1、spark读取文件

#1.spark 读取csv
custid_df = spark.read.format("csv").\
    option("sep", ",").\
    option("header", True).\
    option("encoding", "utf-8").\
    schema("custid STRING").\
    load("/tmp/YB_1340802061021181116357983338500_20220701195704994.csv")
custid_df.createOrReplaceTempView("custid_rdd_table")
spark.spark_df("""
    insert into table dw_bu.dwd_base_groupid_info_outside_d partition(day)
    select 'custgroupid001',custid,'2022-08-02' from custid_rdd_table
""")
spark.catalog.dropTempView("custid_rdd_table")

2、获取hive表schemal

from pyspark.sql.types import StructType, StructField, StringType, IntegerType
df = spark.sql("DESCRIBE dw_bu.ods_base_data_d_info_m_d")
table_schamel = ''
table_schamel = ''
for row in df.collect():
    if "#" in row.col_name:
        break
    table_schamel = table_schamel + " " + row.col_name + " " + row.data_type + ' comment "' + row.comment + '",'
table_schamel = table_schamel[:-1]
print(table_schamel)

3、转换字段类型

from pyspark.sql.types import StructType, StructField, StringType, IntegerType
from pyspark.sql.functions import col, to_date,regexp_replace,when
df = spark.sql("select * from dw_bu.dws_dx_fraud_flag_d limit 1000")
df  = df.select("dt_m_1004","dt_m_1032","dt_m_1034")
df = df.withColumn("dt_m_1004", col("dt_m_1004").cast("string"))
df.show()
df.withColumn("dt_m_1004", regexp_replace(col("dt_m_1004"), "[^0-9\.]", "").cast("bigint")).show()

4、获取hive表对应spark索引

type_mapping = {
    "string": "keyword",
    "int": "long",
    "bigint": "long",
    "float": "float",
    "double": "float",
    "date": "date","format": "yyyy-MM-dd"
}
hive_table_info = spark.sql("DESCRIBE dw_bu.ods_base_data_d_info_m_d").collect()
column_mapping = {}
for row in hive_table_info:
    column_name = row.col_name
    data_type = row.data_type
    es_type = type_mapping.get(data_type, "keyword")
    column_mapping[column_name] = {"type": es_type}
print(column_mapping)