dvs_get()

dvs_get() reads the meta files, finds the matching blobs in storage, and writes the contents back to their original paths. It is the inverse of dvs_add(): the call to run after re-cloning a project whose data is not on disk.

dvs_get(paths = character(0), glob = NULL, dry_run = NULL)

🔗Parameters

A progress bar is shown automatically while files are processed (the wrapper creates an internal progress_callback handle); it is not a parameter. No bar is shown for a dry run.

🔗Setup

Add three files, then delete them so dvs_get() has something to restore.

options(width = 1000)
library(dvs)
proj  <- tempfile("project_")
store <- tempfile("storage_")
dir.create(proj)
dir.create(store)
dir.create(file.path(proj, "data", "sub"), recursive = TRUE)
write.csv(mtcars[1:8, ],  file.path(proj, "data", "f1.csv"))
write.csv(mtcars[9:16, ], file.path(proj, "data", "f2.csv"))
write.csv(iris[1:10, ],   file.path(proj, "data", "sub", "f3.csv"), row.names = FALSE)
setwd(proj)
dvs_init(store)

DVS Initialized

dvs_add(c("data/f1.csv", "data/f2.csv", "data/sub/f3.csv"))

# A tibble: 3 × 5
  path            outcome hash                                                                size stored_size
  <chr>           <chr>   <chr>                                                            <bytes>     <bytes>
1 data/f1.csv     copied  5e2c49dd5c8a24a2ffe102e42804812c7eabc2c49682240480558eac390c5d65   488 B       306 B
2 data/f2.csv     copied  c9c0ca2ba19bf1d65159ba2b7300c3336fdf113c92b79901e06be4b40fcd8871   500 B       275 B
3 data/sub/f3.csv copied  881b133ef44731569b7844e1dc60f10e5fc4a9f2c2ac36fe20b2bba636d6bbce   312 B       140 B

file.remove("data/f1.csv", "data/f2.csv", "data/sub/f3.csv")

[1] TRUE TRUE TRUE

dvs_status(status = "absent")[, c("path", "status")]

# A tibble: 3 × 2
  path            status
  <chr>           <chr> 
1 data/f1.csv     absent
2 data/f2.csv     absent
3 data/sub/f3.csv absent

🔗paths

Restore a single file. The result reports path, outcome, and size.

setwd(proj)
dvs_get("data/f1.csv")

# A tibble: 1 × 3
  path        outcome    size
  <chr>       <chr>   <bytes>
1 data/f1.csv copied    488 B

The file is current again:

setwd(proj)
dvs_status("data/f1.csv")[, c("path", "status")]

# A tibble: 1 × 2
  path        status 
  <chr>       <chr>  
1 data/f1.csv current

🔗glob

Restore by glob. Use ** to cross directory boundaries.

setwd(proj)
dvs_get(glob = "data/**/*.csv")

# A tibble: 3 × 3
  path            outcome    size
  <chr>           <chr>   <bytes>
1 data/f1.csv     present   488 B
2 data/f2.csv     copied    500 B
3 data/sub/f3.csv copied    312 B

🔗dry_run

With dry_run = TRUE the result reports what would be retrieved, but no file is written.

setwd(proj)
file.remove("data/f2.csv")

[1] TRUE

dvs_get("data/f2.csv", dry_run = TRUE)

# A tibble: 1 × 3
  path        outcome    size
  <chr>       <chr>   <bytes>
1 data/f2.csv copied    500 B

The file is still absent after a dry run:

setwd(proj)
dvs_status("data/f2.csv")[, c("path", "status")]

# A tibble: 1 × 2
  path        status
  <chr>       <chr> 
1 data/f2.csv absent

🔗Return value

A tibble with one row per file:

Rows that fail carry an error column. The size column is a dvs_bytes value.

🔗Differences from the CLI

The CLI command is dvs get. It prints lines rather than returning a data frame, and adds --threads and --json, which the R surface does not have. Threads are set process-wide with set_dvs_threads().

🔗See also

• dvs_init(), dvs_add(), dvs_status()
• Storage and meta files