Skip to content

A
Amazon-Selection-Data
Commit 7ad606c5 by wangjing
Options

Merge branch 'developer' of http://47.106.101.75/abel_cjy/Amazon-Selection-Data into developer

parents 14af7a9b 0f05969c
Showing with 440 additions and 333 deletions
Pyspark_job/dim/dim_asin_detail.py
...@@ -159,7 +159,8 @@ class DimAsinDetail(object): ...@@ -159,7 +159,8 @@ class DimAsinDetail(object):
REGEXP_REPLACE(seller_json, chr(10), '') as seller_json, buy_box_seller_type as asin_buy_box_seller_type, REGEXP_REPLACE(seller_json, chr(10), '') as seller_json, buy_box_seller_type as asin_buy_box_seller_type,
customer_reviews_json, parent_asin, img_list, created_at as created_time, updated_at as updated_time, customer_reviews_json, parent_asin, img_list, created_at as created_time, updated_at as updated_time,
updated_at as dt, variat_num as variation_num, fbm_delivery_price as asin_fbm_price, updated_at as dt, variat_num as variation_num, fbm_delivery_price as asin_fbm_price,
get_json_object(product_json, '$.Color') as product_json_color get_json_object(product_json, '$.Color') as product_json_color,
get_json_object(product_json, '$.Number of Items') as product_json_number_of_items
from ods_asin_detail where site_name='{self.site_name}' {self.date_sql}""" from ods_asin_detail where site_name='{self.site_name}' {self.date_sql}"""
print(sql) print(sql)
self.df_asin_detail = self.spark.sql(sqlQuery=sql) self.df_asin_detail = self.spark.sql(sqlQuery=sql)
...@@ -348,15 +349,23 @@ class DimAsinDetail(object): ...@@ -348,15 +349,23 @@ class DimAsinDetail(object):
).withColumn( ).withColumn(
"variat_package_quantity_is_abnormal", self.df_asin_detail.variat_parse.getField("is_package_quantity_abnormal") "variat_package_quantity_is_abnormal", self.df_asin_detail.variat_parse.getField("is_package_quantity_abnormal")
).drop("title_parse", "variat_parse", "variat_attribute") ).drop("title_parse", "variat_parse", "variat_attribute")
# Number of Items：直接从 product_json 提取，cast 失败（脏数据）自动为 null
self.df_asin_detail = self.df_asin_detail.withColumn( self.df_asin_detail = self.df_asin_detail.withColumn(
"package_quantity", F.expr(""" CASE "number_of_items", F.col("product_json_number_of_items").cast("int")
WHEN title_package_quantity is null and variat_package_quantity is not null THEN variat_package_quantity ).drop("product_json_number_of_items")
WHEN title_package_quantity is not null THEN title_package_quantity # 优先级：Number of Items > 属性字段 > 标题解析 > 默认1
self.df_asin_detail = self.df_asin_detail.withColumn(
"package_quantity", F.expr("""CASE
WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN number_of_items
WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity
WHEN title_package_quantity IS NOT NULL THEN title_package_quantity
ELSE 1 END""")).withColumn( ELSE 1 END""")).withColumn(
"is_package_quantity_abnormal", F.expr("""CASE "is_package_quantity_abnormal", F.expr("""CASE
WHEN title_package_quantity is null and variat_package_quantity is not null THEN variat_package_quantity_is_abnormal WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN 0
WHEN title_package_quantity is not null THEN title_package_quantity_is_abnormal WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity_is_abnormal
ELSE 2 END""")).drop("title_package_quantity", "variat_package_quantity", "title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal") WHEN title_package_quantity IS NOT NULL THEN title_package_quantity_is_abnormal
ELSE 2 END""")).drop("number_of_items", "title_package_quantity", "variat_package_quantity",
"title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal")
self.df_asin_detail = self.df_asin_detail.join(self.df_user_package_num, on=['asin', 'asin_title'], how='left') self.df_asin_detail = self.df_asin_detail.join(self.df_user_package_num, on=['asin', 'asin_title'], how='left')
self.df_asin_detail = self.df_asin_detail.withColumn( self.df_asin_detail = self.df_asin_detail.withColumn(
"package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))).withColumn( "package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))).withColumn(
......
Pyspark_job/dwt/dwt_fb_base_report.py
...@@ -99,7 +99,8 @@ class DwtFbBaseReport(object): ...@@ -99,7 +99,8 @@ class DwtFbBaseReport(object):
cur_fd.fb_crawl_date, cur_fd.fb_crawl_date,
round((count_30_day_num - last_30_day_num) / last_30_day_num, 4) as count_30_day_rate, round((count_30_day_num - last_30_day_num) / last_30_day_num, 4) as count_30_day_rate,
round((count_1_year_num - last_1_year_num) / last_1_year_num, 4) as count_1_year_rate, round((count_1_year_num - last_1_year_num) / last_1_year_num, 4) as count_1_year_rate,
round((count_lifetime_num - last_lifetime_num) / last_lifetime_num, 4) as count_life_time_rate round((count_lifetime_num - last_lifetime_num) / last_lifetime_num, 4) as count_life_time_rate,
seller_rating
from from
( (
select select
......
Pyspark_job/dwt/dwt_flow_asin.py
...@@ -521,10 +521,27 @@ class DwtFlowAsin(Templates): ...@@ -521,10 +521,27 @@ class DwtFlowAsin(Templates):
CASE WHEN hide_flag = 1 THEN 1 WHEN category_first_id = 'grocery' and category_id != '6492272011' THEN 1 CASE WHEN hide_flag = 1 THEN 1 WHEN category_first_id = 'grocery' and category_id != '6492272011' THEN 1
WHEN category_id in ('21393128011', '21377129011', '21377127011', '21377130011', '21388218011', '21377132011') THEN 1 WHEN category_id in ('21393128011', '21377129011', '21377127011', '21377130011', '21388218011', '21377132011') THEN 1
ELSE 0 END""")).drop("hide_flag") ELSE 0 END""")).drop("hide_flag")
self.df_asin_detail = self.df_asin_detail.withColumn("asin_is_need", F.expr(""" # 解析 asin_category_desc 取 › 分隔的第一个元素作为补充分类名称
CASE WHEN category_first_id in ('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed') THEN 1 self.df_asin_detail = self.df_asin_detail.withColumn(
WHEN asin NOT LIKE 'B0%' THEN 1 "desc_category_first_name",
F.lower(F.trim(F.split(F.col("asin_category_desc"), "›").getItem(0)))
)
# 读取 Hive 分类维表，获取分类名称与ID的对应关系
sql_dim = f"""
select lower(trim(en_name)) as desc_category_first_name, category_first_id as desc_category_first_id
from dim_bsr_category_tree where site_name = '{self.site_name}' and category_parent_id = 0 and leaf_node = 2
"""
df_bsr_category = F.broadcast(self.spark.sql(sqlQuery=sql_dim))
# join 补充分类ID
self.df_asin_detail = self.df_asin_detail.join(df_bsr_category, on=['desc_category_first_name'], how='left')
# 两个分类ID均在过滤列表中才标记为1
need_categories = "('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed')"
self.df_asin_detail = self.df_asin_detail.withColumn("asin_is_need", F.expr(f"""
CASE WHEN category_first_id in {need_categories}
AND desc_category_first_id in {need_categories} THEN 1
WHEN asin NOT LIKE 'B0%' THEN 1
ELSE 0 END""")) ELSE 0 END"""))
self.df_asin_detail = self.df_asin_detail.drop("desc_category_first_name", "desc_category_first_id")
self.df_asin_detail = self.df_asin_detail.withColumn("asin_type", F.expr(""" self.df_asin_detail = self.df_asin_detail.withColumn("asin_type", F.expr("""
CASE WHEN asin_is_self=1 THEN 1 WHEN asin_is_need=1 THEN 2 WHEN asin_is_hide=1 THEN 3 ELSE 0 END""" CASE WHEN asin_is_self=1 THEN 1 WHEN asin_is_need=1 THEN 2 WHEN asin_is_hide=1 THEN 3 ELSE 0 END"""
)).drop("asin_is_self", "asin_is_need", "asin_is_hide") )).drop("asin_is_self", "asin_is_need", "asin_is_hide")
......
