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Batch Processing Image Conversion At-scale With Go Concurrency
- Launching Vectopus.com without WebP image support led to the need for retroactive conversion of 500,000+ SVG files to WebP for better performance and SEO.
- The image ingestion pipeline involved SQS queues, lambda functions, S3 storage, and a Postgres database.
- A CLI tool was developed for batch image conversion handling tasks like downloading, processing, watermarking, and uploading images.
- Node.js, Python, and Go's concurrency models were compared for handling the image conversion task.
- Node.js utilizes event-driven, non-blocking I/O model suitable for web servers but less efficient for CPU-bound tasks like image conversion.
- Python's multiprocessing module offers parallel processing, bypassing GIL limitations, and leveraging multiple CPU cores.
- Go's concurrency model with goroutines provides efficient parallelism, scalability, and minimal overhead, making it ideal for CPU-bound tasks.
- Go's goroutines demonstrated faster processing times, handling 4,500 images in 27 seconds, showcasing its efficiency for large-scale batch processing.
- By leveraging Go's concurrency model, the article concludes on the importance of selecting tools that meet performance needs and considering system design comprehensively.
- The experience with image conversion reinforced the significance of evaluating tools and system design for optimal performance and scalability.
- The source code for the CLI tool discussed in the article is available on GitHub for reference.
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