def get_top_n(n):
'''
:param n: 前十
:return:
'''
origin_data = rds.zrevrange('ReadCounter', 0, n - 1, withscores=True)
cleaned_data = [[int(post_id), int(count)]
for post_id, count in origin_data]
# for item in cleaned_data:
# post_id = item[0]
# post = Post.objects.get(pk=post_id)
# item[0] = post
post_id_list = [post_id for post_id, _ in cleaned_data]
post = Post.objects.filter(pk__in=post_id_list)
post = sorted(post, key=lambda post:post_id_list.index(post.id))
for item, post in zip(cleaned_data, post):
item[0] = post
# post_id_list = [post_id for post_id,_ in cleaned_data]
# post = Post.objects.in_bulk(post_id_list)
# # print(dir(Post.objects))
# for item in cleaned_data:
# post_id = item[0]
# item[0] = post[post_id]
return cleaned_data
def get_top_n(count):
ori_data = rds.zrevrange(READ_COUNT_KEY, 0, count - 1, withscores=True)
# [(b'34', 17.0), [[34, 17],
# (b'12', 10.0), [12, 10],
# (b'2', 9.0), [ 2, 9],
# (b'1', 9.0), => [ 1, 9],
# (b'20', 8.0), [20, 8],
# (b'38', 6.0), [38, 6],
# (b'35', 3.0)] [35, 3]]
rank_data = [[int(post_id), int(count)] for post_id, count in ori_data]
post_id_list = [post_id for post_id, _ in rank_data] # 取出 post_id 列表
# 方法 1
# # post_dict = {1: <Post: Post object>,
# # 2: <Post: Post object>,
# # 12: <Post: Post object>,
# # ...}
# post_dict = Post.objects.in_bulk(post_id_list)
# post_rank = [[post_dict[pid], count] for pid, count in rank_data]
# 方法 2
posts = Post.objects.filter(id__in=post_id_list)
posts = sorted(posts, key=lambda post: post_id_list.index(post.id))
post_rank = [[post, rank[1]] for post, rank in zip(posts, rank_data)]
return post_rank
def top10(request):
print('5678987656789')
post_id = int(request.GET.get('post_id', 1))
print(post_id)
lists = rds.zrevrange(READ_COUNT, 0, -1, withscores=True)
print(lists)
return render(request, 'top10.html', {'lists': lists})
def top_n(num):
# 从redis中获取原始的数据
ori_data = rds.zrevrange(b'readrank', 0, num - 1, withscores=True)
# 清洗数据 将数据装换成整形
cleaned_rank = [[int(post_id), int(count)] for post_id, count in ori_data]
# 思路一：直接替换
# for item in cleaned_rank:
# item[0] = Post.objects.get(id=item[0])
# rank_data = cleaned_rank
# 思路二：批量获取 Post
# post_id_list = [post_id for post_id, _ in cleaned_rank]
# posts = Post.objects.filter(id__in=post_id_list) # 批量取出 posts
# posts = sorted(posts, key=lambda post: post_id_list.index(post.id)) # 调整为正确的顺序
# # 组装 rank_data
# rank_data = []
# 遍历获取帖子和点击量
# for post, (_, count) in zip(posts, cleaned_rank):
# rank_data.append([post, count])
# 思路三
post_id_list = [post_id for post_id, _ in cleaned_rank]
# post_dict = {
# 1: <Post: Post object>,
# 3: <Post: Post object>,
# 29: <Post: Post object>,
# }
post_dict = Post.objects.in_bulk(post_id_list) # 批量获取 post 字典
for item in cleaned_rank:
post_id = item[0]
item[0] = post_dict[post_id]
rank_data = cleaned_rank
return rank_data
def get_top_n(num):
'''
获取阅读计数前n的文章数据
Args:
num: 排行前 N