Pyspark_job/dwt/dwt_nsr_asin_detail.py
...@@ -154,7 +154,7 @@ class DwtNsrAsinDetail(object): ...@@ -154,7 +154,7 @@ class DwtNsrAsinDetail(object):
select asin, select asin,
asin_ao_val as ao_val, asin_ao_val as ao_val,
bsr_orders as bsr_orders, bsr_orders as bsr_orders,
asin_bsr_orders_change as bsr_orders_change, asin_bsr_orders_mom as bsr_orders_change,
asin_air_freight_gross_margin as asin_air_freight_gross_margin, asin_air_freight_gross_margin as asin_air_freight_gross_margin,
asin_ocean_freight_gross_margin as asin_ocean_freight_gross_margin, asin_ocean_freight_gross_margin as asin_ocean_freight_gross_margin,
cast(asin_bought_month as int ) as asin_bought_month cast(asin_bought_month as int ) as asin_bought_month
......
Pyspark_job/export_es/es_ai_asin_add.py
...@@ -29,6 +29,7 @@ class EsAiAsinAdd(object): ...@@ -29,6 +29,7 @@ class EsAiAsinAdd(object):
self.df_ai_asin_detail = self.spark.sql(f"select 1+1;") self.df_ai_asin_detail = self.spark.sql(f"select 1+1;")
self.df_ai_asin_analyze = self.spark.sql(f"select 1+1;") self.df_ai_asin_analyze = self.spark.sql(f"select 1+1;")
self.df_profit_rate = self.spark.sql(f"select 1+1;")
self.df_save_pg = self.spark.sql(f"select 1+1;") self.df_save_pg = self.spark.sql(f"select 1+1;")
self.df_save_es = self.spark.sql(f"select 1+1;") self.df_save_es = self.spark.sql(f"select 1+1;")
...@@ -126,7 +127,10 @@ class EsAiAsinAdd(object): ...@@ -126,7 +127,10 @@ class EsAiAsinAdd(object):
title_word_content, title_word_content,
array_to_string(package_quantity_arr, ',') as package_quantity_arr, array_to_string(package_quantity_arr, ',') as package_quantity_arr,
package_quantity_flag, package_quantity_flag,
label_content label_content,
festival,
multi_color_flag,
multi_color_content
from {self.site_name}_ai_asin_analyze_detail from {self.site_name}_ai_asin_analyze_detail
""" """
self.df_ai_asin_analyze = SparkUtil.read_jdbc_query( self.df_ai_asin_analyze = SparkUtil.read_jdbc_query(
...@@ -143,6 +147,16 @@ class EsAiAsinAdd(object): ...@@ -143,6 +147,16 @@ class EsAiAsinAdd(object):
print("AI分析数据如下：") print("AI分析数据如下：")
self.df_ai_asin_analyze.show(10, True) self.df_ai_asin_analyze.show(10, True)
# 读取利润率数据
sql3 = f"""
select asin, price, ocean_profit, air_profit
from dim_asin_profit_rate_info
where site_name = '{self.site_name}'
"""
self.df_profit_rate = self.spark.sql(sql3).repartition(40, 'asin').cache()
print("利润率数据如下：")
self.df_profit_rate.show(10, True)
def handle_data(self): def handle_data(self):
self.df_save_pg = self.df_ai_asin_detail.join( self.df_save_pg = self.df_ai_asin_detail.join(
self.df_ai_asin_analyze, 'asin', 'left_anti' self.df_ai_asin_analyze, 'asin', 'left_anti'
...@@ -155,20 +169,63 @@ class EsAiAsinAdd(object): ...@@ -155,20 +169,63 @@ class EsAiAsinAdd(object):
'is_ascending_flag' 'is_ascending_flag'
) )
df_profit = self.df_profit_rate.withColumn(
"profit_rate_extra",
F.when(
F.col("ocean_profit").isNull() & F.col("air_profit").isNull(),
F.lit(None)
).otherwise(
F.struct(
F.col("ocean_profit").alias("ocean_profit"),
F.col("air_profit").alias("air_profit")
)
)
).drop("ocean_profit", "air_profit")
self.df_save_es = self.df_ai_asin_detail.join( self.df_save_es = self.df_ai_asin_detail.join(
self.df_ai_asin_analyze, 'asin', 'inner' self.df_ai_asin_analyze, 'asin', 'inner'
).withColumn(
'profit_key', F.concat_ws("_", F.col("asin"), F.col("price"))
).join(
df_profit, on=["asin", "price"], how="left"
).select( ).select(
'account_addr', 'account_name', 'analyze_id', 'ao_val', 'appearance', 'asin', 'bought_month', 'account_addr', 'account_name', 'analyze_id', 'ao_val', 'appearance', 'asin', 'bought_month',
'bought_month_mom', 'bought_month_yoy', 'brand', 'bsr_rank', 'buy_box_seller_type', 'category', 'bought_month_mom', 'bought_month_yoy', 'brand', 'bsr_rank', 'buy_box_seller_type', 'category',
'category_current_id', 'category_id', 'color', 'crowd', 'fb_country_name', 'function', 'img', 'category_current_id', 'category_id', 'color', 'crowd', 'fb_country_name', 'festival', 'function',
'img_num', 'is_ascending_flag', 'is_new_flag', 'label_content', 'launch_time', 'launch_time_type', 'img', 'img_num', 'is_ascending_flag', 'is_new_flag', 'label_content', 'launch_time', 'launch_time_type',
'material', 'package_quantity', 'package_quantity_arr', 'package_quantity_flag', 'parent_asin', 'material', 'multi_color_content', 'multi_color_flag', 'package_quantity', 'package_quantity_arr',
