import json
import os
import re
import sys
import time
import traceback
import zlib
import pandas as pd
import redis
from datetime import datetime
sys.path.append("/opt/module/spark-3.2.0-bin-hadoop3.2/demo/py_demo/")
sys.path.append(os.path.dirname(sys.path[0])) # 上级目录
from utils.templates import Templates
# from ..utils.templates import Templates
from utils.templates_mysql import TemplatesMysql
# from ..utils.templates_mysql import TemplatesMysql
from pyspark.sql.types import IntegerType
from pyspark.sql import functions as F
from pyspark.sql.types import *
from pyspark.sql import SparkSession
class DimStAsinInfo(Templates):
def __init__(self, site_name='us', date_type="day", date_info='2022-10-01', consumer_type='lastest', topic_name="us_asin_detail", batch_size=100000):
super(DimStAsinInfo, self).__init__()
self.site_name = site_name
self.date_type = date_type
self.date_info = date_info
self.consumer_type = consumer_type # 消费实时还是消费历史
self.topic_name = topic_name # 主题名字
self.batch_size = batch_size
self.batch_size_history = int(batch_size / 10)
self.db_save = f'kafka_test_001'
# self.spark = self.create_spark_object(
# app_name=f"{self.db_save}: {self.site_name},{self.date_type}, {self.date_info}")
# 创建 SparkSession
# self.spark = SparkSession.builder \
# .appName("KafkaConsumerApp") \
# .getOrCreate()
self.spark = self.create_spark_object(
app_name=f"{self.db_save}: {self.site_name},{self.date_type}, {self.date_info}")
self.schema = self.init_schema()
# 连接mysql
self.engine = self.get_connection()
def get_connection(self):
return TemplatesMysql(site_name="us").mysql_connect()
def judge_spider_asin_detail_is_finished(self):
while True:
try:
sql = f'SELECT * from workflow_progress WHERE page="ASIN详情" and site_name="{self.site_name}" and date_type="{self.date_type}" and date_info="{self.date_info}" and status_val=3'
df = pd.read_sql(sql, con=self.engine)
if df.shape[0] == 1:
print(f"ASIN详情状态为3, 抓取完成并终止程序, site_name:{self.site_name}, date_type:{self.date_type}, date_info:{self.date_info}")
self.spark.stop()
quit() # 退出程序
break
except Exception as e:
print(e, traceback.format_exc())
time.sleep(10)
self.engine = self.get_connection()
@staticmethod
def init_schema():
schema = StructType([
StructField("asin", StringType(), True),
StructField("week", StringType(), True),
StructField("title", StringType(), True),
StructField("img_url", StringType(), True),
StructField("rating", StringType(), True),
StructField("total_comments", StringType(), True),
StructField("price", FloatType(), True),
StructField("rank", StringType(), True),
StructField("category", StringType(), True),
StructField("launch_time", StringType(), True),
StructField("volume", StringType(), True),
StructField("weight", StringType(), True),
StructField("page_inventory", IntegerType(), True),
StructField("buy_box_seller_type", IntegerType(), True),
StructField("asin_vartion_list", IntegerType(), True),
StructField("title_len", IntegerType(), True),
StructField("img_num", IntegerType(), True),
StructField("img_type", StringType(), True),
StructField("activity_type", StringType(), True),
StructField("one_two_val", StringType(), True),
StructField("three_four_val", StringType(), True),
StructField("eight_val", StringType(), True),
StructField("qa_num", IntegerType(), True),
StructField("five_star", IntegerType(), True),
StructField("four_star", IntegerType(), True),
StructField("three_star", IntegerType(), True),
StructField("two_star", IntegerType(), True),
StructField("one_star", IntegerType(), True),
StructField("low_star", IntegerType(), True),
StructField("together_asin", StringType(), True),
StructField("brand", StringType(), True),
StructField("ac_name", StringType(), True),
StructField("material", StringType(), True),
StructField("node_id", StringType(), True),
StructField("data_type", IntegerType(), True),
StructField("sp_num", StringType(), True),
StructField("describe", StringType(), True),
StructField("date_info", StringType(), True),
