[I] Unable to run show command on dataframe on iceberg format data [iceberg]

[via GitHub]

knowxyz opened a new issue, #12802:
URL: https://github.com/apache/iceberg/issues/12802

   from pyspark.sql import SparkSession
   spark1 = SparkSession.builder \
       .appName("IcebergSave") \
       .config("spark.sql.extensions", 
"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
       .config("spark.sql.catalog.abccatalog", 
"org.apache.iceberg.spark.SparkCatalog") \
       .config("spark.sql.catalog.abccatalog.type", "hadoop") \
       .config("spark.jars.packages", 
"org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.4.3") \
       .config("spark.sql.catalog.abccatalog.warehouse", 
f"{mount_point}/mywarehouse").getOrCreate()
   
spark1.conf.set(f"fs.azure.account.key.{storage_account_name}.blob.core.windows.net",
 storage_account_key)
   spark1.conf.set("spark.sql.parquet.enableVectorizedReader", "false")
   spark1.conf.set("spark.sql.iceberg.vectorization.enabled", "false")
   df = < read data from some source>
   savetable ="abccatalog.default.table1"
   # Write to Iceberg table with partitioning
   df.createOrReplaceTempView("tempview1")
   
   # Query the temporary view using SQL
   resultdf1 = spark1.sql("SELECT * FROM tempview1")
   # only iceberg here
   resultdf1.write.format("iceberg").mode("overwrite").saveAsTable(savetable)
   dfread = 
spark.read.format("org.apache.iceberg.spark.source.IcebergSource").option("header",
 "true").load(savetable)
   show = dfread.show()
   print(show)
   
![Image](https://github.com/user-attachments/assets/cd606a49-89cf-4acc-b74e-e10f6b66ca56)
   


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