Instacart·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Instacart SWE interview that went deeper than I expected on a database design problem. They took the classic in-memory DB question and layered backup/restore on top, then pushed hard on the design tradeoffs. Left feeling like I handled the mechanics okay but fumbled the discussion part.

Questions Asked (2)

Extend an in-memory key-value database with backup and restore functionality. The backup operation saves the full database state at a given timestamp and returns the count of keys that still have at least one non-expired field. The restore operation rolls back to the most recent backup at or before a target timestamp, but recalculates every restored field's TTL relative to the current timestamp so the remaining lifetime is preserved.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I got the backup part down pretty quick, deep copy the whole state, count valid fields, store it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model and operations, then design a backup mechanism that snapshots the database state and computes the count of keys with non-expired fields. For restore, explain how to find the latest backup at or before the target timestamp and adjust TTLs to preserve remaining lifetimes relative to the current time.

Pro tip: Emphasize the importance of handling time consistently—use a single source of truth for timestamps and consider edge cases like backups during active TTLs and restoring to a time before any backup exists.

1. Clarify requirements and data model

Confirm the key-value store's structure (e.g., fields with TTLs), backup/restore semantics, and how timestamps are provided. Ask about concurrency and persistence expectations.

2. Design backup operation

Propose storing a deep copy of the database state at the given timestamp, including each field's value and absolute expiration time. Compute the count of keys with at least one non-expired field by checking against the backup timestamp.

3. Design restore operation

Find the most recent backup with timestamp ≤ target. For each field in that backup, recalculate its TTL as (original_expiration - backup_timestamp) and set new expiration to current_time + remaining_ttl, preserving remaining lifetime.

4. Address edge cases and trade-offs

Discuss handling of missing backups, expired fields during restore, memory overhead of full snapshots, and potential optimizations like incremental backups or copy-on-write.

5. Summarize and test

Walk through a concrete example to validate the approach, then summarize key decisions and complexity (time/space) of backup and restore.

Key Points to Mention

Deep copy vs. persistent data structures for efficient snapshots
Time source consistency and using absolute timestamps for TTLs
Counting non-expired fields at backup time (checking expiration > backup timestamp)
TTL recalculation formula: new_expiration = current_time + (old_expiration - backup_timestamp)
Handling of keys/fields that expired between backup and restore
Complexity analysis: O(N) for backup and restore, where N is number of fields

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

What are the tradeoffs between doing a full deep copy per backup versus using structural sharing, and how does your choice affect the behavior of subsequent write and read operations after a restore?

System DesignTechnical Trade-offsData Modeling

Author's notes

This is where I kind of stumbled.

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AI HintsAI Generated

Suggested Approach

Start by defining both approaches and their core tradeoffs in storage, time, and complexity. Then explain how each affects write and read behavior after a restore, emphasizing copy-on-write, versioning, and isolation. Conclude with a recommendation based on workload characteristics like backup frequency, data size, and read/write patterns.

Pro tip: Mention that structural sharing often requires immutable data structures or persistent data structures (e.g., copy-on-write B-trees) to avoid accidental mutation, and that restore semantics must consider whether the restored snapshot is isolated or shares pages with the live system.

1. Define the two approaches

Briefly describe full deep copy (duplicating all data) and structural sharing (reusing unchanged data blocks via references or copy-on-write).

2. Compare tradeoffs

Discuss storage overhead, backup/restore speed, write amplification, and complexity for each approach.

3. Analyze post-restore write behavior

Explain how writes after restore differ: deep copy gives full isolation but may cause write amplification; structural sharing may require copy-on-write or reference counting to avoid corrupting the snapshot.

4. Analyze post-restore read behavior

Describe read performance: deep copy may have better locality; structural sharing may incur indirection or cache misses but can enable instant restores.

5. Recommend based on context

Suggest which approach suits which scenario (e.g., frequent backups vs. large datasets) and note hybrid strategies.

Key Points to Mention

Storage and time complexity: deep copy uses O(n) space and time; structural sharing uses O(1) or O(changes) for backup.
Copy-on-write and immutable data structures as enablers for structural sharing.
Write amplification and isolation: deep copy ensures writes don't affect backup; structural sharing requires careful reference management.
Restore speed: structural sharing allows instant restore by switching pointers; deep copy requires data transfer.
Read performance: deep copy may improve locality; structural sharing may add indirection but can leverage caches.
Concurrency and consistency: how each approach handles concurrent writes during backup and after restore.

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