sagarlakshmipathy opened a new issue, #545:
URL: https://github.com/apache/incubator-xtable/issues/545

   ### Search before asking
   
   - [X] I had searched in the 
[issues](https://github.com/apache/incubator-xtable/issues?q=is%3Aissue) and 
found no similar issues.
   
   
   ### Please describe the bug 🐞
   
   I ran into an issue while using Snowflake's polaris catalog. Documenting 
here.
   
   ```
   java -cp 
/Users/sagarl/Downloads/iceberg-spark-runtime-3.4_2.12-1.4.1.jar:/Users/sagarl/latest/incubator-xtable/xtable-utilities/target/xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:/Users/sagarl/Downloads/bundle-2.20.160.jar:/Users/sagarl/Downloads/url-connection-client-2.20.160.jar
 org.apache.xtable.utilities.RunSync --datasetConfig config.yaml 
--icebergCatalogConfig catalog.yaml
   ```
   ### Error
   ```
   2024-09-20 22:55:30 INFO  org.apache.iceberg.RemoveSnapshots:328 - Cleaning 
up expired files (local, incremental)
   2024-09-20 22:55:31 ERROR org.apache.xtable.spi.sync.TableFormatSync:78 - 
Failed to sync snapshot
   org.apache.iceberg.exceptions.ForbiddenException: Forbidden: Delegate access 
to table with user-specified write location is temporarily not supported.
        at 
org.apache.iceberg.rest.ErrorHandlers$DefaultErrorHandler.accept(ErrorHandlers.java:157)
 ~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.rest.ErrorHandlers$CommitErrorHandler.accept(ErrorHandlers.java:88)
 ~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.rest.ErrorHandlers$CommitErrorHandler.accept(ErrorHandlers.java:71)
 ~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.rest.HTTPClient.throwFailure(HTTPClient.java:183) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.rest.HTTPClient.execute(HTTPClient.java:292) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.rest.HTTPClient.execute(HTTPClient.java:226) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.rest.HTTPClient.post(HTTPClient.java:337) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.rest.RESTClient.post(RESTClient.java:112) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.rest.RESTTableOperations.commit(RESTTableOperations.java:152)
 ~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.BaseTransaction.lambda$commitSimpleTransaction$3(BaseTransaction.java:416)
 ~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.util.Tasks$Builder.runTaskWithRetry(Tasks.java:413) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.util.Tasks$Builder.runSingleThreaded(Tasks.java:219) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.util.Tasks$Builder.run(Tasks.java:203) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at org.apache.iceberg.util.Tasks$Builder.run(Tasks.java:196) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.BaseTransaction.commitSimpleTransaction(BaseTransaction.java:412)
 ~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.iceberg.BaseTransaction.commitTransaction(BaseTransaction.java:307) 
~[iceberg-spark-runtime-3.4_2.12-1.4.1.jar:?]
        at 
org.apache.xtable.iceberg.IcebergConversionTarget.completeSync(IcebergConversionTarget.java:221)
 ~[xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:0.2.0-SNAPSHOT]
        at 
org.apache.xtable.spi.sync.TableFormatSync.getSyncResult(TableFormatSync.java:165)
 ~[xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:0.2.0-SNAPSHOT]
        at 
org.apache.xtable.spi.sync.TableFormatSync.syncSnapshot(TableFormatSync.java:70)
 [xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:0.2.0-SNAPSHOT]
        at 
org.apache.xtable.conversion.ConversionController.syncSnapshot(ConversionController.java:182)
 [xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:0.2.0-SNAPSHOT]
        at 
org.apache.xtable.conversion.ConversionController.sync(ConversionController.java:118)
 [xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:0.2.0-SNAPSHOT]
        at org.apache.xtable.utilities.RunSync.main(RunSync.java:191) 
[xtable-utilities-0.2.0-SNAPSHOT-bundled.jar:0.2.0-SNAPSHOT]
   ```
   The sync did not completely happen at this point meaning the table gets 
created in target format in the catalog, but doesn't have data in it.
   
   
   ### config.yaml
   ```
   sourceFormat: HUDI
   targetFormats:
     - ICEBERG
   datasets:
     -
       tableBasePath: s3://xtable-demo-bucket/spark_demo/people
       tableName: people
       partitionSpec: city:VALUE
       namespace: spark_demo
   ```
   
   ### catalog.yaml
   ```
   catalogImpl: org.apache.iceberg.rest.RESTCatalog
   catalogName: iceberg_catalog
   catalogOptions:
     io-impl: org.apache.iceberg.aws.s3.S3FileIO
     warehouse: iceberg_catalog
     uri: https://<polaris-id>.snowflakecomputing.com/polaris/api/catalog
     credential: <client-id>:<client-secret>
     header.X-Iceberg-Access-Delegation: vended-credentials
     scope: PRINCIPAL_ROLE:ALL
     client.region: us-west-2
   ```
   
   I could access the table using spark-shell using command, so the table is 
very much created. I could also create a table directly from the spark shell if 
needed. So I can say spark writes work directly from outside SF, there is 
something wrong with the catalog sync for an existing table.
   
