---
title: "File upload and Volumes reference guide for Seven Bridges API R Client"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_document:
    toc: true
    toc_float: true
    toc_depth: 4
    number_sections: false
    theme: "flatly"
    highlight: "textmate"
    css: "sevenbridges.css"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{File upload and Volumes reference guide for Seven Bridges API R Client}
  %\VignetteEncoding{UTF-8}
---

<a name="top"></a>

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)
```

# File upload

Seven Bridges platforms provide a few different methods for data import:

- Import from FTP or HTTP with the web interface
- The file upload API that you can directly call with the `sevenbridges2` 
package
- The command line uploader
- Import from cloud storage - Volume
- Import from a DRS server

In this chapter we will explain how you can use the `sevenbridges2` API library
to upload your files to the Platform.

Although it is more intuitive to have these operations available on the `File`
object, they are separated and stored directly on the authentication object 
`Auth`, because there are a separate group of endpoints themselves. 

## Upload single file

You can upload files from your local computer to the Platform using the 
`upload()` method on your `Auth` object. The method allows you to upload only
a single file for now. 

To upload a file, you should provide its full path on your local computer
as the `path` parameter.

To specify the upload destination for your file you can use either `project` or
`parent` parameter. These two parameters should not be used together.

* **project** - `Project` object or project ID.
* **parent** - `File` object (of type `Folder`) or its ID.

By calling the `upload()` method you are creating an upload job that by 
default starts to run immediately.
If you don't want to start the job immediately, just set the `init` parameter
to `TRUE` in order to only initialize the object. 

This upload job is wrapped into an object of the class `Upload` where you can
see its details and call other actions on it. 

Let's initialize an upload job that will upload a file into a project:
```{r}
# Authenticate
a <- Auth$new(platform = "aws-us", token = "<your-token>")

# Get the desired project to upload to
destination_project <- a$projects$get(project = "<project_id>")

# Create upload job and set destination project
upload_job <- a$upload(
  path = "/path/to/your/file.txt",
  project = destination_project,
  overwrite = TRUE,
  init = TRUE
)
```

If you would like to upload your file into a folder, you need to set the
`parent` parameter:

```{r}
# Get destination folder object
destination_folder <- a$files$get(id = "<folder_id>")

up <- a$upload(
  path = "/path/to/your/file.txt",
  parent = destination_folder,
  overwrite = TRUE,
  init = TRUE
)
```

## Upload fields and operations

Since we have initialized the upload job, let's see which actions can we run.

### Print upload job

First, let's print the `Upload` object to see what the API returned as the 
response.

```{r}
up$print()
```

```
── Upload ─────────────────────────────────────────────────────────────────────
• initialized: TRUE
• part_length: 1
• part_size: 33554432
• file_size: 232
• overwrite: FALSE
• filename: file.txt
• project: <username_or_division>/api-testing
• path: /path/to/your/file.txt
• upload_id: 4OvRx8Z9vghNoAUqsgYtNuM2IsiIM8kghhjgi7igu79HX9QKZpDEh5TZDrmhPxF

```
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### File size, part size and part number/length

In the previous example we can see that the API returned the upload id and some 
information about sizes. 
First we see the `file_size` in bytes (232), which is the real size of the file. 
File upload actually splits files into parts in the background; parts are then 
being uploaded one by one or in parallel and then merged again on destination.
Each part can weigh a maximum of 5 GB, while the default `part_size` is 
recommended and set to be 32MB (which is 33554432B in our example). 

Lastly, number of parts or `part_length` field, is also an important measure. 
Maximum number of parts can be 10.000. 

Since users can control part size through the `part_size` parameter 
in `upload()` function, they should be careful not to set a size that is too 
small for very large files, so that total number of parts doesn’t exceed the 
limit of maximum 10.000.

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### Start upload

Call the `start()` method on the upload job object do start the upload process.

```{r}
# Start upload
up$start()
```

If you want to skip the step where you need to call the `start()` method
to start the actual upload process, just set the `init` parameter back to 
`FALSE` when creating the upload job and the upload process will start right 
away.

```{r}
# Create upload job and start it immediately
up <- a$upload(
  path = "/path/to/your/file.txt",
  project = destination_project,
  overwrite = TRUE,
  init = FALSE
)
```

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### Get status information about the job

In order to track the progress of the job, you can call the `info()` method
on the upload object.

```{r}
# Get upload progress info
up$info()
```

Apart from basic information, the result will also provide the info on the
number of uploaded parts up to that moment. 

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## List all ongoing uploads

Going back to the authentication object, there are two more operations for 
uploads manipulation. 
One is the method `list_ongoing_uploads()` that allows you to see the list of 
all ongoing upload processes.

