How to make ohif look at s3 for loading studies - amazon-s3

I have built object storage plugin to store orthanc data in s3 bucket in legacy mode. I am now trying to eliminate local storage of files of orthanc and move it to s3 completely. I also have OHIF viewer integrated which is serving orthanc data, How do I make it fetch from s3 bucket? I have read that json file of dicom file can be used to do this, but I dont know how to do that because the json file has url of each instance in s3 bucket. How do i generate this json file if this is the way to do it?

Related

Directly download from a link and upload file to GCS

Is there a way to download a MP4 file directly and store on Google bucket. We have a use-case to get a file URL to download and upload it on cloud. However, since file size can be more than 1 GB, it is not feasible to download in local storage first and then upload the file to cloud bucket. We are specifically looking for google cloud storage to upload files and solution should be specific to same.
Some Ref doc we found but does not look like the feasible solution as it uploads file from local storage not directly from link.
https://googleapis.dev/ruby/google-cloud-storage/latest/Google/Cloud/Storage.html
https://www.mydatahack.com/uploading-and-downloading-files-in-s3-with-ruby/
Google Cloud Storage does not offer compute features. That means you cannot directly load an object into Cloud Storage from a URL. You must fetch the object and then upload it into Cloud Storage.

Is there a way to upload files to the Amazon S3 from SFTP

My idea is this: I have an SFTP host with data on it and I want to create a file in S3 from this data, but to save network resources I don't want to download all of this data to a system first to upload again. So my question is: is it possible to transfer the data directly to the s3 without first downloading it? (preferably with the Amazon S3 Java SDK)

How to import data to Amazon S3 from URL

I have an S3 bucket and the URL of a large file. I would like to store the content located at the URL in the S3 bucket.
I could download the file to my local machine and then upload it to S3 with Cloudberry or Jungledisk or whatever. However, if the file is large, this may take a long time because the file must be transferred twice, and my network connection is much slower than Amazon's.
If I have a lot of data to store in S3, I can start an EC2 instance, retrieve the files to the instance with curl or wget, and then push the data from the EC2 instance to S3. This works, but it's a lot of steps if I just want to archive one file.
Any suggestions?
You can stream the file directly from the source to S3.
If you are using node, you can use streaming-s3.

loading csv file from S3 in neo4j graphdb

I am seeking some suggestion about loading csv files from s3 bucket to neo4j graphdb. In S3 bucket the files are in csv.gz format. I need to import them into my neo4j graph db which is in ec2 instance.
1. Is there any direct way to load csv.gz into neo4j db without unzipping it ?
2. can I set/add s3 bucket path into neo4j.conf file at neo4j.dbms.directory which is by default neo4j/import ?
kindly help me to suggest some idea to load files from S3
Thank you
You can achieve both of these goals with APOC. The docs give you two approaches:
Load from the GZ file directly, assuming the file in the bucket has a public URL
Load the file from S3 directly, with an access token
Here's an example of the first approach - the section after the ! is the filename within the zip file to load, and this should work with .zip, .gz, .tar files etc.
CALL apoc.load.csv("https://pablissimo-so-test.s3-us-west-2.amazonaws.com/mycsv.zip!mycsv.csv")

Is it possible to add files to Amazon S3 buckets using web URL as source?

I am trying to load one of my S3 buckets.
File i am trying to load is huge tarball on the web, I don't want to download file on my disk and then again start uploading it to S3 bucket.
is there any way that I can directly specify this URL and it get added to S3 ?
You have to "put" to S3, and it does not "get".