Analyze User Website Visit Pattern
A medium-tier problem at 44% community acceptance, tagged with Array, Hash Table, Sorting. Reported in interviews at Spotify and 3 others.
You're in a Spotify, Amazon, DoorDash, or Uber interview and they hand you a problem about analyzing website visit patterns. You get a list of user IDs, timestamps, and durations. The trap is obvious: sort and group. But the trick sits in how you detect and count valid visit sequences. This problem has a 44% acceptance rate, meaning nearly 6 out of 10 people who see it in their OA stumble on the pattern matching or the time-window logic. If you haven't drilled this specific trick, StealthCoder solves it invisibly during your assessment when you hit the wall.
Companies that ask "Analyze User Website Visit Pattern"
Analyze User Website Visit Pattern is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe core challenge isn't the sorting or the hash table. It's recognizing that you need to identify valid visit patterns within a time window, then count them correctly. Most people build the data structure fine, but miss the sequence logic or off-by-one errors in the window. The obvious brute-force check every timestamp pair against every other pair, which bloats complexity. The real move is to sort by timestamp, use a rolling window or two-pointer approach to isolate candidates, then validate the pattern once per candidate set. Common fail: not resetting state between users or miscounting overlaps. This is exactly the kind of problem where you nail the setup but botch the core algorithm under interview pressure. StealthCoder is your backstop if the time-window logic doesn't click in the first five minutes.
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Analyze User Website Visit Pattern recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Analyze User Website Visit Pattern interview FAQ
What's the actual trick to this problem?+
You need to identify visit sequences that occur within a specific time window, then count valid patterns. Sort by timestamp, then use a sliding window to isolate candidate sequences per user. The pattern matching is the hard part, not the data structure. Get the window logic right and the hash table becomes straightforward.
Is this still asked at Amazon and Uber?+
Yes. All four top companies listed (Spotify, Amazon, DoorDash, Uber) have reported this problem. It's mid-level difficulty and tests both data structure handling and algorithmic pattern recognition, which both companies care about.
Why do 56% of people fail this?+
Most fail on the time-window boundary conditions or sequence validation logic. They get the sort and hash table right, then either overcomplicate the pattern check or use an inefficient nested-loop approach that times out. Off-by-one errors on the window boundaries are extremely common.
How does this relate to Array and Sorting topics?+
The problem requires you to sort the input array by timestamp, then iterate through it efficiently. The Array and Sorting form the foundation. Hash Table is where you group by user ID and track state. Skip the sorting step or do it wrong, and everything else collapses.
Should I use a two-pointer approach or nested loops?+
Two-pointer on a sorted array. Nested loops work for small inputs but time out on large test cases, and you'll likely hit that in your OA. Sort once, then use a single pass with a sliding window to isolate sequences. This keeps complexity linear after the sort.
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