Started with a stack-based approach, two stacks, one for history and one for redo.
Clarify requirements (operation types, undo depth, concurrency) and then propose a design using a command pattern with an undo stack. Discuss data structures for efficient text editing (e.g., piece table or rope) and how to handle memory and performance trade-offs.
Pro tip: Mention that undo should be implemented via inverse operations or snapshots, and highlight the importance of idempotency and atomicity for operations. Also, consider discussing how to handle collaborative editing scenarios, as Figma deals with real-time collaboration.
Ask about the types of operations (insert, delete, replace), undo depth, whether redo is needed, and concurrency requirements. Confirm if the API should support batching or transactions.
Define the Operation interface with apply and invert methods. Choose a data structure for the document (e.g., piece table, rope, or gap buffer) that balances memory and performance for edits.
Use a stack to store inverse operations or snapshots. For each apply, push the inverse onto the undo stack; undo pops and applies the inverse. Discuss memory implications and potential optimizations like operation coalescing.
Address concurrent edits (e.g., using operational transforms or CRDTs) and ensure thread safety if needed. Discuss how to handle undo/redo across multiple users in a collaborative setting.
Compare approaches (e.g., inverse operations vs. snapshots) in terms of time/space complexity. Suggest optimizations like lazy deletion or periodic compaction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the existing undo/redo design and the atomicity requirements, then propose a batch as a single composite command that groups multiple operations. Explain how beginBatch/apply/commitBatch manage a pending buffer, and how commitBatch pushes one atomic entry onto the undo stack. Finally, discuss trade-offs around memory, performance, and failure handling.
Pro tip: Emphasize that atomicity means either all operations in the batch are applied and undone together, or none are—so you need to handle partial failures during commit and ensure undo reverts the entire batch as one unit. Also mention that batching can improve performance by reducing undo stack churn and enabling coalesced updates.
Ask about the current undo/redo implementation, expected batch sizes, and whether nested batches or concurrent batches are needed. Confirm that undo() must revert the entire batch atomically.
Define beginBatch() to start a pending batch, apply(...) to buffer operations without executing them, and commitBatch() to atomically apply all buffered operations and push a single composite command onto the undo stack.
During commitBatch(), execute all operations in order; if any fails, roll back already-applied operations to maintain atomicity. For undo(), revert the entire batch by undoing each operation in reverse order as a single unit.
Discuss handling of nested batches, empty batches, and errors during apply or commit. Weigh memory overhead of storing batch operations versus performance gains from reduced undo stack entries.
Recap the design, highlight how it meets atomicity and performance goals, and ask if the interviewer wants to explore alternative approaches or optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints: what defines a 'small operation', the expected undo frequency, and whether undo must be immediate or can be asynchronous. Then propose a layered solution: compress the undo log using delta encoding and run-length encoding, and process undo in chunks with lazy evaluation to bound memory and time.
Pro tip: Emphasize that the best optimization often comes from preventing the need to store millions of individual operations in the first place—by coalescing operations at write time or using a persistent data structure with structural sharing.
Ask about the size of operations, memory limits, latency requirements, and whether undo can be batched or streamed. This ensures your solution targets the real bottleneck.
Use delta encoding, run-length encoding, or a persistent data structure (e.g., immutable tree with path copying) to store the undo log efficiently, reducing memory from O(n) to O(changes).
Instead of applying all operations at once, undo in fixed-size chunks or use lazy iterators to avoid loading the entire log into memory. This bounds peak memory and allows incremental progress.
Build an index (e.g., skip list or B-tree) to quickly locate operations to undo, and parallelize independent undo operations where possible to reduce wall-clock time.
Compare approaches: e.g., eager vs. lazy undo, in-memory vs. on-disk storage, and the trade-off between undo speed and memory usage. Mention when to coalesce operations at write time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard redo stack invalidation: any new apply() or commitBatch() clears the redo stack.
Start by clarifying the data model: a command pattern with undo/redo stacks, where each command encapsulates an edit or a batch. Then describe how redo() pops from the redo stack and applies the command, and how new edits clear the redo stack to maintain linear history. Finally, discuss handling batches as composite commands and edge cases like empty stacks.
Pro tip: Emphasize that redo should be symmetric to undo: if undo pushes onto the redo stack, redo should push onto the undo stack. Also, mention that clearing the redo stack on new edits is standard but can be configurable (e.g., for branching history) — showing awareness of product trade-offs.
Ask if batches are atomic and if redo should work across sessions. Propose a command pattern with undo and redo stacks, where each command has execute() and undo() methods.
Explain that redo() checks if the redo stack is non-empty, pops the top command, calls execute() (or redo()), and pushes it onto the undo stack.
Represent a batch as a composite command containing a list of sub-commands. redo() on a batch executes all sub-commands in order, and undo() reverses them in reverse order.
When a new edit is performed, clear the redo stack because the history has branched. This ensures redo only replays edits that were undone and not superseded.
Mention empty stack handling, memory management (e.g., limiting stack size), and potential for merging commands or using persistent data structures for efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran out of steam a little here near the end.
Start by clarifying the scope and constraints of the editing layer, then propose a robust data model that handles overlapping edits and cursor bookkeeping through normalization and transformation. Walk through concrete edge cases and trade-offs, emphasizing correctness, performance, and collaboration scenarios.
Pro tip: Demonstrate awareness of operational transformation (OT) or CRDTs, but focus on how you'd validate and test edge cases—this shows you prioritize reliability over buzzwords.
Ask about the editing layer's scope: single-user vs. collaborative, real-time vs. batch, and performance requirements. This ensures your solution addresses the right problems.
Propose representing edits as operations with ranges and metadata, and cursors as positions that can be transformed. Consider using a normalized structure to avoid ambiguity.
Explain strategies like operational transformation or conflict-free replicated data types (CRDTs) to merge concurrent edits. Discuss how to detect and resolve overlaps deterministically.
Describe how to update cursor positions when edits occur, using transformation functions that adjust positions based on insertions/deletions. Ensure cursors remain stable and intuitive.
Outline a testing strategy with unit tests for overlapping edits, cursor movements, and undo/redo. Mention property-based testing to cover complex scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.