This is a short introduction on how to use AzureStor.
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)
AzureStor provides a rich framework for managing storage. The following generics allow you to manage storage containers:
storage_container: get a storage container (blob container, file share or ADLS filesystem)
# 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)
These functions for working with objects within a storage container:
list_storage_files: list files/blobs in a directory (for ADLSgen2 and file storage) or blob container
create_storage_dir: for ADLSgen2 and file storage, create a directory
delete_storage_dir: for ADLSgen2 and file storage, delete a directory
delete_storage_file: delete a file or blob
storage_file_exists: check that a file or blob exists
storage_download: transfer a file to or from a storage container
storage_multidownload: transfer multiple files in parallel to or from 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
AzureStor includes a number of extra features to make transferring files efficient.
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)
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
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_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)
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
?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)
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.
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))
To get and set user-defined properties (metadata) for storage objects, use the
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)
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.
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