'price', 'rating', 'scene_comment', 'scene_title', 'seller_id', 'shape', 'short_desc', 'site_name', 'package_quantity_flag', 'parent_asin', 'price', 'profit_key', 'profit_rate_extra', 'rating',
'size', 'theme', 'title', 'title_pic_content', 'title_pic_flag', 'title_word_content', 'scene_comment', 'scene_title', 'seller_id', 'shape', 'short_desc', 'site_name', 'size', 'theme',
'title_word_flag', 'total_comments', 'uses', 'variation_flag', 'variation_num', 'weight' 'title', 'title_pic_content', 'title_pic_flag', 'title_word_content', 'title_word_flag',
'total_comments', 'uses', 'variation_flag', 'variation_num', 'weight'
) )
def create_pg_table(self):
"""
根据模板表创建月度 PG 表：
1. LIKE INCLUDING ALL：复制所有字段类型、其他列默认值、约束、索引
2. 重建 id 列独立序列，避免与模板表共享同一序列
"""
template_tb = "us_ai_asin_detail_month_2026_01"
engine = DBUtil.get_db_engine("postgresql", "us")
# 表已存在则跳过
result = DBUtil.engine_exec_sql(engine, f"SELECT to_regclass('{self.export_pg_tb}')")
if list(result)[0][0] is not None:
print(f"PostgreSQL 表 {self.export_pg_tb} 已存在，跳过建表")
return
# 建表 + 为 id 列创建独立序列
sql = f"""
CREATE TABLE {self.export_pg_tb} (LIKE {template_tb} INCLUDING ALL);
ALTER TABLE {self.export_pg_tb} ALTER COLUMN id DROP DEFAULT;
CREATE SEQUENCE {self.export_pg_tb}_id_seq OWNED BY {self.export_pg_tb}.id;
ALTER TABLE {self.export_pg_tb} ALTER COLUMN id SET DEFAULT nextval('{self.export_pg_tb}_id_seq')
"""
DBUtil.exec_sql("postgresql", "us", sql)
print(f"PostgreSQL 表 {self.export_pg_tb} 创建完成（独立自增序列）")
def save_data(self): def save_data(self):
# 创建月度 PG 表
self.create_pg_table()
# 将新增asin导出给济苍 # 将新增asin导出给济苍
try: try:
self.df_save_pg.write.format("jdbc") \ self.df_save_pg.write.format("jdbc") \
...@@ -180,11 +237,12 @@ class EsAiAsinAdd(object): ...@@ -180,11 +237,12 @@ class EsAiAsinAdd(object):
.save() .save()
CommonUtil.send_wx_msg(['wujicang', 'chenyuanjie'], 'ASIN信息库增量数据导出', f'详情：{self.export_pg_tb} {self.site_name} {self.date_type} {self.date_info}') CommonUtil.send_wx_msg(['wujicang', 'chenyuanjie'], 'ASIN信息库增量数据导出', f'详情：{self.export_pg_tb} {self.site_name} {self.date_type} {self.date_info}')
except Exception as e: except Exception as e:
print("An error occurred while writing to Elasticsearch:", str(e)) print("An error occurred while writing to PostgreSQL:", str(e))
CommonUtil.send_wx_msg(['chenyuanjie'], '\u26A0 ASIN信息库增量数据导出失败', f'详情：{self.export_pg_tb} {self.site_name} {self.date_type} {self.date_info}') CommonUtil.send_wx_msg(['chenyuanjie'], '\u26A0 ASIN信息库增量数据导出失败', f'详情：{self.export_pg_tb} {self.site_name} {self.date_type} {self.date_info}')
# 将增量asin导出到es # 将增量asin导出到es
try: try:
EsUtils.create_index(self.es_index, self.es_client, EsUtils.get_es_ai_body())
self.df_save_es.write.format("org.elasticsearch.spark.sql") \ self.df_save_es.write.format("org.elasticsearch.spark.sql") \
.options(**self.es_options) \ .options(**self.es_options) \
.mode("append") \ .mode("append") \
......
Pyspark_job/listen_program/import_st_to_pg14.py
...@@ -21,7 +21,7 @@ class ImportStToPg14(object): ...@@ -21,7 +21,7 @@ class ImportStToPg14(object):
self.df_st_month = pd.DataFrame() self.df_st_month = pd.DataFrame()
self.df_st_month_state = pd.DataFrame() self.df_st_month_state = pd.DataFrame()
self.df_save = pd.DataFrame() self.df_save = pd.DataFrame()
self.fetch_year_month_by_week() # 如果传的date_type='week', 将date_info转换成月的值 # self.fetch_year_month_by_week() # 如果传的date_type='week', 将date_info转换成月的值
self.year, self.month = self.date_info.split("-")[0], int(self.date_info.split("-")[1]) self.year, self.month = self.date_info.split("-")[0], int(self.date_info.split("-")[1])
def fetch_year_month_by_week(self): def fetch_year_month_by_week(self):
...@@ -31,6 +31,7 @@ class ImportStToPg14(object): ...@@ -31,6 +31,7 @@ class ImportStToPg14(object):
self.date_info = list(df.year_month)[0] self.date_info = list(df.year_month)[0]
def read_data(self): def read_data(self):
self.fetch_year_month_by_week() # 如果传的date_type='week', 将date_info转换成月的值
# 1. 读取date_20_to_30表获取月份对应的周 # 1. 读取date_20_to_30表获取月份对应的周
sql_get_week = f"select year_week, year, week from selection.date_20_to_30 WHERE `year_month`='{self.date_info}' and week_day=1" sql_get_week = f"select year_week, year, week from selection.date_20_to_30 WHERE `year_month`='{self.date_info}' and week_day=1"
df_week = pd.read_sql(sql_get_week, con=self.engine_mysql) df_week = pd.read_sql(sql_get_week, con=self.engine_mysql)
......
