← Grammarly Interview Insights
Start by clarifying requirements: scale, consistency needs, and key features. Then propose a high-level architecture using a client-server model with operational transformation or CRDTs for conflict resolution, and dive into data modeling, real-time communication, and scalability considerations.
Pro tip: Demonstrate awareness of trade-offs between consistency and latency, and mention how you would handle offline editing and conflict resolution. Also, relate to Grammarly's focus on real-time collaboration and correctness.
Ask questions to understand scale (number of concurrent users, document size), consistency requirements (strong vs eventual), and key features (formulas, formatting, offline support).
Outline components: clients, real-time communication layer (WebSockets), application servers, collaboration engine (OT/CRDT), and storage. Explain data flow.
Describe how to represent spreadsheet data (cells, formulas, dependencies) and how to handle concurrent edits using OT or CRDTs, including trade-offs.
Discuss scaling WebSocket servers, sharding workbooks, caching, and ensuring fault tolerance and persistence.
Summarize key trade-offs (e.g., consistency vs latency) and mention potential extensions like offline mode, version history, and permissions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a cell-level revision log keyed by (workbook_id, sheet_id, row, col, version).
Start by clarifying the functional and non-functional requirements, such as expected scale, collaboration needs, and revision history granularity. Then propose a normalized relational schema for core entities (workbooks, sheets, cells) and a separate revision history table that captures changes with versioning. Discuss trade-offs between normalization and denormalization, and how to handle concurrent edits and efficient history retrieval.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how you'd handle large sheets (e.g., sparse storage, chunking) and how revision history can be implemented using event sourcing or change data capture (CDC) to avoid performance bottlenecks.
Ask about scale (number of users, cells per sheet), collaboration features (real-time editing), and revision history needs (how far back, granularity). This shows you don't jump to solutions without understanding the problem.
Outline the main tables: Workbooks (id, name, owner, timestamps), Sheets (id, workbook_id, name, order), Cells (id, sheet_id, row, column, value, formula, format). Explain relationships and indexing strategies for efficient queries.
Propose a revisions table (id, entity_type, entity_id, change_type, old_value, new_value, user_id, timestamp) or an event log. Discuss how to reconstruct past states and handle efficient retrieval of history.
Discuss partitioning, sharding by workbook_id, caching, and sparse storage for cells. Mention how to handle large sheets and frequent updates without degrading performance.
Compare SQL vs NoSQL, normalized vs denormalized, and event sourcing vs snapshotting. Explain why you'd choose one approach based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around the client-server interaction lifecycle, emphasizing how each API call supports real-time collaboration and resilience. Start with session establishment, then detail data fetching and mutation, and finally explain the subscription and reconnection strategy with a focus on consistency and conflict resolution.
Pro tip: Highlight the trade-offs between consistency and latency, and mention how you would use versioning or operational transforms to handle concurrent edits, showing you understand Grammarly's real-time collaborative editing challenges.
Explain how a client opens a session: authenticate, establish a session ID, and negotiate capabilities (e.g., supported operations, protocol version). Mention using WebSocket or HTTP long-polling for the initial handshake.
Describe the API for retrieving a sheet: a GET request that returns the sheet content, metadata, and a version identifier. Discuss pagination or partial loading for large sheets.
Outline how edits are sent: a POST/PUT request with the edit operations, base version, and client-generated ID for idempotency. Explain how the server validates, applies, and broadcasts changes.
Detail the subscription mechanism: a WebSocket channel or server-sent events where the server pushes updates to all session participants. Mention how to handle ordering and deduplication.
Explain the reconnection flow: client reconnects, sends its last known version, and requests missed updates. Server responds with a delta or full snapshot if the gap is too large, ensuring consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Row and column insertions are the sneaky hard part here.
Start by clarifying the requirements and constraints, such as real-time collaboration, offline support, and consistency needs. Then compare conflict resolution strategies like OT and CRDTs, explaining trade-offs in terms of complexity, scalability, and user experience. Finally, propose a concrete solution with a fallback mechanism and discuss how you would test and monitor it.
Pro tip: Acknowledge that perfect automatic resolution isn't always possible and that surfacing conflicts to users with clear UI can be a pragmatic choice. Mention that the best approach depends on product priorities, showing you balance technical purity with user needs.
Ask about the expected scale, real-time vs. asynchronous collaboration, offline support, and consistency guarantees. This shows you don't jump to solutions without understanding the problem.