Return:
rank_data: [
[Post(9),100],
[Post(5),91],
[Post(7),79],
]
'''
# origin_data = [
# (b'1',37.0),
# (b'517',16.8),
# (b'510',12.0),
# ]
origin_data = rds.zrevrange('ReadCounter', 0, num - 1, withscores=True)
# cleaned_data = [
# [1,37],
# [517,16],
# [510,12],
# ]
cleaned_data = [[int(post_id), int(count)]
for post_id, count in origin_data]
#方法一：
# for item in cleaned_data:
# post_id = item[0]
# post = Post.objects.get(pk=post_id)
# item[0] = post
#方法二：批量获取，减少对数据库的操作
# post_id_list = [post_id for post_id, _ in cleaned_data] #提取所有的post_id
# posts = Post.objects.filter(id__in = post_id_list)
# posts = sorted(posts,key=lambda post:post_id_list.index(post.id)) #按照post_id_list 顺序进行排序
#
# for item,post in zip(cleaned_data,posts): #zip 起迭代器的作用
# item[0] = post
#方法三：
post_id_list = [post_id for post_id, _ in cleaned_data] # 提取所有的POST ID
posts = Post.objects.in_bulk(post_id_list)
for item in cleaned_data:
post_id = item[0]
item[0] = posts[post_id]
return cleaned_data
def get_top_n(num):
ori_data = rds.zrevrange(keys.READ_COUNTER, 0, num - 1, withscores=True)
rank_data = [[int(post_id), int(count)] for post_id, count in ori_data]
post_id_list = [post_id for post_id, _ in rank_data]
posts = Post.objects.in_bulk(post_id_list)
for item in rank_data:
post_id = item[0]
item[0] = posts[post_id]
return rank_data
def get_top_n(num):
ori_data = rds.zrevrange('ReadRank', 0, num - 1, withscores=True)
cleaned = [[int(post_id), int(count)] for post_id, count in ori_data]
# 方法二
post_id_list = [post_id for post_id, _ in cleaned]
posts = Post.objects.filter(id__in=post_id_list)
posts = sorted(posts, key=lambda post: post_id_list.index(post.id))
for post, item in zip(posts, cleaned):
item[0] = post
return cleaned
def get_top_n(num):
# 从redis中取出原始数据
ori_data = rds.zrevrange("ReadRank", 0, num - 1, withscores=True)
# ori_data = [
# (b'36', 1183.0),
# (b'3', 233.0),
# (b'37', 164.0),
# ...
# ]
# 数据清洗
cleaned_rank = [[int(post_id), int(count)] for post_id, count in ori_data]
# cleaned_rand = [
# [36, 1183],
# [ 3, 233],
# [37, 164],
# ]
# 思路一：直接替换
# for item in cleaned_rank:
# item[0] = Post.objects.get(id=item[0])
# rank_data = cleaned_rank
# 思路二：批量获取 Post
post_id_list = [post_id for post_id, _ in cleaned_rank]
# 批量取出posts
posts = Post.objects.filter(id__in=post_id_list)
# 调整为正确的顺序v
posts = sorted(posts, key=lambda post: post_id_list.index(post.id))
# 组装 rank_data
rank_data = []
for post, (_, count) in zip(posts, cleaned_rank):
rank_data.append([post, count])
# # 思路三
# post_id_list = [post_id for post_id, _ in cleaned_rank]
# # post_dict = {
# # 1: <Post: Post object>,
# # 3: <Post: Post object>,
# # 29: <Post: Post object>,
# # }
# post_dict = Post.objects.in_bulk(post_id_list) # 批量获取 post 字典
# for item in cleaned_rank:
# post_id = item[0]
# item[0] = post_dict[post_id]
# rank_data = cleaned_rank
return rank_data
def get_top_n(num):
'''取出 Top N 的帖子及其阅读计数'''
