Benjamin-Lemaire commented on issue #1835:
URL:
https://github.com/apache/iceberg-python/issues/1835#issuecomment-2748182671
Sure! If you execute this code below on your own AWS account using Glue and
S3, it creates an Iceberg table using the Glue catalog and adds one row where
the column bar is initially NULL. The second UPSERT operation is supposed to
update that NULL to 7, but it doesn't work because the filter sent to the table
to identify the difference is: 7 != NULL. This condition will never be true
since, in SQL, comparisons involving NULLs do not yield true or false but
instead return unknown.
`import boto3
import pyarrow
import pandas
from pyiceberg.catalog import load_catalog
schema = pyarrow.schema([
('foo', pyarrow.string()),
('bar', pyarrow.int32()), # 'bar' is nullable
('baz', pyarrow.bool_())
])
database = "--> YOUR GLUE DATABASE <--"
table = "mytable"
bucket = "--> YOUR S3 BUCKET <--"
# Initialize S3 client
s3 = boto3.client('s3', region_name='us-east-1')
# Load Catalog
catalog = load_catalog("default", **{"type": "glue"})
table_location = f"s3://{bucket}/{database}/{table}"
# Create table in Glue Catalog
table = catalog.create_table_if_not_exists(
identifier=(database,table),
schema=schema,
location=table_location
)
# Upsert Data with NULL
data_with_null = [
{"foo": "apple", "bar": None, "baz": False}, # Ensuring nullable fields
]
data =
pyarrow.Table.from_pandas(pandas.DataFrame(data_with_null),schema=schema)
table.upsert(data,join_cols=["foo"])
# Upsert Data without NULL
data_without_null = [
{"foo": "apple", "bar": 7, "baz": False},
]
data =
pyarrow.Table.from_pandas(pandas.DataFrame(data_without_null),schema=schema)
table.upsert(data,join_cols=["foo"])
`
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