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Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Databricks software engineering interview with a meaty systems question about firewall rule matching. The problem started straightforward then got more interesting with the follow-up about range intersection, which is where things got tricky for me.

Questions Asked (1)

Q1

Design and implement a firewall rule matcher that checks a single IPv4 address against a list of CIDR rules, each tagged as accept or deny. Walk through your choice of data structures and algorithms, then extend the solution to handle an incoming CIDR range instead of a single IP, and discuss efficient approaches for range intersection.

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

Started with a trie on the bit representation of the IP, which felt right since CIDR rules are basically prefix matches.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a trie-based data structure for efficient longest-prefix matching on a single IP. For CIDR range matching, extend the trie to handle range queries or use interval trees, and discuss trade-offs between approaches.

Pro tip: Mention that real-world systems often use a combination of tries and interval trees, and that rule ordering matters for overlapping CIDRs—demonstrating awareness of production complexities.

1. Clarify Requirements and Constraints

Ask about rule priority (e.g., longest prefix match, first match), expected scale, and whether rules can overlap. This shows you think about edge cases before coding.

2. Design for Single IP Matching

Propose a binary trie (prefix tree) where each node represents a bit of the IP address, storing accept/deny at terminal nodes. Explain that lookup is O(32) for IPv4, and discuss handling of default rules.

3. Extend to CIDR Range Matching

For an incoming CIDR range, you need to check if any rule CIDR overlaps or contains it. Discuss approaches: decompose the range into prefixes and query the trie, or use an interval tree for efficient overlap queries.

4. Analyze Trade-offs and Optimizations

Compare trie vs. interval tree vs. sorted arrays with binary search. Mention memory vs. speed trade-offs, and how to handle updates (insert/delete rules) efficiently.

5. Discuss Production Considerations

Bring up real-world aspects: rule ordering, default policies, performance under high throughput, and potential use of hardware acceleration or caching.

Key Points to Mention

  • Longest prefix match semantics for overlapping CIDR rules
  • Binary trie (radix tree) for O(32) single IP lookup
  • Range intersection using prefix decomposition or interval trees
  • Time and space complexity trade-offs between data structures
  • Handling rule updates and concurrency in a production firewall
  • Default accept/deny policy and rule priority ordering

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