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Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Instacart coding round focused on a multi-level database simulation with banking entities. The first three levels were pretty standard CRUD and aggregation stuff, but level 4 is where things got interesting and honestly a bit rough.

Questions Asked (4)

Q1

Design and implement a backup function that captures a full snapshot of the current database state, tagged with a given timestamp.

System DesignData ModelingTechnical Trade-offs
Author's notes

Seemed manageable at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: full snapshot, timestamp tagging, and expected scale (data size, frequency). Then outline a design that balances consistency, performance, and storage, and finally dive into implementation details like snapshot isolation and metadata management.

Pro tip: Mention that you would use the database's native snapshot capabilities (e.g., MVCC, consistent reads) rather than locking the entire database, and discuss how to handle incremental backups for efficiency.

1. Clarify Requirements and Constraints

Ask about data volume, backup frequency, acceptable downtime, retention policy, and whether the backup must be point-in-time consistent. This ensures the design meets actual needs.

2. Choose a Snapshot Mechanism

Decide between logical (e.g., pg_dump) vs physical (e.g., file system snapshot, WAL) backups, and how to achieve a consistent snapshot without blocking writes, using MVCC or snapshot isolation.

3. Design Metadata and Timestamp Tagging

Define a metadata schema to store backup ID, timestamp, size, location, and status. Ensure timestamps are in UTC and include precision to avoid collisions.

4. Implement the Backup Function

Outline the function's steps: acquire snapshot, stream data to durable storage, record metadata, and handle errors/retries. Consider asynchronous execution to avoid blocking.

5. Address Trade-offs and Operational Concerns

Discuss trade-offs between full vs incremental backups, storage cost, restore time, and how to verify backup integrity. Mention monitoring and alerting for failures.

Key Points to Mention

  • Consistency: Use database-native snapshot isolation (e.g., MVCC) to avoid locking and ensure a point-in-time consistent view.
  • Timestamp handling: Store timestamps in UTC with high precision, and consider using monotonic clocks to avoid issues with clock skew.
  • Storage and format: Choose a durable, cost-effective storage (e.g., S3) and a format that balances compression and restore speed (e.g., compressed SQL, custom binary).
  • Metadata management: Maintain a catalog of backups with status, location, and checksums for integrity verification.
  • Performance: Implement incremental or differential backups to reduce load and storage, and use parallel streams for large datasets.
  • Error handling and idempotency: Ensure the backup function is idempotent and can resume or retry on failure without corrupting the snapshot.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Implement a restore function that rolls the database back to a snapshot at a given timestamp, then re-applies any scheduled operations that fall between that timestamp and the current time.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

This is where I started sweating.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level design that separates snapshot restoration from replaying scheduled operations. Discuss data structures and algorithms for efficient timestamp lookup and operation replay, and address trade-offs around consistency, idempotency, and performance.

Pro tip: Emphasize idempotency and ordering of replayed operations to avoid duplicate side effects, and mention how you would handle operations that depend on external systems or have non-deterministic outcomes.

1. Clarify Requirements and Constraints

Ask about snapshot frequency, operation types, consistency requirements, and acceptable downtime. Confirm whether operations are idempotent and if external side effects need special handling.

2. Design Snapshot Restoration

Explain how to locate and load the snapshot at or before the given timestamp. Discuss storage format, indexing by timestamp, and ensuring the snapshot is consistent.

3. Identify and Order Operations to Replay

Describe how to efficiently retrieve scheduled operations between the snapshot timestamp and now, and sort them by their scheduled time to maintain correct order.

4. Replay Operations with Idempotency and Error Handling

Detail the replay mechanism, ensuring each operation is applied exactly once (e.g., using operation IDs or idempotent writes). Discuss handling failures, retries, and logging.

5. Discuss Trade-offs and Optimizations

Compare approaches for snapshot storage (full vs incremental), replay performance (batch vs streaming), and consistency guarantees. Mention monitoring and rollback strategies.

