Memory issues with headless browser when processing audio files

Other Questions Headless Browser
1

I’m having trouble with a headless browser setup. It keeps crashing due to memory problems when I process lots of audio files. Here’s what I’m trying to do:

audioFiles.forEach(file => {
  page.evaluate(() => {
    // Set up audio processor
    // Create a new visualization
    // Get element size for a screenshot
  })
  page.screenshot()
  // Clean up the audio processor after processing
})

The main issue is that I need to create and destroy the audio processor for each file. If I don’t, the visualizations accumulate and eventually cause the crash. I tried passing the processor instance to a separate evaluate call for cleanup, but it didn’t work. Does anyone have suggestions on how to properly manage the memory here?

2

hey, i had similar issues. try using page.evaluate() for cleanup too, like:

page.evaluate(() => {
  // cleanup code here
})

also, consider batching files or using worker threads to spread the load. might help with memory. good luck!

3

I’ve faced similar memory issues in headless browser setups. One approach that worked well for me was to process the files in smaller batches and force garbage collection between those batches to free up memory. You could try something like this:

const chunkSize = 10;
for (let i = 0; i < audioFiles.length; i += chunkSize) {
  const chunk = audioFiles.slice(i, i + chunkSize);
  await Promise.all(chunk.map(async file => {
    await page.evaluate(() => {
      // Your audio processing logic here
    });
    await page.screenshot();
  }));
  await page.evaluate(() => window.gc());
}

Using this strategy can help manage memory more effectively by periodically cleaning up resources. Additionally, you might want to explore using tools like puppeteer-cluster, which offers automated memory management when scaling up your processing tasks.

4

Have you considered using a memory profiler to identify where the leaks are occurring? Tools like Chrome DevTools’ Memory tab can be invaluable for tracking down these issues. Additionally, you might want to look into using Web Workers for processing the audio files. This could offload the heavy lifting from the main thread and potentially mitigate memory issues. Lastly, ensure you’re properly disposing of all resources after each file is processed - this includes not just the audio processor, but any canvases, buffers, or other objects created during visualization. It’s tedious, but methodically cleaning up after each iteration can make a big difference in memory management.