```{r}
# List ongoing uploads
a$list_ongoing_uploads()
```

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## Abort upload process

The other one is `abort()`.
You can abort any upload process using the `upload_abort()` method.
To do so, you need to provide the ID of a process within the `upload_id`
parameter.

```{r}
# Abort upload
a$abort_upload(upload_id = "<id_of_the_upload_process>")
```

**Note** that in practice, if you start a big upload job, your R session 
will be blocked until this process is finished. This functionality is work in 
progress but the idea is to not block your main session in the future,
while the upload is running. 
For now, you can create another R session on your own and track the 
progress of the upload job there. 

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# Volumes

Cloud storage providers come with their own interfaces, features, and 
terminology. At a certain level, though, they all view resources as data objects 
organized in repositories. Authentication and operations are commonly defined 
on those objects and repositories, and while each cloud provider might call 
these things different names and apply different parameters to them, their basic 
behavior is the same.

Seven Bridges environments mediate access to these repositories using volumes. 
A volume is associated with a particular cloud storage repository that you have 
enabled Seven Bridges to read from (and, optionally, to write to). 
Currently, volumes may be created using two types of cloud storage repositories: 
Amazon Web Services' (AWS) S3 buckets and Google Cloud Storage (GCS) buckets.

A volume enables you to treat the cloud repository associated with it as 
external storage. You can 'import' files from the volume to your Seven Bridges 
environment to use them as inputs for computation. Similarly, you can write 
files from the Seven Bridges environment to your cloud storage by 'exporting' 
them to your volume.

Learn more about volumes on the [Seven Bridges Platform](https://docs.sevenbridges.com/docs/connecting-cloud-storage-overview), [CGC](https://docs.cancergenomicscloud.org/docs/connect-cloud-storage-overview),
[BDC](https://sb-biodatacatalyst.readme.io/docs/connect-cloud-storage-overview)
and [CAVATICA](https://docs.cavatica.org/reference/connect-cloud-storage-overview).

All volume related operations for querying volumes, fetching a single volume, 
and creating volumes are grouped under `volumes` path (`Volumes` 
resource class) on the authentication object. 

When operating with a single volume, it is represented as an object of the 
`Volume` class which stores all volume information returned from the API and 
additional methods you are able to call directly on the volume, like volume 
update, deactivation, listing content, volume members management etc.

**Note** that all operations with volumes require `advance_access` parameter 
to be set to TRUE. In most of the volume operations it is pre-set to `TRUE` by 
default.

## List volumes

You can list all volumes you've registered by calling the `volumes$query()` 
method from the authentication object. The method doesn't have any additional 
query parameters that could allow you to search for volumes by specific 
criteria, except the ones that control the number of results returned using 
`limit` and `offset` parameters.

```{r}
# Query volumes
a$volumes$query()
```

The result returned is the `Collection` object with pagination ability.

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## Get single volume information

In order to retrieve information about a single volume of interest, you can get 
it using the `volumes$get()` method using its id as parameter.
Volume ID is usually presented in the `<division_name>/<volume_name>` form for 
Enterprise users, while for public program users it can be in the 
`<volume_owner>/<volume_name>` form.

```{r}
# Get volume
a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")
```

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## Create volumes - AWS (S3) using IAM User authentication type

For creating volumes we have exposed several functions for different cloud 
providers and authentication types:

* `create_s3_using_iam_user`: creates S3 volume using IAM User authentication 
type
* `create_s3_using_iam_role`: creates S3 volume using IAM Role authentication 
type
* `create_google_using_iam_user`: creates GC volume using IAM User 
authentication type
* `create_google_using_iam_role`: creates GC volume using IAM Role 
authentication type
* `create_azure`: creates Azure volume (only RO privileges allowed)
* `create_ali_oss`: creates AliCloud volume (only RO privileges allowed)

For each of the functions it is possible to provide parameters via path 
(`from_path`) to a JSON file where all required fields should be listed.

Examples of use are shown below:

```{r}
# Create AWS volume using IAM User authentication type
aws_iam_user_volume <- a$volumes$create_s3_using_iam_user(
  name = "my_new_aws_user_volume",
  bucket = "<bucket-name>",
  description = "AWS IAM User volume",
  access_key_id = "<access-key>",
  secret_access_key = "<secret-access-key>"
)

aws_iam_user_volume_from_path <- a$volumes$create_s3_using_iam_user(
  from_path = "path/to/my/json/file.json"
)


# Create AWS volume using IAM Role authentication type
aws_iam_role_volume <- a$volumes$create_s3_using_iam_role(
  name = "my_new_aws_role_volume",
  bucket = "<bucket-name>",
  description = "AWS IAM Role volume",
  role_arn = "<role-arn-key>",
  external_id = "<external-id>"
)

aws_iam_role_volume_from_path <- a$volumes$create_s3_using_iam_role(
  from_path = "path/to/my/json/file.json"
)