Key Points to Mention

  • Idempotency of operations to prevent duplicate side effects during replay
  • Efficient timestamp-based lookup using indexes or time-series databases
  • Ordering of operations by scheduled time to maintain causality
  • Handling of external dependencies and non-deterministic operations
  • Trade-offs between full and incremental snapshots (storage vs restore time)
  • Consistency guarantees and potential downtime during restore

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q3

How would you handle a restore call when no snapshot exists for the requested timestamp?

System DesignTechnical Trade-offs
Author's notes

Straightforward error handling question.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints first, then propose a strategy that either fails fast with a clear error or falls back to the nearest available snapshot based on business needs. Emphasize trade-offs between consistency, availability, and data loss, and outline how you would communicate and log the decision.

Pro tip: Mention that you would first check if a snapshot exists at or before the requested timestamp and use the closest one, but also discuss the importance of idempotency and auditability to avoid repeated failures. This shows you think about operational maturity, not just the happy path.

1. Clarify requirements and constraints

Ask about the expected behavior: should the restore fail, use the nearest snapshot, or wait? Understand SLAs, data loss tolerance, and whether the timestamp is a hard requirement.

2. Check for alternative snapshots

Look for the closest snapshot before or after the requested timestamp. Determine if using it is acceptable and how to communicate any data discrepancy.

3. Define fallback and error handling

If no suitable snapshot exists, decide on a fallback (e.g., fail with a clear error, trigger a new backup, or restore from an older snapshot with warnings). Ensure the system logs the event and notifies stakeholders.

4. Implement idempotency and retries

Ensure the restore operation is idempotent so repeated calls don't cause issues. Consider retry logic with backoff if the snapshot might become available later.

5. Communicate and document

Explain the chosen approach to the interviewer, highlighting trade-offs and how you would document the behavior for future reference and user expectations.

Key Points to Mention

  • Trade-offs between consistency (exact timestamp) and availability (using nearest snapshot)
  • Idempotency of restore operations to handle retries safely
  • Error handling and user communication: clear error messages vs. silent fallback
  • Monitoring and alerting for missing snapshots to prevent future issues
  • Data loss implications and how to quantify acceptable loss
  • Potential to trigger an on-demand snapshot if the system supports it

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q4

What should happen if a restore operation is triggered while another restore is already in progress?

System DesignAdaptability & AmbiguityTechnical Trade-offs
Author's notes

Did not see this coming.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the system's requirements and constraints, such as data consistency needs and acceptable downtime. Then, propose a concurrency control mechanism (e.g., locking or queuing) and discuss trade-offs between approaches like rejecting, queuing, or canceling the in-progress restore. Finally, tie your answer to real-world scenarios at Instacart, emphasizing safety and reliability.

Pro tip: Demonstrate awareness of idempotency and failure recovery: even with locking, consider what happens if the lock holder crashes. Mentioning a timeout or lease-based lock shows maturity beyond textbook answers.

1. Clarify requirements and constraints

Ask about the system's consistency requirements, acceptable latency, and whether restores are idempotent. This shows you avoid assumptions and tailor solutions to context.

2. Identify possible strategies

List options: reject the new request, queue it, cancel the ongoing restore, or allow concurrent restores with conflict resolution. Briefly explain each.

3. Evaluate trade-offs

Compare strategies based on data integrity, resource usage, user experience, and complexity. For example, queuing avoids data corruption but may cause delays; rejecting is simple but may frustrate users.

4. Recommend a solution with concurrency control

Propose a specific mechanism like a distributed lock (with lease/timeout) or a queue with idempotent workers. Explain how it prevents conflicts and handles failures.

5. Discuss monitoring and recovery

Mention logging, alerting, and dead-letter queues for failed restores. Highlight the importance of observability to detect stuck locks or backlog.

Key Points to Mention

  • Idempotency of restore operations: if restores are idempotent, concurrent execution might be safe with proper isolation.
  • Distributed locking with lease/timeout to avoid deadlocks if the lock holder crashes.
  • Queuing mechanisms (e.g., message queues) to serialize restores and provide backpressure.
  • Trade-offs between consistency, availability, and latency (CAP theorem considerations).
  • User experience: how to communicate status (e.g., 'restore in progress, your request is queued').
  • Failure scenarios: what happens if the in-progress restore fails? Should the queued restore proceed?

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.