def compute_video_rank_table():
task_name = '计算视频排名对照表'
coll = db['video'] # 获得collection的句柄
count = coll.estimated_document_count()
top_n = 60
print(count)
keys = ['cView', 'cLike', 'cDanmaku', 'cFavorite', 'cCoin', 'cShare']
task = ProgressTask(task_name, top_n * len(keys), collection=db['tracer'])
o = {}
skip = int(count / 100)
for each_key_index in range(len(keys)):
each_key = keys[each_key_index]
o[each_key] = {}
o['name'] = 'video_rank'
o[each_key]['rate'] = []
i = 1
last_value = 9999999999
# logger.info("开始计算视频{}排名对照表".format(each_key))
video = coll.find({}, {
'title': 1
}).limit(200).sort(each_key, DESCENDING).batch_size(200)
top = 1
for each_video in list(video):
o[each_key][each_video['title']] = top
top += 1
while i <= top_n:
task.current_value = i + top_n * each_key_index
video = list(
coll.find({
each_key: {
'$lt': last_value
}
}, {
each_key: 1
}).limit(1).skip(skip).sort(each_key, DESCENDING))
print(video)
if len(video) != 0:
video = video[0]
else:
i += 1
continue
if each_key not in video:
break
last_value = video[each_key]
o[each_key]['rate'].append(last_value)
print(last_value)
i += 1
o['update_time'] = datetime.datetime.utcnow() + datetime.timedelta(hours=8)
output_coll = db['rank_table']
output_coll.update_one({'name': 'video_rank'}, {'$set': o}, upsert=True)
def gen_online():
task_name = "生成在线人数爬取链接"
t = ProgressTask(task_name, 1, collection=db['tracer'])
ONLINE_URL = 'https://www.bilibili.com/video/online.html'
redis_connection.rpush("online:start_urls", ONLINE_URL)
t.current_value = 1
def author_fans_rate_caculate():
logging.basicConfig(
level=logging.INFO,
format='[%(asctime)s] %(levelname)s @ %(name)s: %(message)s')
logger = logging.getLogger(__name__)
coll = db['author'] # 获得collection的句柄
logger.info('开始计算粉丝增速')
c_datetime = datetime.datetime.now()
end_date = (
datetime.datetime(c_datetime.year, c_datetime.month, c_datetime.day) -
datetime.timedelta(1)).timestamp()
start_date = (
datetime.datetime(c_datetime.year, c_datetime.month, c_datetime.day) -
datetime.timedelta(2)).timestamp()
task = ProgressTask("计算粉丝增速",
coll.count_documents({}),
collection=db['tracer'])
c = 0
for each in coll.find({}, {'mid': 1, '_id': 0}).batch_size(200):
c += 1
task.current_value = c
ag = coll.aggregate([{
'$match': {
'mid': each['mid']
}
}, {
'$project': {
'mid': 1,
'data': {
"$filter": {
"input": "$data",
"as": "each_data",
"cond": {
"$gt": [
"$$each_data.datetime",
datetime.datetime.now() - datetime.timedelta(7)
]
}
}
}
}
}]).batch_size(1)
each_author = next(ag)
if 'data' in each_author and each_author['data'] != None:
data = sorted(each_author['data'], key=lambda x: x['datetime'])
if len(data) >= 2:
logger.info(each_author['mid'])
x = tuple(map(lambda x: x['datetime'].timestamp(), data))
y = tuple(map(lambda x: x['fans'], data))
inter_fun = interp1d(x, y, kind='linear')
if start_date > x[0] and end_date < x[-1]:
inter_data = inter_fun([start_date, end_date])
delta_fans = inter_data[1] - inter_data[0]
coll.update_one({'mid': each_author['mid']},
{"$set": {
'cRate': int(delta_fans)
}})
def calculate_author_rank():
task_name = "计算作者排名数据"
keys = ['cFans', 'cArchive_view', 'cArticle_view']
count = coll.count_documents({keys[0]: {'$exists': 1}})
t = ProgressTask(task_name, count * len(keys), collection=db['tracer'])
for each_key in keys:
logger.info("开始计算作者{}排名".format(each_key))
i = 1
authors = coll.find({
each_key: {
'$exists': 1
}
}, {
'mid': 1,
'rank': 1,
each_key: 1
}).batch_size(300).sort(each_key, DESCENDING)
if each_key == 'cFans':
each_rank = 'fansRank'
each_d_rank = 'dFansRank'
each_p_rank = 'pFansRank'
elif each_key == 'cArchive_view':
each_rank = 'archiveViewRank'
each_d_rank = 'dArchiveViewRank'
each_p_rank = 'pArchiveViewRank'
elif each_key == 'cArticle_view':
each_rank = 'articleViewRank'
each_d_rank = 'dArticleViewRank'
each_p_rank = 'pArticleViewRank'
for each_author in authors:
t.current_value += 1
logger.info("计算{}排名".format(each_author['mid']))
# 如果没有data 直接下一个
if each_key in each_author:
# 如果已经计算过rank
if 'rank' in each_author:
rank = each_author['rank']
if each_rank in each_author['rank']:
rank[each_d_rank] = each_author['rank'][each_rank] - i
else:
rank[each_d_rank] = 0
rank[each_rank] = i
rank[each_p_rank] = format_p_rank(i, count)
else:
# 初始化
rank = {
each_rank: i,
each_d_rank: 0,
each_p_rank: format_p_rank(i, count)
}
if each_author[each_key] == 0:
if 'rank' in each_author:
rank = each_author['rank']
rank[each_d_rank] = 0
rank[each_rank] = -1
rank[each_p_rank] = -1
else:
rank = {each_rank: -1, each_d_rank: 0, each_p_rank: -1}
if each_key == 'cArticle_view':
rank['updateTime'] = datetime.datetime.now()
coll.update_one({'mid': each_author['mid']},
{'$set': {
'rank': rank,
}})
i += 1
t.current_value = t.total_value
logger.info("计算作者排名结束")
