I went straight to two stacks, undo and redo, which is the obvious answer.
Start by clarifying requirements: what operations are needed, what types of changes (e.g., property updates, structural changes), and whether collaboration is required. Then propose a command pattern where each operation is an object with apply and inverse methods, stored in an undo stack and a redo stack. Discuss how to handle memory, batching, and potential optimizations like persistent data structures.
Pro tip: Mention that in a collaborative editor like Figma, you need to handle concurrent operations and that undo/redo must be scoped per user, which often requires operational transformation or CRDTs. This shows you understand the real-world complexity beyond a simple stack.
Ask about the types of operations, expected frequency, memory constraints, and whether collaboration or persistence is needed. This ensures your design fits the context.
Represent each operation as a command object with apply and inverse methods. This encapsulates the logic and makes undo/redo straightforward.
Use two stacks: one for undo (past operations) and one for redo (undone operations). On apply, push to undo stack and clear redo stack; on undo, pop from undo, apply inverse, push to redo; on redo, pop from redo, apply operation, push to undo.
Discuss batching operations, using persistent data structures to share state, and limiting history size. Consider lazy inverse computation or storing deltas instead of full snapshots.
Mention how to deal with concurrent edits (e.g., operational transformation or CRDTs), and how to scope undo/redo per user in a multiplayer setting.
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Start by clarifying the requirements and constraints, then design a batching mechanism that groups operations and records a single undo entry. Explain how you would implement beginBatch, apply, and commitBatch, ensuring atomicity and efficient undo.
Pro tip: Emphasize that the batch should be treated as a single transaction: if any operation fails, the entire batch should be rolled back, and the undo stack should only contain the batch as one entry.
Ask about expected batch size, error handling, and whether nested batches are needed. Confirm that undo should revert the entire batch in one step.
Propose a Batch object that stores a list of operations and a composite undo function. The undo stack will hold either individual operations or batches.
beginBatch initializes a new batch and sets a flag to route apply calls into it. apply adds the operation to the batch and executes it. commitBatch finalizes the batch, pushes it onto the undo stack, and clears the batching state.
Ensure that undoing a batch reverses all operations in reverse order. If an error occurs during apply, abort the batch and undo any already-applied operations.
Mention potential optimizations like merging operations or lazy execution, and address edge cases such as empty batches or nested batches.
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Start by clarifying the requirements and constraints of the undo system, then propose a design that uses operation compression and inverse operations to optimize both time and space. Discuss trade-offs between different compression strategies and how they affect undo/redo performance, and consider edge cases like memory limits and concurrency.
Pro tip: Emphasize that undo/redo is a core user experience feature, so optimizations must not compromise correctness or responsiveness; mention that you would instrument the system to measure actual memory and latency improvements before and after optimization.
Ask about the expected scale (number of operations, memory limits), latency requirements, and whether undo/redo must be persistent across sessions. This ensures your optimization targets the right bottlenecks.
Propose compressing consecutive operations of the same type (e.g., multiple character insertions into a single string insert) or using delta encoding. Discuss how to detect compressible sequences and the trade-off between compression ratio and decompression overhead.
Store inverse operations compactly, e.g., by storing only the minimal data needed to revert (like the previous state or a diff). Consider using a command pattern with reversible operations and sharing data between forward and inverse operations.
Compare approaches: eager vs lazy compression, in-memory vs on-disk storage, and the impact on undo/redo speed. Quantify space savings and time costs, and propose a hybrid approach if needed.
Discuss handling of non-compressible operations, memory pressure (e.g., evicting old history), and concurrency (e.g., collaborative editing). Mention how to test and monitor the system.
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Explain the standard undo/redo model: undo moves the last action from the undo stack to the redo stack, and any new edit after an undo clears the redo stack. Then connect this to real-world design tools like Figma, emphasizing why clearing the redo stack is necessary to avoid branching history and maintain a linear timeline.
Pro tip: Mention that some applications offer a 'redo history' that persists after new edits, but this is non-standard and can confuse users; Figma likely follows the standard model for predictability. Also, note that the redo stack is typically cleared to prevent inconsistent states and to simplify the mental model for users.
Briefly describe the undo and redo stacks as LIFO structures that store actions or states. Clarify that undo stack holds actions that can be undone, and redo stack holds actions that can be redone.
State that when an undo is performed, the most recent action is popped from the undo stack, reversed, and pushed onto the redo stack. This allows the action to be redone later.
Describe that when a new edit is applied after an undo, the redo stack is cleared (or becomes invalid) because the new action creates a new branch in history. This prevents redoing actions that are no longer in the current timeline.
Mention why clearing the redo stack is standard: it avoids ambiguity, maintains a linear history, and aligns with user expectations. Optionally, contrast with alternative models (e.g., persistent redo) and their pros/cons.
Connect to Figma's collaborative design tool: emphasize that predictable undo/redo is crucial for user experience, and that clearing redo prevents conflicts in a multi-user environment (though Figma's real-time collaboration may have more complex handling).
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Start by clarifying the requirements and constraints, such as whether the batch must be atomic or if partial success is acceptable. Then discuss trade-offs between different failure handling strategies, and propose a solution that aligns with the system's needs, emphasizing idempotency, retries, and observability.
Pro tip: Demonstrate maturity by acknowledging that the 'right' answer depends on business requirements and system constraints, and that you would collaborate with stakeholders to define the desired behavior. Also, mention that you'd design for idempotency and use dead-letter queues to handle persistent failures.
Ask questions to understand the batch's purpose, whether partial success is acceptable, and the impact of failures on downstream systems. Determine if the batch must be atomic or if it can be processed in a best-effort manner.
Discuss the pros and cons of different approaches: all-or-nothing (atomic) vs. partial success with error handling. Consider factors like data consistency, user experience, and system complexity.
Explain how you would make operations idempotent to allow safe retries, and implement retry mechanisms with exponential backoff for transient failures. Mention the use of unique identifiers to detect and skip already-processed items.
Describe how to isolate and handle items that repeatedly fail, such as using a dead-letter queue for manual inspection or automated alerts. Ensure that failures are logged with sufficient context for debugging.
Outline monitoring and alerting for batch processing, and discuss recovery strategies like reprocessing failed items or compensating transactions. Emphasize the importance of observability and clear error reporting.
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Systematically enumerate edge cases by categorizing them into input, state, and interaction scenarios. For each category, consider empty, single, multiple, and nested cases, and how they affect the system's behavior. Prioritize edge cases based on likelihood and impact, and suggest how to handle them in design or testing.
Pro tip: Demonstrate that you think about edge cases not just as bugs but as opportunities to improve system robustness and user experience. Mention how you would document and test these edge cases to ensure they are handled correctly.
Clarify what the system does, focusing on stacks, batches, and batch calls. Understand the expected normal flow to contrast with edge cases.
Break down edge cases into input (empty, single, multiple), state (empty stack, full stack), and interaction (nested batches, overlapping calls).
For each edge case, assess how it could break the system or cause incorrect behavior, and how likely it is to occur in practice.
Suggest design or implementation approaches to handle each edge case, such as validation, error handling, or fallback mechanisms.
Conclude with a prioritized list of edge cases to address, and mention how you would test them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.