Chat Service: Implement a Multi-Room Chat Backend with Message History
Interview Experience
Round 1 Coding
Problem
Implement a backend for a multi-room chat service. Users can join rooms, send messages, and fetch message history. Each message has a sender, timestamp, and content.
python
from datetime import datetime
class ChatService:
def create_room(self, room_id: str) -> None:
...
def join_room(self, user_id: str, room_id: str) -> bool:
...
def send_message(self, user_id: str, room_id: str,
content: str) -> dict:
**returns** message dict with auto-generated timestamp
...
def get_history(self, room_id: str,
limit: int = 50, before_ts: datetime = None) -> list[dict]:
...
def active_users(self, room_id: str) -> list[str]:
...
Example
chat = ChatService()
chat.create_room("general")
chat.join_room("alice", "general")
chat.join_room("bob", "general")
chat.send_message("alice", "general", "Hello!")
# -> {"id": "uuid", "sender": "alice", "content": "Hello!", "ts": ...}
chat.get_history("general", limit=10)
# -> [{"sender":"alice","content":"Hello!","ts":...}]
chat.active_users("general") -> ["alice", "bob"]
Follow-ups
- How would you implement
get_historywith cursor-based pagination for efficient scrolling? - How do you handle a user sending a message to a room they haven't joined?
- How would you push new messages to connected clients in real time (WebSockets vs. SSE vs. long polling)?
- How do you design the data model if rooms can have thousands of messages and you need fast lookups by time range?
Full Details
Round 1 Coding
Problem
Implement a backend for a multi-room chat service. Users can join rooms, send messages, and fetch message history. Each message has a sender, timestamp, and content.
python
from datetime import datetime
class ChatService:
def create_room(self, room_id: str) -> None:
...
def join_room(self, user_id: str, room_id: str) -> bool:
...
def send_message(self, user_id: str, room_id: str,
content: str) -> dict:
**returns** message dict with auto-generated timestamp
...
def get_history(self, room_id: str,
limit: int = 50, before_ts: datetime = None) -> list[dict]:
...
def active_users(self, room_id: str) -> list[str]:
...
Example
chat = ChatService()
chat.create_room("general")
chat.join_room("alice", "general")
chat.join_room("bob", "general")
chat.send_message("alice", "general", "Hello!")
# -> {"id": "uuid", "sender": "alice", "content": "Hello!", "ts": ...}
chat.get_history("general", limit=10)
# -> [{"sender":"alice","content":"Hello!","ts":...}]
chat.active_users("general") -> ["alice", "bob"]
Follow-ups
- How would you implement
get_historywith cursor-based pagination for efficient scrolling? - How do you handle a user sending a message to a room they haven't joined?
- How would you push new messages to connected clients in real time (WebSockets vs. SSE vs. long polling)?
- How do you design the data model if rooms can have thousands of messages and you need fast lookups by time range?
About This Question
This is a candidate experience report from a notion interview during the phone round.
More Notion Interview Questions
About Notion Interview Reports
This question was reported by a candidate who interviewed at Notion. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Notion are the higher-signal extractions to take from this report.
For broader preparation context, the Notion interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Notion reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Notion Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Notion reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.