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| """
1.准备Spark开发环境
2.读取MySQL数据
3.读取和性别标签相关的4级标签rule并解析
4.根据4级标签加载ES数据
5.读取和性别标签相关的5级标签(根据4级标签的id作为pid查询)
6.根据ES数据和5级标签数据进行匹配,得出userId,tagsId
7.查询ES中的oldDF
8.合并newDF和oldDF
9.将最终结果写到ES
"""
from pyspark.sql import SparkSession
import os
from pyspark.sql.types import StringType
from UserProfile.offline.pojo.RuleMeta import Rule4Meta
import pyspark.sql.functions as F
os.environ['SPARK_HOME'] = '/export/server/spark'
os.environ['PYSPARK_HOME'] = '/root/anaconda3/envs/pyspark_env/bin/python'
@F.udf
def updateTagsId(old_tags:str, new_tag:str, tag5_ids:str):
if new_tag == None:
return old_tags
if old_tags == None:
return new_tag
old_tags_list = old_tags.split(",")
tag5_id_list = tag5_ids.split(',')
new_list = []
for old in old_tags_list:
if old not in tag5_id_list:
new_list.append(old)
new_list.append(new_tag)
return ','.join(new_list)
def strParse(rule_str:str):
all_element_list = rule_str.split("##")
rule_four_dict = {}
for element in all_element_list:
rule_four_dict[element.split("=")[0]] = element.split("=")[1]
return rule_four_dict
if __name__ == '__main__':
spark = SparkSession\
.builder\
.master("local[*]")\
.appName('获取MySQL的数据,读取ES中数据,并将分析结果写入到ES')\
.config("spark.sql.shuffle.partitions", 10)\
.getOrCreate()
url = "jdbc:mysql://192.168.88.166:3306/tfec_tags?useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC&useSSL=false&user=root&password=123456"
tableName = "tbl_basic_tag"
tag4Id = 4
sql = f"select id,rule from {tableName} where id = {tag4Id} or pid = {tag4Id}"
tfec_userprofile_result = "tfec_userprofile_result"
tag_four_df = spark.read\
.format('jdbc')\
.option('url', url)\
.option('query', sql)\
.load()
tag_four_str = tag_four_df.rdd.map(lambda row: row.rule).collect()[0]
tag_four_dict = strParse(tag_four_str)
rule_four_meta = Rule4Meta.dict_to_obj(tag_four_dict)
print(rule_four_meta.esIndex, rule_four_meta.esNodes, rule_four_meta.selectFields)
es_df = spark.read.format('es')\
.option('es.nodes', rule_four_meta.esNodes)\
.option('es.resource', rule_four_meta.esIndex)\
.option('es.read.field.include', rule_four_meta.selectFields)\
.load()
tag_five_df = tag_four_df.where(f'id!={tag4Id}')
result_df = es_df\
.join(tag_five_df,
on=tag_five_df.rule ==es_df.gender,
how='left')\
.select(es_df.id.cast(StringType()).alias('userId'),
tag_five_df.id.cast(StringType()).alias('tagsId'))
old_df = spark.read.format('es')\
.option('es.nodes', rule_four_meta.esNodes)\
.option('es.resource', tfec_userprofile_result)\
.load()
tag5Ids = tag_five_df\
.select(F.col('id').cast(StringType()).alias('id'))\
.rdd.map(lambda row: row.id).collect()
print(tag5Ids)
new_result_df = result_df.withColumn('tag5Ids', F.lit(','.join(tag5Ids)))
merge_df = new_result_df\
.join(old_df, on=new_result_df.userId==old_df.userId,how='right')\
.select(old_df.userId.alias('userId'), updateTagsId(old_df.tagsId, new_result_df.tagsId, new_result_df.tag5Ids).alias('tagsId'))
merge_df.show()
merge_df\
.write\
.format('es')\
.option('es.nodes', rule_four_meta.esNodes)\
.option('es.resource', tfec_userprofile_result)\
.option('es.write.operation','upsert')\
.option('es.mapping.id','userId')\
.option('es.mapping.name',"userId:userId,tagsId:tagsId") \
.mode('append')\
.save()
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