Palantir's Learning round. One candidate report opens by observing that everyone around them seemed to be failing this round; treat that as one person's impression rather than a measured rate, but it is the reason this question is worth more preparation than its frequency suggests. You are given a single-threaded package manager (described as "like NPM") and asked to convert it to multi-threaded. The interviewer first explains what a package manager is, walks you through class definitions for packages and repositories, and asks you to explain the code back. Documentation is provided for every library or concept introduced, so no background knowledge is required — the round measures how fast you absorb an unfamiliar system, not what you already know.
Part 1 — parallelize the blocking fetch. The given code loops over repositories and blocks on each network call in turn:
for repo in self.repos:
try:
return self.__getPackageFuture(repo, pkg).result()
except PackageNotFound:
continue
raise PackageNotFound(pkg)
Every call goes over the network and .result() blocks. Note precisely why this serializes: the future is created and immediately awaited on each iteration, so there is never more than one request in flight. The fix reported by candidates is to separate submission from collection — append every packageFuture to a queue or array first so all requests start, then iterate the collection calling .result() on each. One candidate used a pointer in a while loop over the collected futures; other traversals are equally acceptable.
One refinement worth being able to discuss if pushed: iterating the collected futures in submission order still blocks on a slow early repository even when a later one has already answered. If the goal were simply the fastest successful hit, you would consume in completion order (as_completed / wait(FIRST_COMPLETED)). The reported answer keeps submission order, which also preserves the original code's repository-priority semantics — the loop returns the first repo that has the package, not the fastest one. Knowing which of those you want, and saying so, is the strong version of this answer.
Part 2 — fix the caching bug. You are then shown a larger body of code containing a caching defect. The code volume is large but the error is inside the function logic. The fix: do not cache packages, because they can be too large — cache the repositories they live in instead, correcting the values stored in the hash table.
FDSE variant. Forward Deployed Software Engineer candidates get a different Learning round: Palantir has an internal SQL-like language (create tables, join tables, aggregate rows) exposed as an object-oriented query API in the style of SQLAlchemy. The task is to learn that language during the interview and use it to answer simple query problems — for example querying car objects by attributes such as price, colour, and mileage. Reports describe this as considerably easier than the package-manager version.
This code makes a network call per repository and blocks on each one. How would you make it more efficient?
Can you explain what this code does? (walking through the package and repository class definitions)
There is a bug in the caching logic in this code. Find and fix it.
Using this internal SQL-like query language we've just shown you, answer these queries over the car objects.
(Escalation) A weak Learning round may be repeated in the hiring-manager round.
| Approach | Notes |
|---|---|
| ThreadPoolExecutor / async gather | Using a thread pool (e.g., concurrent.futures.ThreadPoolExecutor with map/as_completed) or async/await (asyncio.gather) provides cleaner concurrency primitives, but the problem specifically exercises manual future batching with the provided API. |
| Pointer-based while loop over queue | Candidate-mentioned alternative: after appending all futures to an array, use a pointer in a while loop to iterate and call result() — functionally equivalent to a for loop over the array, but allows more flexible early-exit or retry logic. |
Common mistakes: Calling .result() inside the original loop (blocking on each network call before dispatching the next) — this is the exact pattern the interviewer wants candidates to fix, so failing to identify it as the problem costs the round.; In the SQL-like query section, candidates tended to write complex joins when the question only required querying a specific field, wasting time and signaling poor reading comprehension of the new language.; Caching the package object itself rather than the repository where the package was found — the interviewer considers package objects too large to cache and expects the candidate to recognize this.
Interviewer hints: The interviewer explicitly teaches each new concept or library before asking questions about it, and provides documentation. Candidates are expected to absorb and apply the material during the interview rather than bring prior knowledge.; The interviewer tells candidates upfront that calling .result() blocks — this is the hint to stop blocking inline and queue the futures instead.; The interviewer warns explicitly: a poor Learning round result will cause this exact problem to be repeated in the hiring-manager round under harder conditions.
What passers do: Asking the interviewer as many clarifying questions as needed was explicitly encouraged and seen positively — candidates who did this reported feeling able to keep up even with unfamiliar material.; Candidates who had read about threads and thread pools before the interview found it much easier to follow along, even though the interviewer provides documentation for everything introduced.; Separating the dispatch of all futures from result collection — queuing all packageFuture objects first, then iterating to call .result() — was the correct move for the parallelization part.; For the SQL-like query section, understanding the specific fields and attributes of the car objects before attempting any query prevented wasted effort on unnecessary joins.
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What you just read — canonical solution, follow-up arc, what passing candidates actually did — exists for all 15 Palantir questions, refreshed monthly from new candidate reports.