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Start by clarifying requirements and edge cases, then design a state-machine-based CSV parser that handles quoted fields, embedded commas, escaped quotes, and newlines inside quotes. After parsing, implement a Table class with projection and predicate filtering, discussing trade-offs like memory usage and API design.
Pro tip: Demonstrate production awareness by discussing how you'd handle malformed CSV input gracefully and how the design would extend to streaming or larger datasets, showing you think beyond the immediate problem.
Ask about CSV format specifics (e.g., delimiter, quote character, escaping rules) and expected behaviors for malformed input. Confirm the query API expectations: projection returns new table or view? Predicate applied per row?
Outline a state machine that tracks whether you're inside quotes, handling escaped quotes by doubling. Explain how to accumulate characters and split rows/fields correctly, including newlines within quotes.
Store parsed data as a list of rows (each row a list of strings) and column headers. Consider using a simple class with methods for projection and filtering.
For projection, create a new table with selected columns. For predicate, iterate rows, apply the predicate function, and return matching rows (possibly as a new table). Discuss whether to return views or copies.
Talk about time/space complexity, memory efficiency, and how the design could be extended to support streaming, indexing, or more complex queries. Mention testing strategies for edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty clean problem once you see it as a linked list reconstruction.
Model the fragments as a linked list by building a map from start to fragment and identifying the head as the start that never appears as an end. Then traverse from the head, concatenating payloads, and handle edge cases like cycles or disconnected fragments.
Pro tip: Before coding, clarify with the interviewer whether the fragments are guaranteed to form a single valid chain and whether start/end values are unique; this shows you think about edge cases and data integrity.
Ask if the chain is guaranteed to be complete, if start/end values are unique, and if there can be cycles or multiple chains. This ensures you handle all scenarios correctly.
Create a dictionary mapping each start value to its fragment, and a set of all end values to identify the head (the start not in the end set).
Iterate through fragments to find the one whose start is not present in the set of ends; that's the head of the chain.
Starting from the head, follow the links using the map, appending each payload to a result list, until no next fragment exists or a cycle is detected.
If the chain is incomplete or cyclic, decide on appropriate behavior (e.g., return partial result or raise error). Join the payloads and return the final string.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the fragments as an undirected multigraph where each fragment is an edge between its two endpoint labels. Check that the graph is connected (ignoring isolated vertices) and has 0 or 2 vertices of odd degree; then use Hierholzer's algorithm to construct an Eulerian path, treating edges as undirected and using all fragments exactly once.
Pro tip: Mention that the path is not unique and that any valid Eulerian path is acceptable; also note that you can start from any odd-degree vertex if two exist, otherwise any vertex with edges, and that using a multiset for adjacency handles parallel edges cleanly.
Treat each fragment as an undirected edge between its two endpoint labels. Build an adjacency list or multiset to allow parallel edges and self-loops.
Verify the graph is connected (considering only vertices with degree > 0) and count vertices with odd degree. An Eulerian path exists iff there are 0 or 2 odd-degree vertices.
If there are two odd-degree vertices, start at either one; if none, start at any vertex with non-zero degree. This ensures the path can traverse all edges exactly once.
Use a stack-based iterative approach: follow edges until stuck, then backtrack and insert vertices into the path. Remove edges as they are used to avoid reusing fragments.
Reverse the collected path to get the final sequence of vertices. Verify that all fragments are used exactly once and that consecutive vertices correspond to an edge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked through using union-find or just BFS/DFS per component.
Model the input as a directed graph and use traversal algorithms to identify connected components and detect structural anomalies. For each component, verify it forms a valid linear chain (no cycles, branches, or duplicate edges) and then extract the payload sequence by following the unique path. Clearly separate the validation phase from the extraction phase to handle invalid inputs gracefully.
Pro tip: Mention that you would first validate the entire graph structure before attempting to extract sequences, as this prevents infinite loops and ensures you only process valid chains. Also, discuss how you would handle edge cases like empty input or self-loops, showing attention to detail.
Represent each node and directed edge from the input, ensuring you capture all connections. Use adjacency lists or maps to store outgoing edges per node.
Check for invalid conditions: cycles (using DFS with recursion stack or Union-Find), branches (nodes with out-degree > 1 or in-degree > 1), and duplicate edges (using a set of edge pairs). If any invalid condition exists, report an error.
Treat the graph as undirected to find weakly connected components (e.g., via BFS/DFS or Union-Find). Each component should be a separate chain.
For each valid component, identify the start node (in-degree 0) and follow the unique outgoing edge until the end, collecting node payloads in order.
Account for isolated nodes (single-node chains), empty input, and ensure the output is a list of sequences. If any component is invalid, decide whether to skip it or fail entirely based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.