Discuss Operational Transformation (OT) and Conflict-free Replicated Data Types (CRDTs), highlighting their strengths and weaknesses for concurrent edits and structural changes like row/column insertion.
Recommend a specific approach (e.g., CRDTs for offline-first or OT for centralized real-time) and explain how it handles the given conflict scenarios, including merge semantics.
Describe how to handle conflicts that can't be auto-resolved, such as user notifications, version history, or manual merge options. Mention testing strategies like property-based testing and chaos engineering.
Summarize the trade-offs of your chosen approach in terms of latency, complexity, and infrastructure, and how it would scale with the number of users and document size.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about write-ahead logging and periodic snapshots so you don't have to replay the entire op history on recovery.
Start by clarifying the session model and consistency requirements, then propose a layered persistence strategy (e.g., write-ahead logging + periodic snapshots) with idempotent recovery. Discuss trade-offs between durability, latency, and cost, and how you'd validate recovery with chaos testing.
Pro tip: Tie your answer to Grammarly's real-time collaborative editing context: emphasize that snapshots must be consistent with the operation log and that recovery should be idempotent to handle duplicate replays safely.
Ask about session semantics (e.g., collaborative document editing), acceptable data loss window, and whether recovery must be automatic. Define what 'mid-session' means: crash of a single server, availability zone, or entire region.
Propose a write-ahead log (WAL) or append-only operation log for durability, combined with periodic snapshots to bound recovery time. Mention using a distributed store like Kafka or a database with strong consistency for the log.
Explain how snapshots are taken (e.g., every N operations or T seconds), stored (e.g., object storage), and versioned. Ensure snapshots are consistent with the log by recording the log offset at snapshot time.
Describe loading the latest snapshot and replaying subsequent log entries to reconstruct state. Emphasize idempotency: operations should be replay-safe, and recovery should handle partial writes or duplicates.
Discuss trade-offs: snapshot frequency vs. recovery time and storage cost; synchronous vs. asynchronous persistence. Propose testing recovery with fault injection and monitoring recovery time objectives (RTO/RPO).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I described a dependency graph and topological sort for propagating updates.
Start by clarifying the requirements and constraints, such as the scale of the dependency graph and performance needs. Then describe a dependency graph approach with topological sorting to determine recalculation order, and discuss optimizations like lazy evaluation and cycle detection. Finally, mention trade-offs and how you would handle edge cases.
Pro tip: Demonstrate awareness of real-world spreadsheet complexities like circular references and incremental updates, and tie your solution to Grammarly's need for efficiency and correctness at scale.
Ask about the scale (number of cells, update frequency), performance requirements, and whether the system must handle cycles or errors. This shows you think before coding.
Represent cells as nodes in a directed graph where an edge from A to B means B depends on A. This allows efficient traversal and cycle detection.
Use topological sorting to order cells so that each cell is recalculated only after all its dependencies. This ensures correctness and avoids redundant computations.
Consider lazy evaluation (recalculate only when needed), incremental updates, and cycle detection (report errors). Discuss trade-offs between eager and lazy approaches.
Talk about data structures (adjacency lists, reverse dependencies), algorithms (DFS/BFS for topo sort), and how to scale (e.g., batching updates). Mention potential pitfalls like stale values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with region-affinity for sessions, a central coordination layer for cross-region conflicts, and eventual consistency with strong consistency only for the op ordering within a session.
Start by clarifying the system's current architecture, scale, and latency requirements, then propose a multi-region deployment with edge caching and data replication. Discuss trade-offs between consistency, cost, and latency, and how you would measure and iterate on performance.
Pro tip: Emphasize that latency is a user experience metric, not just a server metric—focus on reducing round trips and leveraging CDNs for static assets, while being pragmatic about data consistency trade-offs.
Ask about expected user distribution, read/write patterns, data consistency needs, and budget. This ensures your scaling strategy aligns with business goals.
Propose deploying services in multiple regions close to users, using global load balancing to route traffic to the nearest healthy region.
Discuss data replication strategies (e.g., active-active or read replicas) and caching (CDN, edge, in-memory) to reduce database round trips.
Explain how you'd handle data consistency across regions (e.g., eventual consistency, conflict resolution) and the trade-offs with latency and cost.
Outline a plan to monitor latency (e.g., p95, p99) per region, set SLOs, and continuously optimize based on real user data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.