   ```
   pyspark --packages 
org.apache.iceberg:iceberg-spark-runtime-3.4_2.12:1.4.1,software.amazon.awssdk:bundle:2.20.160,software.amazon.awssdk:url-connection-client:2.20.160
 \
   --conf 
spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
 \
   --conf spark.sql.defaultCatalog=polaris \
   --conf spark.sql.catalog.polaris=org.apache.iceberg.spark.SparkCatalog \
   --conf spark.sql.catalog.polaris.type=rest \
   --conf 
spark.sql.catalog.polaris.header.X-Iceberg-Access-Delegation=vended-credentials 
\
   --conf 
spark.sql.catalog.polaris.uri=https://<polaris-id>.snowflakecomputing.com/polaris/api/catalog
 \
   --conf spark.sql.catalog.polaris.credential=<client-id>:<client-secret> \
   --conf spark.sql.catalog.polaris.warehouse=iceberg_catalog \
   --conf spark.sql.catalog.polaris.scope=PRINCIPAL_ROLE:my_spark_admin_role \
   --conf spark.sql.catalog.polaris.client.region=us-west-2
   ```
   
   ```
   >>> spark.sql("USE spark_demo")
   DataFrame[]
   >>> spark.sql("SHOW TABLES").show()
   +----------+----------+-----------+                                          
   
   | namespace| tableName|isTemporary|
   +----------+----------+-----------+
   |spark_demo|    people|      false|
   |spark_demo|test_table|      false|
   +----------+----------+-----------+
   
   >>> spark.sql("SELECT * FROM people").show()
   
+-------------------+--------------------+------------------+----------------------+-----------------+---+----+---+----+---------+
   
|_hoodie_commit_time|_hoodie_commit_seqno|_hoodie_record_key|_hoodie_partition_path|_hoodie_file_name|
 id|name|age|city|create_ts|
   
+-------------------+--------------------+------------------+----------------------+-----------------+---+----+---+----+---------+
   
+-------------------+--------------------+------------------+----------------------+-----------------+---+----+---+----+---------+
   
   >>> 
   
   ### directly creating table using spark
   ```
   spark.sql("USE spark_demo")
   
   from pyspark.sql import SparkSession
   from pyspark.sql.types import StructType, StructField, IntegerType, 
StringType
   
   
   # Define the schema for the records
   schema = StructType([
      StructField("id", IntegerType(), True),
      StructField("name", StringType(), True),
      StructField("age", IntegerType(), True),
      StructField("city", StringType(), True),
      StructField("create_ts", StringType(), True)
   ])
   
   # Create a DataFrame with the records
   records = [
      (1, 'John', 25, 'NYC', '2023-09-28 00:00:00'),
      (2, 'Emily', 30, 'SFO', '2023-09-28 00:00:00'),
      (3, 'Michael', 35, 'ORD', '2023-09-28 00:00:00'),
      (4, 'Andrew', 40, 'NYC', '2023-10-28 00:00:00'),
      (5, 'Bob', 28, 'SEA', '2023-09-23 00:00:00'),
      (6, 'Charlie', 31, 'DFW', '2023-08-29 00:00:00')
   ]
   
   df = spark.createDataFrame(records, schema)
   
   spark.sql("""
   CREATE TABLE people_via_spark (
       id INT,
       name STRING,
       age INT,
       city STRING,
       create_ts STRING
   ) USING iceberg
   """)
   
   df.writeTo("people_via_spark").append()
   ```
   
   ```
   >>> spark.sql("SELECT * FROM people_via_spark").show()                       
   
   +---+-------+---+----+-------------------+                                   
   
   | id|   name|age|city|          create_ts|
   +---+-------+---+----+-------------------+
   |  1|   John| 25| NYC|2023-09-28 00:00:00|
   |  2|  Emily| 30| SFO|2023-09-28 00:00:00|
   |  3|Michael| 35| ORD|2023-09-28 00:00:00|
   |  4| Andrew| 40| NYC|2023-10-28 00:00:00|
   |  5|    Bob| 28| SEA|2023-09-23 00:00:00|
   |  6|Charlie| 31| DFW|2023-08-29 00:00:00|
   +---+-------+---+----+-------------------+
   ```
   
   ### Are you willing to submit PR?
   
   - [ ] I am willing to submit a PR!
   - [ ] I am willing to submit a PR but need help getting started!
   
   ### Code of Conduct
   
   - [X] I agree to follow this project's [Code of 
Conduct](https://www.apache.org/foundation/policies/conduct)
   


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