Pyspark_job/my_kafka/kafka_flow_asin_detail.py
...@@ -99,6 +99,7 @@ class KafkaFlowAsinDetail(Templates): ...@@ -99,6 +99,7 @@ class KafkaFlowAsinDetail(Templates):
self.df_asin_keep_date = self.spark.sql("select 1+1;") self.df_asin_keep_date = self.spark.sql("select 1+1;")
self.df_asin_bsr_end = self.spark.sql("select 1+1;") self.df_asin_bsr_end = self.spark.sql("select 1+1;")
self.df_hide_category = self.spark.sql("select 1+1;") self.df_hide_category = self.spark.sql("select 1+1;")
self.df_bsr_category = self.spark.sql("select 1+1;")
self.df_asin_new_cate = self.spark.sql("select 1+ 1;") self.df_asin_new_cate = self.spark.sql("select 1+ 1;")
self.df_user_package_num = self.spark.sql("select 1+1;") self.df_user_package_num = self.spark.sql("select 1+1;")
self.df_asin_category = self.spark.sql("select 1+1;") self.df_asin_category = self.spark.sql("select 1+1;")
...@@ -190,7 +191,8 @@ class KafkaFlowAsinDetail(Templates): ...@@ -190,7 +191,8 @@ class KafkaFlowAsinDetail(Templates):
StructField("customer_reviews_json", StringType(), True), StructField("customer_reviews_json", StringType(), True),
StructField("img_list", StringType(), True), StructField("img_list", StringType(), True),
StructField("follow_sellers", IntegerType(), True), StructField("follow_sellers", IntegerType(), True),
StructField("fbm_delivery_price", FloatType(), True) StructField("fbm_delivery_price", FloatType(), True),
StructField("product_json", StringType(), True)
]) ])
return schema return schema
...@@ -448,15 +450,26 @@ class KafkaFlowAsinDetail(Templates): ...@@ -448,15 +450,26 @@ class KafkaFlowAsinDetail(Templates):
withColumn("title_package_quantity_is_abnormal", df.title_parse.getField("is_package_quantity_abnormal")). \ withColumn("title_package_quantity_is_abnormal", df.title_parse.getField("is_package_quantity_abnormal")). \
withColumn("variat_package_quantity_is_abnormal", df.variat_parse.getField("is_package_quantity_abnormal")). \ withColumn("variat_package_quantity_is_abnormal", df.variat_parse.getField("is_package_quantity_abnormal")). \
drop("title_parse", "variat_parse", "variat_attribute") drop("title_parse", "variat_parse", "variat_attribute")
# Number of Items：从 product_json 提取，cast 失败（脏数据）自动为 null，提取后立即 drop
df = df.withColumn(
"number_of_items",
F.get_json_object(F.col("product_json"), "$.Number of Items").cast("int")
).drop("product_json")
# 优先级：Number of Items > 属性字段 > 标题解析 > 默认1
df = df.withColumn( df = df.withColumn(
"package_quantity", F.expr(""" "package_quantity", F.expr("""
CASE WHEN title_package_quantity is null and variat_package_quantity is not null THEN variat_package_quantity CASE WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN number_of_items
WHEN title_package_quantity is not null THEN title_package_quantity ELSE 1 END""") WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity
WHEN title_package_quantity IS NOT NULL THEN title_package_quantity
ELSE 1 END""")
).withColumn( ).withColumn(
"is_package_quantity_abnormal", F.expr(""" "is_package_quantity_abnormal", F.expr("""
CASE WHEN title_package_quantity is null and variat_package_quantity is not null THEN variat_package_quantity_is_abnormal CASE WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN 0
WHEN title_package_quantity is not null THEN title_package_quantity_is_abnormal ELSE 2 END""") WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity_is_abnormal
).drop("title_package_quantity", "variat_package_quantity", "title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal") WHEN title_package_quantity IS NOT NULL THEN title_package_quantity_is_abnormal
ELSE 2 END""")
).drop("number_of_items", "title_package_quantity", "variat_package_quantity",
"title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal")
df = df.withColumn("title", F.lower(F.col("title"))) df = df.withColumn("title", F.lower(F.col("title")))
df = df.join(self.df_user_package_num, on=['asin', 'title'], how='left') df = df.join(self.df_user_package_num, on=['asin', 'title'], how='left')
df = df.withColumn("package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))). \ df = df.withColumn("package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))). \
...@@ -567,11 +580,18 @@ class KafkaFlowAsinDetail(Templates): ...@@ -567,11 +580,18 @@ class KafkaFlowAsinDetail(Templates):
df = df.withColumn("bsr_type", F.expr(""" df = df.withColumn("bsr_type", F.expr("""
CASE WHEN limit_rank is null and asin_bs_cate_1_rank <= 500000 THEN 1 WHEN limit_rank is not null and asin_bs_cate_1_rank <= limit_rank THEN 1 ELSE 0 END""" CASE WHEN limit_rank is null and asin_bs_cate_1_rank <= 500000 THEN 1 WHEN limit_rank is not null and asin_bs_cate_1_rank <= limit_rank THEN 1 ELSE 0 END"""
)).drop("limit_rank") )).drop("limit_rank")
# 5. 是否必需ASIN # 5. 是否必需ASIN（双重确认：BSR分类 + 页面描述分类，两者均在排除列表才标记为1）
df = df.withColumn("is_need_asin", F.expr(""" df = df.withColumn(
CASE WHEN asin_bs_cate_1_id in ('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed') THEN 1 "desc_category_first_name",
WHEN asin NOT LIKE 'B0%' THEN 1 F.lower(F.trim(F.split(F.col("category"), "›").getItem(0)))
).join(self.df_bsr_category, on=['desc_category_first_name'], how='left')
need_categories = "('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed')"
df = df.withColumn("is_need_asin", F.expr(f"""
CASE WHEN asin_bs_cate_1_id in {need_categories}
AND desc_category_first_id in {need_categories} THEN 1
WHEN asin NOT LIKE 'B0%' THEN 1
ELSE 0 END""")) ELSE 0 END"""))
df = df.drop("desc_category_first_name", "desc_category_first_id")
# 6. asin_type # 6. asin_type
df = df.withColumn("asin_type", F.expr(""" df = df.withColumn("asin_type", F.expr("""
CASE WHEN is_self_asin=1 THEN 1 WHEN is_need_asin=1 THEN 2 WHEN is_hide_asin=1 THEN 3 ELSE 0 END""" CASE WHEN is_self_asin=1 THEN 1 WHEN is_need_asin=1 THEN 2 WHEN is_hide_asin=1 THEN 3 ELSE 0 END"""
...@@ -805,6 +825,13 @@ class KafkaFlowAsinDetail(Templates): ...@@ -805,6 +825,13 @@ class KafkaFlowAsinDetail(Templates):
username=us_mysql_con_info['username'], query=sql) username=us_mysql_con_info['username'], query=sql)
self.df_hide_category = F.broadcast(df_hide_category) self.df_hide_category = F.broadcast(df_hide_category)
self.df_hide_category.show(10, truncate=False) self.df_hide_category.show(10, truncate=False)
print("5.1 读取BSR分类树（用于双重确认is_need_asin）")
sql = f"""
select lower(trim(en_name)) as desc_category_first_name, category_first_id as desc_category_first_id
from dim_bsr_category_tree where site_name = '{self.site_name}' and category_parent_id = 0 and leaf_node = 2
"""
self.df_bsr_category = F.broadcast(self.spark.sql(sqlQuery=sql))
self.df_bsr_category.show(10, truncate=False)
print("6. 读取asin_label信息") print("6. 读取asin_label信息")
sql = f""" sql = f"""
select asin, label from select asin, label from
...@@ -986,6 +1013,7 @@ class KafkaFlowAsinDetail(Templates): ...@@ -986,6 +1013,7 @@ class KafkaFlowAsinDetail(Templates):
F.col("describe_len").alias("asin_describe_len") F.col("describe_len").alias("asin_describe_len")
) )
df = df.drop("category", "seller_json") df = df.drop("category", "seller_json")
df = df.withColumn("date_info_del", F.lit(self.date_info))
df.write.format("org.elasticsearch.spark.sql").options(**self.es_options).mode("append").save() df.write.format("org.elasticsearch.spark.sql").options(**self.es_options).mode("append").save()
end_time = time.time() end_time = time.time()
elapsed_time = end_time - start_time elapsed_time = end_time - start_time
......
