Where it comes from
- Published by Palantir for all engineering rolesSource 1Analyzing the Efficiency of CodePublisherPalantirSource typecompany hiring page More Palantir questions The Palantir interview guide
- Reported at OpenAISource 2OpenAI Forward Deployed Engineer Interview ExperiencePublisherAced (formerly Exponent), candidate-submitted experienceSource typecandidate’s personal write-up More OpenAI questions The OpenAI interview guide
How to answer
Palantir’s company-wide guide “Analyzing the Efficiency of Code” tells candidates to know how to calculate the efficiency of algorithms and not to forget space complexity. Source 1Analyzing the Efficiency of CodePublisherPalantirSource typecompany hiring page The second half of this question is the half: a customer’s data has a shape, and the shape decides whether your big-O matters.
- Name the variables first. Not “O(n)” but “n payments, m invoices, k invoices that share one amount”. Most real solutions have more than one size.
- Cost every phase, not just the main loop. Building the index, each sort, and the work per item. Say the hidden costs aloud:
list.pop(i),x in some_list, a sort inside a loop, string building. - Give space separately. What is held at the peak, including the sorted copy of the input.
- Say the worst case and the input that causes it. Look for skew first in customer data: one price for every subscription, one giant account, one default value in a key column.
- Tie it to real sizes. Ask how many rows, how often it runs, whether a user waits on it, and how much memory the worker has. Do the arithmetic out loud, or measure.
- Say what you would change, and at what size. If the sizes say it doesn’t matter, say that and stop.
The trap is the one-word answer, “linear”, that names no variables and no worst case. The opposite trap is rewriting for speed before you know the data.
Follow-ups
What the interviewer may ask next, once your first answer is on the table.
- Build the input that makes your solution slowest.
- This moves from a nightly job to an API call a user waits on. What changes in your answer?
- The data no longer fits in memory on one worker. What do you do?
- You said the lookup is constant time. Constant in what, and what is hiding behind it?
Where answers go wrong
- Saying “it’s O(n)” with no named variables, no worst case and no data size, so the answer cannot be checked against anything.
- Calling a dictionary lookup constant time while the list behind each key grows with the data.
- Leaving out space, or counting only the new structures and not the copy of the input you sorted.
- Optimizing before asking how big the data is and how often the code runs.
Answer this in two minutes
Write the answer you would say out loud. The clock starts with your first word.
Model answer
The solution on the board matches bank payments to open invoices: same amount in cents, due day within WINDOW = 3 days either way, nearest day wins, each invoice used once. “Variables: n payments, m invoices, k invoices with the same amount as a given payment, and c of those inside its window.