Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Databricks software engineer interview with a networking-flavored coding problem. The question was about building a CIDR block lookup system that returns the longest-prefix match for any incoming IPv4 address, basically a mini router in code. Felt like a solid systems-adjacent problem, not your typical LeetCode grind.

Questions Asked (1)

Design and implement a class that takes a list of IPv4 CIDR blocks at construction time and, for any given IPv4 address, returns the most specific (longest prefix length) matching CIDR block from the list, or an empty string if none match.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The problem looks like a networking thing at first and I spent a minute mentally panicking about whether I needed to remember subnet math.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a trie-based solution where each node represents a bit of the IP address. Implement insertion of CIDR blocks and lookup by traversing the trie bit by bit, keeping track of the longest matching prefix. Discuss trade-offs between trie and sorted array with binary search, and analyze time/space complexity.

Pro tip: Mention that you would store the original CIDR string at terminal nodes and handle edge cases like overlapping prefixes and invalid inputs. Also, note that using a compressed trie (radix tree) can save memory for sparse prefixes.

1. Clarify requirements and constraints

Ask about expected input size, update frequency, memory limits, and whether the list is static. Confirm that the most specific match means longest prefix length, and that an empty string is returned if no match.

2. Choose data structure

Propose a binary trie (prefix tree) where each level corresponds to a bit of the IP address. Alternatively, consider a sorted array of CIDR blocks with binary search, but explain why trie is more efficient for longest prefix match.

3. Design insertion and lookup algorithms

For insertion, parse each CIDR block into a 32-bit integer and prefix length, then insert bits into the trie, marking terminal nodes with the original CIDR string. For lookup, traverse the trie following the bits of the query IP, remembering the last terminal node encountered.

4. Analyze complexity and trade-offs

Insertion: O(32) per CIDR block, so O(N*32) for N blocks. Lookup: O(32) worst-case. Space: O(N*32) nodes. Compare with sorted array + binary search: O(log N) lookup but O(N) insertion, and more complex to find longest prefix.

5. Handle edge cases and optimizations

Discuss handling invalid CIDR blocks, IPv4 address validation, and overlapping prefixes. Mention possible optimizations like path compression (radix tree) to reduce memory, or using a hash map for exact matches if applicable.

Key Points to Mention

Binary trie (prefix tree) for efficient longest prefix match
Bitwise operations to parse IP addresses and CIDR blocks
Time complexity: O(32) for lookup and insertion per block
Space complexity: O(N*32) nodes, with possible compression
Handling of overlapping prefixes and most specific match
Edge cases: invalid inputs, empty list, no match

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