1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
from utils import con_sql
from datetime import datetime
from config import *
import pandas as pd
import os
import time
import pymysql
import time
def fetch_qa(device_id, card_type, size):
try:
key = '{device_id}-{card_type}-{date}'.format(device_id=device_id,
card_type=card_type, date=RecommendFeed.current_date())
if (device_id != '0'):
search_qa_recommend_key = "TS:search_recommend_answer_queue:device_id:" + str(device_id)
search_qa_recommend_list = list()
search_cursor_ts = 0
if redis_client.exists(search_qa_recommend_key):
search_qa_recommend_dict = redis_client.hgetall(search_qa_recommend_key)
if b'cursor' in search_qa_recommend_dict:
search_cursor_ts = json.loads(search_qa_recommend_dict[b'cursor'])
if search_cursor_ts < 10:
search_qa_recommend_list = json.loads(search_qa_recommend_dict[b'answer_queue'])
if search_cursor_ts < len(search_qa_recommend_list):
size = size - 1
try:
que = DeviceQAQueue.objects.get(device_id=device_id)
except DeviceQAQueue.DoesNotExist:
que = AnswerQueue.objects.last()
if not que:
return []
que = list(filter(None, que.queue.split(',')))
# adjust args.
cursor = redis_client.get(key) or 0
cursor = int(cursor) % len(que)
size = min(size, len(que))
# redis_client.set(key, cursor + size, ex=24 * 60 * 60)
data = list(islice(cycle(que), cursor, cursor + size))
data = list(map(int, data))
if cursor + 2 * size < len(que):
redis_client.set(key, cursor + size, ex=24 * 60 * 60)
else:
try:
context.request_logger.app(reset_answer_queue=True)
cursor = 0
redis_client.set(key, cursor, ex=24 * 60 * 60)
except:
redis_client.set(key, cursor + size, ex=24 * 60 * 60)
if device_id != '0':
if len(search_qa_recommend_list) > 0 and search_cursor_ts < len(search_qa_recommend_list):
queue = search_qa_recommend_list[search_cursor_ts:search_cursor_ts + 1]
queue.extend(data)
data = queue
new_search_cursor = search_cursor_ts + 1
redis_client.hset(search_qa_recommend_key, 'cursor', new_search_cursor)
redis_client.expire(search_qa_recommend_key, 30 * 24 * 60 * 60)
read_qa_key = "TS:recommend_answer_set:device_id:" + str(device_id)
if len(data) > 0:
redis_client.sadd(read_qa_key, *data)
return data
except:
logging_exception()
return []
def fetch_user_topic(device_id, card_type, size):
try:
key = '{device_id}-{card_type}-{date}'.format(device_id=device_id, card_type=card_type,
date=RecommendFeed.current_date())
if (device_id != '0') and size >= 2:
search_topic_recommend_key = "TS:search_recommend_tractate_queue:device_id:" + str(device_id)
search_topic_recommend_list = list()
search_cursor_ts = 0
if redis_client.exists(search_topic_recommend_key):
search_topic_recommend_dict = redis_client.hgetall(search_topic_recommend_key)
if b'cursor' in search_topic_recommend_dict:
search_cursor_ts = json.loads(search_topic_recommend_dict[b'cursor'])
if search_cursor_ts < 30:
search_topic_recommend_list = json.loads(search_topic_recommend_dict[b'tractate_queue'])
if search_cursor_ts < len(search_topic_recommend_list):
size = size - 2
try:
que = DeviceUserTopicQueue.objects.get(device_id=device_id)
except DeviceUserTopicQueue.DoesNotExist:
que = UserTopicQueue.objects.last()
if not que:
return []
que = list(filter(None, que.queue.split(',')))
# adjust args.
cursor = redis_client.get(key) or 0
cursor = int(cursor) % len(que)
size = min(size, len(que))
data = list(islice(cycle(que), cursor, cursor + size))
data = list(map(int, data))
if cursor + 2 * size < len(que):
redis_client.set(key, cursor + size, ex=24 * 60 * 60)
else:
try:
context.request_logger.app(reset_queue=True)
cursor = 0
redis_client.set(key, cursor, ex=24 * 60 * 60)
except:
redis_client.set(key, cursor + size, ex=24 * 60 * 60)
if device_id != '0' and size >= 2:
if len(search_topic_recommend_list) > 0 and search_cursor_ts < len(search_topic_recommend_list):
queue = search_topic_recommend_list[search_cursor_ts:search_cursor_ts + 2]
queue.extend(data)
data = queue
new_search_cursor = search_cursor_ts + 2
redis_client.hset(search_topic_recommend_key, 'cursor', new_search_cursor)
redis_client.expire(search_topic_recommend_key, 30 * 24 * 60 * 60)
read_topic_key = "TS:recommend_tractate_set:device_id:" + str(device_id)
if len(data) > 0:
redis_client.sadd(read_topic_key, *data)
return data
except:
logging_exception()
return []
def fetch_diary(cls, device_id, card_type, city_id, size):
