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fix: schema check of iceberg logical types #856

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fix: schema check of iceberg logical types #856

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751d87b
fix: schema check of iceberg logical types
raphaelauv Jun 25, 2024
d1c48bf
review 1
raphaelauv Jun 26, 2024
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20 changes: 18 additions & 2 deletions pyiceberg/table/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,12 +128,14 @@
TableVersion,
)
from pyiceberg.types import (
FixedType,
IcebergType,
ListType,
MapType,
NestedField,
PrimitiveType,
StructType,
UUIDType,
transform_dict_value_to_str,
)
from pyiceberg.utils.concurrent import ExecutorFactory
Expand All @@ -153,7 +155,21 @@
ALWAYS_TRUE = AlwaysTrue()
TABLE_ROOT_ID = -1

_JAVA_LONG_MAX = 9223372036854775807

def _apply_logical_conversion(table_schema: Schema, task_schema: Schema) -> Schema:
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We can defer this to a later PR, but just want to put it out here.

In PyIceberg we have a little bit an obsession with the visitor pattern. Using the SchemaWithPartnerVisitor you can traverse two schema's at once. An example is the ArrowProjectionVisitor and can be found in pyarrow.py. The ArrowAccessor does the lookups by ID, but I think here name makes more sense since we don't have the IDs (yet).

all_fields = []
for task_field in task_schema.fields:
table_field = table_schema.find_field(task_field.name)
new_type = None
if isinstance(table_field.field_type, UUIDType):
if isinstance(task_field.field_type, FixedType) and len(task_field.field_type) == 16:
new_type = UUIDType()
if new_type is not None:
all_fields.append(NestedField(task_field.field_id, task_field.name, new_type, task_field.required))
else:
all_fields.append(task_field)

return Schema(*all_fields)


def _check_schema_compatible(table_schema: Schema, other_schema: "pa.Schema") -> None:
Expand All @@ -176,7 +192,7 @@ def _check_schema_compatible(table_schema: Schema, other_schema: "pa.Schema") ->
raise ValueError(
f"PyArrow table contains more columns: {', '.join(sorted(additional_names))}. Update the schema first (hint, use union_by_name)."
) from e

task_schema = _apply_logical_conversion(table_schema, task_schema)
if table_schema.as_struct() != task_schema.as_struct():
from rich.console import Console
from rich.table import Table as RichTable
Expand Down