gmweaver commented on issue #19:
URL: https://github.com/apache/iceberg-python/issues/19#issuecomment-2643719088
I ran into the same issue as @cee-shubham and my initial guess is that S3
bucket authentication is not using the S3 keys configured on the server and is
instead trying to use local S3 creds/keys?
I confirmed that the S3 keys I have on my Nessie server have access to the
bucket by running a similar append operation via Spark.
Example code with output:
```
>>> catalog = load_catalog(
... "nessie",
... **{
... "uri": "http://nessie:19120/iceberg/main";,
... },
... )
>>>
>>> print(catalog.list_namespaces())
[('demo',)]
>>> schema = Schema(
... NestedField(1, "id", IntegerType(), required=True),
... NestedField(2, "name", StringType(), required=False),
... )
>>>
>>> catalog.create_table("demo.test_pyiceberg_table", schema)
test_pyiceberg_table(
1: id: required int,
2: name: optional string
),
partition by: [],
sort order: [],
snapshot: null
>>> table = catalog.load_table("demo.test_pyiceberg_table")
>>> table.scan().to_pandas()
Empty DataFrame
Columns: [id, name]
Index: []
>>> data = pa.Table.from_pydict(
... {
... "id": np.array([1, 2, 3], dtype="int32"),
... "name": ["Alice", "Bob", "Charlie"],
... },
... schema=schema.as_arrow(),
... )
>>>
>>> table.append(data)
Traceback (most recent call last):
File
"/Users/garrett.weaver/Library/Caches/pypoetry/virtualenvs/testing-py3.12/lib/python3.12/site-packages/s3fs/core.py",
line 114, in _error_wrapper
return await func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/Users/garrett.weaver/Library/Caches/pypoetry/virtualenvs/testing-py3.12/lib/python3.12/site-packages/aiobotocore/client.py",
line 412, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.exceptions.ClientError: An error occurred (403) when calling the
PutObject operation: Forbidden
```
Similar code on Spark works:
```
SPARK_PACKAGES = [
"org.apache.iceberg:iceberg-spark-runtime-3.4_2.12:1.7.1",
"org.projectnessie.nessie-integrations:nessie-spark-extensions-3.4_2.12:0.99.0",
"software.amazon.awssdk:bundle:2.20.126",
"software.amazon.awssdk:url-connection-client:2.20.126",
]
SPARK_SQL_EXTENSIONS = [
"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions",
"org.projectnessie.spark.extensions.NessieSparkSessionExtensions",
]
SPARK_CONFIG = {
"spark.jars.packages": ",".join(SPARK_PACKAGES),
"spark.sql.extensions": ",".join(SPARK_SQL_EXTENSIONS),
"spark.sql.catalog.nessie": "org.apache.iceberg.spark.SparkCatalog",
"spark.sql.catalog.nessie.uri": "http://nessie:19120/iceberg/main";,
"spark.sql.catalog.nessie.type": "rest",
}
spark = SparkSession.builder.config(map=SPARK_CONFIG).getOrCreate()
>>> spark.sql(
... """
... CREATE TABLE IF NOT EXISTS nessie.demo.test_spark_table (
... id INTEGER,
... name STRING
... ) USING iceberg
... """
... )
DataFrame[]
>>>
>>> spark.sql(
... """
... INSERT INTO nessie.demo.test_spark_table VALUES
... (1, 'Alice'),
... (2, 'Bob')
... """
... ).show()
++
||
++
++
>>> spark.read.format("iceberg").load("nessie.demo.test_spark_table").show()
+---+-----+
| id| name|
+---+-----+
| 1|Alice|
| 2| Bob|
+---+-----+
```
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