# Create Google Cloud volume using IAM User authentication type
gc_iam_user_volume <- a$volumes$create_google_using_iam_user(
  name = "my_new_gc_user_volume",
  access_mode = "RW",
  bucket = "<bucket-name>",
  description = "GC IAM User volume",
  client_email = "<client_email>",
  private_key = "<private_key-string>"
)

gc_iam_user_volume_from_path <- a$volumes$create_google_using_iam_user(
  from_path = "path/to/my/json/file.json"
)

# Create Google Cloud volume using IAM Role authentication type
# by passing configuration parameter as named list
gc_iam_role_volume <- a$volumes$create_google_using_iam_role(
  name = "my_new_gc_role_volume",
  access_mode = "RO",
  bucket = "<bucket-name>",
  description = "GC IAM Role volume",
  configuration = list(
    type = "<type-name>",
    audience = "<audience-link>",
    subject_token_type = "<subject_token_type>",
    service_account_impersonation_url = "<service_account_impersonation_url>",
    token_url = "<token_url>",
    credential_source = list(
      environment_id = "<environment_id>",
      region_url = "<region_url>",
      url = "<url>",
      regional_cred_verification_url = "<regional_cred_verification_url>"
    )
  )
)

# Create Google Cloud volume using IAM Role authentication type
# by passing configuration parameter as string path to configuration file
gc_iam_role_volume_config_file <- a$volumes$create_google_using_iam_role(
  name = "my_new_gc_role_volume_cnf_file",
  access_mode = "RO",
  bucket = "<bucket-name>",
  description = "GC IAM Role volume - using config file",
  configuration = "path/to/config/file.json"
)

# Create Google Cloud volume using IAM Role authentication type
# using from_path parameter
gc_iam_role_volume_from_path <- a$volumes$create_google_using_iam_role(
  from_path = "path/to/full/config/file.json"
)

# Create Azure volume
azure_volume <- a$volumes$create_azure(
  name = "my_new_azure_volume",
  description = "Azure volume",
  endpoint = "<endpoint>",
  container = "<bucket-name",
  storage_account = "<storage_account-name>",
  tenant_id = "<tenant_id>",
  client_id = "<client_id>",
  client_secret = "<client_secret>",
  resource_id = "<resource_id>"
)

azure_volume_from_path <- a$volumes$create_azure(
  from_path = "path/to/my/json/file.json"
)

# Create Ali Cloud volume
ali_volume <- a$volumes$create_ali_oss(
  name = "my_new_azure_volume",
  description = "Ali volume",
  endpoint = "<endpoint>",
  bucket = "<bucket-name",
  access_key_id = "<access_key_id>",
  secret_access_key = "<secret_access_key>"
)

ali_volume_from_path <- a$volumes$create_ali_oss(
  from_path = "path/to/my/json/file.json"
)
```

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## Volume object operations

When you've created a new volume, you can notice it is represented as an object
of the `Volume` class. To preview all volume information, use the `print()` 
method:

```{r}
# Print volume info
print(aws_iam_user_volume)
```

Within this volume you have the following operations available to 
execute:

* `update`: update volume information
* `list_contents` : list volume content
* `get_file`: get single volume file info
* `deactivate` : deactivate volume
* `reactivate` : reactivate previously deactivated volume
* `list_members`: list all volume members
* `add_member`: add new volume member
* `remove_member`: remove volume member
* `get_member`: get a volume member information
* `modify_member_permissions`: modify member permissions on the volume
* `delete` : delete previously deactivated volume
* `reload` : reload volume object to sync information
* `list_imports`: list all imports from the specified volume
* `list_exports`: list all exports to the specified volume

### Update volume

You can update volume's `description`, `access_mode` and `service` information. 
Please consult our [API documentation](https://docs.sevenbridges.com/reference/update-a-volume-v2) 
on how to use the `service` parameter. 

```{r}
# If the volume is created with RO access mode and RO credential parameters,
# and now we want to change it to RW, we should also set proper credential
# parameters that are connected to the RW user on the bucket.
# If it's created with RW credentials, but access mode is set to RO, then no
# change is needed in the credentials parameters.
aws_iam_user_volume$update(
  description = "Updated to RW",
  access_mode = "RW",
  service = list(
    credentials = list(
      access_key_id = "<access_key_id_for_rw>",
      secret_access_key = "<secret_access_key_for_rw>",
    )
  )
)
```

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### Reload volume

To keep your local `Volume` object up to date with the volume on the platform, 
you can always call the `reload()` function:

```{r}
# Reload volume object
aws_iam_user_volume$reload()
```

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### List volume's content

This operation lists all volume files in the root directory of the bucket, 
unless the `parent` parameter is specified. In that case, it lists the content 
of that directory on the bucket. 
The output is a `VolumeContentCollection` collection object, that contains 
two fields: 

-`items` for storing a list of `VolumeFile` objects (files on the 
volume) and 
-`prefixes` for storing a list of `VolumePrefix` objects or folders on the 
volume.

You can also specify the `limit` parameter to control the number of results 
returned.

Same as `Collection` objects, here we also have pagination functions to 
return either the next page of results or all results. However, backward 
pagination is not available for volume contents.

Users can also navigate through pages of results by using the 
`continuation token` parameter or `link` to fetch the next chunk of results. 
If you use the `link` parameter, it will overwrite all other parameters if set, 
since it already contains the `limit` and `continuation_token` info.

```{r}
# List all files in root bucket directory
content_collection <- aws_iam_user_volume$list_contents(limit = 20)

# Print collection
content_collection

# List all files from a specific directory on the bucket
folder_files_collection <- aws_iam_user_volume$list_contents(
  prefix = "<directory_name>"
)

# Get the next group of results by setting the continuation token
content_collection <- aws_iam_user_volume$list_contents(
  limit = 20,
  continuation_token = "<continuation_token>"
)

# Preview volume files
content_collection$items

# Preview volume prefixes/folders
content_collection$prefixes

# Preview links
aws_iam_user_volume$links

# Get the next group of results by setting the link parameter
aws_iam_user_volume$list_contents(link = "<link_to_next_results>")

# Or use VolumeContentCollection object's next_page() method for this:
content_collection$next_page()

# You can also fetch all results with the all() method
content_collection$all()
```

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### Volume files and prefixes

Volume files and prefixes are also treated as objects and they contain some 
operations that can be called on them.

#### Get VolumeFile info

This operation returns a single volume file information. The input parameter 
can be file's id which is represented as location on the bucket 
(`location)`, or a link to that file resource. The link is a `href` field 
of the desired file received from the response when returning a list of volume 
contents with `list_contents()`.
Empty arguments are not allowed along with setting both parameters together.

```{r}
# Get single volume file info - by setting file_location
vol_file1 <- aws_iam_user_volume$get_file(
  location = "<file_location_on_bucket>"
)

# Get single volume file info - by setting link
vol_file1 <- aws_iam_user_volume$get_file(link = "full/request/link/to/file")
```

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#### Reload VolumeFile object

To keep your local VolumeFile object up to date with the volume file on the 
platform, you can always call the `reload()` function:

```{r}
vol_file1$reload()
```

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#### Get VolumePrefix info

Unfortunately we don't have a separate operation to fetch only prefixes on the
volume, therefore, we can get its prefixes only by using the `list_contents()`
operation and look for the `prefixes` field in the returned 
`VolumeContentCollection` object. 

```{r}
# List content
volume_content <- aws_iam_user_volume$list_contents()

# Extract prefixes
volume_prefixes <- volume_content$prefixes

# Select one of the volume folders to list its content
volume_folder <- volume_prefixes[[1]]

# Print volume prefix information
volume_folder$print()
```

You can also list the content of a volume prefix/folder on the volume, by
calling `list_contents()` directly on the `VolumePrefix` object.

```{r}
## Select one of the volume folders to list its content
volume_folder <- volume_prefixes[[1]]

# List content
volume_folder_content <- volume_folder$list_contents()
```

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### List volume members

In order to fetch members of one volume or a specific member by its username, 
you can use `list_members()` and `get_member()` operations:

```{r}
# List volume members
aws_iam_user_volume$list_members() # limit = 2

# Get single member
aws_iam_user_volume$get_member(member = "<member-username>")
```

This action works also with teams and divisions as volume members if ran by
user with ADMIN account privileges. The input should be either such Member 
object or team/division string ID.

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### Remove members

Volume admins can remove volume members by providing its username or object of 
the `Member` class to the `remove_member()` function:

```{r}
# Remove member
aws_iam_user_volume$remove_member(member = "<member-username>")

# Remove member using the Member object
members <- aws_iam_user_volume$list_members()
aws_iam_user_volume$remove_member(members$items[[3]])
```

This action works also with teams and divisions as volume members if ran by
user with ADMIN account privileges. The input should be either such Member 
object or team/division string ID.

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### Adding new members

The function for adding new members to the volume allows a `Member` object 
(for example used in a project) or its username as input. 

```{r}
# Add member via username
aws_iam_user_volume$add_member(
  user = "<member-username>",
  permissions = list(read = TRUE, copy = TRUE, write = FALSE, admin = FALSE)
)

# Add member via Member object
aws_iam_user_volume$add_member(
  user = Member$new(
    username = "<member-username>",
    id = "<member-username>"
  ),
  permissions = list(
    read = TRUE, copy = TRUE, write = FALSE,
    admin = FALSE
  )
)
```

#### Users with ADMIN privileges on divisions or enterprises

Users with Enterprise account (not linked to any division) can add whole teams  
and divisions as members of a specified volume. However, be aware that those  
users (if MEMBER role) cannot list divisions or fetch single division/team.

Users with ADMIN and MEMBER privileges on some Division can add teams from 
this division to a volume created in the same division.

