rdblue commented on code in PR #6437:
URL: https://github.com/apache/iceberg/pull/6437#discussion_r1050287724
##########
python/pyiceberg/io/pyarrow.py:
##########
@@ -437,3 +457,103 @@ def visit_or(self, left_result: pc.Expression,
right_result: pc.Expression) -> p
def expression_to_pyarrow(expr: BooleanExpression) -> pc.Expression:
return boolean_expression_visit(expr, _ConvertToArrowExpression())
+
+
+class _ConstructFinalSchema(SchemaVisitor[pa.ChunkedArray]):
+ file_schema: Schema
+ table: pa.Table
+
+ def __init__(self, file_schema: Schema, table: pa.Table):
+ self.file_schema = file_schema
+ self.table = table
+
+ def schema(self, schema: Schema, struct_result: List[pa.ChunkedArray]) ->
pa.Table:
+ return pa.table(struct_result, schema=schema_to_pyarrow(schema))
+
+ def struct(self, _: StructType, field_results: List[pa.ChunkedArray]) ->
List[pa.ChunkedArray]:
+ return field_results
+
+ def field(self, field: NestedField, _: pa.ChunkedArray) -> pa.ChunkedArray:
+ column_name = self.file_schema.find_column_name(field.field_id)
+
+ if column_name:
+ column_idx = self.table.schema.get_field_index(column_name)
+ else:
+ column_idx = -1
+
+ expected_arrow_type = schema_to_pyarrow(field.field_type)
+
+ # The idx will be -1 when the column can't be found
+ if column_idx >= 0:
+ column_field: pa.Field = self.table.schema[column_idx]
+ column_arrow_type: pa.DataType = column_field.type
+ column_data: pa.ChunkedArray = self.table[column_idx]
+
+ # In case of schema evolution
+ if column_arrow_type != expected_arrow_type:
+ column_data = column_data.cast(expected_arrow_type)
+ else:
+ import numpy as np
+
+ column_data = pa.array(np.full(shape=len(self.table),
fill_value=None), type=expected_arrow_type)
+ return column_data
+
+ def list(self, _: ListType, element_result: pa.ChunkedArray) ->
pa.ChunkedArray:
+ pass
+
+ def map(self, _: MapType, key_result: pa.ChunkedArray, value_result:
pa.ChunkedArray) -> pa.DataType:
+ pass
+
+ def primitive(self, primitive: PrimitiveType) -> pa.ChunkedArray:
+ pass
+
+
+def to_final_schema(final_schema: Schema, schema: Schema, table: pa.Table) ->
pa.Table:
+ return visit(final_schema, _ConstructFinalSchema(schema, table))
+
+
+def project_table(
+ files: Iterable["FileScanTask"], table: "Table", row_filter:
BooleanExpression, projected_schema: Schema, case_sensitive: bool
+) -> pa.Table:
+ if isinstance(table.io, PyArrowFileIO):
+ scheme, path = PyArrowFileIO.parse_location(table.location())
+ fs = table.io.get_fs(scheme)
+ else:
+ raise ValueError(f"Expected PyArrowFileIO, got: {table.io}")
+
+ projected_field_ids = projected_schema.field_ids
+
+ tables = []
+ for task in files:
Review Comment:
I think the inner part of this loop should be a Parquet method that we
provide, so that the caller can read progressively or read parts in parallel
tasks. This is a great start for single process, though.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: issues-unsubscr...@iceberg.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@iceberg.apache.org
For additional commands, e-mail: issues-h...@iceberg.apache.org
