The inversion part was fine, flipping '#' and ' ' is trivial once you decompress.
Clarify the bitmap format and compression scheme, then propose a pipeline: decompress the bitmap, apply the transformation on the raw pixel grid, and recompress using the existing routines. Discuss trade-offs between transforming before vs. after decompression, and consider optimizations for each transformation type.
Pro tip: Mention that for certain transformations like horizontal mirror or 90-degree rotation, you can sometimes transform the compressed representation directly without full decompression, but always validate correctness against the decompressed version.
Ask about the bitmap dimensions, compression algorithm (e.g., RLE, LZ), and whether transformations must be lossless. Confirm that reusing existing compression/decompression routines is mandatory.
Outline a straightforward approach: decompress the bitmap into a pixel grid, apply the transformation (inversion, mirror, rotation), then recompress using the existing routine. Discuss memory and time complexity.
For each transformation, consider if it can be applied directly on the compressed data or if it benefits from partial decompression. For example, horizontal mirror might be done by reversing the order of compressed blocks if the compression is row-based.
Address non-square bitmaps, padding, and alignment issues for rotation. Ensure the transformed bitmap is correctly recompressed and renders as expected. Test with small examples.
Compare the naive decompress-transform-recompress approach with more complex in-place or streaming transformations. Consider performance, memory usage, and code maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and honestly it's the more interesting question.
Start by defining the core principle: a transformation can be applied directly on compressed data if it commutes with the compression scheme or can be expressed as an operation on the compressed domain. Then systematically walk through the tradeoffs—computational cost, memory, latency, and correctness—and illustrate with concrete examples like filtering on dictionary-encoded columns or map-side aggregation in MapReduce.
Pro tip: Emphasize that the decision hinges on whether the transformation is 'compression-aware' and whether the compressed format supports random access or partial decompression. Mention that in practice, you often choose a compression scheme that aligns with your query patterns to enable direct operations.
Clarify what transformation is needed (e.g., filter, aggregate, project) and what compression is used (e.g., run-length encoding, dictionary encoding, delta encoding). The compatibility depends on both.
Determine if the transformation commutes with decompression—i.e., whether applying the transformation on compressed data yields the same result as decompressing first. Look for homomorphisms or pushdown capabilities.
Consider CPU cost (decompression overhead vs. direct operation), memory usage (holding compressed vs. decompressed data), latency (streaming vs. batch), and implementation complexity (custom operators vs. standard libraries).
Assess whether the compressed format allows random access, partial decompression, or requires full decompression. Also consider selectivity: if the transformation filters out most data, decompressing only relevant blocks may be better.
Summarize when direct application is beneficial (e.g., when transformation is simple, compression is block-wise, and data is large) and provide a concrete example like predicate pushdown on Parquet or map-side aggregation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.