Pyspark_job/my_kafka/kafka_rank_asin_detail.py
...@@ -98,6 +98,7 @@ class KafkaRankAsinDetail(Templates): ...@@ -98,6 +98,7 @@ class KafkaRankAsinDetail(Templates):
self.df_asin_keep_date = self.spark.sql("select 1+1;") self.df_asin_keep_date = self.spark.sql("select 1+1;")
self.df_asin_bsr_end = self.spark.sql("select 1+1;") self.df_asin_bsr_end = self.spark.sql("select 1+1;")
self.df_hide_category = self.spark.sql("select 1+1;") self.df_hide_category = self.spark.sql("select 1+1;")
self.df_bsr_category = self.spark.sql("select 1+1;")
self.df_asin_new_cate = self.spark.sql("select 1+ 1;") self.df_asin_new_cate = self.spark.sql("select 1+ 1;")
self.df_user_package_num = self.spark.sql("select 1+1;") self.df_user_package_num = self.spark.sql("select 1+1;")
self.df_asin_category = self.spark.sql("select 1+1;") self.df_asin_category = self.spark.sql("select 1+1;")
...@@ -189,7 +190,8 @@ class KafkaRankAsinDetail(Templates): ...@@ -189,7 +190,8 @@ class KafkaRankAsinDetail(Templates):
StructField("customer_reviews_json", StringType(), True), StructField("customer_reviews_json", StringType(), True),
StructField("img_list", StringType(), True), StructField("img_list", StringType(), True),
StructField("follow_sellers", IntegerType(), True), StructField("follow_sellers", IntegerType(), True),
StructField("fbm_delivery_price", FloatType(), True) StructField("fbm_delivery_price", FloatType(), True),
StructField("product_json", StringType(), True)
]) ])
return schema return schema
...@@ -447,15 +449,26 @@ class KafkaRankAsinDetail(Templates): ...@@ -447,15 +449,26 @@ class KafkaRankAsinDetail(Templates):
withColumn("title_package_quantity_is_abnormal", df.title_parse.getField("is_package_quantity_abnormal")). \ withColumn("title_package_quantity_is_abnormal", df.title_parse.getField("is_package_quantity_abnormal")). \
withColumn("variat_package_quantity_is_abnormal", df.variat_parse.getField("is_package_quantity_abnormal")). \ withColumn("variat_package_quantity_is_abnormal", df.variat_parse.getField("is_package_quantity_abnormal")). \
drop("title_parse", "variat_parse", "variat_attribute") drop("title_parse", "variat_parse", "variat_attribute")
# Number of Items：从 product_json 提取，cast 失败（脏数据）自动为 null，提取后立即 drop
df = df.withColumn(
"number_of_items",
F.get_json_object(F.col("product_json"), "$.Number of Items").cast("int")
).drop("product_json")
# 优先级：Number of Items > 属性字段 > 标题解析 > 默认1
df = df.withColumn( df = df.withColumn(
"package_quantity", F.expr(""" "package_quantity", F.expr("""
CASE WHEN title_package_quantity is null and variat_package_quantity is not null THEN variat_package_quantity CASE WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN number_of_items
WHEN title_package_quantity is not null THEN title_package_quantity ELSE 1 END""") WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity
WHEN title_package_quantity IS NOT NULL THEN title_package_quantity
ELSE 1 END""")
).withColumn( ).withColumn(
"is_package_quantity_abnormal", F.expr(""" "is_package_quantity_abnormal", F.expr("""
CASE WHEN title_package_quantity is null and variat_package_quantity is not null THEN variat_package_quantity_is_abnormal CASE WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN 0
WHEN title_package_quantity is not null THEN title_package_quantity_is_abnormal ELSE 2 END""") WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity_is_abnormal
).drop("title_package_quantity", "variat_package_quantity", "title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal") WHEN title_package_quantity IS NOT NULL THEN title_package_quantity_is_abnormal
ELSE 2 END""")
).drop("number_of_items", "title_package_quantity", "variat_package_quantity",
"title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal")
df = df.withColumn("title", F.lower(F.col("title"))) df = df.withColumn("title", F.lower(F.col("title")))
df = df.join(self.df_user_package_num, on=['asin', 'title'], how='left') df = df.join(self.df_user_package_num, on=['asin', 'title'], how='left')
df = df.withColumn("package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))). \ df = df.withColumn("package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))). \
...@@ -566,11 +579,18 @@ class KafkaRankAsinDetail(Templates): ...@@ -566,11 +579,18 @@ class KafkaRankAsinDetail(Templates):
df = df.withColumn("bsr_type", F.expr(""" df = df.withColumn("bsr_type", F.expr("""
CASE WHEN limit_rank is null and asin_bs_cate_1_rank <= 500000 THEN 1 WHEN limit_rank is not null and asin_bs_cate_1_rank <= limit_rank THEN 1 ELSE 0 END""" CASE WHEN limit_rank is null and asin_bs_cate_1_rank <= 500000 THEN 1 WHEN limit_rank is not null and asin_bs_cate_1_rank <= limit_rank THEN 1 ELSE 0 END"""
)).drop("limit_rank") )).drop("limit_rank")
# 5. 是否必需ASIN # 5. 是否必需ASIN（双重确认：BSR分类 + 页面描述分类，两者均在排除列表才标记为1）
df = df.withColumn("is_need_asin", F.expr(""" df = df.withColumn(
CASE WHEN asin_bs_cate_1_id in ('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed') THEN 1 "desc_category_first_name",
WHEN asin NOT LIKE 'B0%' THEN 1 F.lower(F.trim(F.split(F.col("category"), "›").getItem(0)))
).join(self.df_bsr_category, on=['desc_category_first_name'], how='left')
need_categories = "('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed')"
df = df.withColumn("is_need_asin", F.expr(f"""
CASE WHEN asin_bs_cate_1_id in {need_categories}
AND desc_category_first_id in {need_categories} THEN 1
WHEN asin NOT LIKE 'B0%' THEN 1
ELSE 0 END""")) ELSE 0 END"""))
df = df.drop("desc_category_first_name", "desc_category_first_id")
# 6. asin_type # 6. asin_type
df = df.withColumn("asin_type", F.expr(""" df = df.withColumn("asin_type", F.expr("""
CASE WHEN is_self_asin=1 THEN 1 WHEN is_need_asin=1 THEN 2 WHEN is_hide_asin=1 THEN 3 ELSE 0 END""" CASE WHEN is_self_asin=1 THEN 1 WHEN is_need_asin=1 THEN 2 WHEN is_hide_asin=1 THEN 3 ELSE 0 END"""
...@@ -804,6 +824,13 @@ class KafkaRankAsinDetail(Templates): ...@@ -804,6 +824,13 @@ class KafkaRankAsinDetail(Templates):
username=us_mysql_con_info['username'], query=sql) username=us_mysql_con_info['username'], query=sql)
self.df_hide_category = F.broadcast(df_hide_category) self.df_hide_category = F.broadcast(df_hide_category)
self.df_hide_category.show(10, truncate=False) self.df_hide_category.show(10, truncate=False)
print("5.1 读取BSR分类树（用于双重确认is_need_asin）")
sql = f"""
select lower(trim(en_name)) as desc_category_first_name, category_first_id as desc_category_first_id
from dim_bsr_category_tree where site_name = '{self.site_name}' and category_parent_id = 0 and leaf_node = 2
"""
self.df_bsr_category = F.broadcast(self.spark.sql(sqlQuery=sql))
self.df_bsr_category.show(10, truncate=False)
print("6. 读取asin_label信息") print("6. 读取asin_label信息")
sql = f""" sql = f"""
select asin, label from select asin, label from
...@@ -982,6 +1009,7 @@ class KafkaRankAsinDetail(Templates): ...@@ -982,6 +1009,7 @@ class KafkaRankAsinDetail(Templates):
F.col("describe_len").alias("asin_describe_len") F.col("describe_len").alias("asin_describe_len")
) )
df = df.drop("category", "seller_json") df = df.drop("category", "seller_json")
df = df.withColumn("date_info_del", F.lit("1970-01"))
df.write.format("org.elasticsearch.spark.sql").options(**self.es_options).mode("append").save() df.write.format("org.elasticsearch.spark.sql").options(**self.es_options).mode("append").save()
end_time = time.time() end_time = time.time()
elapsed_time = end_time - start_time elapsed_time = end_time - start_time
......
