← Sigmacomputing Interview Insights
I started with a list of lists because it felt natural, then the interviewer pushed on what happens when most cells are empty and I had to walk back and pitch a dict keyed on (row, col) tuples instead.
Start by clarifying requirements and constraints, then design a simple data structure like a list of lists or a dictionary keyed by (row, col). Implement the methods with attention to edge cases, and ensure pretty-print handles empty cells and alignment. Discuss trade-offs and potential extensions.
Pro tip: Mention that using a dictionary for sparse data can save memory, but for a fixed number of columns and dynamic rows, a list of lists is simpler and more efficient. Also, consider using a sentinel value for empty cells and handle type consistency.
Ask about expected data types, default empty value, maximum rows, and whether columns are truly fixed. Confirm if pretty-print should align columns and how to represent empty cells.
Decide between list of lists (dense) or dictionary (sparse). For fixed columns and dynamic rows, a list of lists is straightforward; each row is a list of length num_cols.
Implement get_cell(row, col) and set_cell(row, col, value) with bounds checking. For set_cell, extend rows if needed. Use a default empty value (e.g., None or empty string).
Print the first N rows, including empty cells. Determine column widths based on max content length, and format each cell with padding. Handle cases where N exceeds current rows.
Test with empty spreadsheet, setting values in new rows, out-of-bounds access, and varying cell content lengths. Discuss time/space complexity and possible optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview actually got interesting.
Start by clarifying the requirements and constraints of the spreadsheet application, then compare the two data structures across the three dimensions: memory usage, access time, and handling of sparse data. Conclude with a recommendation based on the expected usage patterns, such as read/write ratio and density of populated cells.
Pro tip: Mention that the optimal choice often depends on the specific operations (e.g., random access vs. iteration) and that a hybrid approach or a more advanced structure like a hash map of rows to lists might be worth considering.
Ask about the expected size of the spreadsheet, typical density of populated cells, and the most frequent operations (read, write, iterate). This ensures your comparison is relevant to the actual use case.
Compare the memory overhead of a list of lists (which allocates space for every cell, even empty ones) versus a sparse dictionary (which only stores non-empty cells). Quantify the trade-off for mostly-empty spreadsheets.
Discuss the time complexity for common operations: random access, insertion, deletion, and iteration. For a list of lists, access is O(1) but iteration over empty cells is wasteful; for a sparse dictionary, access is O(1) average but with higher constant factors and potential hash collisions.
Explain how each structure performs when the spreadsheet is mostly empty. The list of lists wastes memory and time on empty cells, while the sparse dictionary efficiently skips them but may have overhead for dense regions.
Based on the analysis, recommend one structure or a hybrid approach, and justify it by linking back to the requirements. Acknowledge that the choice may depend on factors like memory constraints, performance needs, and expected data density.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sketched out storing either a raw value or a callable/expression object per cell, and mentioned you'd need dependency tracking to avoid circular refs.
Start by clarifying the current API design and requirements, then propose a formula engine that parses expressions into an AST, builds a dependency graph, and evaluates cells with cycle detection. Discuss trade-offs between eager and lazy evaluation, and how to handle updates efficiently.
Pro tip: Mention that you would separate the formula parsing and evaluation logic from the spreadsheet storage to keep the system modular and testable, and consider using a topological sort for evaluation order.
Ask about the expected formula syntax, supported functions, performance needs, and whether cells can reference ranges or other sheets. Understand if the API is for a single-user or collaborative environment.
Propose using an abstract syntax tree (AST) to represent formulas, with a parser that converts strings into ASTs. Mention handling of cell references, operators, and functions.
Explain how to track dependencies between cells to determine evaluation order, detect cycles, and support incremental updates. Discuss eager vs. lazy evaluation and their trade-offs.
Describe how to extend the current API to set formulas, retrieve computed values, and propagate changes when dependencies update. Consider caching and invalidation strategies.
Cover error handling (e.g., circular references, invalid formulas), scalability for large sheets, and potential optimizations like parallel evaluation or memoization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.