Erigara commented on code in PR #2029:
URL: https://github.com/apache/iceberg-python/pull/2029#discussion_r2100859083
##########
pyiceberg/expressions/visitors.py:
##########
@@ -894,12 +895,17 @@ def visit_unbound_predicate(self, predicate:
UnboundPredicate[L]) -> BooleanExpr
def visit_bound_predicate(self, predicate: BoundPredicate[L]) ->
BooleanExpression:
file_column_name =
self.file_schema.find_column_name(predicate.term.ref().field.field_id)
+ field_name = predicate.term.ref().field.name
if file_column_name is None:
# In the case of schema evolution, the column might not be present
# in the file schema when reading older data
if isinstance(predicate, BoundIsNull):
return AlwaysTrue()
+ # Projected fields are only available for identity partition fields
+ # Which mean that partition pruning excluded partition field which
evaluates to false
+ elif field_name in self.projected_missing_fields:
+ return AlwaysTrue()
Review Comment:
@Fokko here is a as small example as i could produce (it still quite big in
terms of LoC due to setup).
Basically it works with the following iceberg table:
| col1 | col2 |
| ---- | ---- |
| 1 | 1 |
| 1 | 2 |
| 2 | 1 |
| 2 | 2 |
Where `col1` is used for partitioning and absent from parquet data file.
So physical layout is following:
- `col1 = 1 -> warehouse/1.parquet`
- `col2 = 2 -> warehouse/2.parquet`
Both files `1.parquet` and `2.parquet` are the same with following structure
`{'col2: [1, 2]}`.
```python
#!/usr/bin/env python
import pyarrow as pa
import pyarrow.parquet as pq
from pyiceberg import expressions as expr
from pyiceberg.catalog import load_catalog
from pyiceberg.partitioning import PartitionSpec, PartitionField
from pyiceberg.io.pyarrow import parquet_file_to_data_file
from pyiceberg.transforms import IdentityTransform
from pyiceberg.schema import Schema
from pyiceberg.table.name_mapping import NameMapping, MappedField
from pyiceberg.types import (
NestedField,
LongType,
)
catalog = load_catalog(
"default",
**{
"type": "in-memory",
"warehouse": "warehouse/",
}
)
catalog.create_namespace("default")
# create iceberg table
schema = Schema(
NestedField(field_id=1, name="col1", field_type=LongType()),
NestedField(field_id=2, name="col2", field_type=LongType()),
)
mapping = NameMapping([
MappedField(field_id=1,names=["col1"]),
MappedField(field_id=2,names=["col2"]),
])
table = catalog.create_table_if_not_exists(
"default.table",
schema=schema,
partition_spec=PartitionSpec(
PartitionField(source_id=1, field_id=1001,
transform=IdentityTransform(), name="col1"),
),
properties={"schema.name-mapping.default": mapping.model_dump_json()},
)
# write 2 parquet files: one for col1 partition values 1 and 2
file_1 = "warehouse/1.parquet"
file_2 = "warehouse/2.parquet"
df = pa.Table.from_arrays([[1, 2]], names=["col2"])
pq.write_table(df, file_1)
pq.write_table(df, file_2)
# add_files into iceberg table assign each file partition value
with table.transaction() as tx:
with tx.update_snapshot().fast_append() as fast_append:
data_file_1 = parquet_file_to_data_file(tx._table.io,
tx.table_metadata, file_1)
data_file_1.partition[0] = 1
fast_append.append_data_file(data_file_1)
data_file_2 = parquet_file_to_data_file(tx._table.io,
tx.table_metadata, file_1)
data_file_2.partition[0] = 2
fast_append.append_data_file(data_file_2)
expr_1 = expr.And(expr.EqualTo("col1", 1), expr.EqualTo("col2", 1))
scan_1 = table.scan(row_filter=expr_1)
len_1 = len(scan_1.to_arrow())
assert len_1 == 1
expr_2 = expr.And(expr.EqualTo("col1", 2), expr.EqualTo("col2", 2))
scan_2 = table.scan(row_filter=expr_2)
len_2 = len(scan_2.to_arrow())
assert len_2 == 1
expr_3 = expr.Or(expr_1, expr_2)
scan_3 = table.scan(row_filter=expr_3)
len_3 = len(scan_3.to_arrow())
assert len_3 == len_1 + len_2
```
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