Go IPFS VS Apache Hadoop

nice/renice isn’t what it used to be[0].

Bandwidth limiting is not built in[1].

[0] - https://stackoverflow.com/questions/10342470/process-nicenes...

[1] - https://github.com/ipfs/go-ipfs/issues/3065

For me, https://github.com/ipfs/go-ipfs/issues/9044 is the main blocker atm and https://github.com/ipfs/go-ipfs/issues/2167 is still around and annoying.

It makes me wonder if you use the IPFS FUSE mount and symlink an IPNS address of files that you want to sync if it would be read by Dropbox.

I'm using the IPFS fuse mount to load mine into Plex/Jellyfin. It's nice that I can load a movie into a virtual directory on IPFS and my home and remote servers get updated automatically. (when I update my IPNS) So you could run an official solidaritycinema IPNS address that people load into their Plex as a library.

Maybe tangentially related, I've been interested in IPFS as a network medium. ( Using the IPFS fuse mount ) Rather than syncing the entire file it syncs the Library list. When the Plex server makes the request for the file, IPFS negotiates the download. It makes it more like Netflix.

I've been messing with Plex + IPFS and I think it's pretty cool. The IPFS FUSE Mount gives IPFS network access to Plex as just a regular filesystem. To keep it private within my family I'm using a Private Swarm.

"Implement bandwidth limiting" https://github.com/ipfs/go-ipfs/issues/3065

Going on six years now. You can use external tools (like "trickle") or your OS knobs.

So my plan is to setup plex on a relative's Raspberry Pi so that it works off the IPFS mounted network directories in the same way. They'll have a virtual library that takes basically no memory on their Pi unless they request a video, then it'll start caching to their machine.

Did you check out tools like https://hadoop.apache.org/ ?

check out the various dockerfiles bundled with hadoop on GitHub. you can point to them from within docker-compose. they haven't been updated in a couple years tho.

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.

There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.

A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks.

The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing.

So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.

This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools.