Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

Databricks coding interview with a networking/IP filtering problem that felt deceptively algorithmic once the follow-ups started piling on. The core question was clear enough but the edge cases around overlapping rules kept me second-guessing myself the whole way through.

Questions Asked (1)

Given a list of CIDR rules each labeled as approve or reject, and a single query IP address, determine whether that IP is approved or rejected. You need to handle the matching policy (longest-prefix-match vs first-match-wins), parse CIDR notation into network ranges, check IP membership, and build an efficient lookup structure for many rules.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I spent probably two minutes just clarifying the matching policy before writing a single line, which in hindsight was the right call because the interviewer seemed to appreciate it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the matching policy (longest-prefix-match vs first-match-wins) and the expected scale of rules and queries. Then design an efficient data structure, such as a binary trie for longest-prefix-match or a sorted list for first-match-wins, and walk through the algorithm for parsing CIDR, checking IP membership, and resolving conflicts.

Pro tip: Explicitly discuss the trade-offs between longest-prefix-match and first-match-wins, and mention that in real systems like Databricks, longest-prefix-match is often preferred for its deterministic and intuitive semantics. Also, highlight the importance of handling IPv4 and IPv6 uniformly and considering memory vs. speed trade-offs.

1. Clarify requirements and constraints

Ask about the matching policy, the number of rules, query frequency, and whether IPv4 and IPv6 need support. Confirm if rules can overlap and how conflicts should be resolved.

2. Choose the right data structure

For longest-prefix-match, a binary trie (or Patricia trie) is ideal; for first-match-wins, a sorted list of CIDR ranges with binary search can work. Discuss the trade-offs in time and space complexity.

3. Parse CIDR and implement IP membership

Convert each CIDR to a network address and prefix length, then represent the IP as an integer. Check membership by masking the IP with the prefix and comparing to the network address.

4. Resolve matches according to policy

For longest-prefix-match, traverse the trie to find the deepest node with a rule; for first-match-wins, iterate rules in order and return the first match. Handle the case where no rule matches (default action).

5. Analyze complexity and optimize

Discuss time complexity for building and querying, and suggest optimizations like precomputing ranges, using bitwise operations, or caching frequent queries. Mention scalability for many rules.

Key Points to Mention

Longest-prefix-match vs first-match-wins semantics and their implications
Binary trie (or Patricia trie) for efficient longest-prefix-match
CIDR parsing: converting IP to integer and using bitwise operations for masking
Handling IPv4 and IPv6 uniformly (e.g., using 128-bit integers)
Time and space complexity trade-offs between different data structures
Default action when no rule matches and conflict resolution strategies

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