Pyspark_job/script/dolphinscheduler_task_monitor.py 0 → 100644
import os
import sys
sys.path.append(os.path.dirname(sys.path[0])) # 上级目录
from utils.DolphinschedulerHelper import DolphinschedulerHelper
from utils.redis_utils import RedisUtils
from utils.common_util import CommonUtil
if __name__ == '__main__':
# 监听半小时之内报错的任务
client = RedisUtils.getClient()
redis_key = "dolphinscheduler_task_monitor:lateTime"
lateTime = client.get(redis_key)
lateTime = lateTime or "2026-03-20 00:00:00"
import json
print(lateTime)
req_params = {
"pageNo": 1,
"pageSize": 100,
"stateType": "FAILURE",
"startDate": lateTime
}
project_name = "big_data_selection"
errList = DolphinschedulerHelper.list_projects_task(project_name=project_name, req_params=req_params)
errMsg = []
for it in errList:
task_name = "-".join(it['name'].split("-")[:-2])
paramMap = DolphinschedulerHelper.view_process_instance_variables(project_name, it["id"])
errMsg.append(f"""任务[{task_name}]执行失败,启动参数为:{json.dumps(paramMap)}""")
from datetime import datetime
now = datetime.now()
formatted_time = now.strftime("%Y-%m-%d %H:%M:%S")
if len(errMsg) > 0:
all_msg = "\n".join(errMsg)
msg = f"""截止到日期{lateTime}到{formatted_time},海豚任务报错详情如下\n{all_msg}"""
CommonUtil.send_msg_robot(msg)
client.set(redis_key, formatted_time)
Pyspark_job/sqoop_export/export_dwt_ai_asin_add.py deleted 100644 → 0
import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.common_util import CommonUtil
from utils.secure_db_client import get_remote_engine
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
date_type = CommonUtil.get_sys_arg(2, None)
date_info = CommonUtil.get_sys_arg(3, None)
print(f"执行参数为{sys.argv}")
# 获取数据库引擎
db_type = "postgresql_15"
engine = get_remote_engine(
site_name='us',
db_type=db_type
)
if site_name == 'us':
export_tb = f"ai_asin_detail_month_{date_info.replace('-', '_')}"
else:
export_tb = f"{site_name}_ai_asin_detail_month_{date_info.replace('-', '_')}"
# 导出数据
engine.sqoop_raw_export(
hive_table="dwt_ai_asin_add",
import_table=export_tb,
partitions={
"site_name": site_name,
"date_type": date_type,
"date_info": date_info
},
m=30,
cols="site_name,asin,weight,bought_month,category,img,title,brand,account_name,account_addr,buy_box_seller_type,"
"launch_time,img_num,variation_flag,variation_num,ao_val,category_id,category_current_id,parent_asin,bsr_rank,"
"price,rating,total_comments,seller_id,fb_country_name,review_json_list,launch_time_type,describe,product_json,"
"product_detail_json,bought_month_mom,bought_month_yoy,is_new_flag,is_ascending_flag"
)
print("success")
Pyspark_job/sqoop_import/ods_merchantwords_brand_analytics.py deleted 100644 → 0
import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.ssh_util import SSHUtil
from utils.common_util import CommonUtil
from utils.hdfs_utils import HdfsUtils
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
date_type = CommonUtil.get_sys_arg(2, None)
date_info = CommonUtil.get_sys_arg(3, None)
assert site_name is not None, "site_name 不能为空！"
assert date_type is not None, "date_type 不能为空！"
assert date_info is not None, "date_info 不能为空！"
hive_table = f"ods_merchantwords_brand_analytics"
partition_dict = {
"site_name": site_name,
"date_type": date_type,
"date_info": date_info
}
# 落表路径校验
hdfs_path = CommonUtil.build_hdfs_path(hive_table, partition_dict=partition_dict)
print(f"hdfs_path is {hdfs_path}")
year, month, day = date_info.split("-")
db_type = 'postgresql_16'
import_table = f"{site_name}_merchantwords_brand_analytics_{year}_{month}_{day}"
sql_query = f"""
select
id,
search_term,
quantity_being_sold,
created_time,
updated_time,
quantity_being_sold_str,
result_count,
departments
from {import_table}
where 1=1
and \$CONDITIONS
"""
# 进行schema和数据校验
CommonUtil.check_schema_before_import(db_type=db_type,
site_name=site_name,
query=sql_query,
hive_tb_name=hive_table,
msg_usr=['chenyuanjie'],
partition_dict=partition_dict)
# 生成导出脚本
import_sh = CommonUtil.build_import_sh(site_name=site_name,
db_type=db_type,
query=sql_query,
hdfs_path=hdfs_path,
map_num=1,
key='id'
)
HdfsUtils.delete_hdfs_file(hdfs_path)
client = SSHUtil.get_ssh_client()
SSHUtil.exec_command_async(client, import_sh, ignore_err=False)
CommonUtil.after_import(hdfs_path=hdfs_path, hive_tb=hive_table)
client.close()
CommonUtil.check_import_sync_num(db_type=db_type,
partition_dict=partition_dict,
import_query=sql_query,
hive_tb_name=hive_table,
msg_usr=['chenyuanjie'])
\ No newline at end of file
Pyspark_job/sqoop_import/ods_merchantwords_other_search_term_data.py deleted 100644 → 0
import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.ssh_util import SSHUtil
from utils.common_util import CommonUtil
from utils.hdfs_utils import HdfsUtils
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
date_type = CommonUtil.get_sys_arg(2, None)
date_info = CommonUtil.get_sys_arg(3, None)
assert site_name is not None, "site_name 不能为空！"
assert date_type is not None, "date_type 不能为空！"
assert date_info is not None, "date_info 不能为空！"
hive_table = f"ods_merchantwords_other_search_term_data"
partition_dict = {
"site_name": site_name,
"date_type": date_type,
"date_info": date_info
}
# 落表路径校验
hdfs_path = CommonUtil.build_hdfs_path(hive_table, partition_dict=partition_dict)
print(f"hdfs_path is {hdfs_path}")
year, month, day = date_info.split("-")
db_type = 'postgresql_16'
import_table = f"{site_name}_merchantwords_other_search_term_{year}_{month}_{day}"
sql_query = f"""
select
id,
search_term,
asin,
page,
buy_data,
label,
created_time,
updated_time
from {import_table}
where 1=1
and \$CONDITIONS
"""
# 进行schema和数据校验
CommonUtil.check_schema_before_import(db_type=db_type,
site_name=site_name,
query=sql_query,
hive_tb_name=hive_table,
msg_usr=['chenyuanjie'],
partition_dict=partition_dict)
import_sh = CommonUtil.build_import_sh(site_name=site_name,
db_type=db_type,
query=sql_query,
hdfs_path=hdfs_path,
map_num=35,
key='id'
)
HdfsUtils.delete_hdfs_file(hdfs_path)
client = SSHUtil.get_ssh_client()