# ori_data = [
# (b'36', 1183.0),
# (b'3', 233.0),
# (b'37', 164.0),
# ...
# ]
ori_data = rds.zrevrange(b'ReadRank', 0, num - 1,
withscores=True) # 从 redis 取出原始的阅读数据
# cleaned_rank = [
# [36, 1183],
# [ 3, 233],
# [37, 164],
# ]
cleaned_rank = [[int(post_id), int(count)]
for post_id, count in ori_data] # 数据清洗
# 思路一：直接替换
# for item in cleaned_rank:
# item[0] = Post.objects.get(id=item[0])
# rank_data = cleaned_rank
# 思路二：批量获取 Post
# post_id_list = [post_id for post_id, _ in cleaned_rank]
# posts = Post.objects.filter(id__in=post_id_list) # 批量取出 posts
# posts = sorted(posts, key=lambda post: post_id_list.index(post.id)) # 调整为正确的顺序
# # 组装 rank_data
# rank_data = []
# for post, (_, count) in zip(posts, cleaned_rank):
# rank_data.append([post, count])
# 思路三
post_id_list = [post_id for post_id, _ in cleaned_rank]
# post_dict = {
# 1: <Post: Post object>,
# 3: <Post: Post object>,
# 29: <Post: Post object>,
# }
post_dict = Post.objects.in_bulk(post_id_list) # 批量获取 post 字典
for item in cleaned_rank:
post_id = item[0]
item[0] = post_dict[post_id]
rank_data = cleaned_rank
return rank_data
def get_top_n(num):
#获取排名前n的数据
'''
ori_data = [
(b'33', 344.0),
(b'31', 34.0),
(b'29', 32.0),
]
'''
ori_data = rds.zrevrange('ReadRank', 0, num - 1,
withscores=True) #取出原始排名数据
# cleaned_data = [
# [33, 344],
# [31, 34],
# [29, 32],
# ]
cleaned_data = [[int(post_id), int(count)]
for post_id, count in ori_data] #数据清洗
#方法一 跟数据库交互多 性能不好
# for item in cleaned_data:
# item[0] = Post.objects.get(id=item[0])
# rank_data = cleaned_data
#方法二
# post_id_list = [post_id for post_id, _ in cleaned_data]
# posts = Post.objects.filter(id__in = post_id_list) #批量取出posts
# posts = sorted(posts, key=lambda post: post_id_list.index(post.id)) #根据id的索引进行排序
# # 拼接rank_data
# rank_data = []
# for post, (_, count) in zip(posts, cleaned_data):
# rank_data.append([post, count])
#方法三
post_id_list = [post_id for post_id, _ in cleaned_data]
# post_dict = {
# 1: <Post: Post object>,
# 2: <Post: Post object>,
# 3: <Post: Post object>,
# }
post_dict = Post.objects.in_bulk(post_id_list)
for item in cleaned_data:
item[0] = post_dict[item[0]]
rank_data = cleaned_data
return rank_data
def get_top_n(num):
'''获取排名数据'''
# ori_data = [
# (b'33', 843.0),
# ( b'1', 384.0),
# (b'29', 261.0),
# ]
ori_data = rds.zrevrange('ReadRank', 0, num - 1,
withscores=True) # 取出原始排名数据
# cleaned_data = [
# [33, 1779],
# [ 1, 384],
# [29, 261],
# ]
cleaned_data = [[int(post_id), int(count)]
for post_id, count in ori_data] # 数据清洗
# 方法一
# for item in cleaned_data:
# item[0] = Post.objects.get(id=item[0])
# rank_data = cleaned_data
# 方法二
# post_id_list = [post_id for post_id, _ in cleaned_data]
# posts = Post.objects.filter(id__in=post_id_list) # 批量取出 posts
# posts = sorted(posts, key=lambda post: post_id_list.index(post.id)) # 按照id的索引进行排序
# # 拼装 rank_data
# rank_data = []
# for post, (_, count) in zip(posts, cleaned_data):
# rank_data.append([post, count])
# 方法三
post_id_list = [post_id for post_id, _ in cleaned_data]
