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I got the basic first-match logic down pretty quick but then they pushed on efficiency and I fumbled a bit.
Start by clarifying the problem: rules are evaluated in order, first match wins, and CIDR blocks can overlap. Then implement a straightforward linear scan that converts each CIDR to a range and checks if the IP falls within it, returning the action of the first matching rule. For efficiency, discuss preprocessing rules into a data structure like a binary trie or interval tree to enable faster lookups, especially for large rule sets.
Pro tip: Mention that in real systems, firewall rules are often updated frequently, so the data structure should support efficient insertions and deletions, not just lookups. Also, consider using a binary trie (prefix tree) where each node represents a bit of the IP address, which naturally handles CIDR prefixes and first-match semantics by storing the action at the node where the prefix ends.
Confirm that rules are evaluated in order, first match wins, and that CIDR blocks may overlap. Ask about input format (e.g., IPv4 vs IPv6), expected rule set size, and whether rules can be updated dynamically.
For each rule, convert the CIDR to a range of IP addresses (start and end). Iterate through rules in order, and for each, check if the given IP is within the range. Return the action of the first matching rule, or a default (e.g., deny) if none match.
The naive solution is O(N) per lookup. For large N, preprocess rules into a more efficient structure. Discuss options like sorting rules by prefix length and using binary search, building a binary trie (radix tree) for O(W) lookup where W is bit width, or using an interval tree.
Compare approaches: tries offer fast lookup and support dynamic updates but have memory overhead; interval trees are good for static sets; sorting with binary search is simple but requires careful handling of overlapping ranges. Mention handling of IPv6 and the importance of first-match semantics in the data structure.
Recap the chosen approach, emphasizing correctness and efficiency. Mention potential extensions like caching frequent lookups or using hardware acceleration, and confirm the solution meets the requirements.
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