SSHUtil.exec_command_async(client, import_sh, ignore_err=False)
CommonUtil.after_import(hdfs_path=hdfs_path, hive_tb=hive_table)
client.close()
\ No newline at end of file
Pyspark_job/sqoop_import/ods_merchantwords_search_term_type.py deleted 100644 → 0
import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.ssh_util import SSHUtil
from utils.common_util import CommonUtil
from utils.hdfs_utils import HdfsUtils
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
st_type = CommonUtil.get_sys_arg(2, None)
date_type = CommonUtil.get_sys_arg(3, None)
date_info = CommonUtil.get_sys_arg(4, None)
assert site_name is not None, "site_name 不能为空！"
assert st_type is not None, "st_type 不能为空！"
assert date_type is not None, "date_type 不能为空！"
assert date_info is not None, "date_info 不能为空！"
if site_name == 'us':
if date_info == '2024-05-05':
if st_type == "bs":
quit()
elif date_info in ['2024-06-06', '2024-06-07', '2024-06-08']:
if st_type == "hr":
quit()
hive_tb = f"ods_merchantwords_search_term_{st_type}"
partition_dict = {
"site_name": site_name,
"date_type": date_type,
"date_info": date_info
}
if st_type in ["zr", "sp"]:
cols = "search_term,asin,page,page_row,id,created_time,updated_time"
elif st_type in ["sb"]:
cols = "search_term,asin,page,data_type,id,created_time,updated_time"
else:
cols = "search_term,asin,page,created_time,updated_time"
db_type = 'postgresql_16'
year, month, day = date_info.split("-")
import_tb = f"{site_name}_merchantwords_search_term_rank_{st_type}_{year}_{month}_{day}"
query = f"""
select
{cols}
from
{import_tb}
where 1 = 1
and \$CONDITIONS
"""
print(f"当前同步的表为：{import_tb}")
hdfs_path = CommonUtil.build_hdfs_path(hive_tb, partition_dict=partition_dict)
print(f"hdfs_path is {hdfs_path}")
empty_flag, check_flag = CommonUtil.check_schema_before_import(db_type=db_type,
site_name=site_name,
query=query,
hive_tb_name=hive_tb,
msg_usr=['chenyuanjie'],
partition_dict=partition_dict
)
assert check_flag, f"导入hive表{hive_tb}表结构检查失败！请检查query是否异常！！"
if not empty_flag:
if st_type in ["zr", "sp", "sb"]:
if st_type == "zr":
map_num = 50
elif st_type == "sp":
map_num = 25
else:
map_num = 15
sh = CommonUtil.build_import_sh(site_name=site_name,
db_type=db_type,
query=query,
hdfs_path=hdfs_path,
map_num=map_num,
key="id")
else:
sh = CommonUtil.build_import_sh(site_name=site_name,
db_type=db_type,
query=query,
hdfs_path=hdfs_path)
HdfsUtils.delete_hdfs_file(hdfs_path)
client = SSHUtil.get_ssh_client()
SSHUtil.exec_command_async(client, sh, ignore_err=False)
CommonUtil.after_import(hdfs_path=hdfs_path, hive_tb=hive_tb)
client.close()
pass
\ No newline at end of file
Pyspark_job/utils/DolphinschedulerHelper.py
...@@ -498,3 +498,26 @@ class DolphinschedulerHelper(object): ...@@ -498,3 +498,26 @@ class DolphinschedulerHelper(object):
return resp_json['msg'] return resp_json['msg']
else: else:
raise Exception(f"任务停止失败") raise Exception(f"任务停止失败")
@classmethod
def list_projects_task(cls, project_name: str = _def_project_name, req_params=None):
"""
根据当前运行脚本判断是否是海豚上正在运行的任务并获取任务参数
:param project_name: 默认是 big_data_selection
"""
if req_params is None:
req_params = {}
project_map = cls.get_project_map()
project_code = project_map.get(project_name)
url = f"{cls._ip_port}/dolphinscheduler/projects/{project_code}/process-instances"
resp = requests.get(
url,
headers=cls.get_http_header(),
params=req_params
)
resp_json = json.loads(resp.content.decode("utf-8"))
if bool(resp_json['success']):
return resp_json['data']['totalList']
else:
return None
Pyspark_job/utils/common_util.py
...@@ -1884,17 +1884,24 @@ outputformat 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' ...@@ -1884,17 +1884,24 @@ outputformat 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
df_latest_asin_detail_with_parent = df_latest_asin_detail_with_parent.withColumnRenamed(f"new_{column}", f"{column}") df_latest_asin_detail_with_parent = df_latest_asin_detail_with_parent.withColumnRenamed(f"new_{column}", f"{column}")
return df_asin_detail, df_latest_asin_detail_with_parent return df_asin_detail, df_latest_asin_detail_with_parent
@staticmethod
def send_msg_robot(msg: str):
webhook_url = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=c519c702-6164-45c8-98b5-a87e52150f19"
headers = {
"Content-Type": "application/json"
}
data = {
"msgtype": "text",
"text": {
"content": msg
}
}
resp = requests.post(webhook_url, json=data, headers=headers)
if resp.status_code == 200:
result = resp.json()
if result.get("errcode") == 0:
print("发送成功")
else:
print("发送失败:", result)
else:
print("HTTP错误:", resp.status_code, resp.text)
Pyspark_job/utils/es_util.py
...@@ -1044,6 +1044,141 @@ class EsUtils(object): ...@@ -1044,6 +1044,141 @@ class EsUtils(object):
}, },
"img_type_arr": { "img_type_arr": {
"type": "integer" "type": "integer"
},
"date_info_del": {
"type": "keyword"
}
}
}
}
@staticmethod
def get_es_ai_body():
return {
"settings": {
"number_of_shards": "3",
"number_of_replicas": "1",
"analysis": {
"filter": {
"en_snowball": {
"type": "snowball",
"language": "English"
},
"en_synonym": {
"type": "synonym_graph",
"synonyms_path": "analysis/synonyms_en.txt",
"updateable": "true"
}
},
"analyzer": {
"en_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"en_snowball"
]
},
"en_search_analyzer": {
"tokenizer": "standard",
"filter": [
"lowercase",
"en_synonym",
"en_snowball"
]
}
},
"normalizer": {
"lowercase_normalizer": {
"type": "custom",
"char_filter": [],
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"asin": {"type": "keyword"},
"parent_asin": {"type": "keyword"},
"site_name": {"type": "keyword"},
"analyze_id": {"type": "integer"},
"seller_id": {"type": "keyword"},
"title": {
"type": "text",
"analyzer": "en_analyzer",
"search_analyzer": "en_search_analyzer"
},
"img": {"type": "keyword"},
"img_num": {"type": "integer"},
"launch_time": {"type": "keyword"},
"launch_time_type": {"type": "integer"},
"price": {"type": "scaled_float", "scaling_factor": 100},