```{r}
# Add team via team ID
aws_iam_user_volume$add_member_team(
  team = "<team-id>",
  permissions = list(read = TRUE, copy = TRUE, write = FALSE, admin = FALSE)
)

# Add member via Team object
# Query teams in division and fetch first team to add to a volume
teams <- a$teams$query(division = "division-id")
team <- teams$items[[1]]

aws_iam_user_volume$add_member_team(
  team = team,
  permissions = list(
    read = TRUE, copy = TRUE, write = FALSE,
    admin = FALSE
  )
)
```

Enterprise users with ADMIN privileges that are part of default division can 
add both divisions and teams from any division to a volume created in this 
default division:

```{r}
# Add division via division ID
aws_iam_user_volume$add_member_division(
  division = "<division-id>",
  permissions = list(read = TRUE, copy = TRUE, write = FALSE, admin = FALSE)
)

# Add member via Division object
# Query divisions and fetch first division to add to a volume
divisions <- a$divisions$query()
division <- divisions$items[[1]]

aws_iam_user_volume$add_member_division(
  division = division,
  permissions = list(
    read = TRUE, copy = TRUE, write = FALSE,
    admin = FALSE
  )
)
```

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### Modifying a member's permissions

Users can modify specific member's permissions on the volume by providing the
privileges they want to change:

```{r}
# Modify member permissions
aws_iam_user_volume$modify_member_permissions(
  member = "<member-username>",
  permissions = list(write = TRUE)
)
```

This action works also with teams and divisions as volume members if ran by
user with ADMIN account privileges. The input should be either such Member 
object or team/division string ID.

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### Deactivate and reactivate the volume

Once deactivated, you cannot import from, export to, or browse within a volume. 
As such, the content of the files imported from this volume will no longer be 
accessible on the platform. However, you can update the volume and manage 
members. 
Note that you cannot deactivate the volume if you have running imports or 
exports unless you force the operation using the query parameter `force = TRUE`.

**Note** that to delete a volume, first you must deactivate it and delete all 
files which have been imported from the volume to the platform.

To reactivate the volume, just use the `reactivate()` function.

```{r}
# Deactivate volume
aws_iam_user_volume$deactivate()

# Reactivate volume
aws_iam_user_volume$reactivate()
```

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### Delete volume

To be able to delete a volume, you first need to deactivate it and then delete 
all files on the Platform that were previously imported from the volume.

```{r}
# Deactivate volume
aws_iam_user_volume$deactivate()

# Delete volume
aws_iam_user_volume$delete()
```

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# Imports

Creating and connecting volumes to the Platform allows you to import your 
files/folders from a cloud bucket to the Platform. Imports operations are 
related to volumes, but in the API they are separated under `/imports` 
endpoints, so in our library they are also grouped under imports path on the 
authentication object (`Imports` resource class). 

A single import job is represented as an `Import` class object containing 
information about which file/folder has been or is being imported, from which 
volume, to which project/folder on the platform, import start and finish time, 
status of the job, logs etc.

## List volume imports

To preview and query all import jobs you've created use the `query()` function 
on `Auth$imports` path:

```{r}
# List imports
all_imports <- a$imports$query()

# Limit results to 5
imp_limit5 <- a$imports$query(limit = 5)

# Load next page of 5 results
imp_limit5$next_page(advance_access = TRUE)

# Load all results at once until last page
imp_limit5$all(advance_access = TRUE)
```

It is possible to use some query parameters as different criteria for filtering
results like `volume`, `project`, `state` etc:

```{r}
# List imports with state being RUNNING or FAILED
imp_states <- auth$imports$query(state = c("RUNNING", "FAILED"))

# List imports to the specific project
imp_project <- auth$imports$query(project = "<project_id>")
```

Listing imports is also available within `Project` and `Volume` objects, where 
resulting imports are related to the specific project or volume where they're 
called from.

```{r}
## Get the volume from which you want to list all imports
vol1 <- auth$volumes$get(id = "<volumes_owner_or_division>/<volume-name>")
vol1$list_imports()

## Get the project object for which you want to list imports
test_proj <- auth$projects$get("<project_id>")
test_proj$list_imports()
```

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## Get a single import job

Similar to other resource classes, the `get()` method will return a single 
import job object when provided with a job id.

