---
title: "Working with Cloud Storage (S3)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Working with Cloud Storage (S3)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

The Arrow C++ library includes a generic filesystem interface and specific
implementations for some cloud storage systems. This setup allows various
parts of the project to be able to read and write data with different storage
backends. In the `arrow` R package, support has been enabled for AWS S3.
This vignette provides an overview of working with S3 data using Arrow.

> In Windows and macOS binary packages, S3 support is included. On Linux when 
installing from source, S3 support is not enabled by default, and it has 
additional system requirements. See `vignette("install", package = "arrow")` 
for details.

## URIs

File readers and writers (`read_parquet()`, `write_feather()`, et al.)
accept an S3 URI as the source or destination file,
as do `open_dataset()` and `write_dataset()`.
An S3 URI looks like:

```
s3://[access_key:secret_key@]bucket/path[?region=]
```

For example, one of the NYC taxi data files used in `vignette("dataset", package = "arrow")` is found at

```
s3://ursa-labs-taxi-data/2019/06/data.parquet
```

Given this URI, you can pass it to `read_parquet()` just as if it were a local file path:

```r
df <- read_parquet("s3://ursa-labs-taxi-data/2019/06/data.parquet")
```

Note that this will be slower to read than if the file were local,
though if you're running on a machine in the same AWS region as the file in S3,
the cost of reading the data over the network should be much lower.

## Creating a FileSystem object

Another way to connect to S3 is to create a `FileSystem` object once and pass
that to the read/write functions.
`S3FileSystem` objects can be created with the `s3_bucket()` function, which
automatically detects the bucket's AWS region. Additionally, 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).
This may be convenient when dealing with
long URIs, and it's necessary for some options and authentication methods
that aren't supported in the URI format.

With a `FileSystem` object, you can point to specific files in it with the `$path()` method.
In the previous example, this would look like:

```r
bucket <- s3_bucket("ursa-labs-taxi-data")
df <- read_parquet(bucket$path("2019/06/data.parquet"))
```

You can list the files and/or directories in an S3 bucket or subdirectory using
the `$ls()` method:

```r
bucket$ls()
```

See `help(FileSystem)` for a list of options that `s3_bucket()` and `S3FileSystem$create()`
can take. `region`, `scheme`, and `endpoint_override` can be encoded as query
parameters in the URI (though `region` will be auto-detected in `s3_bucket()` or from the URI if omitted).
`access_key` and `secret_key` can also be included,
but other options are not supported in the URI.

The object that `s3_bucket()` returns is technically a `SubTreeFileSystem`, which holds a path and a file system to which it corresponds. `SubTreeFileSystem`s can be useful for holding a reference to a subdirectory somewhere (on S3 or elsewhere).

One way to get a subtree is to call the `$cd()` method on a `FileSystem`

```r
june2019 <- bucket$cd("2019/06")
df <- read_parquet(june2019$path("data.parquet"))
```

`SubTreeFileSystem` can also be made from a URI:

```r
june2019 <- SubTreeFileSystem$create("s3://ursa-labs-taxi-data/2019/06")
```

## 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](https://en.wikipedia.org/wiki/Percent-encoding) 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](https://docs.aws.amazon.com/sdk-for-cpp/v1/developer-guide/credentials.html).

- Use an [AccessRole](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html)
for temporary access by passing the `role_arn` identifier to `S3FileSystem$create()` or `s3_bucket()`.

## 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:

```r
bucket <- s3_bucket("ursa-labs-taxi-data", 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](https://min.io/) 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:

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

or, as a URI, it would be

```
s3://minioadmin:minioadmin@?scheme=http&endpoint_override=localhost%3A9000
```

(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 the use of 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:

```r
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:

```{r, eval = FALSE}
Sys.unsetenv("AWS_DEFAULT_REGION")
Sys.unsetenv("AWS_S3_ENDPOINT")
```

By default, the AWS SDK tries to retrieve metadata about user configuration, 
which can cause conficts 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`.

```{r, eval = FALSE}
Sys.setenv(AWS_EC2_METADATA_DISABLED = TRUE)
```
