How can you handle memory usage when dealing with large files in Node.js streams?

0-2 years experience

1. 1You need to copy a 2 GB log file to another location using Node.js streams. How would you set up the pipeline to ensure you don't run out of memory?
2. 2If you notice the process memory spikes while reading a large CSV with a readable stream, what immediate change could you make to the stream configuration?
3. 3What does the highWaterMark option control, and how would you adjust it when processing a large file?

2-5 years experience

1. 1Our service streams user‑uploaded video files through a transcoding library and has started leaking memory under load. Walk me through how you'd debug this and what stream‑related changes you'd consider.
2. 2A nightly batch job pipes a huge JSON file through a transform that aggregates data, but it sometimes crashes with ‘JavaScript heap out of memory’. What trade‑offs would you evaluate between adjusting backpressure, chunk size, or switching APIs?
3. 3Explain why calling .resume() on a paused stream could cause memory pressure in a long‑running ETL pipeline.

5-8 years experience

1. 1Design a file ingestion component that can handle tens of gigabytes per minute across multiple worker nodes while keeping each Node.js process under a fixed memory budget. What architectural patterns and stream settings would you employ?
2. 2We need to process gzip‑compressed log archives on the fly. How would you integrate compression streams without blowing up memory, and what monitoring would you put in place?
3. 3If a downstream service occasionally slows down, causing backpressure, how would you ensure the upstream file‑read stream doesn't buffer unbounded data, and what fallback mechanisms could you implement?

8+ years experience

1. 1Our platform is moving from a monolithic Node.js service that reads large data dumps to a microservices architecture. How would you redesign the ingestion pipeline to minimize memory usage across services, and what cross‑team standards would you establish for stream handling?
2. 2Legacy code uses fs.readFileSync for large files, causing OOM in production. Propose a migration strategy to streams, including testing, rollout, and handling edge cases like partial failures.
3. 3At scale we need to enforce consistent backpressure handling across dozens of services. What governance, tooling, or library abstractions would you introduce to ensure memory safety company‑wide?