Learn the vocabulary of diving as deep as possible down one path before backtracking to explore siblings.
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At standup, a dev mentions exploring a graph by diving as deep as possible down one path before backtracking, only exploring a sibling branch once the current path is fully exhausted. What is this algorithm called?
Depth-first search (DFS) is exactly this: depth-first search explores a graph or tree by diving as deep as possible down one path, using a stack or recursion to remember where to backtrack to, and only explores a sibling branch once the current path is fully exhausted. A hash collision is an unrelated hash-table concept about two keys sharing a bucket. This dive-deep-then-backtrack behavior is exactly why depth-first search is a natural fit for problems like detecting cycles or finding any path at all, rather than the shortest one.
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During a design review, the team picks depth-first search to detect a cycle in a graph, specifically because diving deep along a path and tracking the current path's visited nodes makes revisiting a node still on that path immediately detectable. Which capability does this provide?
Depth-first search (DFS) here provides Cheap cycle detection via the current-path visited set, since the algorithm only needs to track which nodes are on the currently active path, and encountering one of those nodes again while still exploring that path immediately reveals a cycle, with no need to compare every pair of nodes in the graph. Comparing every pair of nodes across the whole graph would cost far more than tracking just the current path. This current-path-tracking behavior is exactly why depth-first search is the standard tool for cycle detection in a graph.
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In a code review, a dev notices a graph-traversal feature explores every node in wide, level-by-level layers using a queue, when the actual goal is simply to detect whether any cycle exists anywhere in the graph. What does this represent?
This is a missed depth-first-search opportunity, since diving deep along one path and tracking that path's visited nodes would detect a cycle as soon as one is encountered, without needing to expand every node layer by layer. A cache eviction policy is an unrelated concept about discarded cache entries. This layer-by-layer pattern is exactly the kind of mismatch a reviewer flags once the actual goal is simple cycle detection rather than shortest-path distances.
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An incident report shows a cycle-detection feature ran far slower than expected on a large graph, because it explored every node level by level with a queue instead of diving deep along a path and tracking that path's visited nodes. What practice would prevent this?
Switching to depth-first search with current-path tracking avoids the cost of expanding every node in wide layers. Continuing to explore every node level by level with a queue to detect a cycle regardless of how large the graph grows is exactly what caused the issue described in this incident. This current-path-tracking approach is the standard fix once cycle detection, not shortest-path distance, is the actual goal.
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During a PR review, a teammate asks why the team reaches for depth-first search instead of breadth-first search, given that breadth-first search also visits every reachable node in a graph. What is the reasoning?
Depth-first search uses less memory since it only needs to track the current path being explored, and it naturally supports cycle detection and backtracking-style problems, while breadth-first search explores level by level, needing a queue that can hold an entire frontier of nodes at once, and is the natural choice whenever the shortest path by number of edges is actually needed. This is exactly why the choice between them depends on whether the goal is memory-efficient exploration or a shortest-path guarantee.