# -*-coding:utf8-*-#
__author__ = 'hash'
"""
create time:16/7/5 15:42
"""
from datetime import datetime, timedelta
# os.environ['SPARK_HOME'] = "/Users/play/software/spark" # 絕對路徑
# sys.path.append("/Users/play/software/spark/python")
# print os.environ['SPARK_HOME']
from pyspark import SparkContext, SparkConf
# Initialize SparkContext
# sc = SparkContext('local')
conf = SparkConf().setAppName("The GMV Sum BeiBei")
conf.set('spark.logConf', False)
sc = SparkContext(conf=conf)
today_str='2016-07-05'
today = datetime.strptime(today_str, "%Y-%m-%d")
# today = datetime.today()
today_str = today.strftime("%Y-%m-%d")
# 指定日期,前一天
ytday = today - timedelta(days=1)
ytday_str = ytday.strftime("%Y-%m-%d")
base_path = "hdfs://master:9000/super_db/"
category = "beibei-com"
# source_path_current='/Users/play/TEMP/7-4/log/2016-07-03/beibei-com_2016-07-03*'
# source_path_current = '/Users/play/TEMP/7-4/log/test/test.txt'
# source_path_next = '/Users/play/TEMP/7-4/log/2016-07-04/beibei-com_2016-07-04-00*'
# yesterday
# source_path_current = '/super_db/raw_db/' + category + '/' + category + '_item/2016/' + ytday_str + '/' + category + '_' + ytday_str + '*'
# today
# source_path_next = '/super_db/raw_db/' + category + '/' + category + '_item/2016/' + today_str + '/' + category + '_' + today_str + '-00*'
#
source_path_current = '/super_db/raw_db/beibei/beibei_item/2016/beibei_item_07_04.txt'
source_path_next = '/super_db/raw_db/beibei/beibei_item/2016/beibei_item_07_05.txt'
# target_data_path = '/Users/play/TEMP/7-4/data/rs.txt'
# target_stat_path = '/Users/play/TEMP/7-4/stat/rs.txt'
# target_cat_stat_path = '/Users/play/TEMP/7-4/stat_by_cat/rs.txt'
target_stat_path = base_path + "analytics_db/" + category + "/" + category + "_item/2016/" + ytday_str + "_stat2"
print target_stat_path
# def filter_log_item(x):
# sp1 = x.split("#&#")
# if len(sp1) == 10:
# try:
# x1 = datetime.strptime(sp1[9], '%Y-%m-%d %H:%M:%S')
# return True
# except:
# return False
# # return True
# else:
# return False
def filter_log_item2(x):
sp1 = x.split("#&#")
if len(sp1) == 28:
try:
x1 = datetime.strptime(sp1[-1], '%Y-%m-%d %H:%M:%S')
return True
except:
return False
# return True
else:
return False
def get_some_data(x):
sp1 = x.split("#&#")
# if len(sp1)<10:return None
id = sp1[0].strip()
cat = sp1[2].strip()
brand = sp1[3].strip()
price = float(sp1[4])
orig_price = float(sp1[5])
sales = int(sp1[6])
start_date = sp1[8].split(":")[0].strip()
start_date1 = datetime.strptime(start_date, "%Y-%m-%d %H")
price= 0 if price==-1 else price
orig_price= 0 if orig_price==-1 else orig_price
sales= 0 if sales==-1 else sales
return ((id, start_date1), (start_date1, sales, price, cat, brand, orig_price))
def get_some_data2(x):
sp1 = x.split("#&#")
# if len(sp1)<10:return None
# id = sp1[0].strip()
# cat = sp1[2].strip()
# brand = sp1[3].strip()
# price = float(sp1[4])
# orig_price = float(sp1[5])
# # sales = int(sp1[6])
# start_date = sp1[8].split(":")[0].strip()
# start_date1 = datetime.strptime(start_date, "%Y-%m-%d %H")
id = sp1[3]
start_date = sp1[27].split(":")[0].strip()
start_date1 = datetime.strptime(start_date, "%Y-%m-%d %H")
sales = sp1[8]
price = sp1[6]
orig_price = sp1[9]
# stock = int(sp1[17])
if "\N" == sales:sales = 0
else:sales = int(sales)
if "\N" == price:
price = 0.0
else:
price = float(price)
if "\N" == orig_price:
orig_price = 0.0
else:
