hdfs VS juicefs

I have completed the part for MapReduce in Go on a local machine with multiple processes. Now, I want to run it over different machines for which I'll need a distributed file system like Hadoop. I am using HDFS Client for making queries to HDFS, but I am not able to set the appropriate file path in Go for input files. It is giving me an error similar to this issue.

I find Go and Hadoop combination good when it comes loading loading data to Hadoop. For example this library and client is way faster that the Java based hdfs cli. https://github.com/colinmarc/hdfs

This is a library and a cli to connect to hdfs. I have used it from inside docker containers. https://github.com/colinmarc/hdfs

Feel free to download and try JuiceFS 1.2-beta1 here. If you have any questions, join JuiceFS discussions on GitHub and our community on Slack.

Support for Kerberos keytab files

JuiceFS under the Apache License 2.0, is a high-performance POSIX file system optimized for cloud-native environments. It stores data in Object Storage (e.g., Amazon S3) and metadata in databases like Redis, MySQL, or TiKV. JuiceFS integrates massive cloud storage with big data, machine learning, and AI applications efficiently, akin to local storage. It features full POSIX and Hadoop compatibility, S3 interface, Kubernetes support, and shared file storage for numerous clients. Some cool features are - strong consistency, scalable performance, data encryption, global file locks, and compression with LZ4 or Zstandard.

To improve the handling process of capacity overrun, the JuiceFS client supports deletion operations in the case of Ceph cluster fullness (see related code changes in JuiceFS Community Edition). Therefore, for newer client versions, there is no need to use set-full-ratio for temporary adjustments.

If you have any questions or would like to learn more, feel free to join discussions about JuiceFS on GitHub and the JuiceFS community on Slack.

The architecture image shows GCS and others, so I suspect it does.

https://github.com/juicedata/juicefs#architecture

See also: JuiceFS: https://juicefs.com/

Adds a DBMS or key-value store for metadata, making the filesystem much faster (POSIX, small overwrites don't have to replace a full object in the GCS/S3 backend).

Almost certainly a better solution if you want to turn your object storage into a mountable filesystem, with the (big) caveat that you can't access the files directly in the bucket (they are not stored transparently).

JuiceFS can do the same thing as s3fs-fuse, but better. Because it supports robust data consistency and caching policies to improve performance.