Data Stream interview questions
18 data stream problems tagged across recent interview reports. Drilled most heavily by atlassian, citadel, and databricks.
Data Stream problems ask you to process unbounded sequences of values and answer queries on the fly, no reprocessing, no storing everything. You get a value, you update your state, you respond. 18 problems tagged this way. Atlassian, Citadel, Databricks, and Spotify all ask variants heavily. The pattern surfaces everywhere: median tracking, rate limiting, autocomplete, hit counters. It's deceptively hard because the naive approach, store all data, fails at scale. StealthCoder solves the variant you didn't drill the moment it lands in your live OA.
Most-asked data stream problems
| # | Problem | Diff | # Companies | Pass % |
|---|---|---|---|---|
| 01 | Find Median from Data Stream | HARD | 31 | 53% |
| 02 | Design Hit Counter | MEDIUM | 16 | 69% |
| 03 | Logger Rate Limiter | EASY | 10 | 77% |
| 04 | Moving Average from Data Stream | EASY | 9 | 80% |
| 05 | Design Browser History | MEDIUM | 5 | 78% |
| 06 | Kth Largest Element in a Stream | EASY | 5 | 60% |
| 07 | Number of Recent Calls | EASY | 5 | 77% |
| 08 | Design Search Autocomplete System | HARD | 4 | 49% |
| 09 | Stock Price Fluctuation | MEDIUM | 4 | 48% |
| 10 | Online Stock Span | MEDIUM | 2 | 67% |
| 11 | Product of the Last K Numbers | MEDIUM | 2 | 63% |
| 12 | Stream of Characters | HARD | 2 | 51% |
| 13 | Design a File Sharing System | MEDIUM | 1 | 42% |
| 14 | Design Front Middle Back Queue | MEDIUM | 1 | 56% |
| 15 | Find Consecutive Integers from a Data Stream | MEDIUM | 1 | 49% |
| 16 | Finding MK Average | HARD | 1 | 38% |
| 17 | First Unique Number | MEDIUM | 1 | 56% |
| 18 | Two Sum III - Data structure design | EASY | 1 | 39% |
You can't drill every data stream variant before the assessment. StealthCoder runs invisibly during screen share and solves whichever variant they throw at you. No browser extension. No detection signature. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderRecognize Data Stream problems by their stateful setup: a constructor that initializes a data structure, then repeated calls to add, query, or process incoming values. Common subtypes include sliding-window aggregates (moving average, kth largest), online statistics (median, mode), and event-based logic (rate limiter, autocomplete). The trick is always the same: choose a data structure that lets you update in O(1) or O(log n) time and answer queries fast without re-scanning. Drill moving-average and kth-largest-element first, they teach the heap and deque patterns. Hit rate-limiter and logger next for state machines. Then tackle design problems like browser-history and file-sharing. When a hard variant appears live, StealthCoder reads the problem spec and surfaces the right approach in seconds, invisible to the proctor.
Companies that hire most on data stream
18 data stream problems.
You won't drill them all. Pass anyway.
Data Stream is one of the patterns interviews actually filter on. Memorizing every variant in a week is a fantasy. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds, no matter which data stream flavor lands in your live OA. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Data Stream interview FAQ
How many Data Stream problems should I drill before my OA?+
Start with 5 to 7 core problems: moving-average, kth-largest, find-median, logger-rate-limiter, and first-unique-number. Then pick 2 to 3 design problems relevant to the company. At 18 total, you won't see every variant, but these teach the heap, deque, and hash-map tricks that unlock the rest.
How do I recognize a Data Stream problem in an interview?+
Look for a class or object with an __init__ method and repeated method calls (add, next, query). The problem describes continuous or sequential input without upfront access to all data. Phrases like 'stream', 'online', 'real-time', or 'incoming' are strong signals. Design problems often hide the pattern in a system description.
Which companies ask Data Stream problems the most?+
Atlassian leads with 12 tagged problems, followed by Citadel and Databricks at 8 and 7 respectively. Spotify, Google, Pinterest, and Snowflake each ask 6 to 7. If you're targeting any of these, Data Stream is non-negotiable, drill it early and hard.
What data structures do I need for Data Stream?+
Heaps (priority queues) for kth-largest and median. Deques for sliding windows and moving averages. Hash maps for frequency tracking and rate limiting. Balanced trees help with online statistics. Master heap insertion and extraction first; it's the bottleneck in most solutions.
Are Data Stream problems harder than other patterns?+
They're harder because they require upfront data structure design and careful state management. You can't brute force by reprocessing. However, the conceptual set, heaps, deques, hash maps, is smaller than many other patterns. Once you drill 6 to 8 core problems, new variants feel repetitive, not novel.