# post_dict = {
# 1: <Post: Post object>,
# 2: <Post: Post object>,
# 3: <Post: Post object>,
# }
post_dict = Post.objects.in_bulk(post_id_list)
for item in cleaned_data:
item[0] = post_dict[item[0]]
rank_data = cleaned_data
return rank_data
def get_top_n(num):
'''获取阅读排行前 N 的数据'''
# ori_data = [
# (b'31', 507.0),
# (b'2', 88.0),
# (b'34', 24.0),
# ]
ori_data = rds.zrevrange('ReadRank', 0, num - 1, withscores=True) # 取出原始数据
# rank_data = [
# [31, 507],
# [ 2, 88],
# [34, 24],
# ]
rank_data = [[int(post_id), int(count)]
for post_id, count in ori_data] # 清洗原始数据
# 方法一：思路简单，效率低，性能差
# for item in rank_data:
# post = Post.objects.get(pk=item[0]) # 替换每一项的第一个元素
# item[0] = post
# 方法二
# post_id_list = [post_id for post_id, _ in rank_data] # 批量取出 id 列表
# posts = Post.objects.filter(id__in=post_id_list) # 批量获取 post
# posts = sorted(posts, key=lambda post: post_id_list.index(post.id)) # 重新按阅读量排序
# # 按位置逐一替换
# for post, item in zip(posts, rank_data):
# item[0] = post
# 方法三
post_id_list = [post_id for post_id, _ in rank_data] # 批量取出 id 列表
# posts = {
# 2: <Post: Post object>,
# 3: <Post: Post object>,
# 25: <Post: Post object>,
# }
posts = Post.objects.in_bulk(post_id_list) # 批量获取 post
# 按位置逐一替换
for item in rank_data:
item[0] = posts[item[0]]
return rank_data
def get_top_n(num):
'''获取阅读排行前 N 的文章'''
# ori_data = [
# (b'16', 53.0),
# (b'27', 39.0),
# (b'31', 37.0),
# ]
ori_data = rds.zrevrange('ReadCounter', 0, num - 1, withscores=True)
# cleaned_data = [
# [16, 53],
# [27, 39],
# [31, 37],
# ]
cleaned_data = [[int(post_id), int(count)] for post_id, count in ori_data]
# 方法一
for item in cleaned_data:
key = 'Post-%s' % item[0]
post = cache.get(key)
if post is None:
post = Post.objects.get(pk=item[0])
item[0] = post
# 方法二
# post_id_list = [post_id for post_id, _ in cleaned_data]
# posts = Post.objects.filter(id__in=post_id_list)
# posts = sorted(posts, key=lambda post: post_id_list.index(post.id)) # 按照post_id_list的顺序进行排序
# for index, item in enumerate(cleaned_data):
# item[0] = posts[index]
# for post, item in zip(posts, cleaned_data):
# item[0] = post
# 方法三
post_id_list = [post_id for post_id, _ in cleaned_data]
posts = Post.objects.in_bulk(post_id_list)
for item in cleaned_data:
post_id = item[0]
item[0] = posts[post_id]
return cleaned_data
def get_top_n(num):
'''获取排行前 N 的数据'''
# ori_data = [
# (b'38', 369.0),
# (b'37', 216.0),
# (b'40', 52.0),
# ]
ori_data = rds.zrevrange('ReadRank', 0, num - 1, withscores=True)
# 数据清洗
# cleaned = [
# [38, 369],
# [37, 216],
# [40, 52],
# ]
cleaned = [[int(post_id), int(count)] for post_id, count in ori_data]
# 方法一：循环操作数据库，性能差
# for item in cleaned:
# post = Post.objects.get(pk=item[0])
# item[0] = post
# 方法二
post_id_list = [post_id for post_id, _ in cleaned] # 取出 post id 列表
posts = Post.objects.filter(id__in=post_id_list) # 根据 id 批量获取 post
