Start by clarifying requirements and constraints, then propose a high-level architecture that separates reading, processing, and writing stages. Focus on how to handle line boundaries and exactly-once semantics, and discuss trade-offs between parallelism, buffering, and compression.
Pro tip: Emphasize idempotency and checkpointing to achieve exactly-once semantics, and consider using a distributed file system like Google Cloud Storage with a manifest-based approach to track processed files.
Ask about input size, file distribution, expected output, and any specific technologies (e.g., GFS, MapReduce). Confirm that output files must be exactly 100 MB and that line boundaries must be preserved.
Propose a pipeline with three stages: reading input files, processing lines into 100 MB chunks, and writing output files. Consider using a distributed processing framework like MapReduce or Apache Beam.
Explain how to handle partial lines at chunk boundaries by buffering incomplete lines and carrying them over to the next chunk. Discuss buffer size trade-offs and the need to flush buffers at the end.
Describe mechanisms like idempotent writes, checkpointing, and transactional writes to guarantee exactly-once processing. Discuss how to recover from failures without duplicating or losing lines.
Explain how to parallelize reading and writing while maintaining output file size constraints. Discuss compression options (e.g., gzip) and their impact on file size and processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.