orig_price = float(orig_price)
# return (id, (start_date, sales, stock, price))
cat=""
brand=""
return ((id, start_date1), (start_date1, sales, price, cat, brand, orig_price))
# 昨天銷售數據
yesterday_sales_rdd = sc.textFile(source_path_current).filter(filter_log_item2).map(get_some_data2)
# ys1 = yesterday_sales_rdd.collect()
# print ys1
# print get_some_data(ys1)
# 今天0點的數據
today_sales_rdd = sc.textFile(source_path_next).filter(filter_log_item2).map(get_some_data2)
# print today_sales_rdd.collect()
# 每小時的商品數,只要有日誌記錄
gbk = yesterday_sales_rdd.groupByKey()
# from collections import Counter
def couter(x, y):
k1 = x.keys()
k2 = y.keys()
s1 = set(k1 + k2)
d1 = dict()
for h in s1:
d1[h] = x.get(h, 0) + y.get(h, 0)
return d1
# hour_sale_num = gbk.keys().map(lambda dd: {dd[1].hour: 1}).reduce(lambda x, y: dict(Counter(x) + Counter(y)))
hour_sale_num = gbk.keys().map(lambda dd: {dd[1].hour: 1}).reduce(lambda x, y: couter(x, y))
print hour_sale_num
# 在售商品個數
spu_sum = yesterday_sales_rdd.count()
#
# s = '2016-07-04 00:15:02'
# datetime.strptime(s, "%Y-%m-%d %H:%M:%S")
# 銷售彙總，昨天和今天
all_data = yesterday_sales_rdd.union(today_sales_rdd)
# print 'all_data.first()',all_data.collect()
sales_rdd = all_data.map(lambda x: (x[0][0], x[1]))
print sales_rdd.first()
grouped_sales_rdd = sales_rdd.groupByKey().mapValues(list)
print grouped_sales_rdd.first()
# start_date, sales, price, cat, brand, orig_price
def date_dict(lst):
d2 = dict()
for i, x in enumerate(lst):
# print x
# print x[0].hour
d2[x[0]] = x[1:]
return d2
# date_dict(sale_1[1])
def sale_count24(dd):
sale_list = date_dict(dd)
# print sale_list
sort = sorted(sale_list)
sales = []
for x in reversed(range(24)):
# print x
td1 = ytday + timedelta(hours=x)
td2 = ytday + timedelta(hours=x - 1) # 前1小時
try:
indx = sort.index(td1)
td2 = sort[indx - 1] # 列表的前一項
s1 = int(sale_list[td1][0])
p1 = float(sale_list[td1][1])
s2 = int(sale_list[td2][0])
p2 = float(sale_list[td2][1])
sale = s1 - s2
sale = sale if sale > 0 else 0
money = s1 * p1 - s2 * p2
money = money if money > 0 else 0
sales.append((sale, money))
continue
except:
# 不在列表內
sale = 0
money = 0.0
sales.append((sale, money))
continue
return list(reversed(sales)) # 要倒置一下
# gsr=grouped_sales_rdd.collect()
# example1=gsr[1][1]
# for x in example1:
# print x
# print sale_count24(example1)
hour_sales_rdd = grouped_sales_rdd.map(lambda (a, b): (a, sale_count24(b)))
def sum2(a, b):
if len(a) == 2:
a = a[1]
if len(b) == 2:
b = b[1]
su = []
for x, y in zip(a, b):
su.append((x[0] + y[0], x[1] + y[1]))
return su
hour_sales = hour_sales_rdd.reduce(lambda a, b: sum2(a, b))
# print hour_sales
# 每小時，累積銷量，累積銷售額
num = [x[0] for x in hour_sales]
money = [x[1] for x in hour_sales]
sale_num_sum = sum(num)
# sum(money) / float(len(money))
sale_money_sum = sum(money)
# SPU 每小時銷售額>0的商品個數
spu_sale = hour_sales_rdd.map(lambda a: [1 if x[0] > 0 else 0 for x in a]).reduce(
lambda a, b: [x + y for x, y in zip(a, b)])
print 'spu_sale', spu_sale, len(spu_sale)
# 平均價格
# yesterday_sales_rdd.first()
# 取出現價
now_price_rdd = yesterday_sales_rdd.map(lambda x: x[1][2])
# now_price_rdd.first()
# 取出原價
org_price_rdd = yesterday_sales_rdd.map(lambda x: x[1][-1])
# org_price_rdd.first()
avg_price = now_price_rdd.reduce(lambda a, b: (a + b) / 2.0)
avg_org_price = org_price_rdd.reduce(lambda a, b: (a + b) / 2.0)
# 折扣
discount = avg_price / avg_org_price