# first, we fetch data from personal-queue city-queue, if not both, get data
# from world queue.
user_portrait_diary_part_list = list()
click_diary_size = 1
search_diary_size = 4
if device_id != '0':
user_portrait_diary_key = 'user_portrait_recommend_diary_queue:device_id:%s:%s' % (device_id, datetime.datetime.now().strftime('%Y-%m-%d'))
if redis_client.exists(user_portrait_diary_key):
user_portrait_diary_dict = redis_client.hgetall(user_portrait_diary_key)
user_portrait_cursor = str(user_portrait_diary_dict[b'cursor'],encoding='utf-8')
if user_portrait_cursor == '0':
if b'len_cursor' in user_portrait_diary_dict.keys():
user_portrait_diary_list = json.loads(user_portrait_diary_dict[b'diary_queue'])
len_cursor = str(user_portrait_diary_dict[b'len_cursor'],encoding='utf-8')
len_cursor = int(len_cursor)
if len(user_portrait_diary_list) - len_cursor >size:
user_portrait_diary_part_list = user_portrait_diary_list[len_cursor:len_cursor+size]
redis_client.hset(user_portrait_diary_key,'len_cursor',len_cursor+size)
size = 0
else:
user_portrait_diary_list = json.loads(user_portrait_diary_dict[b'diary_queue'])
diary_list_len = len(user_portrait_diary_list) - len_cursor
size = size - diary_list_len
user_portrait_diary_part_list = user_portrait_diary_list[len_cursor:len_cursor + diary_list_len]
redis_client.hset(user_portrait_diary_key, 'len_cursor', len_cursor + diary_list_len)
user_portrait_cursor = int(user_portrait_cursor) + 1
redis_client.hset(user_portrait_diary_key, 'cursor', user_portrait_cursor)
else:
user_portrait_diary_part_list = json.loads(user_portrait_diary_dict[b'diary_queue'])
size = size - len(user_portrait_diary_part_list)
user_portrait_cursor = int(user_portrait_cursor) + 1
redis_client.hset(user_portrait_diary_key, 'cursor', user_portrait_cursor)
try:
# obj = DeviceDiaryQueue.objects.filter(device_id=device_id, city_id=city_id).first()
(local, nearby, nation, megacity, city_id) = cls.fetch_device_diary_queue_data(city_id, device_id)
if len(local) == 0 and len(nearby) == 0 and len(nation) == 0 and len(megacity) == 0:
(local, nearby, nation, megacity, city_id) = cls.fetch_diary_queue_data(city_id)
# if not obj:
# (local, nearby, nation, megacity,city_id) = cls.fetch_diary_queue_data(city_id)
# else:
# local = list(filter(None, obj.native_queue.split(','))) if obj.native_queue else []
# nearby = list(filter(None, obj.nearby_queue.split(','))) if obj.nearby_queue else []
# nation = list(filter(None, obj.nation_queue.split(','))) if obj.nation_queue else []
# megacity = list(filter(None, obj.megacity_queue.split(','))) if obj.megacity_queue else []
except:
logging_exception()
(local, nearby, nation, megacity, city_id) = cls.fetch_diary_queue_data(city_id)
if(device_id!='0'):
search_diary_recommend_key = "TS:search_recommend_diary_queue:device_id:" + str(device_id)
search_diary_recommend_list = list()
search_cursor_ts = 0
if redis_client.exists(search_diary_recommend_key) and size >3:
search_diary_recommend_dict = redis_client.hgetall(search_diary_recommend_key)
if b'cursor' in search_diary_recommend_dict:
search_cursor_ts = json.loads(search_diary_recommend_dict[b'cursor'])
search_diary_recommend_list = json.loads(search_diary_recommend_dict[b'diary_queue'])
if search_cursor_ts +search_diary_size < len(search_diary_recommend_list) :
size = size - search_diary_size
if (device_id != '0') :
diary_recommend_key = "TS:recommend_diary_queue:device_id:" + str(device_id)
diary_recommend_list = list()
if redis_client.exists(diary_recommend_key) and size > 0:
diary_recommend_dict = redis_client.hgetall(diary_recommend_key)
diary_recommend_list = json.loads(diary_recommend_dict[b'diary_queue'])
if len(diary_recommend_list)>0:
size = size -click_diary_size
key = '{device_id}-{city_id}-{date}'.format(device_id=device_id,
city_id=city_id, date=RecommendFeed.current_date())
