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#!/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()