Databricks·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Databricks SWE interview that went pretty deep into streaming systems. Part one was manageable but the follow-up on memory-bounded RLE caught me off guard in a good way, really made me think about what 'correctness' even means when you're flushing state.

Questions Asked (2)

Implement run-length encoding over a stream of tokens, outputting (value, run_length) pairs for consecutive equal tokens. Your implementation must also handle backpressure from a slow downstream consumer.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The core RLE part was fine, just track current token and count, emit when it changes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the streaming context and backpressure semantics, then outline a stateful operator that buffers the current run and emits pairs only when the token changes or the stream ends. Emphasize how you handle backpressure by propagating demand upstream and using non-blocking emission, and discuss trade-offs between latency, memory, and throughput.

Pro tip: Mention that you would use a bounded buffer and only emit when downstream requests, avoiding unbounded memory growth; also note that run-length encoding can be done with O(1) state per stream, which is a key selling point for streaming systems.

1. Clarify requirements and constraints

Ask about the token type, stream characteristics (ordered, infinite), backpressure model (pull-based like Reactive Streams or push-based with flow control), and expected output format.

2. Design the stateful operator

Maintain a current token and a run length counter. On each incoming token, if it equals the current token, increment the counter; otherwise, emit the previous (token, count) pair and reset state.

3. Integrate backpressure handling

Use a pull-based model where downstream requests drive emission. Buffer at most one pending pair and only emit when there is demand; if no demand, pause consumption from upstream to avoid buffering.

4. Handle stream termination and edge cases

On completion, emit the final run if any. Handle empty streams, single-token streams, and ensure no data loss when backpressure delays emission.

5. Discuss trade-offs and optimizations

Compare latency vs. throughput (e.g., emitting eagerly vs. batching), memory usage (constant vs. buffering), and potential for parallelization or windowing if needed.

Key Points to Mention

Backpressure propagation: use request(n) or similar to control upstream demand and avoid unbounded buffering.
State management: only need O(1) state (current token and count) regardless of stream length.
Emission strategy: emit only when token changes or stream ends, and only when downstream is ready.
Thread safety and concurrency: if multiple threads, ensure state is confined or synchronized.
Error handling: how to handle downstream failures or cancellation, and cleanup of state.
Testing: unit tests for run-length encoding logic and integration tests for backpressure scenarios.

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

Now imagine the token stream is extremely high volume and you can't keep all encoded pairs in memory. How do you redesign the RLE to only retain the last K encoded pairs, emit earlier ones to a downstream sink, and still guarantee correctness at the boundary between flushed and in-memory state?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This is where the interview got interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the constraints: what 'last K encoded pairs' means (e.g., most recent K pairs in the stream), what the downstream sink expects, and whether ordering matters. Then propose a bounded buffer with a flush policy that emits the oldest pair when the buffer exceeds K, and handle boundary correctness by ensuring the flushed state and in-memory state are consistent (e.g., by never splitting a run across the boundary or by tracking the last emitted pair's value and count).

Pro tip: Emphasize that correctness at the boundary requires either atomic flush of complete runs or maintaining a small amount of state (like the last emitted value and its count) to merge with the next run if needed. This shows you understand the subtlety of streaming RLE.

1. Clarify requirements and constraints

Ask about the definition of 'last K encoded pairs', whether the sink can handle partial runs, and if ordering must be preserved. Confirm if K is fixed or dynamic.

2. Design a bounded buffer with flush policy

Use a FIFO queue of size K to hold encoded pairs. When a new pair is added and the queue exceeds K, emit the oldest pair to the sink. Ensure the queue never holds more than K pairs.

3. Handle boundary correctness

To avoid splitting a run across the boundary, either delay emitting a pair until you know it's complete (i.e., the next value differs), or maintain the last emitted pair's value and count so you can merge if the next in-memory pair has the same value.

4. Address failure and recovery

Consider what happens if the sink fails or if the process restarts. Discuss idempotency, checkpointing the last emitted state, or using a transactional sink to guarantee exactly-once semantics.

5. Discuss trade-offs and alternatives

Compare this approach with alternatives like using a larger buffer, windowing, or approximate algorithms. Mention memory vs. correctness trade-offs and how K affects latency and throughput.

Key Points to Mention

Bounded buffer (FIFO) of size K with flush of oldest pair when exceeding K.
Boundary correctness: never split a run; either flush complete runs or track last emitted value/count to merge.
State management: maintain the last emitted pair's value and count to handle continuation of a run.
Backpressure and flow control: if the sink is slow, the buffer may fill; discuss blocking or dropping policies.
Fault tolerance: checkpointing the buffer and last emitted state for recovery, or using a transactional sink.
Trade-offs: memory usage vs. latency, and the impact of K on correctness and performance.

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