Data Stream problems focus on processing elements that arrive continuously rather than all at once. Instead of working with a fixed dataset, you must design algorithms that update results efficiently as new values appear. This pattern is common in real-world systems such as live analytics, recommendation engines, network monitoring, and log processing.
In coding interviews, Data Stream questions test your ability to maintain information incrementally while keeping time and space complexity low. Instead of recomputing everything after each update, you typically rely on specialized data structures to maintain running results.
Common techniques used in Data Stream problems include:
Mastering Data Stream problems helps you design efficient real-time algorithms and prepares you for system-style interview questions. Practice these problems to get comfortable with maintaining dynamic state and updating results as new data arrives.
Helps manage elements in arrival order, a common requirement when processing streams in real time.
Frequently used to track counts, frequencies, or recently seen elements in a stream efficiently.
Useful for handling stream problems that require analyzing the most recent k elements.
Essential for maintaining dynamic minimums, maximums, or medians in streaming data.
| Status | Title | Video | Leetcode | Solve | Difficulty | Companies | Topics |
|---|---|---|---|---|---|---|---|
| 170. Two Sum III - Data structure design | Solve | Easy | LinkedIn | ||||
| 346. Moving Average from Data Stream | Solve | Easy | Amazon+3 | ||||
| 359. Logger Rate Limiter | Solve | Easy | Amazon+5 | ||||
| 703. Kth Largest Element in a Stream | Solve | Easy | Adobe+4 | ||||
| 933. Number of Recent Calls | Solve | Easy | Bloomberg+1 | ||||
| 1656. Design an Ordered Stream | Solve | Easy | Bloomberg |
Start Easy, progress to Hard.
Frequently appear alongside Data Stream.
Common questions about Data Stream.
Focus on understanding how to update results efficiently when a new element arrives. Practice problems involving heaps, hash maps, and sliding windows to build strong intuition.
Data Stream problems involve processing numbers or events as they arrive rather than after collecting the entire dataset. You typically maintain running results using efficient data structures without recomputing everything.
A classic example is finding the median of a continuously incoming stream of numbers. The optimal solution maintains two heaps to keep track of the lower and upper halves of the data.
Yes, many companies use them to test incremental algorithm design and data structure knowledge. Problems like running median, moving averages, and frequency tracking are popular examples.
Common choices include heaps, hash tables, queues, and sometimes balanced trees. These structures allow efficient updates and queries as new elements arrive in the stream.