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Joined 1 year ago
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Cake day: June 17th, 2023

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  • Personally I think child processes are the right approach for this. Launch a new process* for each query and it can (if you choose to go that route) dynamically load in compiled code. Exit when you’re done, and the dynamically loaded code is gone. A side benefit of that is memory leaks are contained, since all memory you allocate is about to be removed anyway.

    I’d probably be fine with hundreds or thousands of these hanging in memory. I suspect the generated code for a single query would be in hundreds of kilobytes, maybe a megabyte. But yeah, this is one of those technical details I’d worry about.

    Honestly, I wonder if you could just use an actual HTTP server for this? They can handle hundreds or even thousands of simultaneous requests. They can handle requests that complete in a fraction of a millisecond or ones that run for several hours. And they have good tools to catch/deal with code that segfaults, hits an endless loop, attempts to allocate terabytes of swap, etc. HTTP also has wonderful tools to load balance across multiple servers if you do need to scale to massive numbers of requests.

    Not sure how a HTTP server would solve the CPU bottleneck of scanning terabytes of data per query?









  • Kernel is not a monolithic application, and you cannot develop it like one. There are tons of actors: independent developers, small support companies (like Collabora), corporations, all with different priorities. There is a large number of independent forks (e.g. for obscure devices), that will never be merged, but need to merge e.g. security patches from the mainline. A single project management tool won’t do, not your typical business grade tracking&reporting tool.

    CI is already there. Not a central one—again, distributed across different organizations. Different organizations have different needs for CI, e.g. supporting weird architectures that they need to develop against.

    There is a reason Torvalds created git—existing tools just wouldn’t work. There might be a place for a similar revolution regarding a bugtracker…







  • Another idea that just occurred to me. Maybe position: absolute; both the real content and the gibberish content with the same top, left, width, and height attributes so that the real content and the gibberish overlap and occupy the same location on the page. Make sure both the real and gibberish content elements have no background so that remains clear. Put the gibberish content in the DOM before the real content. (I think that will ensure that the gibberish appears behind the real content even without setting the z-index.) And then make JS set the color of the text in the gibberish element the same color as the background so humans can’t see it.

    Be aware that these techniques can affect accessibility for people using screen readers.










  • liori@lemm.eetodatahoarder@lemmy.mlCloud Backup
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    1 year ago

    Yep, it’s EU. File transfer shouldn’t be bad if your files are large, though it’s best if you tested it first—it might depend on your ISP’s peering and your prefered transfer protocols/tooling. Whether it’s reputable for your purpose, you probably have to do your own research. Also, remember that the offer I mentioned would only be equivalent in durability to a single-box RAID5 for your purposes, so not exactly equivalent to Google’s.


  • liori@lemm.eetodatahoarder@lemmy.mlCloud Backup
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    1 year ago

    There’s Jottacloud with unlimited storage for 10 EUR/month, but they gradually slow down after first 5 TB. 30 TB might be a bit too much. There’s Hetzner with their dedicated 4×10TB machines for ~52 EUR, you could do RAID5 and have somewhat redundant 30 TB, at the cost of self-managing a dedicated machine. There are several providers doing regular S3 (which you can take advantage of with tools like rclone) with decent redundancy for 4-5 USD/TB + egress. For high-value data you should be probably spending more than 100 USD/month for 30TB in the cloud, or invest in actual hardware. Do you need hot access to this dataset, or is a cold storage archive enough?