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Databricks·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Databricks technical phone screen with a networking/IP routing problem that sounds deceptively simple until you realize they want a full trie implementation. Pretty standard for the level but the longest-prefix-match angle threw me a bit.

Questions Asked (1)

Q1

Given a list of CIDR rules each tagged as approve or reject, and a query IP address, determine whether the IP is approved or rejected using longest-prefix-match semantics. If no rule matches, default to reject.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I parsed the CIDR blocks into integer intervals first, which worked for basic membership checks but felt clunky when they asked about efficiency at scale.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a solution using a binary trie (prefix tree) to store CIDR rules for efficient longest-prefix match. Discuss trade-offs between trie and sorted list approaches, and handle IPv4/IPv6 considerations.

Pro tip: Mention that you would normalize IP addresses and CIDR blocks to binary form and consider using a compressed trie (Patricia trie) for memory efficiency, especially if rules are numerous.

1. Clarify requirements and edge cases

Ask about IP version (IPv4/IPv6), rule format, expected scale, and whether rules can overlap. Confirm default behavior when no match.

2. Choose data structure

Propose a binary trie where each node represents a bit of the IP address, storing the rule action at the node corresponding to the prefix length. Alternatively, discuss sorted list with binary search.

3. Insert rules into trie

For each CIDR rule, convert IP and prefix length to binary, traverse/create nodes, and mark the terminal node with the action (approve/reject).

4. Query IP for longest-prefix match

Traverse the trie bit by bit following the query IP's bits, keeping track of the last seen rule action. After traversal, return the last action or default to reject.

5. Analyze complexity and trade-offs

Discuss time complexity O(W) per query where W is bit width (32 or 128), and space O(N*W). Compare with sorted list approach O(log N) but with more complex matching.

Key Points to Mention

  • Longest-prefix match semantics: most specific rule wins.
  • Binary trie (prefix tree) for efficient matching.
  • Handling IPv4 vs IPv6: separate tries or unified with bit width.
  • Default action when no rule matches: reject.
  • Time and space complexity analysis.
  • Potential optimizations: path compression, Patricia trie, or using a sorted list with binary search.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.