knowxyz commented on issue #12802:
URL: https://github.com/apache/iceberg/issues/12802#issuecomment-2806998151
Code i am trying
# Databricks notebook source
#Mianly used variables
from pyspark.sql import SparkSession
# Azure Storage Account Details
container_name = "csv"
mount_point = "/mnt/icebergdata8" # Choose a mount point
mount_point= "dbfs:/FileStore/tables"
storage_account_name = "abc"
storage_account_key = "zZHI+AStlGv+3w=="
file_name = "data.csv"
catalog = "catalog5"
store = "default"
table = "sampleoutput24"
warehouseName ="warehousenew9"
icebergtable_name = catalog + "." + store + "." + table
tempview ="tempviewice12"
# Initialize Spark Session (if not already initialized)
spark = SparkSession.builder \
.appName("IcebergSave") \
.config("spark.sql.extensions",
"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
.config("spark.sql.catalog.catalog5",
"org.apache.iceberg.spark.SparkCatalog") \
.config("spark.sql.catalog.catalog5.type", "hadoop") \
.config("spark.jars.packages",
"org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.7.1,
org.apache.iceberg:iceberg-azure-bundle:1.7.1") \
.config("spark.sql.catalog.catalog5.warehouse",
f"{mount_point}/{warehouseName}").getOrCreate()
spark.conf.set(f"fs.azure.account.key.{storage_account_name}.blob.core.windows.net",
storage_account_key)
spark.conf.set("spark.sql.parquet.enableVectorizedReader", "false")
spark.conf.set("spark.sql.iceberg.vectorization.enabled", "false")
spark.conf.set("spark.databricks.io.cache.enabled","false")
spark.conf.set("spark.sql.files.useFsCache", "false")
spark.conf.set("spark.sql.execution.photon.enabled", "false")
spark.conf.set("spark.sql.catalog.catalog5.io-impl",
"org.apache.iceberg.azure.AzureBlobStorageIO")
spark.conf.set("spark.sql.catalog.catalog5.io-impl",
"org.apache.iceberg.azure.adlsv2.ADLSFileIO")
file_path =
f"wasbs://{container_name}@{storage_account_name}.blob.core.windows.net/{file_name}"
try:
dbutils.fs.unmount(mount_point)
print(f"{mount_point} unmounted successfully.")
except:
print("{mount_point} was not mounted or unmounting failed.")
try:
#dbutils.fs.mount(
#
source=f"wasbs://{container_name}@{storage_account_name}.blob.core.windows.net/",
# mount_point=mount_point,
#
extra_configs={f"fs.azure.account.key.{storage_account_name}.blob.core.windows.net":
storage_account_key}
#)
print(f"Mounted {container_name} to {mount_point} using Access Key.")
except Exception as e:
print(f"Error mounting using Access Key: ")
print(f"fs.azure.account.key ready to excute")
spark.conf.set(f"fs.azure.account.key.{storage_account_name}.blob.core.windows.net",
storage_account_key)
print(f"fs.azure.account.key Excuted")
# COMMAND ----------
sc = spark.sparkContext
print(spark.conf.get("spark.jars.packages"))
spark.conf.get("spark.sql.catalog.catalog5.warehouse")
# COMMAND ----------
#spark.conf.set("spark.sql.parquet.enableVectorizedReader", "false")
# Create the Spark configuration
# Read the CSV file into a Spark DataFrame
df = spark.read.format("csv").option("header", "true").load(file_path)
# Show the DataFrame
#df.show()
# COMMAND ----------
# Write to Iceberg table with partitioning
#df2 = spark.createDataFrame(df.collect(), df.schema)
df.createOrReplaceTempView(tempview)
# Query the temporary view using SQL
sql = f"SELECT * FROM {tempview}"
resultdf = spark.sql(sql)
#print(icebergtable_name)
# only iceberg here
#resultdf.write.format("iceberg").mode("overwrite").saveAsTable(icebergtable_name)
#resultdf.unpersist()
# COMMAND ----------
schema = resultdf.schema
columns_sql= ""
columns_sql = ", ".join(f"{field.name}
{field.dataType.simpleString().upper()}" for field in schema)
query =f"""
CREATE TABLE {icebergtable_name}({columns_sql}) TBLPROPERTIES(
'delta.columnMapping.mode' = 'name',
'delta.enableIcebergCompatV2' = 'true',
'delta.universalFormat.enabledFormats' = 'iceberg'
);"""
#print (query)
spark.sql(query)
query1 = f"INSERT INTO {icebergtable_name} SELECT * FROM {tempview}"
#r =spark.sql(query1)
#print(query1)
spark.sql(query1)
dfread =
spark.read.format("org.apache.iceberg.spark.source.IcebergSource").load(icebergtable_name)
count = dfread.count()
#print(count)
dfread.show()
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