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Destroy the data store.

Source: R/tar_destroy.R
tar_destroy.Rd

Destroy the data store written by the pipeline.

Usage

tar_destroy(
  destroy = c("all", "cloud", "local", "meta", "process", "progress", "objects",
    "scratch", "workspaces", "user"),
  batch_size = 1000L,
  verbose = TRUE,
  ask = NULL,
  script = targets::tar_config_get("script"),
  store = targets::tar_config_get("store")
)

Arguments

destroy

Character of length 1, what to destroy. Choices:

  • "all": entire data store (default: _targets/) including cloud data, as well as download/upload scratch files.

  • "cloud": cloud data, including metadata as well as target object data from targets with tar_target(..., repository = "aws"). Also deletes temporary staging files in file.path(tempdir(), "targets") that may have been accidentally left over from incomplete uploads or downloads.

  • "local": all the local files in the data store but nothing on the cloud.

  • "meta": metadata file at meta/meta in the data store, which invalidates all the targets but keeps the data.

  • "process": progress data file at meta/process in the data store, which resets the metadata of the main process.

  • "progress": progress data file at meta/progress in the data store, which resets the progress tracking info.

  • "objects": all the target return values in objects/ in the data store but keep progress and metadata. Dynamic files are not deleted this way.

  • "scratch": temporary files in saved during tar_make() that should automatically get deleted except if R crashed.

  • "workspaces": compressed lightweight files in workspaces/ in the data store with the saved workspaces of targets. See tar_workspace() for details.

  • "user": custom user-supplied files in the user/ folder in the data store.

batch_size

Positive integer between 1 and 1000, number of target objects to delete from the cloud with each HTTP API request. Currently only supported for AWS. Cannot be more than 1000.

verbose

Logical of length 1, whether to print console messages to show progress when deleting each batch of targets from each cloud bucket. Batched deletion with verbosity is currently only supported for AWS.

ask

Logical of length 1, whether to pause with a menu prompt before deleting files. To disable this menu, set the TAR_ASK environment variable to "false". usethis::edit_r_environ() can help set environment variables.

script

Character of length 1, path to the target script file. Defaults to tar_config_get("script"), which in turn defaults to _targets.R. If the script does not exist, then cloud metadata will not be deleted.

store

Character of length 1, path to the targets data store. Defaults to tar_config_get("store"), which in turn defaults to _targets/. When you set this argument, the value of tar_config_get("store") is temporarily changed for the current function call. See tar_config_get() and tar_config_set() for details about how to set the data store path persistently for a project.

Value

NULL (invisibly).

Details

The data store is a folder created by tar_make() (or tar_make_future() or tar_make_clustermq()). The details of the data store are explained at https://books.ropensci.org/targets/data.html#local-data-store. The data store folder contains the output data and metadata of the targets in the pipeline. Usually, the data store is a folder called _targets/ (see tar_config_set() to customize), and it may link to data on the cloud if you used AWS or GCP buckets. By default, tar_destroy() deletes the entire _targets/ folder (or wherever the data store is located), including custom user-supplied files in _targets/user/, as well as any cloud data that the pipeline uploaded. See the destroy argument to customize this behavior and only delete part of the data store, and see functions like tar_invalidate(), tar_delete(), and tar_prune() to remove information pertaining to some but not all targets in the pipeline. After calling tar_destroy() with default arguments, the entire data store is gone, which means all the output data from previous runs of the pipeline is gone (except for input/output files tracked with tar_target(..., format = "file")). The next run of the pipeline will start from scratch, and it will not skip any targets.

Storage access

Several functions like tar_make(), tar_read(), tar_load(), tar_meta(), and tar_progress() read or modify the local data store of the pipeline. The local data store is in flux while a pipeline is running, and depending on how distributed computing or cloud computing is set up, not all targets can even reach it. So please do not call these functions from inside a target as part of a running pipeline. The only exception is literate programming target factories in the tarchetypes package such as tar_render() and tar_quarto().

Cloud target data versioning

Some buckets in Amazon S3 or Google Cloud Storage are "versioned", which means they track historical versions of each data object. If you use targets with cloud storage (https://books.ropensci.org/targets/cloud-storage.html) and versioning is turned on, then targets will record each version of each target in its metadata.

Functions like tar_read() and tar_load() load the version recorded in the local metadata, which may not be the same as the "current" version of the object in the bucket. Likewise, functions tar_delete() and tar_destroy() only remove the version ID of each target as recorded in the local metadata.

If you want to interact with the latest version of an object instead of the version ID recorded in the local metadata, then you will need to delete the object from the metadata.

  1. Make sure your local copy of the metadata is current and up to date. You may need to run tar_meta_download() or tar_meta_sync() first.

  2. Run tar_unversion() to remove the recorded version IDs of your targets in the local metadata.

  3. With the version IDs gone from the local metadata, functions like tar_read() and tar_destroy() will use the latest version of each target data object.

  4. Optional: to back up the local metadata file with the version IDs deleted, use tar_meta_upload().

See also

Other clean: tar_delete(), tar_invalidate(), tar_prune(), tar_prune_list(), tar_unversion()

Examples

if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
tar_script({
  library(targets)
  library(tarchetypes)
  list(tar_target(x, 1 + 1))
})
tar_make() # Creates the _targets/ data store.
tar_destroy()
print(file.exists("_targets")) # Should be FALSE.
})
}