#!/usr/bin/env python3
"""pyspark_001 database initialization: create tables + load seed data"""
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName('dataclaw_eval_init_pyspark_001') \
    .enableHiveSupport() \
    .config('spark.sql.warehouse.dir', '/tmp/hive_warehouse') \
    .getOrCreate()

spark.sql('CREATE DATABASE IF NOT EXISTS internal_platform_db')

# Create input table
spark.sql('''
CREATE TABLE IF NOT EXISTS internal_platform_db.caseR1_dwd_ww_kf_session_satify_attrib_v3 (
    `reason` STRING, `reason_detail` STRING, `session_cnt` BIGINT,
    `satify_score` DOUBLE, `session_rate` DOUBLE, `all_session_cnt` BIGINT,
    `all_session_satify_score` DOUBLE, `satify_attrib` DOUBLE,
    `session_type` BIGINT, `total_session_cnt` BIGINT,
    `total_all_session_cnt` BIGINT, `total_session_rate` DOUBLE,
    `imp_date` STRING
) STORED AS ORC
''')

# Load seed data - use explicit schema to avoid inferSchema overriding DDL types
from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType

schema = StructType([
    StructField("reason", StringType(), True),
    StructField("reason_detail", StringType(), True),
    StructField("session_cnt", LongType(), True),
    StructField("satify_score", DoubleType(), True),
    StructField("session_rate", DoubleType(), True),
    StructField("all_session_cnt", LongType(), True),
    StructField("all_session_satify_score", DoubleType(), True),
    StructField("satify_attrib", DoubleType(), True),
    StructField("session_type", LongType(), True),
    StructField("total_session_cnt", LongType(), True),
    StructField("total_all_session_cnt", LongType(), True),
    StructField("total_session_rate", DoubleType(), True),
    StructField("imp_date", StringType(), True),
])

print('Loading seed data...')
df = spark.read.csv(
    "/tmp_workspace/seed_data/caseR1_dwd_ww_kf_session_satify_attrib_v3.csv",
    header=True, schema=schema,
)
df.write.mode("overwrite").insertInto("internal_platform_db.caseR1_dwd_ww_kf_session_satify_attrib_v3")

print('Database initialization complete')
spark.stop()