DSA pattern
Heap / Priority Queue
Heaps maintain partial ordering where root is always min (min-heap) or max (max-heap). Perfect for problems requiring quick access to extreme values, streaming data, or merging sorted sequences.
When to use
5 signals- 01Finding top K elements
- 02Streaming median
- 03Merge K sorted lists
- 04Task scheduling
- 05Dijkstra's shortest path
In interviews, call out the pattern early. It helps interviewers follow your reasoning and keeps your implementation focused. After solving, explain tradeoffs in plain language: why this approach is efficient, where it can break, and what edge cases you would test first.
Practice problems
4 listed- 01Kth Largest ElementPractice →
- 02Merge K Sorted ListsPractice →
- 03Find Median from Data StreamPractice →
- 04Top K FrequentPractice →
Build a review loop
Treat each pattern as a repeating cycle, not a one-time read. Solve a fresh problem today, revisit the same pattern in 2-3 days, and run one timed attempt at the end of the week. This turns short-term familiarity into reliable recall for live interviews.
When reviewing, re-derive the core steps from scratch before checking a reference solution. If you can explain the state transitions and edge cases clearly, you are much more likely to execute well under interview pressure.