import json
import os
import re
import ast
import sys
import time
import logging
import traceback
import threading
import zlib
import pandas as pd
import numpy as np
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 sqlalchemy import create_engine
from sqlalchemy.exc import PendingRollbackError
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 utils.mysql_db import sql_connect, sql_update_many, sql_delete, get_country_engine
from pyspark.sql import SparkSession
class SpiderMerchantwordSearch(Templates):
def __init__(self, site_name='us', date_type='day', date_info='2023-11-16', consumer_type='lastest', batch_size=100000):
super(SpiderMerchantwordSearch, self).__init__()
self.site_name = site_name
self.date_info = date_info
self.consumer_type = consumer_type # 消费实时还是消费历史
self.date_type = date_type
# 通过date_type 获取 topic
self.get_topic_name()
# 通过date_type 获取 schema
self.init_schema()
# 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 = self.topic_name
# self.spark = self.create_spark_object(
# app_name=f"{self.db_save}: {self.site_name},{self.date_type}, {self.date_info}, {self.consumer_type}")
self.app_name = self.get_app_name()
self.spark = self.create_spark_object(app_name=f"{self.app_name}")
# self.schema = self.init_schema()
# 连接mysql
self.engine = get_country_engine(self.site_name)
self.pg16_engine = self.get_16pg_country_engine(self.site_name)
sql_connect(self.site_name)
logging.basicConfig(format='%(asctime)s %(name)s %(levelname)s %(message)s',
level=logging.INFO)
# 爬虫类型--流程表
self.spider_type = "Merchantword搜索词"
def init_schema(self):
self.schema = StructType([
StructField("cate_type", StringType(), True),
StructField("data_list", StringType(), True),
StructField("date_info", StringType(), True),
StructField("spider_time", StringType(), True),
])
# self.col = ['search_term', 'asin', 'page', 'buy_data', 'label']
def get_topic_name(self):
if self.date_type == "day":
# self.topic_name = f"merchantwords_search_term"
self.topic_name = f"{self.site_name}_merchantwords_{self.date_info.replace('-', '_')}"
else:
logging.info("self.date_type error -----")
quit()
def get_16pg_country_engine(self, site_name="us"):
h16_pg_us = {
"user": "postgres",
"password": "fazAqRRVV9vDmwDNRNb593ht5TxYVrfTyHJSJ3BS",
"host": "192.168.10.225",
"port": "5432",
"database": "selection",
}
if site_name == 'us' or site_name == 'mx' or site_name == 'ca':
h16_pg_us["database"] = f"selection"
db_ = 'postgresql+psycopg2://{}:{}@{}:{}/{}'.format(*h16_pg_us.values())
else:
h16_pg_us["database"] = f"selection_{site_name}"
db_ = 'postgresql+psycopg2://{}:{}@{}:{}/{}'.format(*h16_pg_us.values())
engine = create_engine(db_, encoding='utf-8') # , pool_recycle=3600
return engine
def cate_type(self, name, data_list):
cate_type = name[0]
# df_1['date_info'] = name[1]
columns = ['search_term', 'asin', 'page', 'page_row', 'data_type',
'title', 'img', 'price', 'rating', 'reviews', 'created_time']
if cate_type in ['buy']:
df = pd.DataFrame(data=data_list, columns=['search_term', 'asin', 'page', 'buy_data', 'label', 'created_time'])
df.label = df.label.apply(lambda x: str(x)[:200] if x is not None else None) # 截取字符
df.buy_data = df.buy_data.apply(lambda x: str(x)[:200] if x is not None else None) # 截取字符
else:
df = pd.DataFrame(data=data_list, columns=columns)
df['asin'] = df['asin'].str.replace('/', '')
df['date_info'] = self.date_info
df_asin_detail = pd.DataFrame([])
if cate_type in ['zr', 'sp']:
df_asin_detail = df.loc[:,
['asin', 'title', 'img', 'price', 'rating', 'reviews', 'date_info', 'created_time']]
if cate_type in ['zr', 'sp']:
df = df.loc[:, ['search_term', 'asin', 'page', 'page_row', 'date_info', 'created_time']]
df.drop_duplicates(['search_term', 'asin', 'page', 'page_row'], inplace=True)
elif cate_type in ['buy']:
df = df.loc[:, ['search_term', 'asin', 'page', 'buy_data', 'date_info', 'label', 'created_time']]
df.drop_duplicates(['search_term', 'asin', 'page', 'buy_data', 'label'], inplace=True)
else:
if cate_type in ['sb', 'tr']:
df = df.loc[:, ['search_term', 'asin', 'page', 'data_type', 'date_info', 'created_time']]
df.drop_duplicates(['search_term', 'asin', 'page', 'data_type'], inplace=True)
elif cate_type in ['buy']:
df = df.loc[:, ['search_term', 'asin', 'page', 'buy_data', 'date_info', 'label', 'created_time']]
