cache_exec | R Documentation |
Caching is based on the assumption that if the input does not change, the output will not change. After an expression is executed for the first time, its result will be saved (either in memory or on disk). The next run will be skipped and the previously saved result will be loaded directly if all external inputs of the expression remain the same, otherwise the cache will be invalidated and the expression will be re-executed.
cache_exec(expr, path = "cache/", id = NULL, ...)
expr |
An R expression to be cached. |
path |
The path to save the cache. The special value |
id |
A stable and unique string identifier for the expression to be used
to identify a unique copy of cache for the current expression from all
cache files (or in-memory elements). If not provided, an MD5 digest of the
deparsed expression will be used, which means if the expression does not
change (changes in comments or white spaces do not matter), the |
... |
More arguments to control the behavior of caching (see ‘Details’). |
Arguments supported in ...
include:
vars
: Names of local variables (which are created inside the expression).
By default, local variables are automatically detected from the expression
via codetools::findLocalsList()
. Locally created variables are cached along
with the value of the expression.
hash
and extra
: R objects to be used to determine if cache should be
loaded or invalidated. If (the MD5 hash of) the objects is not changed, the
cache is loaded, otherwise the cache is invalidated and rebuilt. By default,
hash
is a list of values of global variables in the expression (i.e.,
variables created outside the expression). Global variables are automatically
detected by codetools::findGlobals()
. You can provide a vector of names to
override the automatic detection if you want some specific global variables
to affect caching, or the automatic detection is not reliable. You can also
provide additional information via the extra
argument. For example, if the
expression reads an external file foo.csv
, and you want the cache to be
invalidated after the file is modified, you may use extra = file.mtime("foo.csv")
.
keep
: By default, only one copy of the cache corresponding to an id
under path
is kept, and all other copies for this id
is automatically
purged. If TRUE
, all copies of the cache are kept. If FALSE
, all copies
are removed, which means the cache is always invalidated, and can be useful
to force re-executing the expression.
rw
: A list of functions to read/write the cache files. The list is of the
form list(name = 'xxx', load = function(file) {}, save = function(x, file) {})
. By default, readRDS()
and saveRDS()
are used. This argument can
also take a character string to use some built-in read/write methods.
Currently available methods include rds
(the default), raw
(using
serialize()
and unserialize()
), and qs
(using qs::qread()
and
qs::qsave()
). The rds
and raw
methods only use base R functions (the
rds
method generates smaller files because it uses compression, but is
often slower than the raw
method, which does not use compression). The qs
method requires the qs package, which can be much faster than base R
methods and also supports compression.
If the cache is found, the cached value of the expression will be loaded and returned (other local variables will also be lazy-loaded into the current environment as a side-effect). If cache does not exist, the expression is executed and its value is returned.
# the first run takes about 1 second
y1 = xfun::cache_exec({
x = rnorm(1e+05)
Sys.sleep(1)
x
}, path = ":memory:", id = "sim-norm")
# the second run takes almost no time
y2 = xfun::cache_exec({
# comments won't affect caching
x = rnorm(1e+05)
Sys.sleep(1)
x
}, path = ":memory:", id = "sim-norm")
# y1, y2, and x should be identical
stopifnot(identical(y1, y2), identical(y1, x))
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