Description Usage Arguments Details Value References See Also Examples
Retrieve an object from an S3 bucket. To check if an object exists, see head_object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | get_object(
object,
bucket,
headers = list(),
parse_response = FALSE,
as = "raw",
...
)
save_object(
object,
bucket,
file = basename(object),
headers = list(),
overwrite = TRUE,
...
)
select_object(
object,
bucket,
request_body,
headers = list(),
parse_response = FALSE,
...
)
s3connection(object, bucket, headers = list(), ...)
|
object |
Character string with the object key, or an object of class “s3_object”. In most cases, if |
bucket |
Character string with the name of the bucket, or an object of class “s3_bucket”. |
headers |
List of request headers for the REST call. |
parse_response |
Passed through to |
as |
Passed through to |
... |
Additional arguments passed to |
file |
An R connection, or file name specifying the local file to save the object into. |
overwrite |
A logical indicating whether to overwrite |
request_body |
For |
get_object
retrieves an object into memory as a raw vector. This page describes get_object
and several wrappers that provide additional useful functionality.
save_object
saves an object to a local file without bringing it into memory.
s3connection
provides a connection
interface to an S3 object.
select_object
uses the SELECT API to select part of a CSV or JSON object. This requires constructing and passing a fairly tedious request body, which users will have to construct themselves according to the documentation.
Some users may find the raw vector response format of get_object
unfamiliar. The object will also carry attributes, including “content-type”, which may be useful for deciding how to subsequently process the vector. Two common strategies are as follows. For text content types, running charToRaw
may be the most useful first step to make the response human-readable. Alternatively, converting the raw vector into a connection using rawConnection
may also be useful, as that can often then be passed to parsing functions just like a file connection would be.
Higher-level functions
If file = NULL
, a raw object. Otherwise, a character string containing the file name that the object is saved to.
API Documentation: GET Object API Documentation: GET Object torrent API Documentation: SELECT Object
get_bucket
, object_exists
, head_object
, put_object
, delete_object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ## Not run:
# get an object in memory
## create bucket
b <- put_bucket("myexamplebucket")
## save a dataset to the bucket
s3save(mtcars, bucket = b, object = "mtcars")
obj <- get_bucket(b)
## get the object in memory
x <- get_object(obj[[1]])
load(rawConnection(x))
"mtcars" %in% ls()
# save an object locally
y <- save_object(obj[[1]], file = object[[1]][["Key"]])
y %in% dir()
# return object using 'S3 URI' syntax, with progress bar
get_object("s3://myexamplebucket/mtcars", show_progress = TRUE)
# return parts of an object
## use 'Range' header to specify bytes
get_object(object = obj[[1]], headers = list('Range' = 'bytes=1-120'))
# example of streaming connection
## setup a bucket and object
b <- put_bucket("myexamplebucket")
s3write_using(mtcars, bucket = b, object = "mtcars.csv", FUN = utils::write.csv)
## setup the connection
con <- s3connection("mtcars.csv", bucket = b)
## line-by-line read
while(length(x <- readLines(con, n = 1L))) {
print(x)
}
## use data.table::fread without saving object to file
library(data.table)
s3write_using(as.data.table(mtcars), bucket = b, object = "mtcars2.csv", FUN = data.table::fwrite)
fread(get_object("mtcars2.csv", bucket = b, as = "text"))
## cleanup
close(con)
delete_bucket("myexamplebucket")
## End(Not run)
|
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