Lyft·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Lyft coding round with two meaty problems back to back. Both were more involved than typical leetcode fare and required you to actually think through data structures and tradeoffs rather than just pattern-match to a solution.

Questions Asked (2)

Implement a cursor-based pagination function get_page(user_id, page_size, cursor) over an in-memory collection of transaction records. Records have txn_id, user_id, amount, and created_at fields. Results should be ordered by created_at descending with txn_id ascending as a tiebreaker. The cursor must be opaque and guarantee stable pages even when new transactions are inserted mid-session. Discuss your data structures, how you encode and decode the cursor, and the time and space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The stable-page constraint is what makes this hard.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a sorted data structure (e.g., balanced BST or skip list) keyed by (created_at DESC, txn_id ASC) to enable efficient range queries. Design an opaque cursor that encodes the last seen key (created_at and txn_id) and possibly a snapshot version to ensure stable pagination despite concurrent inserts. Finally, analyze time and space complexity, and discuss trade-offs between different implementations.

Pro tip: Emphasize that the cursor must be opaque to clients and that using a composite key (created_at, txn_id) ensures a total order, preventing duplicates or missed records when new transactions are inserted. Also, mention that you would validate the cursor to prevent tampering or injection attacks.

1. Clarify requirements and constraints

Ask about expected data size, read/write patterns, concurrency, and whether the in-memory collection is mutable. Confirm that pagination must be stable under concurrent inserts.

2. Choose data structures

Propose a balanced BST (e.g., red-black tree) or skip list to maintain records sorted by (created_at DESC, txn_id ASC). Alternatively, use a sorted array with binary search if inserts are infrequent, or a hash map for O(1) access combined with a sorted index.

3. Design cursor encoding/decoding

Encode the last returned record's created_at and txn_id into an opaque string (e.g., base64 of JSON or a compact binary format). Optionally include a snapshot version or timestamp to ensure stability. Decode by parsing the string back into the key.

4. Implement pagination logic

Given a cursor, find the first record strictly after the cursor key in the sorted order, then collect up to page_size records. Return the new cursor based on the last record in the page.

5. Analyze complexity and trade-offs

Discuss time complexity: O(log n) for finding the start position in a balanced BST, O(k) for collecting k records. Space complexity: O(n) for the data structure plus O(1) for the cursor. Compare with alternatives like offset pagination and explain why cursor-based is better for stability.

Key Points to Mention

Composite key ordering: (created_at DESC, txn_id ASC) ensures a total order and stable pagination.
Opaque cursor: encode the last seen key (and possibly a snapshot version) to prevent clients from manipulating pagination.
Data structure choice: balanced BST or skip list for O(log n) inserts and range queries; sorted array for O(log n) reads but O(n) inserts.
Stability under inserts: new records with later created_at won't affect pages because cursor points to a specific key, not an offset.
Time complexity: O(log n + k) per page; space complexity: O(n) for storage, O(1) for cursor.
Trade-offs: cursor-based vs offset-based pagination; handling of deleted records; cursor expiration and security.

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

Design and implement a time-versioned key-value store supporting set(key, value, t) and get(key, t), where get returns the value with the largest timestamp less than or equal to t, or null if none exists. Optimize for heavy read workloads (millions of queries). Achieve O(log n) per operation per key, handle duplicate timestamps deterministically, and discuss memory tradeoffs and concurrency safety for readers.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

Classic problem but the follow-ups are where it gets real.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a per-key sorted structure (e.g., balanced BST or sorted array) to achieve O(log n) operations. Discuss duplicate timestamp handling, memory tradeoffs, and concurrency strategies, emphasizing read optimization. Finally, outline implementation details and potential optimizations.

Pro tip: Mention that for heavy read workloads, you can use a read-optimized structure like a sorted array with binary search, and handle writes by appending and periodically merging, or use a concurrent balanced BST with fine-grained locking. Also, consider using a versioned skip list for lock-free reads.

1. Clarify Requirements and Constraints

Ask about expected read/write ratio, timestamp granularity, memory limits, and concurrency requirements. Confirm that get should return the value with the largest timestamp <= t, and that duplicate timestamps should be handled deterministically (e.g., last write wins).

2. Choose Data Structure

Propose a per-key sorted data structure: a balanced BST (e.g., red-black tree) or a skip list for O(log n) operations. For read-heavy workloads, consider a sorted array with binary search for O(log n) reads, but note O(n) writes; alternatively, use a B-tree or a log-structured merge tree for better write performance.

3. Handle Duplicate Timestamps and Edge Cases

Decide on a deterministic policy for duplicate timestamps (e.g., overwrite with the latest value). Ensure get returns null if no timestamp <= t exists. Discuss handling of out-of-order writes and timestamp collisions.

4. Address Memory and Concurrency

Discuss memory tradeoffs: storing all versions vs. compacting old versions. For concurrency, propose using read-write locks, copy-on-write, or lock-free data structures to allow concurrent reads. Mention that readers can use snapshot isolation or immutable data structures.

5. Optimize for Heavy Reads

Suggest caching frequently accessed keys, using in-memory indexes, or employing a read-optimized structure like a sorted array with binary search. Consider sharding by key to distribute load and using asynchronous writes to avoid blocking reads.

Key Points to Mention

Time complexity: O(log n) for both set and get per key using balanced BST or skip list.
Duplicate timestamps: deterministic policy (e.g., last write wins) and handling out-of-order writes.
Memory tradeoffs: storing all versions vs. compaction, and impact on read performance.
Concurrency: read-write locks, copy-on-write, or lock-free structures for scalable reads.
Read optimization: caching, sorted arrays with binary search, or LSM trees for heavy read workloads.
Implementation details: per-key data structure, global index, and potential use of versioned skip lists.

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