```{r}
# Get single import
imp_obj <- a$imports$get(id = "<import_job_id>")
```

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## Get bulk import jobs

Users are able to fetch details for multiple import jobs by calling one bulk
action - the `bulk_get()` method. The accepted input can be a list of import
job IDs or a list of import job objects (of class Import).
The result will be a Collection object containing a list of (updated) 
import jobs.

```{r}
# Get details of multiple import jobs
import_jobs <- a$imports$bulk_get(
  imports = list("<import_job_id-1>", "<import_job_id-1>")
)
```

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## Submit new import - import a volume file into the project

In order to import volume files into a project, users can use the
`submit_import()` method from the `Auth$imports` path, or directly on the 
selected `VolumeFile` object (file they want to import) where this function is 
also available. 

```{r}
## First, get the volume you want to import files from
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

## Then, get the project object/id where you want to import files
test_proj <- a$projects$get("<project_id>")

## List all volume files on the volume
vol1_content <- vol1$list_contents()

## Select one of the volume files
volume_file_import <- vol1_content$items[[3]]

## Perform a file import
imp_job1 <- a$imports$submit_import(
  source_location = volume_file_import$location,
  destination_project = test_proj,
  autorename = TRUE
)

# Alternatively you can also call import() directly on the VolumeFile object
imp_job1 <- volume_file_import$import(
  destination_project = test_proj,
  autorename = TRUE
)
```

Preview import job details with the `print()` method:
```{r}
# Print Import object
print(imp_job1)
```

You can also import folders from the volume into the project, with the option to 
preserve or not to preserve folder structure:

```{r}
# Select one of the volume folders to import
volume_folder_import <- vol1_content$prefixes[[1]]

# Perform a folder import
imp_job2 <- a$imports$submit_import(
  source_location = volume_folder_import$prefix,
  destination_project = test_proj,
  overwrite = TRUE,
  preserve_folder_structure = TRUE
)

# Alternatively you can also call import() directly on the VolumePrefix object
imp_job2 <- volume_folder_import$import(
  destination_project = test_proj,
  overwrite = TRUE,
  preserve_folder_structure = TRUE
)

# Print Import object
print(imp_job2)
```

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## Reload import job

In order to refresh the import job object and get the up to date info 
about its state, you can always call the `reload()` function:

```{r}
# Reload import object
imp_job1$reload()
```

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## Submit bulk import - import multiple volume files or folders into a project

Users are able to perform the bulk action on multiple volume files or folders
and import them into a project using a single call of the 
`bulk_submit_import()` method from the `Auth$imports` path.

The required input should be a nested list of elements for each file/folder you
want to import containing specific fields: `source_volume`, `source_location`, 
`destination_project`, `destination_parent`, `name`, `autorename` and 
`perserve_folder_structure.`

```{r}
## First, get the volume you want to import files from
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

## Then, get the project object or the ID of the project into which you want
# to import files
test_proj <- a$projects$get("<project_id>")

## List all volume files
vol1_content <- vol1$list_contents()

## Preview the content and select one VolumeFile object and two VolumePrefix
## objects (folders) for the purpose of this example
volume_file_import <- vol1_content$items[[1, 2]]
volume_file_import

volume_folder_import <- vol1_content$prefixes[[1]]
volume_folder_import

## Construct the inputs list by filling the necessary information for each
# file/folder to import
to_import <- list(
  list(
    source_volume = "rfranklin/my-volume",
    source_location = "chimeras.html.gz",
    destination_project = "rfranklin/my-project"
  ),
  list(
    source_volume = vol1,
    source_location = "my-folder/",
    destination_project = test_proj,
    autorename = TRUE,
    preserve_folder_structure = TRUE
  ),
  list(
    source_volume = "rfranklin/my-volume",
    source_location = "my-volume-folder/",
    destination_parent = "parent-id",
    name = "new-folder-name",
    autorename = TRUE,
    preserve_folder_structure = FALSE
  )
)
bulk_import_jobs <- a$imports$bulk_submit_import(items = to_import)

# Preview the results
bulk_import_jobs

# Get updated status by fetching details with bulk_get() and by passing the
# list of import jobs created in the previous step
a$imports$bulk_get(imports = bulk_import_jobs$items)
```

As you may see from the example above, users are able to import folders from
the volume into the project or a project directory, with the option to 
preserve or not to preserve folder structure.

Moreover, you are able to pass the objects of classes Volume, Project or File 
(with type = 'folder') for `source_volume`, `destination_project` and 
`destination_parent` fields when constructing the inputs list, besides using 
their string IDs.


In the example above, the list of items for the bulk import job was manually
created. Alternatively, you can use the `prepare_items_for_bulk_import()`
utility function to generate the items list.

This function allows you to prepare the list of bulk import items based on the
provided `VolumeFile` or `VolumePrefix` objects, filling the following fields
for each item: `source_volume`, `source_location`, `destination_project` or
`destination_parent`, `autorename`, `preserve_folder_structure`.

Note that the same `destination_project`/`destination_parent` and `autorename`
values will be applied uniformly across all items in the resulting list.
The `preserve_folder_structure` parameter, if provided, applies exclusively to
`VolumePrefix` items.