df.drop_duplicates(['search_term', 'asin', 'page', 'buy_data', 'label'], inplace=True)
else:
df = df.loc[:, ['search_term', 'asin', 'page', 'date_info', 'created_time']]
df.drop_duplicates(['search_term', 'asin', 'page'], inplace=True)
return df, df_asin_detail
def add_column_to_list(self, row):
l = []
for sub_list in json.loads(row['data_list']):
l.append(sub_list + [row["spider_time"]])
return l
def save_data_common(self, name, group):
logging.info(f"name: {name}")
search_exploded_list = group['data_list'].explode()
# 展开后转换为一个大列表
search_list = [i for i in search_exploded_list.tolist() if not isinstance(i, float)]
if search_list:
logging.info(f"搜索词处理{search_list[0:5]}")
# 列表等分
# self.list_svg(search_list, chunk_size=100)
# 转换为df对象
# 通过类别对对应数据字段进行清洗
df_search_term, df_asin_detail = self.cate_type(name, search_list)
logging.info(f"{name} {df_search_term.shape} \n {df_search_term.keys()} {df_search_term.head()}")
# 通过站点 类别 和date_info 拼接表名
if name[0] == "buy":
table_name = f"{self.site_name}_merchantwords_other_search_term_{name[1].replace('-', '_')}"
else:
table_name = f"{self.site_name}_merchantwords_search_term_rank_{name[0]}_{name[1].replace('-', '_')}"
while True:
try:
start_time = time.time()
df_search_term.to_sql(name=f'{table_name}', con=self.pg16_engine, if_exists='append', index=False)
end_time = time.time()
logging.info(f"入库 {table_name} 表 {df_search_term.shape} {df_search_term.head(10)} 成功, 耗时：{end_time - start_time}s")
break
except PendingRollbackError as e:
time.sleep(3)
logging.info(f"error {e} sleep 3")
continue
logging.info(f"detail --> save name: {name}")
if df_asin_detail.shape[0]:
detail_table = f"{self.site_name}_merchantwords_search_term_asin_detail_{name[1].replace('-', '_')}"
while True:
try:
start_time = time.time()
df_asin_detail.to_sql(name=detail_table, con=self.pg16_engine, if_exists='append', index=False)
end_time = time.time()
logging.info(f"入库 {detail_table} 表 {df_asin_detail.shape} {df_asin_detail.head(10)} 成功, 耗时：{end_time - start_time}s")
break
except PendingRollbackError as e:
time.sleep(3)
logging.info(f"error {e} sleep 3")
continue
def save_data(self, df):
threads = []
# 将字符串类型改为 python list
# df['data_list'] = df['data_list'].apply(json.loads)
df["data_list"] = df.apply(self.add_column_to_list, axis=1)
for name, group in df.groupby(['cate_type', 'date_info']):
thread = threading.Thread(target=self.save_data_common, args=(name, group))
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
logging.info("线程处理完成")
def handle_kafka_df(self, kafka_df):
kafka_df.show(20)
# pyspark的kafka_df对象转换成pandas的df对象
pdf = kafka_df.toPandas()
# pdf['asin'] = pdf['asin'].apply(lambda x: str(x).replace('/', ''))
# 过滤--不符合当前周期的数据
pdf = pdf.loc[(~pdf.date_info.isna()) & (pdf.date_info == self.date_info)]
if pdf.shape[0]:
logging.info(f"{pdf.keys()}")
logging.info(f"----------------------------")
self.save_data(pdf)
else:
logging.info(f"{pdf.shape}")
def handle_kafka_history(self, kafka_df):
# kafka_df = kafka_df.withColumn("asin", F.regexp_replace("asin", "/", ""))
self.handle_kafka_df(kafka_df)
def handle_kafka_stream(self, kafka_df, epoch_id):
# kafka_df = kafka_df.withColumn("asin", F.translate("asin", "/", ""))
self.handle_kafka_df(kafka_df)
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：实时 lastest 历史 history
handle_obj = SpiderMerchantwordSearch(site_name=site_name, date_type=date_type, date_info=date_info, consumer_type=consumer_type, batch_size=10000)
handle_obj.run_kafka()
# for i in `ps -ef|grep "spider_asin_search_day" |awk '{print $2}' `; do kill -9 $i ; done;
# 历史
# /opt/module/spark/bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.1.3 --master yarn --driver-memory 10g --executor-memory 20g --executor-cores 4 --num-executors 2 --queue spark /opt/module/spark/demo/py_demo/my_kafka/spider_asin_search_day.py us day 2024-04-10 history > amazon_history_search_day_us.log 2>&1 &
# 实时
# /opt/module/spark/bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.1.3 --master yarn --driver-memory 10g --executor-memory 20g --executor-cores 4 --num-executors 2 --queue spark /opt/module/spark/demo/py_demo/my_kafka/spider_asin_search_day.py us day 2024-04-13 history > amazon_history_search_day13_us.log 2>&1 &