Datadog Coding Interview Questions
100 Datadog coding interview problems with full optimal solutions — 31 easy, 54 medium, 15 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Datadog interviewer values, and a FAQ section.
Showing 6 problems of 100
- #87hardfrequently asked
87. Serialize and Deserialize Binary Tree
Design serialize and deserialize for an arbitrary binary tree. Datadog uses this as the canonical wire-format question — same shape as their distributed metric-tree snapshot format that flows between ingest and query nodes.
- #88hardfrequently asked
88. Find Median from Data Stream
Design addNum and findMedian over a streaming sequence. Datadog asks this as the canonical streaming-percentile problem — two-heap solution is the exact pattern they use for p50 over a sliding metric window.
- #89hardfrequently asked
89. Sliding Window Maximum
Return the max of each sliding window of size k. Datadog asks this constantly — the monotonic deque is the exact algorithm they use for streaming max-over-window aggregations on real-time metric dashboards.
- #92hardfrequently asked
92. Merge k Sorted Lists
Merge k sorted linked lists into one sorted list. Datadog asks this because their query layer routinely merges K ordered metric shards into a unified stream — the min-heap pattern is identical.
- #98hardfrequently asked
98. Trapping Rain Water
Compute how much water gets trapped between bars. Datadog uses this for the two-pointer greedy proof — same shape as bounded peak-valley estimation in a streaming metric series.
- #100hardfrequently asked
100. Largest Rectangle in Histogram
Find the largest rectangle area in a histogram. Datadog uses this for the monotonic-stack pattern — same shape as their algorithm for detecting the largest sustained burst window in a metric stream.
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