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Working with data stored in cloud storage systems like Amazon Simple Storage Service (S3) and Google Cloud Storage (GCS) is a very common task. Because of this, the Arrow C++ library provides a toolkit aimed to make it as simple to work with cloud storage as it is to work with the local filesystem.

To make this work, the Arrow C++ library contains a general-purpose interface for file systems, and the arrow package exposes this interface to R users. For instance, if you want to you can create a LocalFileSystem object that allows you to interact with the local file system in the usual ways: copying, moving, and deleting files, obtaining information about files and folders, and so on (see help("FileSystem", package = "arrow") for details). In general you probably don’t need this functionality because you already have tools for working with your local file system, but this interface becomes much more useful in the context of remote file systems. Currently there is a specific implementation for Amazon S3 provided by the S3FileSystem class, and another one for Google Cloud Storage provided by GcsFileSystem.

This article provides an overview of working with both S3 and GCS data using the Arrow toolkit.

S3 and GCS support on Linux

Before you start, make sure that your arrow install has support for S3 and/or GCS enabled. For most users this will be true by default, because the Windows and macOS binary packages hosted on CRAN include S3 and GCS support. You can check whether support is enabled via helper functions:

If these return TRUE then the relevant support is enabled.

In some cases you may find that your system does not have support enabled. The most common case for this occurs on Linux when installing arrow from source. In this situation S3 and GCS support is not always enabled by default, and there are additional system requirements involved. See the installation article for details on how to resolve this.

Connecting to cloud storage

One way of working with filesystems is to create ?FileSystem objects. ?S3FileSystem objects can be created with the s3_bucket() function, which automatically detects the bucket’s AWS region. Similarly, ?GcsFileSystem objects can be created with the gs_bucket() function. The resulting FileSystem will consider paths relative to the bucket’s path (so for example you don’t need to prefix the bucket path when listing a directory).

With a FileSystem object, you can point to specific files in it with the $path() method and pass the result to file readers and writers (read_parquet(), write_feather(), et al.).

Often the reason users work with cloud storage in real world analysis is to access large data sets. An example of this is discussed in the datasets article, but new users may prefer to work with a much smaller data set while learning how the arrow cloud storage interface works. To that end, the examples in this article rely on a multi-file Parquet dataset that stores a copy of the diamonds data made available through the ggplot2 package, documented in help("diamonds", package = "ggplot2"). The cloud storage version of this data set consists of 5 Parquet files totaling less than 1MB in size.

The diamonds data set is hosted on both S3 and GCS, in a bucket named voltrondata-labs-datasets. To create an S3FileSystem object that refers to that bucket, use the following command:

bucket <- s3_bucket("voltrondata-labs-datasets")

To do this for the GCS version of the data, the command is as follows:

bucket <- gs_bucket("voltrondata-labs-datasets", anonymous = TRUE)

Note that anonymous = TRUE is required for GCS if credentials have not been configured.

Within this bucket there is a folder called diamonds. We can call bucket$ls("diamonds") to list the files stored in this folder, or bucket$ls("diamonds", recursive = TRUE) to recursively search subfolders. Note that on GCS, you should always set recursive = TRUE because directories often don’t appear in the results.

Here’s what we get when we list the files stored in the GCS bucket:

bucket$ls("diamonds", recursive = TRUE)
## [1] "diamonds/cut=Fair/part-0.parquet"     
## [2] "diamonds/cut=Good/part-0.parquet"     
## [3] "diamonds/cut=Ideal/part-0.parquet"    
## [4] "diamonds/cut=Premium/part-0.parquet"  
## [5] "diamonds/cut=Very Good/part-0.parquet"

There are 5 Parquet files here, one corresponding to each of the “cut” categories in the diamonds data set. We can specify the path to a specific file by calling bucket$path():

parquet_good <- bucket$path("diamonds/cut=Good/part-0.parquet")

We can use read_parquet() to read from this path directly into R:

diamonds_good <- read_parquet(parquet_good)
## # A tibble: 4,906 × 9
##    carat color clarity depth table price     x     y     z
##    <dbl> <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 E     VS1      56.9    65   327  4.05  4.07  2.31
##  2  0.31 J     SI2      63.3    58   335  4.34  4.35  2.75
##  3  0.3  J     SI1      64      55   339  4.25  4.28  2.73
##  4  0.3  J     SI1      63.4    54   351  4.23  4.29  2.7 
##  5  0.3  J     SI1      63.8    56   351  4.23  4.26  2.71
##  6  0.3  I     SI2      63.3    56   351  4.26  4.3   2.71
##  7  0.23 F     VS1      58.2    59   402  4.06  4.08  2.37
##  8  0.23 E     VS1      64.1    59   402  3.83  3.85  2.46
##  9  0.31 H     SI1      64      54   402  4.29  4.31  2.75
## 10  0.26 D     VS2      65.2    56   403  3.99  4.02  2.61
## # … with 4,896 more rows
## # ℹ Use `print(n = ...)` to see more rows

Note that this will be slower to read than if the file were local.