```{r}
## First, get the volume you want to import files from
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

## Then, get the project object or the ID of the project into which you want
# to import files
test_proj <- a$projects$get("<project_id>")

## List all volume files
vol1_content <- vol1$list_contents()

## Select two VolumeFile objects
volume_file_1_import <- vol1_content$items[[1]]
volume_file_2_import <- vol1_content$items[[2]]

volume_files_to_import <- list(volume_file_1_import, volume_file_2_import)

## Construct the inputs list using the prepare_items_for_bulk_import() utility
# function
to_import <- prepare_items_for_bulk_import(
  volume_items = volume_files_to_import,
  destination_project = test_proj
)

bulk_import_jobs <- a$imports$bulk_submit_import(items = to_import)

# Preview the results
bulk_import_jobs

# Get updated status by fetching details with bulk_get() and by passing the
# list of import jobs created in the previous step
a$imports$bulk_get(imports = bulk_import_jobs$items)
```

Keep in mind that `prepare_items_for_bulk_import()` is designed solely to
assist in constructing the list of items for submitting a bulk import job. It
operates under certain constraints; refer to the function's documentation for
further details. After obtaining the function's output, you can manually adjust
individual items as needed.

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# Exports

Exports are the actions of exporting your files from the Platform into a
cloud bucket represented as a volume.
Export operations are also related to volumes, but in the API they are 
separated under `/exports` endpoints, so in our library they are also grouped
under the `exports` path on the authentication object (`Exports` resource 
class). 

A single export job is represented as an `Export` class object containing 
information about a file has been or is being exported, from which 
project/folder on the Platform, to which volume, export start and finish time, 
status of the job, logs etc.

## List file exports to the volumes

Users can preview and query all export jobs they've created for the purpose of 
exporting their files from the Platform into a cloud bucket using volumes. 
The output is a `Collection` object storing a list of exports in its `items`
field and providing pagination options.

```{r}
# List exports
all_exports <- a$exports$query()

# Limit results to 5
exp_limit5 <- a$exports$query(limit = 5)

# Load next page of 5 results
exp_limit5$next_page(advance_access = TRUE)

# List all results until last page
exp_limit5$all()
```

It is possible to use some query parameters as different criteria for filtering
results like `volume`, `state` etc:

```{r}
# List exports with status RUNNING or FAILED
exp_states <- a$exports$query(state = c("RUNNING", "FAILED"))

# List exports into a specific volume
exp_volume <- a$exports$query(
  volume = "<volume_owner_or_division>/<volume_name>" # volume object or id
)
```

Listing exports is also available within `Volume` objects, where results 
contain all files exported to the specific volume they're being called from.

```{r}
# Get the volume for which you want to list all exports
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

# List exports
vol1$list_exports()
```

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## Get a single export job

Similar to other resource classes, the `get()` method will return a single
export job object when provided with job id.

```{r}
# Get a single export
exp_obj <- auth$exports$get(id = "<export_job_id>")
```

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## Get bulk export jobs

Users are able to fetch details for multiple export jobs by calling one bulk
action - the `bulk_get()` method. The accepted input can be a list of export
job IDs or a list of export job objects (of class Export).
The result will be a Collection object containing a list of (updated) 
export jobs.

```{r}
# Get details of multiple export jobs
export_jobs <- a$exports$bulk_get(
  exports = list("<export_job_id-1>", "<export_job_id-1>")
)
```

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## Submit a new export - export a file from the platform to a volume

In order to export platform files into volumes, users can use the  
`submit_export()` method from the `auth$exports` path, or directly on the 
selected `File` object (file they want to export) where this function is also 
available. 

```{r}
# First, get the volume you want to export files to
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

# Get the File object/id you want to export from the platform
test_file <- a$files$get("<file_id>")

# Perform a file export
exp_job1 <- a$exports$submit_export(
  source_file = test_file,
  destination_volume = vol1,
  destination_location = "new_volume_file.txt" # new name
)
```

Preview export job details with the `print()` method:

```{r}
# Print export job info
print(exp_job1)
```

Bear in mind that **folders export** from the platform to volumes is 
**not possible** with this function. For such cases (or export of multiple 
files) it is better to use bulk actions that will be added to the package soon.

Users can also export files into specific volume directories, by providing the 
`prefix` within the `location` parameter as a folder name, which will then be 
virtually created on the volume:

```{r}
# Export file into the folder 'test_folder'
exp_job2 <- a$exports$submit_export(
  source_file = test_file,
  destination_volume = vol1,
  destination_location = "test_folder/new_volume_file.txt" # new name
)

# Print export job info
print(exp_job2)
```

__Important__ :

- The file selected for export must not be a public file or an alias. 
Aliases are objects stored in your cloud storage bucket which have been made 
available on a Seven Bridges environment. The volume you are exporting to must 
be configured for read-write access. To do this, set the `access_mode` parameter 
to RW when creating or modifying a volume.
- If this call is successful, the original project file will become an alias to 
the newly exported object on the volume. The source file will be deleted from 
the Seven Bridges environment and, if no more copies of this file exist, it 
will no longer count towards your total storage price on the Seven Bridges 
environment.

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## Reload export job

In order to refresh the export job object and get the up to date info about 
its state, you can always call the `reload()` function:

```{r}
# Reload export object
exp_job1$reload()
```

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## Submit bulk export - export multiple files into a volume

Users are able to perform the bulk action on multiple project files and
export them into a volume using a single call of the `bulk_submit_export()`
method from the `Auth$exports` path.

The required input should be a nested list of elements for each file you
want to export containing specific fields: `source_file`, `destination_volume`, 
`destination_location`, `overwrite` and 
`properties` that accepts a list with fields: `sse_algorithm`, 
`sse_aws_kms_key_Id` and/or `aws_canned_acl`.

```{r}
## First, get the project and files you want to export
test_proj <- a$projects$get("<project_id>")
proj_files <- test_proj$list_files()

## Choose the first 3 files to export
files_to_export <- proj_files$items[1:3]

## Then, get the volume you want to export files into
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

## Construct the inputs list by filling the necessary information for each
# file to export
to_export <- list(
  list(
    source_file = files_to_export[[1]],
    destination_volume = vol1,
    destination_location = files_to_export[[1]]$name
  ),
  list(
    source_file = "second-file-id",
    destination_volume = vol1,
    destination_location = "my-folder/exported_second_file.txt",
    overwrite = TRUE
  ),
  list(
    source_file = files_to_export[[3]],
    destination_volume = vol1,
    destination_location = files_to_export[[3]]$name,
    overwrite = FALSE,
    properties = list(
      sse_algorithm = "AES256"
    )
  ),
  copy_only = FALSE
)
bulk_export_jobs <- a$exports$bulk_submit_export(items = to_export)

# Preview the results
bulk_export_jobs

# Get updated status by fetching details with bulk_get() and by passing the
# list of export jobs created in the previous step
a$exports$bulk_get(exports = bulk_export_jobs$items)
```

As you may see from the example above, users are able to export files into a 
folder on a volume.

Moreover, you are able to pass the objects of classes Volume or File 
(with type = 'file' only, since folders can't be exported) when 
constructing the inputs list, in addition to using their string IDs for
`source_file` and `destination_volume` fields. However, `destination_location`
on the volume must be set as character field. 

Lastly, `copy_only` field will apply to all files being exported, which means
that each file would be copied to the volume, while source location would
remain on the Platform if `copy_only` is set to TRUE.

In the example above, the list of items for the bulk export job was manually
created. Alternatively, you can use the `prepare_items_for_bulk_export()`
utility function to generate the items list.

This function allows you to prepare the list of bulk export items based on the
provided `File` objects, filling the following fields for each item: 
`source_file`, `destination_volume`, `destination_location`, `overwrite`,
`properties`.

Check the function's documentation and [API documentation](https://docs.sevenbridges.com/reference/start-a-bulk-export-job)
for more details.


```{r}
## First, get the project and files you want to export
test_proj <- a$projects$get("<project_id>")
proj_files <- test_proj$list_files()

## Then, get the volume you want to export files into
vol1 <- a$volumes$get(id = "<volume_owner_or_division>/<volume_name>")

## Select two File objects
file_1_export <- proj_files[[1]]
file_2_export <- proj_files[[2]]

files_to_export <- list(file_1_export, file_2_export)

## Construct the inputs list using the prepare_items_for_bulk_export() utility
# function
to_export <- prepare_items_for_bulk_export(
  files = files_to_export,
  destination_volume = vol1,
  destination_location_prefix = "my-folder/"
)

bulk_export_jobs <- a$exports$bulk_submit_export(items = to_export)

# Preview the results
bulk_export_jobs

# Get updated status by fetching details with bulk_get() and by passing the
# list of export jobs created in the previous step
a$exports$bulk_get(exports = bulk_export_jobs$items)
```

Keep in mind that `prepare_items_for_bulk_export()` is designed solely to
assist in constructing the list of items for submitting a bulk export job. It
operates under certain constraints; refer to the function's documentation for
further details. After obtaining the function's output, you can manually adjust
individual items as needed.

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