# strategy rule: when user refresh over 30 loadings, reset native nearby nation queue cursor.
counter_key = key + '-counter_v1'
counter = redis_client.incr(counter_key)
if counter == 1:
redis_client.expire(counter_key, 24 * 60 * 60)
cursor_key = key + '-cursor_v1'
cursor = redis_client.get(cursor_key) or b'0-0-0-0'
# if counter > 30:
# cursor = b'0-0-0-0'
# redis_client.delete(counter_key)
cx, cy, cm, cz = map(int, cursor.split(b'-'))
x, y, m, z = cls.get_city_scale(city_id)
data, ncx, ncy, ncm, ncz = cls.get_scale_data(
local, nearby, nation, megacity,
cx, cy, cm, cz,
x, y, z, m, size
)
if ncx == cx and ncy == cy: # native queue and nearby queue
logger.info("diary queue reach end,cx:%d,cy:%d,cm:%d,cz:%d",cx,cy,cm,cz)
# redis_client.delete(counter_key)
# data, ncx, ncy, ncm, ncz = cls.get_scale_data(
# local, nearby, nation, megacity,
# 0, 0, 0, 0,
# x, y, z, m, size
# )
ncx = ncy = ncm = ncz = 0
val = '-'.join(map(str, [ncx, ncy, ncm, ncz]))
redis_client.set(cursor_key, val, ex=24 * 60 * 60)
data = list(map(int, data))
if device_id != '0':
if search_cursor_ts<len(search_diary_recommend_list)-search_diary_size:
queue = search_diary_recommend_list[search_cursor_ts:search_cursor_ts+search_diary_size]
queue.extend(data)
data = queue
new_search_cursor = search_cursor_ts +search_diary_size
redis_client.hset(search_diary_recommend_key,'cursor',new_search_cursor)
redis_client.expire(search_diary_recommend_key,30*24*60*60)
if len(diary_recommend_list) >0:
diary_id = diary_recommend_list.pop(0)
data.insert(0,diary_id)
if len(diary_recommend_list)>0:
diary_recommend_list_json = json.dumps(diary_recommend_list)
redis_client.hset(diary_recommend_key,'diary_queue',diary_recommend_list_json)
redis_client.expire(diary_recommend_key,30*24*60*60)
else:
redis_client.delete(diary_recommend_key)
if len(user_portrait_diary_part_list)>0:
user_portrait_diary_part_list.extend(data)
data = user_portrait_diary_part_list
#已读
read_diary_key = "TS:recommend_diary_set:device_id:" + str(device_id)
if len(data)>0:
redis_client.sadd(read_diary_key,*data)
return data
def get_scale_data(local, nearby, nation, megacity, cx, cy, cm, cz, x, y, z, m, size):
"""
:param local: local diary queue
:param nearby: nearby diary queue
:param nation: nation diary queue
:param megacity: megacity diary queue
:param cx: seen local diary offset
:param cy: seen nearby diary offset
:param cz: seen nation diary offset
:param cm: seen megacity diary offset
:param x: local diary scale factor
:param y: nearby diary scale factor
:param z: nation diary scale factor
:param m: megacity diary scale factor
:param size: nubmer of diary
:return:
"""
# 本地 临近 特大城市 全国 四个层级 都按照的是四舍五入取得方式
# 针对出现的问题，本次相应的优化是：
# 1、如果出现两个层级为零，且有剩余坑位时，则按照本地 临近 全国的优先级，先给优先级高且为零的层级一个坑位。
# 2、如果所有层级都非零，且有剩余坑位时，则优先给权重占比大的层级一个坑位。
# 3、如果只有一个层级为零，且有剩余坑位时，则优先填充权重占比大的层级一个坑位。
nx = int(round(x * 1.0 / (x + y + z + m) * size))
ny = int(round(y * 1.0 / (x + y + z + m) * size))
nz = int(round(z * 1.0 / (x + y + z + m) * size))
nm = int(round(m * 1.0 / (x + y + z + m) * size))
nxyz = [nx, ny, nm, nz]
xyz = [x, y, m, z]
counter = Counter([nx, ny, nm, nz])
if counter[0] == 2:
nxyz[nxyz.index(0)] += size - sum(nxyz)
else:
nxyz[xyz.index(max(xyz))] += size - sum(nxyz)
nx, ny, nm, nz = nxyz
slocal = local[cx:cx + nx]
cx = min(cx + nx, len(local))
ny += (nx - len(slocal))
snearby = nearby[cy:cy + ny]
cy = min(cy + ny, len(nearby))
nm += (ny - len(snearby))
smegacity = megacity[cm: cm + nm]
cm = min(cm + nm, len(megacity))
nz += (nm - len(smegacity))
snation = nation[cz:cz + nz]
cz = min(cz + nz, len(nation))
return chain(slocal, snearby, smegacity, snation), cx, cy, cm, cz