← Block (Square) Interview Insights
Clarify the input format and edge cases (nullable referred_by, duplicates, cycles) before designing the solution. Propose a hash map from user to referrer for O(1) lookups, then traverse upward from the given user while tracking visited nodes to detect cycles. If a cycle is found, return the acyclic prefix and the cycle nodes in encounter order; otherwise, reverse the path to get the chain from the earliest ancestor.
Pro tip: Emphasize that you would validate the graph structure early (e.g., check for self-referrals or duplicate edges) and discuss how you'd handle memory constraints for 1M users, such as using a compact data structure or streaming the CSV.
Ask about the CSV schema, whether referred_by can be null, how duplicates should be handled, and the expected output format for cycles. Confirm that the referral chain should be ordered from earliest ancestor to the given user.
Propose using a hash map (dictionary) to store each user's referrer for O(1) lookups. Plan to traverse upward from the given user, maintaining a list of visited nodes and a set for cycle detection.
During traversal, if a node repeats, identify the cycle nodes in encounter order and the acyclic prefix. Otherwise, collect the path and reverse it to get the chain from the earliest ancestor.
Discuss memory usage for 1M users (e.g., using arrays or compact structures), and how to handle duplicates (e.g., last-write-wins or validate consistency). Mention time complexity O(n) for traversal.
Walk through test cases: normal chain, cycle, null referrer, duplicate entries, and large input. Explain how you'd verify correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The justification part is where I fumbled.
Model the user hierarchy as a tree and perform a single post-order traversal to compute root_ancestor and chain_depth for all nodes. Use memoization to avoid recomputing paths, ensuring O(n) time and space. Justify by noting each node is visited once and stored once.
Pro tip: Explicitly discuss handling of edge cases like cycles, multiple roots, or disconnected components, and mention that the solution scales to large datasets by avoiding recursion depth issues with an iterative approach.
Confirm the definition of root_ancestor and chain_depth, and whether the hierarchy is a tree (single root, no cycles). Ask about input format and constraints.
Represent the hierarchy as an adjacency list or parent pointers. Use post-order DFS (iterative to avoid stack overflow) to compute values bottom-up.
For each node, root_ancestor is the root of its subtree (or itself if root), and chain_depth is 1 + max child chain_depth. Compute these during traversal.
Store computed values in a hash map or array to reuse for parent computations. Ensure each node is processed exactly once.
Argue O(n) time because each node is visited once and O(n) space for storage. Discuss handling of cycles, multiple roots, or missing parents.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the tie-breaking rule caught me off guard.
Clarify the definition of a valid acyclic chain and the input graph structure, then propose an algorithm that finds all maximal chains, filters for acyclic ones, and sorts them by length descending, root ancestor ID ascending, and lexicographic order of the chain list. Discuss time and space complexity, and consider optimizations like memoization or topological sorting if the graph is a DAG.
Pro tip: Always confirm edge cases with the interviewer, such as empty graph, multiple components, or chains of equal length and root ID, to show thoroughness. Also, mention that you would validate the acyclic property using cycle detection (e.g., DFS with recursion stack) before processing.
Ask about the graph representation (adjacency list/matrix), whether it's directed/undirected, and the exact definition of a 'valid acyclic chain' (e.g., simple path with no repeated nodes). Confirm tie-breaking rules and output format.
Use DFS from each node to explore all simple paths, pruning when a cycle is detected (e.g., node already in current path). Alternatively, if the graph is a DAG, use topological order and DP to find longest paths from each root.
Collect all valid chains, then sort them by length descending, root ancestor ID ascending, and finally lexicographically by the sequence of node IDs. Select the top 3.
Discuss worst-case time complexity (exponential for general graphs) and suggest optimizations like memoization for DAGs or early termination if only top 3 are needed. Mention space complexity.
Walk through a small example to verify correctness, including ties and cycles. Consider edge cases: empty graph, single node, disconnected components, and chains with same length and root.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like the 'real world' part of the question and I actually liked it.
Start by clarifying the data schema and business rules (e.g., what defines a duplicate, how to determine 'earliest seen', and what constitutes a self-referral). Then outline a step-by-step preprocessing pipeline that handles each requirement in a logical order, ensuring data integrity and explainability. Finally, discuss trade-offs and edge cases, such as performance implications and how to validate the output.
Pro tip: Emphasize the importance of documenting assumptions and validating the preprocessing with unit tests or sample data, as this demonstrates production-ready thinking and reduces downstream errors.
Ask questions to understand the CSV structure, what 'earliest seen' means (e.g., timestamp or row order), and how null/empty values are represented. Confirm the definition of a self-referral and external roots.
Identify duplicate rows based on a key (e.g., user ID). When conflicts exist in the parent field, keep the parent from the earliest seen record, using a timestamp or original row order.
Standardize null or empty referred_by values to a consistent representation (e.g., None or a sentinel value) to simplify downstream logic.
For referred_by values not in the user list, treat them as external roots (e.g., mark as 'external'). For rows where referred_by equals the user ID, flag them as self-referrals (e.g., add a boolean column).
Test the preprocessing with edge cases (e.g., all nulls, all self-referrals) and document assumptions and transformations for reproducibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They gave the exact expected behaviors so this was more about writing clean assertions than figuring out the logic.
First, clarify the function under test and the input format (e.g., a list of edges or a mapping). Then, design three minimal test cases: one acyclic chain, one self-cycle, and one two-node cycle, using the provided example rows to construct the inputs and expected outputs. Write concise unit tests that assert the correct cycle detection behavior for each case.
Pro tip: Mention that you would also test edge cases like empty input or a single node without a self-loop, and that you keep tests minimal but meaningful to avoid overfitting to the example.
Identify what the function does (e.g., detect cycles in a directed graph) and how the input is structured (e.g., list of edges, adjacency list). Confirm the expected output format (e.g., boolean, list of cycles).
Create a simple chain like A→B→C with no cycles. Assert that the function returns False or an empty list, depending on the output contract.
Create a single node with a self-loop (e.g., A→A). Assert that the function correctly identifies a cycle.
Create a cycle between two nodes (e.g., A→B→A). Assert that the function detects the cycle.
Use a testing framework (e.g., pytest, unittest) to write three separate test functions. Keep each test minimal and focused on one scenario, using the provided example rows to construct inputs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.