kevinjqliu commented on issue #992:
URL: https://github.com/apache/iceberg-python/issues/992#issuecomment-2266221072
Here's what I ran based on the video, I cannot reproduce the issue using
0.7.0.
In Jupyter,
```
!pip install pyiceberg==0.7.0 --force -q
```
With pyiceberg integration docker running, `docker compose -f
dev/docker-compose-integration.yml up -d`,
```
from pyiceberg.catalog import load_catalog
import pyarrow as pa
from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType
from pyiceberg.expressions import EqualTo
catalog = load_catalog(
"demo",
**{
"type": "rest",
"uri": "http://localhost:8181";,
"s3.endpoint": "http://localhost:9000";,
"s3.access-key-id": "admin",
"s3.secret-access-key": "password",
},
)
catalog.create_namespace_if_not_exists("default")
schema = Schema(
NestedField(field_id=1, name="table_id", field_type=StringType(),
required=True),
NestedField(field_id=2, name="name", field_type=StringType(),
required=True),
NestedField(field_id=3, name="dataset", field_type=StringType(),
required=True),
NestedField(
field_id=4, name="description", field_type=StringType(),
required=False
),
identifier_field_ids=[1],
)
try:
catalog.purge_table("default.some_table")
except:
pass
table = catalog.create_table("default.some_table", schema=schema)
schema = pa.schema([
("table_id", pa.string(), False),
("name", pa.string(), False),
("dataset", pa.string(), False),
("description", pa.string(), True)
])
# Create data arrays
data = [
['id_1', 'id_2', 'id_3', 'id_4'],
['name_1', 'name_2', 'name_3', 'name_4'],
['dataset_1', 'dataset_1', 'dataset_2', 'dataset_3'],
['desc_1', 'desc_2', 'desc_3', 'desc_4']
]
# Create PyArrow Table
table_data = pa.Table.from_arrays(data, schema=schema)
table = catalog.load_table("default.some_table")
table.append(table_data)
from pyiceberg.expressions import BooleanExpression, And, EqualTo, AlwaysTrue
def build_row_filter(equality_key_value: dict) -> BooleanExpression:
if not equality_key_value or len(equality_key_value) == 0:
return AlwaysTrue
else:
expressions: list[BooleanExpression] = []
for key in equality_key_value:
expressions.append(EqualTo(key, equality_key_value[key]))
if len(expressions) == 1:
return expressions[0]
else:
return And(*expressions)
requested_columns = ["*"]
scan = table.scan(
selected_fields=(requested_columns),
row_filter=build_row_filter({
"dataset": "dataset_1",
})
)
scan.to_pandas()
```
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