Design Twitter
Detailed guide and Python implementation for the 'Design Twitter' problem.
1. 学ぶ
The 'Design Twitter' problem is a key challenge in the Heap / Priority Queue section.
This implementation focuses on easy-level logic in Python.
We prioritize technical accuracy and code readability in our provided solutions.
2. Real-World Applications
3. Visual Intuition
Visualizing the logic flow for Design Twitter.
4. Prerequisites
5. Step-by-Step Thinking
1. Understand the problem
Read the problem statement for Design Twitter carefully.
2. Formulate brute force
Draft a simple iterative solution.
3. Identify inefficiency
Look for redundant calculations.
4. Optimize search path
Use hashing or sorting to speed up the process.
5. Final Implementation
Clean up the code for production standards.
問題提起
Design a simplified version of Twitter where users can post tweets, follow/unfollow another user, and is able to see the 10 most recent tweets in the user's news feed.
Implement the Twitter class:
- Twitter() Initializes your twitter object.
- postTweet(userId: int, tweetId: int) Composes a new tweet with ID tweetId by the user userId.
- getNewsFeed(userId: int) -> List[int] Retrieves the 10 most recent tweet IDs in the user's news feed.
- follow(followerId: int, followeeId: int) The user with ID followerId started following the user with ID followeeId.
- unfollow(followerId: int, followeeId: int) The user with ID followerId started unfollowing the user with ID followeeId.
Input is a list of operations and arguments. Implement a function twitter(operations: list, arguments: list) -> list that returns a list of results (None for constructor/postTweet/follow/unfollow, and List[int] for getNewsFeed).
- •1 <= userId, followerId, followeeId <= 500
- •0 <= tweetId <= 10^4
- •All the tweets have unique IDs
- •At most 30000 calls will be made in total
例
operations = ["Twitter", "postTweet", "getNewsFeed", "follow", "postTweet", "getNewsFeed", "unfollow", "getNewsFeed"], arguments = [[], [1, 5], [1], [1, 2], [2, 6], [1], [1, 2], [1]]
[None, None, [5], None, None, [6, 5], None, [5]]
User 1 posts tweet 5. News feed: [5]. User 1 follows 2. User 2 posts tweet 6. News feed: [6, 5]. User 1 unfollows 2. News feed: [5].
Need a Hint?
Edge Cases to Watch
- Empty input structures
- Single element inputs
- Large numerical bounds
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インタビューの洞察とバリエーション
複雑さの分析の内訳
なぜ時間がかかるのか: Directly evaluates all possibilities.
なぜ宇宙なのか: Uses standard local memory.
なぜ時間がかかるのか: Optimized paths reduce total operations.
なぜ宇宙なのか: May trade memory for speed.
最適化されたソリューションの Python コード
最適化されたソリューションの Python コード
import heapq, collections
class TwitterOpt:
def __init__(self):
self.count = 0
self.tweetMap = collections.defaultdict(list)
self.followMap = collections.defaultdict(set)
def postTweet(self, userId, tweetId):
self.tweetMap[userId].append([self.count, tweetId])
self.count -= 1
def getNewsFeed(self, userId):
res = []
minHeap = []
self.followMap[userId].add(userId)
for followeeId in self.followMap[userId]:
if followeeId in self.tweetMap:
index = len(self.tweetMap[followeeId]) - 1
count, tweetId = self.tweetMap[followeeId][index]
minHeap.append([count, tweetId, followeeId, index - 1])
heapq.heapify(minHeap)
while minHeap and len(res) < 10:
count, tweetId, followeeId, index = heapq.heappop(minHeap)
res.append(tweetId)
if index >= 0:
count, tweetId = self.tweetMap[followeeId][index]
heapq.heappush(minHeap, [count, tweetId, followeeId, index - 1])
return res
def follow(self, followerId, followeeId):
self.followMap[followerId].add(followeeId)
def unfollow(self, followerId, followeeId):
if followeeId in self.followMap[followerId]: self.followMap[followerId].remove(followeeId)ブルート フォース コード (スポイラーガード付き)
ブルート フォース コード (スポイラーガード付き)
class TwitterBrute:
def __init__(self):
self.tweets = []
self.following = collections.defaultdict(set)
def postTweet(self, userId, tweetId):
self.tweets.append((userId, tweetId))
def getNewsFeed(self, userId):
res = []
for u, t in reversed(self.tweets):
if u == userId or u in self.following[userId]:
res.append(t)
if len(res) == 10: break
return res
def follow(self, followerId, followeeId):
self.following[followerId].add(followeeId)
def unfollow(self, followerId, followeeId):
if followeeId in self.following[followerId]: self.following[followerId].remove(followeeId)Algorithm Pattern Checklist
When dealing with Heap / Priority Queue data patterns.
- Are constraints clear?
- Is there a linear or logarithmic optimization possible?
Key Revision Notes
Standard Heap / Priority Queue problem properties apply.
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