """Read a Pandas DataFrame eagerly from this Iceberg table scan.
+
+ Returns:
+ pd.DataFrame: Materialized Pandas Dataframe from the Iceberg table
scan
+ """
+ return self.to_arrow().to_pandas(**kwargs)
+
+ def to_duckdb(self, table_name: str, connection:
Optional[DuckDBPyConnection] = None) -> DuckDBPyConnection:
+ """Shorthand for loading this table scan in DuckDB.
+
+ Returns:
+ DuckDBPyConnection: In memory DuckDB connection with the Iceberg
table scan.
+ """
+ import duckdb
+
+ con = connection or duckdb.connect(database=":memory:")
+ con.register(table_name, self.to_arrow())
+
+ return con
+
+ def to_ray(self) -> ray.data.dataset.Dataset:
+ """Read a Ray Dataset eagerly from this Iceberg table scan.
+
+ Returns:
+ ray.data.dataset.Dataset: Materialized Ray Dataset from the
Iceberg table scan
+ """
+ import ray
+
+ return ray.data.from_arrow(self.to_arrow())
+
+ def to_polars(self) -> pl.DataFrame:
+ """Read a Polars DataFrame from this Iceberg table scan.
+
+ Returns:
+ pl.DataFrame: Materialized Polars Dataframe from the Iceberg table
scan
+ """
+ import polars as pl
+
+ result = pl.from_arrow(self.to_arrow())
+ if isinstance(result, pl.Series):
+ result = result.to_frame()
+
+ return result
+
+
+class FileBasedScan(AbstractTableScan, ABC):
+ """A base class for table scans that plan FileScanTasks."""
+
+ @cached_property
+ def _manifest_group_planner(self) -> ManifestGroupPlanner:
Review Comment:
The motivation for a manifest-based file scan task planner comes from the
Java-side
https://github.com/apache/iceberg/blob/1911c94ea605a3d3f10a1994b046f00a5e9fdceb/core/src/main/java/org/apache/iceberg/BaseIncrementalAppendScan.java#L76-L97
(class
[here](https://github.com/apache/iceberg/blob/main/core/src/main/java/org/apache/iceberg/ManifestGroup.java)).
I cache this property on this class because the class has a
`partition_filters` method that is cached on that class. It's therefore cached
for the `FileBasedScan`'s lifetime, similar to what we had before:
https://github.com/apache/iceberg-python/blob/f47513b8fc972dd26b99b1a9fbb102c712dd07fe/pyiceberg/table/__init__.py#L1696-L1698
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