Databricks·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Databricks coding interview for a software engineering role. The problem was a deep dive into compression encoding, specifically building both sides of an RLE/bit-packing hybrid from scratch, plus tests. Pretty involved for a single session.

Questions Asked (1)

Implement an encoder and decoder for a hybrid RLE/bit-packing compression format. The encoder receives integers one at a time and must decide which encoding scheme to use based on the input pattern and a minimum-length threshold. The decoder is initialized with the encoder's output and exposes an iterator interface that yields the original integers in order.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one took me a while to even fully parse.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: the exact encoding schemes (RLE and bit-packing), the minimum-length threshold, and the expected interface for the encoder and decoder. Then outline a design that buffers input to detect runs and decide encoding, and implement the decoder as an iterator that parses the encoded stream on the fly. Discuss trade-offs between compression ratio, speed, and memory usage.

Pro tip: Mention that you would use a buffering strategy with a small window to detect runs and bit-packing opportunities, and that the decoder should be lazy to handle large streams efficiently. Also, highlight the importance of handling edge cases like runs shorter than the threshold and ensuring the decoder can handle malformed input gracefully.

1. Clarify requirements and constraints

Ask about the exact encoding formats, the minimum-length threshold, expected input sizes, and whether the decoder needs to handle corrupted data. Confirm the interface: encoder takes integers one at a time, decoder is initialized with encoded output and yields integers.

2. Design the encoder

Use a buffer to accumulate input integers. Detect runs of identical values and sequences of small integers suitable for bit-packing. When a run or sequence reaches the threshold, emit the corresponding encoded block; otherwise, flush as bit-packed or raw.

3. Design the decoder

Implement an iterator that reads the encoded stream block by block. For each block, decode according to its type (RLE or bit-packed) and yield integers one by one. Maintain state to handle partial blocks.

4. Discuss trade-offs and optimizations

Compare buffering strategies (e.g., fixed-size window vs. dynamic) and their impact on compression ratio and latency. Consider bit-packing width selection (e.g., based on max value in block) and how to encode block headers efficiently.

5. Test and validate

Walk through examples: runs of identical values, sequences of small integers, mixed patterns, and edge cases like empty input or runs just below threshold. Ensure the decoder correctly reconstructs the original sequence.

Key Points to Mention

RLE encoding: store run length and value; bit-packing: store a bit width and packed values.
Minimum-length threshold: only use RLE if run length >= threshold, otherwise bit-pack or store raw.
Buffering strategy: need to buffer to detect patterns; consider memory vs. compression trade-off.
Decoder as iterator: lazy evaluation to handle large streams and avoid loading everything into memory.
Block header design: include encoding type, length, and bit width if applicable.
Edge cases: empty input, runs shorter than threshold, values exceeding bit width, and malformed encoded data.

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