# discount
# SPU 每小時銷售額>0的商品個數
def sale_item_to_hour(x):
idd = x[0]
salist = x[1]
hd = dict()
for h in range(24):
if salist[h][0] > 0: # 有銷量
hd[h] = [idd]
return hd
def reduce_hour_dict_sum(x, y):
# print Counter(x),Counter(y)
nd = dict()
for h in range(24):
nd[h] = x.get(h, []) + y.get(h, [])
return nd
# reduce_hour_dict_sum(ssc[6],ssc[10])
spu_sale = hour_sales_rdd.map(sale_item_to_hour).reduce(lambda x, y: reduce_hour_dict_sum(x, y))
##每小時銷售商品的個數
spu_sale_num = dict()
for h, lst in spu_sale.items():
spu_sale_num[h] = len(lst)
set1 = reduce(set.union, [set(x) for x in spu_sale.values()])
spu_sale_sum = len(set1) # 商品去重後的總數
# json
js = dict()
for hour in range(24):
sale_num = hour_sales[hour][0]
sale_money = hour_sales[hour][1]
spu = hour_sale_num.get(hour, 0)
spu_sale_hour = spu_sale_num.get(hour, 0)
js[hour] = {'sales': sale_num, 'gmv': sale_money, 'spu': spu, 'spu_saled': spu_sale_hour}
# spu_sale_sum ,set 去重
js['sum'] = {'sales_sum': sale_num_sum, 'gmv_sum': sale_money_sum,
'spu_sum': spu_sum, 'spu_saled_sum': spu_sale_sum,
'avg_price': avg_price, 'avg_org_price': avg_org_price, 'discount': discount
}
print '----------------------------'
print js
"""
js={0: {'spu': 3, 'spu_saled': 2, 'sales': 4394, 'gmv': 1739302.0}, 1: {'spu': 3, 'spu_saled': 2, 'sales': 8048, 'gmv': 716863.0}, 2: {'spu': 3, 'spu_saled': 3, 'sales': 3, 'gmv': 798.0}, 3: {'spu': 3, 'spu_saled': 2, 'sales': -8046, 'gmv': -715563.0}, 4: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 5: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 6: {'spu': 3, 'spu_saled': 3, 'sales': 8057, 'gmv': 718666.0}, 7: {'spu': 3, 'spu_saled': 2, 'sales': -2480, 'gmv': -1687642.0}, 8: {'spu': 3, 'spu_saled': 2, 'sales': 560, 'gmv': 1632733.0}, 9: {'spu': 3, 'spu_saled': 1, 'sales': -8611, 'gmv': -2349332.0}, 10: {'spu': 3, 'spu_saled': 2, 'sales': 8055, 'gmv': 716835.0}, 11: {'spu': 3, 'spu_saled': 1, 'sales': -7496, 'gmv': 916460.0}, 12: {'spu': 3, 'spu_saled': 1, 'sales': 5571, 'gmv': -972225.0}, 13: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 14: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 15: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 16: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 17: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 18: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 19: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 20: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 21: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 22: {'spu': 3, 'spu_saled': 0, 'sales': -8055, 'gmv': -716895.0}, 23: {'spu': 0, 'spu_saled': 0, 'sales': 0, 'gmv': 0.0}, 'sum': {'discount': 0.639766081882126, 'gmv_sum': 0.0, 'avg_org_price': 244.28571429350995, 'sales_sum': 0, 'avg_price': 156.28571429333533, 'spu_sum': 36, 'spu_saled_sum': 3}}
"""
# 保存
# stat_rdd = sc.parallelize(json.dumps(str(js),ensure_ascii=False),numSlices=1)
stat_rdd = sc.parallelize([str(js)])
# stat_rdd.saveAsTextFile(target_stat_path)
# stat_rdd.repartition(1).saveAsTextFile(target_stat_path)
# stat_rdd.map(json.dumps).saveAsPickleFile(target_stat_path)
stat_rdd.repartition(1).saveAsTextFile(target_stat_path)
print 'Saved:', target_stat_path
# from pyspark import SQLContext
# languagesDF= SQLContext.load(js)
# languagesDF.write.json(target_stat_path)
beibei_sum_spark(python)
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