tar_delete: Delete target output values.

In targets: Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Delete target output values.

Description

Delete the output values of targets in ⁠_targets/objects/⁠ (or the cloud if applicable) but keep the records in the metadata.

Usage

tar_delete(
  names,
  cloud = TRUE,
  batch_size = 1000L,
  verbose = TRUE,
  store = targets::tar_config_get("store")
)

Arguments

names	Optional, names of the targets to delete. If supplied, the names argument restricts the targets which are deleted. The value is a tidyselect expression like any_of() or starts_with() from tidyselect itself, or tar_described_as() to select target names based on their descriptions.
cloud	Logical of length 1, whether to delete objects from the cloud if applicable (e.g. AWS, GCP). If FALSE, files are not deleted from the cloud.
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.
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.

Details

If you have a small number of data-heavy targets you need to discard to conserve storage, this function can help. Local external files files (i.e. format = "file" and repository = "local") are not deleted. For targets with repository not equal "local", tar_delete() attempts to delete the file and errors out if the deletion is unsuccessful. If deletion fails, either log into the cloud platform and manually delete the file (e.g. the AWS web console in the case of repository = "aws") or call tar_invalidate() on that target so that targets does not try to delete the object. For patterns recorded in the metadata, all the branches will be deleted. For patterns no longer in the metadata, branches are left alone.

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_destroy(), 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(y1, 1 + 1),
    tar_target(y2, 1 + 1),
    tar_target(z, y1 + y2)
  )
}, ask = FALSE)
tar_make()
tar_delete(starts_with("y")) # Only deletes y1 and y2.
tar_make() # y1 and y2 rerun but return the same values, so z is up to date.
})
}

targets documentation built on Oct. 3, 2024, 1:11 a.m.