StructField("weight_str", StringType(), True),
StructField("package_quantity", StringType(), True),
StructField("pattern_name", StringType(), True),
StructField("seller_id", StringType(), True),
StructField("variat_num", IntegerType(), True),
StructField("site_name", StringType(), True),
StructField("best_sellers_rank", StringType(), True),
StructField("best_sellers_herf", StringType(), True),
StructField("account_url", StringType(), True),
StructField("account_name", StringType(), True),
StructField("parentAsin", StringType(), True),
StructField("asinUpdateTime", StringType(), True),
])
return schema
@staticmethod
def clean_kafka_df(df):
df = df.withColumnRenamed("seller_id", "account_id")
# cols_python = ["asin", "parentAsin", "variat_num", "best_sellers_rank", "best_sellers_herf", "price", "rating",
# "brand", "brand", "account_id", "account_name", "account_url", "buy_box_seller_type",
# "volume", "weight", "weight_str", "launchTime", "total_comments", "page_inventory"]
# oneCategoryRank, aoVal, bsrOrders, bsrOrdersSale
# siteName volumeFormat weightFormat asinUpdateTime
# java那边插件的字段名称
cols_java = ['asin', 'parentAsin', 'asinVarNum', 'oneCategoryRank', 'bestSellersRank', 'lastHerf', 'aoVal', 'price', 'rating',
'bsrOrders', 'bsrOrdersSale', 'brandName', 'accountId', 'accountName', 'accountUrl', 'siteName', 'buyBoxSellerType',
'volume', 'volumeFormat', 'weight', 'weightFormat', 'launchTime', 'totalComments', 'pageInventory', 'asinUpdateTime']
df = df.select("asin", "parentAsin", "variat_num", "best_sellers_rank", "best_sellers_herf", "price", "rating",
"brand", "account_id", "account_name", "account_url", "buy_box_seller_type",
"volume", "weight", "weight_str", "launch_time", "total_comments", "page_inventory", "asinUpdateTime", "site_name", "node_id")
return df
def get_topic_name(self):
if self.site_name == "us" and self.date_type == "month":
self.topic_name = f"{site_name}_asin_detail_{self.date_type}"
else:
self.topic_name = f"{site_name}_asin_detail"
def handle_history(self):
self.get_topic_name()
consumer = self.get_kafka_object_by_python(topic_name=self.topic_name)
partition_data_count = self.get_kafka_partitions_data(consumer=consumer, topic_name=self.topic_name)
beginning_offsets_list = []
end_offsets_list = []
for values in partition_data_count.values():
beginning_offsets_list.append(values['beginning_offsets'])
end_offsets_list.append(values['end_offsets'])
min_offset = min(beginning_offsets_list)
max_offset = max(end_offsets_list)
# max_offset = max(partition_data_count.values())
# for start_offset in range(0, max_offset+1, self.batch_size_history):
for start_offset in range(min_offset, max_offset+1, self.batch_size_history):
end_offset = start_offset + self.batch_size_history
starting_offsets_json = json.dumps({self.topic_name: {str(p): start_offset for p in partition_data_count.keys()}})
ending_offsets_json = json.dumps({self.topic_name: {str(p): end_offset for p in partition_data_count.keys()}})
kafka_df = self.spark.read \
.format("kafka") \
.option("kafka.bootstrap.servers", self.kafka_servers) \
.option("subscribe", self.topic_name) \
.option("startingOffsets", starting_offsets_json) \
.option("endingOffsets", ending_offsets_json) \
.option("failOnDataLoss", "false") \
.load() \
.select(F.from_json(F.col("value").cast("string"), schema=self.schema).alias("data")) \
.select("data.*")
print(f"kafka_df.count():{kafka_df.count()}, start_offset:{start_offset}, end_offset:{end_offset}")
pdf = kafka_df.toPandas()
# pdf.to_sql()
# 关闭SparkSession
self.spark.stop()
def run(self):
self.handle_history()
if __name__ == '__main__':
site_name = sys.argv[1] # 参数1：站点
date_type = sys.argv[2] # 参数2：类型：week/4_week/month/quarter/day
date_info = sys.argv[3] # 参数3：年-周/年-月/年-季/年-月-日, 比如: 2022-1
consumer_type = sys.argv[4] # 参数3：年-周/年-月/年-季/年-月-日, 比如: 2022-1
handle_obj = DimStAsinInfo(site_name=site_name, date_type=date_type, date_info=date_info, consumer_type=consumer_type, batch_size=100000)
handle_obj.run()