posts = sorted(posts,
key=lambda post: post_id_list.index(post.id)) # 根据 id 位置排序
for post, item in zip(posts, cleaned):
item[0] = post # 逐个替换 post
# 方法三
# post_id_list = [post_id for post_id, _ in cleaned] # 取出 post id 列表
# # posts = {
# # 1: <Post: Post object>,
# # 4: <Post: Post object>,
# # 6: <Post: Post object>,
# # }
# posts = Post.objects.in_bulk(post_id_list) # 批量获取 post
# for item in cleaned:
# item[0] = posts[item[0]]
return cleaned
def get_top_n(count):
ori_data = rds.zrevrange(READ_COUNT_KEY, 0, count - 1, withscores=True)
# [(b'3', 24.0), (b'2', 18.0), (b'5', 15.0), (b'38', 10.0), (b'1', 6.0)]
# [[3, 24], [2, 18], [5, 15], [38, 10], [1, 6]]
rank_data = [[int(post_id), int(count)] for post_id, count in ori_data]
post_id_list = [post_id for post_id, _ in rank_data]
# 方法 1
# # post_dict = {1: <Post: Post object>,
# # 2: <Post: Post object>,
# # 3: <Post: Post object>,
# # 5: <Post: Post object>,
# # 38: <Post: Post object>,
# # ...}
# post_dict = Post.objects.in_bulk(post_id_list)
# post_rank = [[post_dict[pid], count] for pid, count in rank_data]
# 方法 2
posts = Post.objects.filter(id__in=post_id_list)
posts = sorted(posts, key=lambda post: post_id_list.index(post.id))
post_rank = [[post, rank[1]] for post, rank in zip(posts, rank_data)]
return post_rank
def get_top_n(n):
# 获取排名原始数据
# [
# (b'28', 3.0),
# (b'26', 3.0),
# (b'31', 2.0),
# (b'35', 1.0),
# (b'30', 1.0)
# ]
ori_data = rds.zrevrange(keys.READ_RANK, 0, n - 1, withscores=True)
# rank_data =[]
# for data in ori_data:
# post_id = int(data[0])
# count = int(data[1])
# rank_data.append([post_id, count])
# ======================================
# rank_data = [
# (28, 3),
# (26, 3),
# (31, 2),
# (35, 1),
# (30, 1),
# ]
rank_data = [[int(data[0]), int(data[1])] for data in ori_data]
# 获取对应的 Post
# for data in rank_data:
# post = Post.objects.get(id=data[0])
# data[0] = post
id_list = [post_id for post_id, _ in rank_data]
posts = Post.objects.filter(id__in=id_list)
posts = sorted(posts, key=lambda post: id_list.index(post.id))
# 整理数据格式
rank_data = [(post, data[1]) for post, data in zip(posts, rank_data)]
return rank_data
def get_top_n(num):
'''获取阅读量前 N 的文章'''
# ori_data = [
# (b'38', 321.0),
# (b'1', 239.0),
# (b'39', 111.0)
# ...
# ]
ori_data = rds.zrevrange(b'ReadRank', 0, num - 1,
withscores=True) # 取出原始排名数据
# rank_data = [
# [1, 1035],
# [38, 687],
# [39, 214],
# ]
rank_data = [[int(post_id), int(count)]
for post_id, count in ori_data] # 数据清洗
# 批量获取 post 对象
post_id_list = [post_id for post_id, _ in rank_data] # 取出每一个 post_id
posts = Post.objects.filter(id__in=post_id_list) # filter 的结果会按 id 生序排列
posts = sorted(
posts,
key=lambda p: post_id_list.index(p.id)) # 重新整理成按 post_id_list 的顺序排列
# 修改 rank_data 每一项的第一个元素为 post 对象
# rank_data = [
# [<Post(1)>, 1035],
# [<Post(38)>, 687],
# [<Post(39)>, 214],
# ...
# ]
for item, post in zip(rank_data, posts):
item[0] = post
return rank_data
