Table Operations: Implement a Lightweight In-Memory Table with Insert, Delete, and Query Operations
Question Details
Problem
Implement an in-memory database table that supports:
- insert(row: dict) -- add a row (rows have a unique id field).
- delete(id) -- remove a row by id.
- query(filters: dict) -> list[dict] --
return all rows matching all key-value filters (AND semantics).
- update(id, changes: dict) -- update specific fields of a row.
python
class Table:
def insert(self, row: dict) -> None: ...
def delete(self, id: int) -> None: ...
def query(self, filters: dict) -> list[dict]: ...
def update(self, id: int, changes: dict) -> None: ...
Example:
t.insert({"id":1,"name":"Alice","dept":"Eng"})
t.insert({"id":2,"name":"Bob", "dept":"Eng"})
t.query({"dept":"Eng"}) -> [{"id":1,...},{"id":2,...}]
t.update(1, {"dept":"PM"})
t.query({"dept":"Eng"}) -> [{"id":2,...}]
Follow-ups
1. query scans all rows. How would you add an index on a specific column to speed up equality lookups?
2. Support range queries (age > 30). What data structure would you use for a range index?
3. How do you handle schema evolution -- adding a new column to existing rows?
4. Implement query with OR semantics as well as AND. How does the filter language change?
Full Details
Problem
Implement an in-memory database table that supports:
- insert(row: dict) -- add a row (rows have a unique id field).
- delete(id) -- remove a row by id.
- query(filters: dict) -> list[dict] --
return all rows matching all key-value filters (AND semantics).
- update(id, changes: dict) -- update specific fields of a row.
python
class Table:
def insert(self, row: dict) -> None: ...
def delete(self, id: int) -> None: ...
def query(self, filters: dict) -> list[dict]: ...
def update(self, id: int, changes: dict) -> None: ...
Example:
t.insert({"id":1,"name":"Alice","dept":"Eng"})
t.insert({"id":2,"name":"Bob", "dept":"Eng"})
t.query({"dept":"Eng"}) -> [{"id":1,...},{"id":2,...}]
t.update(1, {"dept":"PM"})
t.query({"dept":"Eng"}) -> [{"id":2,...}]
Follow-ups
1. query scans all rows. How would you add an index on a specific column to speed up equality lookups?
2. Support range queries (age > 30). What data structure would you use for a range index?
3. How do you handle schema evolution -- adding a new column to existing rows?
4. Implement query with OR semantics as well as AND. How does the filter language change?
About This Question
This is a reported interview question from a airtable interview during the phone round.
It covers the following topics: Coding, Sql, Phone, Onsite .
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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.
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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 Airtable 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.