← Databricks Interview Insights
I started with RLE because it felt easier and I needed to build confidence.
Start by clarifying requirements and constraints, then design both encoding schemes with clear interfaces. Implement encode and decode for each, ensuring round-trip correctness, and discuss trade-offs between compression ratio, speed, and complexity.
Pro tip: Demonstrate awareness of edge cases like empty input, single-character runs, and non-ASCII data, and mention how you would test round-trip integrity and handle errors gracefully.
Ask about input types (strings, bytes), expected data characteristics, and whether encoding must be lossless. Confirm if both schemes need to be interoperable or standalone.
Define function signatures for encode and decode for each scheme. Choose appropriate data structures (e.g., list of tuples for RLE, bit buffer for bit-packing) and decide on output format (string, bytes, etc.).
Write encode: iterate through input, count consecutive identical elements, and output pairs of (count, value). Write decode: parse pairs and reconstruct the original sequence.
Write encode: determine the minimum number of bits needed per value, pack values into a continuous bit stream, and output the packed bytes along with metadata (e.g., bit width). Write decode: read metadata, unpack bits, and reconstruct values.
Test both schemes with various inputs (empty, single element, alternating, long runs) to ensure round-trip correctness. Compare compression effectiveness, speed, and memory usage, and discuss when to use each.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I found most interesting and also where I fumbled a bit.
Start by clarifying requirements: the decoder must identify the compression scheme per segment, so the header should include a scheme identifier, length, and possibly checksum. Propose a fixed-size header with a version field, scheme ID, and payload length, then discuss trade-offs like overhead vs. flexibility and extensibility.
Pro tip: Mention that the header should be self-describing and forward-compatible: reserve bits for future schemes and include a version field so the format can evolve without breaking decoders.
Ask about constraints: are segments fixed or variable size? Is random access needed? What compression schemes must be supported? This ensures the design meets actual needs.
Propose a fixed-size header containing: magic number, version, compression scheme ID (e.g., 1 byte), payload length (e.g., 4 bytes), and optional checksum. This allows the decoder to read the header, identify the scheme, and know how many bytes to read.
Include a version field and reserved bits for future schemes. Consider using a scheme ID registry or a TLV (Type-Length-Value) format for additional metadata.
Compare fixed vs. variable header size, overhead vs. flexibility, and complexity of parsing. For example, a fixed header is simpler but may waste space; a variable header is more flexible but requires more complex parsing.
Include a checksum or magic number to detect corruption. Specify behavior for unknown scheme IDs (e.g., skip segment or error out).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining both encoding schemes and their trade-offs: RLE excels for data with long runs of identical values, while bit-packing is better for data with high cardinality and small value ranges. Then explain that the decision at runtime often involves sampling the data to estimate run lengths and value distribution, and choosing the encoding that minimizes storage or maximizes query performance. Finally, mention that in systems like Databricks, adaptive encoding selection is common, and you might combine both (e.g., RLE then bit-packing) for optimal results.
Pro tip: Emphasize that the decision isn't just about compression ratio but also about query performance and CPU overhead—RLE can be faster for scans on run-heavy data, while bit-packing reduces I/O. Also, mention that modern columnar formats like Parquet use statistics and sampling to choose encodings per column chunk.
Briefly explain RLE (run-length encoding) and bit-packing, highlighting that RLE is ideal for repetitive data and bit-packing for small-range, high-cardinality data.
Discuss how to analyze the data: compute run lengths, distinct value counts, and value range to determine which encoding is more suitable.
Explain that the choice depends on factors like compression ratio, decompression speed, and query patterns (e.g., scans vs. point lookups).
Outline a runtime strategy: sample the data, estimate costs for each encoding, and pick the one with the best expected performance, possibly using a hybrid approach.
Mention how systems like Databricks/Parquet implement this, e.g., per-column chunk encoding selection based on statistics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on negative numbers with bit-packing.
Systematically enumerate edge cases by category: single-value inputs, boundary values (min/max of the type), and negative numbers in signed integer schemes. For each, explain how the algorithm behaves, whether it handles the case correctly, and any adjustments needed. Tie the discussion back to the problem's constraints and the chosen data structures.
Pro tip: Don't just list edge cases—explain the reasoning behind each and how you'd test it. Mention that you'd write unit tests for these cases and consider using property-based testing to catch unexpected boundaries.
Clarify the data types and constraints (e.g., 32-bit signed integers, array length, etc.) to know what boundaries exist.
Consider inputs like empty arrays, single-element arrays, zero, or null—cases where the algorithm might not loop or might divide by zero.
For each numeric type, check minimum and maximum values (e.g., INT_MIN, INT_MAX) and how operations like addition or multiplication might overflow.
Discuss how negative values affect indexing, comparisons, absolute values, and bitwise operations, especially with two's complement representation.
Explain how you would test these edge cases, including unit tests and potential use of assertions or property-based testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.