kevinjqliu commented on code in PR #1043:
URL: https://github.com/apache/iceberg-python/pull/1043#discussion_r1717682714


##########
pyiceberg/io/pyarrow.py:
##########
@@ -1304,6 +1305,195 @@ def _read_all_delete_files(fs: FileSystem, tasks: 
Iterable[FileScanTask]) -> Dic
     return deletes_per_file
 
 
+def _fs_from_file_path(file_path: str, io: FileIO) -> FileSystem:
+    scheme, netloc, _ = _parse_location(file_path)
+    if isinstance(io, PyArrowFileIO):
+        return io.fs_by_scheme(scheme, netloc)
+    else:
+        try:
+            from pyiceberg.io.fsspec import FsspecFileIO
+
+            if isinstance(io, FsspecFileIO):
+                from pyarrow.fs import PyFileSystem
+
+                return PyFileSystem(FSSpecHandler(io.get_fs(scheme)))
+            else:
+                raise ValueError(f"Expected PyArrowFileIO or FsspecFileIO, 
got: {io}")
+        except ModuleNotFoundError as e:
+            # When FsSpec is not installed
+            raise ValueError(f"Expected PyArrowFileIO or FsspecFileIO, got: 
{io}") from e
+
+
+class PyArrowProjector:
+    _table_metadata: TableMetadata
+    _io: FileIO
+    _fs: FileSystem
+    _projected_schema: Schema
+    _bound_row_filter: BooleanExpression
+    _case_sensitive: bool
+    _limit: Optional[int]
+    """Projects an Iceberg Table to a PyArrow construct.
+
+    Attributes:
+        _table_metadata: Current table metadata of the Iceberg table
+        _io: PyIceberg FileIO implementation from which to fetch the io 
properties
+        _fs: PyArrow FileSystem to use to read the files
+        _projected_schema: Iceberg Schema to project onto the data files
+        _bound_row_filter: Schema bound row expression to filter the data with
+        _case_sensitive: Case sensitivity when looking up column names
+        _limit: Limit the number of records.
+    """
+
+    def __init__(
+        self,
+        table_metadata: TableMetadata,
+        io: FileIO,
+        projected_schema: Schema,
+        row_filter: BooleanExpression,
+        case_sensitive: bool = True,
+        limit: Optional[int] = None,
+    ) -> None:
+        self._table_metadata = table_metadata
+        self._io = io
+        self._fs = _fs_from_file_path(table_metadata.location, io)  # TODO: 
use different FileSystem per file
+        self._projected_schema = projected_schema
+        self._bound_row_filter = bind(table_metadata.schema(), row_filter, 
case_sensitive=case_sensitive)
+        self._case_sensitive = case_sensitive
+        self._limit = limit
+
+    @property
+    def _use_large_types(self) -> bool:
+        """Whether to represent data as large arrow types.
+
+        Defaults to True.
+        """
+        return property_as_bool(self._io.properties, 
PYARROW_USE_LARGE_TYPES_ON_READ, True)
+
+    @property
+    def _projected_field_ids(self) -> Set[int]:
+        """Set of field IDs that should be projected from the data files."""
+        return {
+            id
+            for id in self._projected_schema.field_ids
+            if not isinstance(self._projected_schema.find_type(id), (MapType, 
ListType))
+        }.union(extract_field_ids(self._bound_row_filter))
+
+    def project_table(self, tasks: Iterable[FileScanTask]) -> pa.Table:
+        """Project the Iceberg table to a pa.Table.
+
+        Returns a pa.Table with data from the Iceberg table by resolving the
+        right columns that match the current table schema. Only data that
+        matches the provided row_filter expression is returned.
+
+        Args:
+            tasks: FileScanTasks representing the data files and delete files 
to read from.
+
+        Returns:
+            A PyArrow table. Result is capped at the limit, if specified.
+
+        Raises:
+            ResolveError: When a required field cannot be found in the file
+            ValueError: When a field type in the file cannot be projected to 
the schema type
+        """
+        deletes_per_file = _read_all_delete_files(self._fs, tasks)
+        executor = ExecutorFactory.get_or_create()
+
+        def _project_table_from_scan_task(task: FileScanTask) -> pa.Table:
+            batches = 
list(self._project_batches_from_scan_tasks_and_deletes([task], 
deletes_per_file))
+            if len(batches) > 0:
+                return pa.Table.from_batches(batches)
+            else:
+                return None
+
+        futures = [
+            executor.submit(
+                _project_table_from_scan_task,
+                task,
+            )
+            for task in tasks
+        ]
+        total_row_count = 0
+        # for consistent ordering, we need to maintain future order
+        futures_index = {f: i for i, f in enumerate(futures)}
+        completed_futures: SortedList[Future[pa.Table]] = 
SortedList(iterable=[], key=lambda f: futures_index[f])
+        for future in concurrent.futures.as_completed(futures):
+            completed_futures.add(future)
+            if table_result := future.result():
+                total_row_count += len(table_result)
+            # stop early if limit is satisfied
+            if self._limit is not None and total_row_count >= self._limit:
+                break
+
+        # by now, we've either completed all tasks or satisfied the limit
+        if self._limit is not None:
+            _ = [f.cancel() for f in futures if not f.done()]
+
+        tables = [f.result() for f in completed_futures if f.result()]
+
+        if len(tables) < 1:
+            return pa.Table.from_batches([], 
schema=schema_to_pyarrow(self._projected_schema, include_field_ids=False))
+
+        result = pa.concat_tables(tables, promote_options="permissive")

Review Comment:
   
[pyarrow.concat_tables](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)
 docs
   
[pyarrow.Table.from_batches](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.from_batches)
 docs
   
   Not sure if these operations are 0 copy. 
   
   @Fokko would you know about the performance characteristics of these two 
functions? 



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