"rating": {"type": "scaled_float", "scaling_factor": 100},
"total_comments": {"type": "integer"},
"bought_month": {"type": "integer"},
"bought_month_mom": {"type": "scaled_float", "scaling_factor": 100},
"bought_month_yoy": {"type": "scaled_float", "scaling_factor": 100},
"bsr_rank": {"type": "integer"},
"bsr_rank_str": {"type": "keyword"},
"ao_val": {"type": "scaled_float", "scaling_factor": 100},
"variation_flag": {"type": "integer"},
"variation_num": {"type": "integer"},
"is_ascending_flag": {"type": "integer"},
"is_new_flag": {"type": "integer"},
"buy_box_seller_type": {"type": "keyword"},
"category": {"type": "keyword"},
"category_id": {"type": "keyword"},
"category_current_id": {"type": "keyword"},
"festival": {"type": "keyword"},
"fb_country_name": {"type": "keyword"},
"profit_key": {"type": "keyword"},
"multi_color_flag": {"type": "keyword"},
"package_quantity_flag": {"type": "keyword"},
"title_pic_flag": {"type": "keyword"},
"title_word_flag": {"type": "keyword"},
"account_addr": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"account_name": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"appearance": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"brand": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"color": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"crowd": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"function": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"material": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"multi_color_content": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"package_quantity": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"package_quantity_arr": {"type": "integer"},
"scene_comment": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"scene_title": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"shape": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"short_desc": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"size": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"theme": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"title_pic_content": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"title_word_content": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"uses": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"weight": {"type": "keyword", "normalizer": "lowercase_normalizer"},
"label_content": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"normalizer": "lowercase_normalizer"
}
}
},
"profit_rate_extra": {
"type": "object",
"properties": {
"ocean_profit": {
"type": "float"
},
"air_profit": {
"type": "float"
}
}
} }
} }
} }
......
Pyspark_job/utils/templates.py
...@@ -93,6 +93,8 @@ class Templates(object): ...@@ -93,6 +93,8 @@ class Templates(object):
# 测试标识 # 测试标识
self.test_flag = 'normal' self.test_flag = 'normal'
self.beginning_offsets_dict = {} # history消费时, 初始的偏移量 self.beginning_offsets_dict = {} # history消费时, 初始的偏移量
# 记录最后一次收到非空批次的时间，用于无新数据超时检测
self.last_data_time = time.time()
# redis连接对象--用来锁定--解决并发 # redis连接对象--用来锁定--解决并发
self.client = get_redis_h14() self.client = get_redis_h14()
...@@ -453,11 +455,25 @@ class Templates(object): ...@@ -453,11 +455,25 @@ class Templates(object):
self.query.awaitTermination() self.query.awaitTermination()
def handle_kafka_stream_templates(self, kafka_df, epoch_id): def handle_kafka_stream_templates(self, kafka_df, epoch_id):
if self.spider_type == 'asin详情' and kafka_df.count() > 0: has_data = self.spider_type == 'asin详情' and kafka_df.count() > 0
if has_data:
kafka_df = self.deduplication_kafka_data(kafka_df, "asin", "asinUpdateTime") kafka_df = self.deduplication_kafka_data(kafka_df, "asin", "asinUpdateTime")
self.handle_kafka_stream(kafka_df, epoch_id) self.handle_kafka_stream(kafka_df, epoch_id)
if has_data:
# 处理完成后更新时间戳，避免长批次处理耗时误触发超时
self.last_data_time = time.time()
if self.test_flag == 'normal': if self.test_flag == 'normal':
self.kafka_consumption_is_finished() self.kafka_consumption_is_finished()
# 仅当前批次无新数据时才做超时检测
# 若距上次有效数据已超过 30 分钟，说明爬虫可能已完成但状态表尚未更新
# 进入轮询，每 2 分钟重新检查一次，直到状态更新后 kafka_consumption_is_finished() 内部 exit(0)
if not has_data:
elapsed = time.time() - self.last_data_time
if elapsed > 30 * 60:
print(f"[超时检测] 已 {elapsed / 60:.1f} 分钟无新数据，进入状态轮询（每2分钟检查一次），等待爬虫状态更新")
while True:
time.sleep(120)
self.kafka_consumption_is_finished()
def handle_kafka_stream(self, kafka_df, epoch_id): def handle_kafka_stream(self, kafka_df, epoch_id):
pass pass
...@@ -657,14 +673,19 @@ class Templates(object): ...@@ -657,14 +673,19 @@ class Templates(object):
wx_msg = f"站点： {self.site_name}, {self.date_type}, {self.date_info} asin详情实时消费数据到redis准备工作已完成，可以开启详情爬取！" wx_msg = f"站点： {self.site_name}, {self.date_type}, {self.date_info} asin详情实时消费数据到redis准备工作已完成，可以开启详情爬取！"
else: else:
pass pass
try: sql = f"UPDATE selection.workflow_progress SET {kafka_field}=3, updated_at=CURRENT_TIMESTAMP where site_name='{self.site_name}' and date_type='{self.date_type}' and date_info='{self.date_info}' and page='asin详情'"
sql = f"UPDATE selection.workflow_progress SET {kafka_field}=3, updated_at=CURRENT_TIMESTAMP where site_name='{self.site_name}' and date_type='{self.date_type}' and date_info='{self.date_info}' and page='asin详情'" for retry in range(5):
DBUtil.exec_sql('mysql', 'us', sql) try:
CommonUtil.send_wx_msg(wx_users, f"asin详情kafka消费", wx_msg) DBUtil.exec_sql('mysql', 'us', sql)
except Exception as e: CommonUtil.send_wx_msg(wx_users, f"asin详情kafka消费", wx_msg)
print(e, traceback.format_exc()) break
CommonUtil.send_wx_msg(wx_users, f"\u26A0asin详情kafka实时消费\u26A0", except Exception as e:
f"站点： {self.site_name} asin详情实时消费准备失败，请等待处理！") print(f"UPDATE workflow_progress 失败（第{retry + 1}次），等待10s重试", e, traceback.format_exc())
if retry == 4:
CommonUtil.send_wx_msg(wx_users, f"\u26A0asin详情kafka实时消费\u26A0",
f"站点： {self.site_name} asin详情实时消费准备失败，请等待处理！")
else:
time.sleep(10)
else: else:
pass pass
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment