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fix: Use binary(16) for UUID type to ensure Spark compatibility #2881

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fix: Use binary(16) for UUID type to ensure Spark compatibility #2881

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d84868d
fix: Use binary(16) for UUID type to ensure Spark compatibility
ndrluis Jan 5, 2026
a7bdfca
add TODO comment for PyArrow UUID filtering workaround
ndrluis Jan 12, 2026
d6dd092
Merge branch 'main' into uuid-fix
Fokko Jan 22, 2026
1751f0c
Use session_catalog fixture in UUID write test
ndrluis Jan 22, 2026
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6 changes: 5 additions & 1 deletion pyiceberg/io/pyarrow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -789,7 +789,11 @@ def visit_string(self, _: StringType) -> pa.DataType:
return pa.large_string()

def visit_uuid(self, _: UUIDType) -> pa.DataType:
return pa.uuid()
# TODO: Change to uuid when PyArrow implements filtering for UUID types
# Using binary(16) instead of pa.uuid() because filtering is not
# implemented for UUID types in PyArrow
# (context: https://github.com/apache/iceberg-python/issues/2372)
return pa.binary(16)

def visit_unknown(self, _: UnknownType) -> pa.DataType:
"""Type `UnknownType` can be promoted to any primitive type in V3+ tables per the Iceberg spec."""
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2 changes: 1 addition & 1 deletion tests/integration/test_add_files.py
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Original file line number Diff line number Diff line change
Expand Up @@ -828,7 +828,7 @@ def test_add_files_with_valid_upcast(
pa.field("list", pa.list_(pa.int64()), nullable=False),
pa.field("map", pa.map_(pa.string(), pa.int64()), nullable=False),
pa.field("double", pa.float64(), nullable=True),
pa.field("uuid", pa.uuid(), nullable=True),
pa.field("uuid", pa.binary(16), nullable=True),
)
)
)
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8 changes: 4 additions & 4 deletions tests/integration/test_reads.py
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Original file line number Diff line number Diff line change
Expand Up @@ -610,15 +610,15 @@ def test_partitioned_tables(catalog: Catalog) -> None:
def test_unpartitioned_uuid_table(catalog: Catalog) -> None:
unpartitioned_uuid = catalog.load_table("default.test_uuid_and_fixed_unpartitioned")
arrow_table_eq = unpartitioned_uuid.scan(row_filter="uuid_col == '102cb62f-e6f8-4eb0-9973-d9b012ff0967'").to_arrow()
assert arrow_table_eq["uuid_col"].to_pylist() == [uuid.UUID("102cb62f-e6f8-4eb0-9973-d9b012ff0967")]
assert arrow_table_eq["uuid_col"].to_pylist() == [uuid.UUID("102cb62f-e6f8-4eb0-9973-d9b012ff0967").bytes]

arrow_table_neq = unpartitioned_uuid.scan(
row_filter="uuid_col != '102cb62f-e6f8-4eb0-9973-d9b012ff0967' and uuid_col != '639cccce-c9d2-494a-a78c-278ab234f024'"
).to_arrow()
assert arrow_table_neq["uuid_col"].to_pylist() == [
uuid.UUID("ec33e4b2-a834-4cc3-8c4a-a1d3bfc2f226"),
uuid.UUID("c1b0d8e0-0b0e-4b1e-9b0a-0e0b0d0c0a0b"),
uuid.UUID("923dae77-83d6-47cd-b4b0-d383e64ee57e"),
uuid.UUID("ec33e4b2-a834-4cc3-8c4a-a1d3bfc2f226").bytes,
uuid.UUID("c1b0d8e0-0b0e-4b1e-9b0a-0e0b0d0c0a0b").bytes,
uuid.UUID("923dae77-83d6-47cd-b4b0-d383e64ee57e").bytes,
]


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63 changes: 61 additions & 2 deletions tests/integration/test_writes/test_writes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1500,7 +1500,7 @@ def test_table_write_schema_with_valid_upcast(
pa.field("list", pa.list_(pa.int64()), nullable=False),
pa.field("map", pa.map_(pa.string(), pa.int64()), nullable=False),
pa.field("double", pa.float64(), nullable=True), # can support upcasting float to double
pa.field("uuid", pa.uuid(), nullable=True),
pa.field("uuid", pa.binary(16), nullable=True),
)
)
)
Expand Down Expand Up @@ -2138,7 +2138,7 @@ def test_uuid_partitioning(session_catalog: Catalog, spark: SparkSession, transf
tbl.append(arr_table)

lhs = [r[0] for r in spark.table(identifier).collect()]
rhs = [str(u.as_py()) for u in tbl.scan().to_arrow()["uuid"].combine_chunks()]
rhs = [str(uuid.UUID(bytes=u.as_py())) for u in tbl.scan().to_arrow()["uuid"].combine_chunks()]
assert lhs == rhs


Expand Down Expand Up @@ -2530,3 +2530,62 @@ def test_v3_write_and_read_row_lineage(spark: SparkSession, session_catalog: Cat
assert tbl.metadata.next_row_id == initial_next_row_id + len(test_data), (
"Expected next_row_id to be incremented by the number of added rows"
)


@pytest.mark.integration
def test_write_uuid_in_pyiceberg_and_scan(session_catalog: Catalog, spark: SparkSession) -> None:
"""Test UUID compatibility between PyIceberg and Spark.

UUIDs must be written as binary(16) for Spark compatibility since Java Arrow
metadata differs from Python Arrow metadata for UUID types.
"""
identifier = "default.test_write_uuid_in_pyiceberg_and_scan"

session_catalog.create_namespace("ns")

schema = Schema(NestedField(field_id=1, name="uuid_col", field_type=UUIDType(), required=False))

test_data_with_null = {
"uuid_col": [
uuid.UUID("00000000-0000-0000-0000-000000000000").bytes,
None,
uuid.UUID("11111111-1111-1111-1111-111111111111").bytes,
]
}

try:
session_catalog.drop_table(identifier=identifier)
except NoSuchTableError:
pass

table = _create_table(session_catalog, identifier, {"format-version": "2"}, schema=schema)

arrow_table = pa.table(test_data_with_null, schema=schema.as_arrow())

# Write with pyarrow
table.append(arrow_table)

# Write with pyspark
spark.sql(
f"""
INSERT INTO {identifier} VALUES ("22222222-2222-2222-2222-222222222222")
"""
)
df = spark.table(identifier)

table.refresh()

assert df.count() == 4
assert len(table.scan().to_arrow()) == 4

result = df.where("uuid_col = '00000000-0000-0000-0000-000000000000'")
assert result.count() == 1

result = df.where("uuid_col = '22222222-2222-2222-2222-222222222222'")
assert result.count() == 1

result = table.scan(row_filter=EqualTo("uuid_col", uuid.UUID("00000000-0000-0000-0000-000000000000").bytes)).to_arrow()
assert len(result) == 1

result = table.scan(row_filter=EqualTo("uuid_col", uuid.UUID("22222222-2222-2222-2222-222222222222").bytes)).to_arrow()
assert len(result) == 1