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I jumped straight into the happy path and forgot about multi-digit counts for way too long.
Start by clarifying the encoding format and edge cases, then implement compression by scanning the string and counting consecutive characters, and decompression by parsing counts and characters. Discuss how to handle counts larger than 9 or 255, such as using multi-digit counts or a delimiter, and consider trade-offs like readability vs. compactness.
Pro tip: Demonstrate awareness of real-world constraints: mention that RLE is often used in formats like BMP or fax, where counts are limited to 255, and propose a robust encoding scheme (e.g., count as decimal string followed by character) that handles arbitrary run lengths without ambiguity.
Ask about the expected encoding format (e.g., count as decimal digits followed by character) and how to handle runs longer than 9 or 255. Confirm behavior for empty string, single character, and non-alphanumeric characters.
Iterate through the string, count consecutive identical characters, and append the count (as a string) followed by the character to the output. Ensure counts are correctly represented even if they exceed one digit.
Parse the compressed string by reading digits to form the count, then read the next character and repeat it count times. Handle cases where the count may be multi-digit and ensure the input is valid.
Discuss time and space complexity (O(n) for both compression and decompression). Compare alternative encoding schemes (e.g., fixed-width counts vs. variable-length) and their impact on compression ratio and simplicity.
Walk through examples: empty string, single character, runs of length 1, runs >9, runs >255, and strings with mixed runs. Verify that compression and decompression are inverses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said O(n) for both without much explanation and they pushed back a little, asking me to be more precise about what n means in each case and what the space usage looks like relative to input vs output size.
Start by clearly defining the input and output formats for both compression and decompression, then derive time and space complexity by analyzing the number of operations relative to input size. For compression, focus on the linear scan and output construction; for decompression, focus on parsing counts and expanding characters. Mention best, average, and worst cases, especially for compression where output size depends on data compressibility.
Pro tip: Explicitly state that compression time is O(n) where n is the input length, but space is O(m) where m is the compressed output size, which can be up to 2n in the worst case (e.g., alternating characters). This shows you understand the nuance beyond just saying O(n) space.
Clarify that compression takes a string of length n and produces a compressed string of length m, while decompression takes a compressed string of length m and produces a string of length n. This sets the stage for complexity analysis.
Explain that compression requires a single pass through the input, comparing each character with the previous one and appending counts to the output. Thus, time complexity is O(n).
Discuss that the output string can be up to 2n in the worst case (e.g., 'abc' -> 'a1b1c1'), so space complexity is O(m) or O(n) in the worst case. Mention that if using a list of characters and joining, auxiliary space is O(m).
For decompression, parsing the compressed string takes O(m) time, and building the output takes O(n) time, so overall time is O(m + n). Space is O(n) for the output string, plus O(m) for the input, so O(n + m).
Conclude with a summary table or statement of complexities, and note that RLE is efficient for data with many consecutive repeats but can expand data in the worst case. Mention potential optimizations like using a StringBuilder to avoid O(n^2) string concatenation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the exact bit layout and edge cases, then present a clean implementation using bitwise operations. Discuss trade-offs like memory savings versus access speed, and mention potential optimizations or alternative approaches.
Pro tip: Demonstrate awareness of endianness and sign issues by explicitly stating that you treat values as unsigned 4-bit integers and that the packing order is independent of machine endianness. Also, mention that you would write unit tests for edge cases like empty array, single element, and maximum values.
Confirm the value range (0-15), packing order (first value high nibble, second low nibble), and handling of odd-length arrays (zero-pad last byte). Ask about expected input size and performance requirements.
Iterate through the array in steps of two. For each pair, combine the first value shifted left by 4 bits with the second value. If only one value remains, pack it in the high nibble and set the low nibble to zero.
Use bitwise OR and left shift to combine values. Ensure masking with 0x0F to prevent overflow if values exceed 4 bits. Write clear, commented code.
Test with empty array, single element, even and odd lengths, and boundary values (0 and 15). Verify that unpacking returns the original array (with possible trailing zero if odd length).
Compare memory savings (4x reduction) versus access overhead. Mention alternative approaches like using a bit vector or SIMD for large arrays. Consider if random access is needed and how to implement it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you've written the compression side.
First clarify the bit-packing scheme (e.g., fixed-width packing, bit order, endianness) and handle edge cases like n=0 or invalid input. Then implement a bit-by-bit or chunk-based extraction using bitwise operations, ensuring correct handling of partial bytes and sign extension if needed. Validate with small examples and discuss time/space complexity.
Pro tip: Mention that you would use a 64-bit buffer to accumulate bits and extract values in chunks, which reduces the number of bitwise operations and improves performance. Also, proactively discuss how you would handle signed integers and potential overflow.
Ask about the bit width per value, bit order (MSB-first or LSB-first), and whether values are signed or unsigned. Confirm the expected output format and edge cases.
Decide on a bit-by-bit or chunk-based approach. For efficiency, use a buffer to accumulate bits and extract multiple values at once.
Write code that reads bits from the packed array, reconstructs each integer, and handles partial bytes at the end. Use masks and shifts appropriately.
Test with n=0, n not a multiple of values per byte, and maximum values. Ensure no out-of-bounds access and correct sign extension if needed.
Discuss time complexity O(n * bits_per_value / word_size) and space complexity O(n). Suggest optimizations like using larger buffers or SIMD if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.