I have 2pe files in ceph rgw bucket.
How can I find files in ceph rgw without access (atime) in 60 days ( not modify time)
The S3 standard does not include access time meta information in its store. Please see this answer to a similar question concerning S3 storage.
So to answer your question: access time is not defined in the S3 standard.
But you could probably synthesise this information yourself by parsing the access logs if available. But yeah. That requires some scripting.
Related
My app uses Google Cloud Storage through Firebase with Java, Angular & Flutter. It stores pictures and such there. Now, a lot of older files recently disappeared from Google Cloud Storage. A test version of my app is probably the culprit. But I want to make sure that I got the storage bucket configured correctly.
Please note that I don't have object versioning enabled. From what I know, it would keep a copy of deleted files around. That's why I plan to enable it in the future. But it doesn't help me with files deleted in the past.
Right now, my storage bucket is configured as follows:
Default storage class: Standard
Object versioning: Off
Retention policy: None
Lifecycle rules: None
So with that configuration, would Google Cloud Storage automatically delete files? Like, say, after a year or so?
No. If you don't ask Cloud Storage to delete your files, your files will stay around forever. There's no expiration of any sort by default. Cloud Storage is a popular tool for long term storage/backup/retention.
If you want to be especially careful not to delete certain objects, retention policies and object holds can be used to make it harder to delete objects by accident. For example, if you wanted to temporarily ensure that your scripts would not delete your most important object, you could run:
gsutil retention temp set gs://my_bucket_name/my_important_file.txt
This would set a "temporary object hold" on the object, which would make it so that my_important_file.txt could not be deleted with the delete command until you released the hold.
I want to take Oracle database backup using RMAN directly into the Google Cloud Storage
I am unable to find the plugin to use to take the RMAN backups into Cloud Storage. We have a plugin for Amazon S3 and am looking for one such related to Google Cloud Storage.
I don't believe there's an official way of doing this. Although I did file a Feature Request for the Cloud Storage engineering team to look into that you can find here.
I recommend you to star the Feature Request, for easy visibility and access, allowing you to view its status updates. The Cloud Storage team might ask questions there too.
You can use gcsfuse to mount GCS bucket as file systems on your machine and use RMAN to create backups there.
You can find more information about gcsfuse on its github page. Here are the basic steps to mount a bucket and run RMAN:
Create a bucket oracle_bucket. Check that it doesn't have a retention policy defined on it (it looks like gcsfuse has some issues with retention policies).
Please have a look at mounting.md that describes credentials for GCS. For example, I created a service account with Storage Admin role and created a JSON key for it.
Next, set up credentials for gcsfuse on your machine. In my case, I set GOOGLE_APPLICATION_CREDENTIALS to the path to JSON key from step 1. Run:
sudo su - oracle
mkdir ./mnt_bucket
gcsfuse --dir-mode 755 --file-mode 777 --implicit-dirs --debug_fuse oracle_bucket ./mnt_bucket
From gcsfuse docs:
Important: You should run gcsfuse as the user who will be using the
file system, not as root. Do not use sudo.
Configure RMAN to create a backup in mnt_bucket. For example:
configure controlfile autobackup format for device type disk to '/home/oracle/mnt_bucket/%F';
configure channel device type disk format '/home/oracle/mnt_bucket/%U';
After you run backup database you'll see a backup files created in your GCS bucket.
I am following Zero to JupyterHub with Kubernetes to create a jupyterHub environment for my team to use.
Using Google Kubernetes Engine and every user gets his/her own storage and files are stored - this setup works fine.
I am having trouble as how should I create a volume or shared database so that everyone in team can see each other's notebooks, share file's and data.
To explain more, in desired setup - when a user signs in and goes to his/her jupyter image - every user sees the same folder "shared" and one can create individual folders for themselves inside that folder but are able to reuse code that someone else has already written.
I looked into NFS with Firestore but that seems very expensive.
As in the documentation gcePersistenceDisk do not support multiple read and write.
There is alternative solution for the problem. Rook is a storage backend various storage provisioner available through it. One of them is Ceph which has shared filesystem solution on kubernetes.
I would like to know if there is a way to mount google cloud storage bucket as a folder for the first time
and each time we read the file, cache it locally (so it won't use money/bandwidth).
GCSFUSE has two type of caching available, Stat caching and type caching. You can refer to this document which provide detailed information on these types of caching with there trade-offs.
I've been using gcsfuse (FUSE) for some weeks and everything was running smoothly until my instance disk(10GB) got filled up out of nowhere.
I was trying to identify the cause and erasing some temporal files and found out that unmounting the bucket fixed the issue.
It's supposed to upload to the cloud right? So why is it taking up space as if it was counted as local instance space?
Thanks for the help guys.
Here is a reason why you would see this behaviour.
Pasting from the gcsfuse doc
https://cloud.google.com/storage/docs/gcs-fuse
Local storage: Objects that are new or modified will be stored in
their entirety in a local temporary file until they are closed or
synced. When working with large files, be sure you have enough local
storage capacity for temporary copies of the files, particularly if
you are working with Google Compute Engine instances. For more
information, see the readme documentation.