Introduction to AzureStor

Hong Ooi

This is a short introduction on how to use AzureStor.

Storage endpoints

The interface for accessing storage is similar across blobs, files and ADLSGen2. You call the storage_endpoint function and provide the endpoint URI, along with your authentication credentials. AzureStor will figure out the type of storage from the URI.

AzureStor supports all the different ways you can authenticate with a storage endpoint:

In the case of an AAD token, you can also provide an object obtained via AzureAuth::get_azure_token(). If you do this, AzureStor can automatically refresh the token for you when it expires.

# various endpoints for an account: blob, file, ADLS2
bl_endp_key <- storage_endpoint("https://mystorage.blob.core.windows.net", key="access_key")
fl_endp_sas <- storage_endpoint("https://mystorage.file.core.windows.net", sas="my_sas")
ad_endp_tok <- storage_endpoint("https://mystorage.dfs.core.windows.net", token="my_token")

# alternative (recommended) way of supplying an AAD token
token <- AzureRMR::get_azure_token("https://storage.azure.com",
                                   tenant="myaadtenant", app="app_id", password="mypassword"))
ad_endp_tok2 <- storage_endpoint("https://mystorage.dfs.core.windows.net", token=token)

Listing, creating and deleting containers

AzureStor provides a rich framework for managing storage. The following generics allow you to manage storage containers:

# example of working with containers (blob storage)
list_storage_containers(bl_endp_key)
cont <- storage_container(bl_endp_key, "mycontainer")
newcont <- create_storage_container(bl_endp_key, "newcontainer")
delete_storage_container(newcont)

Files and blobs

These functions for working with objects within a storage container:

# example of working with files and directories (ADLSgen2)
cont <- storage_container(ad_end_tok, "myfilesystem")
list_storage_files(cont)
create_storage_dir(cont, "newdir")
storage_download(cont, "/readme.txt")
storage_multiupload(cont, "N:/data/*.*", "newdir")  # uploading everything in a directory

Uploading and downloading

AzureStor includes a number of extra features to make transferring files efficient.

Parallel connections

The storage_multiupload/download functions transfer multiple files in parallel, which usually results in major speedups when transferring multiple small files. The pool is created the first time a parallel file transfer is performed, and persists for the duration of the R session; this means you don’t have to wait for the pool to be (re-)created each time.

# uploading/downloading multiple files at once: use a wildcard to specify files to transfer
storage_multiupload(cont, src="N:/logfiles/*.zip")
storage_multidownload(cont, src="/monthly/jan*.*", dest="~/data/january")

# or supply a vector of file specs as the source and destination
src <- c("file1.csv", "file2.csv", "file3.csv")
dest <- file.path("data/", src)
storage_multiupload(cont, src, dest)

File format helpers

AzureStor includes convenience functions to transfer data in a number of commonly used formats: RDS, RData, TSV (tab-delimited), CSV, and CSV2 (semicolon-delimited). These work via connections and so don’t create temporary files on disk.

# save an R object to storage and read it back again
obj <- list(n=42L, x=pi, c="foo")
storage_save_rds(obj, cont, "obj.rds")
objnew <- storage_load_rds(cont, "obj.rds")
identical(obj, objnew)  # TRUE

# reading/writing data to CSV format
storage_write_csv(mtcars, cont, "mtcars.csv")
mtnew <- storage_read_csv(cont, "mtcars.csv")
all(mapply(identical, mtcars, mtnew))  # TRUE

Transfer to and from connections

You can upload a (single) in-memory R object via a connection, and similarly, you can download a file to a connection, or return it as a raw vector. This lets you transfer an object without having to create a temporary file as an intermediate step.

# uploading serialized R objects via connections
json <- jsonlite::toJSON(iris, pretty=TRUE, auto_unbox=TRUE)
con <- textConnection(json)
storage_upload(cont, src=con, dest="iris.json")

rds <- serialize(iris, NULL)
con <- rawConnection(rds)
storage_upload(cont, src=con, dest="iris.rds")

# downloading files into memory: as a raw vector with dest=NULL, and via a connection
rawvec <- storage_download(cont, src="iris.json", dest=NULL)
rawToChar(rawvec)

con <- rawConnection(raw(0), "r+")
storage_download(cont, src="iris.rds", dest=con)
unserialize(con)

Copy from URLs (blob storage only)

The copy_url_to_storage function lets you transfer the contents of a URL directly to storage, without having to download it to your local machine first. The multicopy_url_to_storage function does the same, but for a vector of URLs. Currently, these only work for blob storage.

# copy from a public URL: Iris data from UCI machine learning repository
copy_url_to_storage(cont,
    "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
    "iris.csv")

# copying files from another storage account, by appending a SAS to the URL(s)
sas <- "?sv=...."
files <- paste0("https://srcstorage.blob.core.windows.net/container/file", 0:9, ".csv", sas)
multicopy_url_to_storage(cont, files)

Appending (blob storage only)

AzureStor supports uploading to append blobs. An append blob is comprised of blocks and is optimized for append operations; it is well suited for data that is constantly growing, but should not be modified once written, such as server logs.

