seaweedfs VS Apache Hadoop

Whoops: WebDAV:

https://news.ycombinator.com/item?id=39417503

SeaweedFS supports WebDAV. https://github.com/seaweedfs/seaweedfs/wiki/WebDAV

I'm not able to find if both/restic supports mounting backups as WebDAV, but in theory there's nothing stopping you.

It's 100% user space (expose a rest service) and supported by a bunch of file-browsers with a bit of a network aware component to it as well.

If you want/need to take out the metadata, there's some nice solutions for that https://github.com/seaweedfs/seaweedfs

I posted this on https://github.com/seaweedfs/seaweedfs/discussions/5290

I like the idea of using SeaweedFS as an intermediate layer with object write notifications going to SQS, RabbitMQ, or a local file, which could also allow me to observe the changes to different files through a metric collection layer like Prometheus and Grafana.

> Theoretically they could swap with minio but last time we used it it was not a drop-in replacement yet.

Depends on whether AGPL v3 works for you or not (or whether you decide to pay them), I guess: https://min.io/pricing

I've actually been looking for more open alternatives, but haven't found much.

Zenko CloudServer seemed to be somewhat promising, but doesn't seem to be managed very actively: https://github.com/scality/cloudserver/issues/4986 (their Docker images on DockerHub were last updated 10 months ago, which is what the homepage links to; blog doesn't seem active since 2019, forums don't have much going on, despite some action on GitHub still)

There was also Garage, but that one is also AGPL v3: https://garagehq.deuxfleurs.fr/

The closest I got was discovering that SeaweedFS has an S3 compatible mode: https://github.com/seaweedfs/seaweedfs

SeaweedFS S3 Gateway for Joplin notes

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.