Given the input string "AAABBBCCCDDDAAA," compress it to output "A3B3C3D3A3."

This problem describes Run-Length Encoding (RLE), a simple, lossless data compression technique. It works by replacing sequences of identical data values with a single data value and its count.

Mechanics and Why it's Used

RLE encodes data by iterating through a sequence, identifying consecutive runs of the same character, and then storing the character and the length of its run. For the input "AAABBBCCCDDDAAA," the process is:

'A' appears 3 times: "A3"

'B' appears 3 times: "B3"

'C' appears 3 times: "C3"

'D' appears 3 times: "D3"

'A' appears 3 times: "A3"

Concatenating these yields "A3B3C3D3A3." Decoding is the reverse: read a character, then its count, and repeat the character that many times.

RLE is valuable for its simplicity and effectiveness on data with long runs of identical values. In data engineering, it's frequently used internally within columnar storage formats like Parquet and ORC, especially for columns with low cardinality or those that are sorted, where it significantly reduces storage footprint and improves query performance by minimizing I/O. It can also be implicitly leveraged in systems like Snowflake (within micro-partitions) or Spark (during shuffle operations where data might be grouped or sorted), as it optimizes the underlying storage of repetitive data.

def run_length_encode(s: str) -> str:
    if not s: return ""
    encoded_string = []
    i = 0
    while i < len(s):
        j = i
        while j < len(s) and s[j] == s[i]:
            j += 1
        encoded_string.append(f'{s[i]}{j - i}')
        i = j
    return "".join(encoded_string)

Trade-offs and Interview Considerations

While effective for repetitive data, RLE can actually increase data size if the input has very few or no consecutive repetitions (e.g., "ABC" becomes "A1B1C1"). This highlights its specific use case.

In the interview, also mention handling single characters. Some implementations might output "A1" for a single 'A', while others might optimize to just "A" to save space. The former is more explicit and simplifies decoding logic, while the latter requires a more complex parser but is more compact. Discussing this trade-off demonstrates an understanding of practical implementation details.