Datadog·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Datadog SWE interview with two back-to-back coding problems, both leaning more system-design-ish than pure leetcode. The questions were practical enough that I didn't feel totally lost, but the complexity analysis follow-ups were rougher than I expected.

Questions Asked (2)

Design a log store that accepts entries with ISO-8601 timestamps and unique IDs. Implement add(id, timestamp) and a query(start, end, granularity) method where granularity can be year, month, day, hour, minute, or second. Walk through your data structures, time and space complexity, and how you handle ordering and memory.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went with a sorted structure keyed on parsed timestamps and figured that was enough.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure that supports efficient insertion and range queries with granularity-based aggregation. Walk through the design, analyze time and space complexity, and discuss trade-offs and optimizations.

Pro tip: Mention that timestamps can be converted to epoch time for easy bucketing, and consider using a balanced BST or skip list for ordered storage with efficient range queries. Also, discuss how to handle out-of-order inserts and memory constraints.

1. Clarify Requirements

Ask about expected data volume, query patterns, latency requirements, and whether entries can be out of order. Confirm that granularity means grouping results by the specified time unit.

2. Choose Data Structures

Propose using a balanced binary search tree (e.g., Red-Black Tree) or a skip list to store entries keyed by timestamp, enabling O(log n) insertion and efficient range queries. Alternatively, consider a time-bucketed hash map for fixed granularities.

3. Design add and query Operations

For add, insert the entry into the ordered structure. For query, traverse the range [start, end] and aggregate entries into buckets based on the granularity (e.g., truncate timestamps to the granularity level).

4. Analyze Complexity and Trade-offs

Discuss time complexity: O(log n) for add, O(log n + k) for query where k is the number of entries in range. Space complexity: O(n). Compare with alternatives like sorted arrays (O(n) insert) or hash maps (O(1) insert but O(n) range query).

5. Address Memory and Ordering

Explain how to handle memory constraints: use compression, eviction policies, or disk-based storage. For ordering, ensure the structure maintains sorted order by timestamp; handle duplicate timestamps with unique IDs.

Key Points to Mention

Use of epoch time for easy bucketing and comparison
Balanced BST or skip list for O(log n) insertion and range queries
Granularity truncation: converting timestamps to the start of the period (e.g., year, month)
Handling out-of-order inserts and duplicate timestamps
Memory optimization techniques like bucketed storage or compression
Trade-offs between different data structures and their impact on performance

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

Implement a BufferedWriter that wraps an abstract sink whose write(bytes) method may only accept partial writes. Your implementation should support write(data), flush(), and close(). How do you handle ordering, chunking large inputs into fixed-size buffers, minimizing system calls, partial write errors, and thread safety?

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This one felt more familiar coming from backend work.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then outline a design that uses an internal fixed-size buffer to batch writes, handles partial writes by looping until all bytes are written, and ensures thread safety with synchronization. Discuss trade-offs between buffer size, system calls, and latency, and cover error handling and flush/close semantics.

Pro tip: Mention that you would flush the buffer before close and ensure that close is idempotent, and consider using a lock or thread-local buffering to balance thread safety and performance.

1. Clarify requirements and constraints

Ask about the expected usage patterns, thread safety requirements, and whether the sink is thread-safe. Confirm that partial writes must be handled and that ordering must be preserved.

2. Design the buffering strategy

Use an internal byte array of fixed size (e.g., 8KB) to accumulate data. When the buffer is full, write it to the sink, handling partial writes by looping until all bytes are written.

3. Implement write, flush, and close

write(data) copies data into the buffer, flushing when full. flush() writes any buffered data to the sink. close() flushes and then closes the sink, ensuring idempotency.

4. Handle partial writes and errors

When writing to the sink, loop until all bytes are written, handling partial writes. Propagate errors appropriately, possibly wrapping them in a custom exception.

5. Ensure thread safety

Synchronize write, flush, and close methods to prevent concurrent access issues. Alternatively, use a lock per buffer or document that the class is not thread-safe if performance is critical.

Key Points to Mention

Ordering: maintain FIFO order by writing buffered data sequentially and never reordering.
Chunking: use a fixed-size buffer to batch small writes, reducing system calls.
Minimizing system calls: flush only when buffer is full or on explicit flush/close.
Partial write handling: loop until all bytes are written, tracking offset and remaining length.
Thread safety: synchronize methods or use a lock; consider performance implications.
Error handling: propagate exceptions, ensure buffer state remains consistent, and close resources properly.

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