To upload to an append blob, specify type="AppendBlob" in the storage_upload call. To append data (rather than overwriting an existing blob), include the argument append=TRUE. See ?upload_blob for more details.

# create a new append blob
storage_upload(cont, src="logfile1.csv", dest="logfile.csv", type="AppendBlob")

# appending to an existing blob
storage_upload(cont, src="logfile2.csv", dest="logfile.csv", type="AppendBlob", append=TRUE)

Interface to AzCopy

AzureStor includes an interface to AzCopy, Microsoft’s high-performance commandline utility for copying files to and from storage. To take advantage of this, simply include the argument use_azcopy=TRUE on any upload or download function. AzureStor will then call AzCopy to perform the file transfer, rather than using its own internal code. In addition, a call_azcopy function is provided to let you use AzCopy for any task.

# use azcopy to download
myfs <- storage_container(ad_endp, "myfilesystem")
storage_download(myfs, "/incoming/bigfile.tar.gz", "/data", use_azcopy=TRUE)

# use azcopy to sync a local and remote dir
call_azcopy("sync", "c:/local/path", "https://mystorage.blob.core.windows.net/mycontainer", "--recursive=true")

For more information, see the AzCopy repo on GitHub.

Note that AzureStor uses AzCopy version 10. It is incompatible with versions 8.1 and earlier.

Other features

Parallel connections

The storage_multiupload/download functions mentioned above use a background process pool supplied by AzureRMR. You can also use this pool to parallelise tasks for which there is no built-in function. For example, the following code will delete multiple files in parallel:

files_to_delete <- list_storage_files(container, "datadir", info="name")

# initialise the background pool with 10 nodes
AzureRMR::init_pool(10)

# export the container object to the nodes
AzureRMR::pool_export("cont")

# delete the files
AzureRMR::pool_sapply(files_to_delete, function(f) AzureStor::delete_storage_file(cont, f))

Metadata

To get and set user-defined properties (metadata) for storage objects, use the get_storage_metadata and set_storage_metadata functions.

fs <- storage_container("https://mystorage.dfs.core.windows.net/myshare", key="access_key")
storage_upload(share, "iris.csv", "newdir/iris.csv")

set_storage_metadata(fs, "newdir/iris.csv", name1="value1")
# will be list(name1="value1")
get_storage_metadata(fs, "newdir/iris.csv")

set_storage_metadata(fs, "newdir/iris.csv", name2="value2")
# will be list(name1="value1", name2="value2")
get_storage_metadata(fs, "newdir/iris.csv")

set_storage_metadata(fs, "newdir/iris.csv", name3="value3", keep_existing=FALSE)
# will be list(name3="value3")
get_storage_metadata(fs, "newdir/iris.csv")

# deleting all metadata
set_storage_metadata(fs, "newdir/iris.csv", keep_existing=FALSE)

# if no filename supplied, get/set metadata for the container
get_storage_metadata(fs)

Admin interface

Finally, AzureStor’s admin-side interface allows you to easily create and delete resource accounts, as well as obtain access keys and generate a SAS. Here is a sample workflow:

library(AzureStor)

# authenticate with Resource Manager
az <- AzureRMR::get_azure_login("mytenant")
sub1 <- az$get_subscription("subscription_id")
rg <- sub1$get_resource_group("resgroup")


# get an existing storage account
rdevstor1 <- rg$get_storage("rdevstor1")
rdevstor1
#<Azure resource Microsoft.Storage/storageAccounts/rdevstor1>
#  Account type: Storage 
#  SKU: name=Standard_LRS, tier=Standard 
#  Endpoints:
#    blob: https://rdevstor1.blob.core.windows.net/
#    queue: https://rdevstor1.queue.core.windows.net/
#    table: https://rdevstor1.table.core.windows.net/
#    file: https://rdevstor1.file.core.windows.net/ 
# ...

# retrieve admin keys
rdevstor1$list_keys()

# create a shared access signature (SAS)
rdevstor1$get_account_sas(permissions="rw")

# obtain an endpoint object for accessing storage (will have the access key included by default)
rdevstor1$get_blob_endpoint()
#Azure blob storage endpoint
#URL: https://rdevstor1.blob.core.windows.net/
#Access key: <hidden>
#Azure Active Directory token: <none supplied>
#Account shared access signature: <none supplied>
#Storage API version: 2018-03-28

# create a new storage account
blobstor2 <- rg$create_storage_account("blobstor2", location="australiaeast", kind="BlobStorage")

# delete it (will ask for confirmation)
blobstor2$delete()

For more information about the different types of storage, see the Microsoft Docs site. Note that there are other types of storage (queue, table) that do not have a client interface exposed by AzureStor.