Connecting directly with a URI

In most use cases, the easiest and most natural way to connect to cloud storage in arrow is to use the FileSystem objects returned by s3_bucket() and gs_bucket(), especially when multiple file operations are required. However, in some cases you may want to download a file directly by specifying the URI. This is permitted by arrow, and functions like read_parquet(), write_feather(), open_dataset() etc will all accept URIs to cloud resources hosted on S3 or GCS. The format of an S3 URI is as follows:


For GCS, the URI format looks like this:


For example, the Parquet file storing the “good cut” diamonds that we downloaded earlier in the article is available on both S3 and CGS. The relevant URIs are as follows:

uri <- "s3://voltrondata-labs-datasets/diamonds/cut=Good/part-0.parquet"
uri <- "gs://anonymous@voltrondata-labs-datasets/diamonds/cut=Good/part-0.parquet"

Note that “anonymous” is required on GCS for public buckets. Regardless of which version you use, you can pass this URI to read_parquet() as if the file were stored locally:

df <- read_parquet(uri)

URIs accept additional options in the query parameters (the part after the ?) that are passed down to configure the underlying file system. They are separated by &. For example,


is equivalent to:

bucket <- S3FileSystem$create(

Both tell the S3FileSystem object that it should allow the creation of new buckets and to talk to Google Storage instead of S3. The latter works because GCS implements an S3-compatible API – see File systems that emulate S3 below – but if you want better support for GCS you should refer to a GcsFileSystem but using a URI that starts with gs://.

Also note that parameters in the URI need to be percent encoded, which is why :// is written as %3A%2F%2F.

For S3, only the following options can be included in the URI as query parameters are region, scheme, endpoint_override, access_key, secret_key, allow_bucket_creation, and allow_bucket_deletion. For GCS, the supported parameters are scheme, endpoint_override, and retry_limit_seconds.

In GCS, a useful option is retry_limit_seconds, which sets the number of seconds a request may spend retrying before returning an error. The current default is 15 minutes, so in many interactive contexts it’s nice to set a lower value:



S3 Authentication

To access private S3 buckets, you need typically need two secret parameters: a access_key, which is like a user id, and secret_key, which is like a token or password. There are a few options for passing these credentials:

  • Include them in the URI, like s3://access_key:secret_key@bucket-name/path/to/file. Be sure to URL-encode your secrets if they contain special characters like “/” (e.g., URLencode("123/456", reserved = TRUE)).

  • Pass them as access_key and secret_key to S3FileSystem$create() or s3_bucket()

  • Set them as environment variables named AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, respectively.

  • Define them in a ~/.aws/credentials file, according to the AWS documentation.

  • Use an AccessRole for temporary access by passing the role_arn identifier to S3FileSystem$create() or s3_bucket().

GCS Authentication

The simplest way to authenticate with GCS is to run the gcloud command to setup application default credentials:

gcloud auth application-default login

To manually configure credentials, you can pass either access_token and expiration, for using temporary tokens generated elsewhere, or json_credentials, to reference a downloaded credentials file.

If you haven’t configured credentials, then to access public buckets, you must pass anonymous = TRUE or anonymous as the user in a URI:

bucket <- gs_bucket("voltrondata-labs-datasets", anonymous = TRUE)
fs <- GcsFileSystem$create(anonymous = TRUE)
df <- read_parquet("gs://anonymous@voltrondata-labs-datasets/diamonds/cut=Good/part-0.parquet")

Using a proxy server

If you need to use a proxy server to connect to an S3 bucket, you can provide a URI in the form http://user:password@host:port to proxy_options. For example, a local proxy server running on port 1316 can be used like this:

bucket <- s3_bucket(
  bucket = "voltrondata-labs-datasets", 
  proxy_options = "http://localhost:1316"

File systems that emulate S3

The S3FileSystem machinery enables you to work with any file system that provides an S3-compatible interface. For example, MinIO is and object-storage server that emulates the S3 API. If you were to run minio server locally with its default settings, you could connect to it with arrow using S3FileSystem like this:

minio <- S3FileSystem$create(
  access_key = "minioadmin",
  secret_key = "minioadmin",
  scheme = "http",
  endpoint_override = "localhost:9000"

or, as a URI, it would be


(Note the URL escaping of the : in endpoint_override).

Among other applications, this can be useful for testing out code locally before running on a remote S3 bucket.

Disabling environment variables

As mentioned above, it is possible to make use of environment variables to configure access. However, if you wish to pass in connection details via a URI or alternative methods but also have existing AWS environment variables defined, these may interfere with your session. For example, you may see an error message like:

Error: IOError: When resolving region for bucket 'analysis': AWS Error [code 99]: curlCode: 6, Couldn't resolve host name 

You can unset these environment variables using Sys.unsetenv(), for example:


By default, the AWS SDK tries to retrieve metadata about user configuration, which can cause conflicts when passing in connection details via URI (for example when accessing a MINIO bucket). To disable the use of AWS environment variables, you can set environment variable AWS_EC2_METADATA_DISABLED to TRUE.


Further reading