Streams process data piece by piece, enabling efficient memory usage for large files and real-time data. Here's how to use them effectively.
Stream Types
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Creating Custom Streams
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Pipeline and Error Handling
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Object Mode Streams
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Backpressure Handling
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Async Iterators with Streams
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Practical Examples
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Memory-Efficient Processing
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Best Practices
Design:
✓ Use pipeline() for chaining
✓ Handle errors in all streams
✓ Set appropriate highWaterMark
✓ Use objectMode for non-binary data
Performance:
✓ Process in chunks, not all at once
✓ Respect backpressure
✓ Use streams for large files
✓ Avoid loading entire files into memory
Error Handling:
✓ Always handle 'error' event
✓ Use pipeline for automatic cleanup
✓ Implement _destroy() for cleanup
✓ Propagate errors correctly
Conclusion
Node.js streams enable efficient processing of large data sets with constant memory usage. Use Readable for data sources, Writable for destinations, and Transform for processing. Always use pipeline() for proper error handling and backpressure management. Streams are essential for scalable Node.js applications.