Introduction to Cache
Eliot J. B. McIntire
December 23 2022
Source:vignettes/Intro-to-Cache.Rmd
Intro-to-Cache.RmdReproducible workflow
As part of a reproducible workflow, caching of function calls, code
chunks, and other elements of a project is a critical component. The
objective of a reproducible workflow is is likely that an entire work
flow from raw data to publication, decision support, report writing,
presentation building etc., could be built and be reproducible anywhere,
on any computer, operating system, with any starting conditions, on
demand. The reproducible::Cache function is built to work
with any R function.
Differences with other approaches
Cache users DBI as a backend, with key
functions, dbReadTable, dbRemoveTable,
dbSendQuery, dbSendStatement,
dbCreateTable and dbAppendTable. These can all
be accessed via Cache, showCache,
clearCache, and keepCache. It is optimized for
speed of transactions, using fastdigest::fastdigest on R
memory objects and digest::digest on files. The main
function is superficially similar to archivist::cache,
which uses digest::digest in all cases to determine whether
the arguments are identical in subsequent iterations. It also but does
many things that make standard caching with
digest::digest don’t work reliably between systems. For
these, the function .robustDigest is introduced to make
caching transferable between systems. This is relevant for file paths,
environments, parallel clusters, functions (which are contained within
an environment), and many others (e.g., see ?.robustDigest
for methods). Cache also adds important elements like
automated tagging and the option to retrieve disk-cached values via
stashed objects in memory using memoise::memoise. This
means that running Cache 1, 2, and 3 times on the same
function will get progressively faster. This can be extremely useful for
web apps built with, say shiny.
Function-level caching
Any function can be cached by wrapping Cache around the
function call, or by using base pipe |> or using:
Cache(FUN = functionName, ...)
This will be a slight change to a function call, such as:
projectRaster(raster, crs = crs(newRaster)) to
Cache(projectRaster, raster, crs = crs(newRaster)).
This is particularly useful for expensive operations.
## Loading required package: sp##
## Attaching package: 'data.table'## The following object is masked from 'package:raster':
##
## shift
setDTthreads(1) # user can omit this with little effect
tmpDir <- file.path(tempdir(), "reproducible_examples", "Cache")
dir.create(tmpDir, recursive = TRUE)
ras <- raster(extent(0, 1000, 0, 1000), vals = 1:1e6, res = 1)
crs(ras) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +datum=WGS84"
newCRS <- "+init=epsg:4326" # A longlat crs
# No Cache
system.time(suppressWarnings(map1 <- projectRaster(ras, crs = newCRS))) # Warnings due to new PROJ## user system elapsed
## 2.532 0.409 2.941
# Try with memoise for this example -- for many simple cases, memoising will not be faster
opts <- options("reproducible.useMemoise" = TRUE)
# With Cache -- a little slower the first time because saving to disk
system.time({
suppressWarnings({
map1 <- Cache(projectRaster, ras, crs = newCRS, cachePath = tmpDir, notOlderThan = Sys.time())
})
})## user system elapsed
## 2.813 0.282 3.100
# faster the second time
system.time({
map2 <- Cache(projectRaster, ras, crs = newCRS, cachePath = tmpDir)
})## ...(Object to retrieve (b96ade7aaca8821a.rds) is large: 6.5 Mb)## loaded memoised result from previous projectRaster call## user system elapsed
## 0.486 0.000 0.489## [1] TRUECaching examples
Basic use
##
## Attaching package: 'magrittr'## The following object is masked from 'package:raster':
##
## extract
try(clearCache(tmpDir, ask = FALSE), silent = TRUE) # just to make sure it is clear
ranNumsA <- Cache(rnorm, 10, 16, cachePath = tmpDir)
# All same
ranNumsB <- Cache(rnorm, 10, 16, cachePath = tmpDir) # recovers cached copy## ...(Object to retrieve (4fca280cda001fc9.rds))## loaded cached result from previous rnorm call## ...(Object to retrieve (4fca280cda001fc9.rds))## loaded cached result from previous quote call## ...(Object to retrieve (4fca280cda001fc9.rds))## loaded cached result from previous rnorm call
# Any minor change makes it different
ranNumsE <- Cache(rnorm, 10, 6, cachePath = tmpDir) # differentExample 1: Basic cache use with tags
ranNumsA <- Cache(rnorm, 4, cachePath = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(runif(4), cachePath = tmpDir, userTags = "objectName:b")
showCache(tmpDir, userTags = c("objectName"))## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue
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## createdDate## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue
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clearCache(tmpDir, userTags = c("runif"), ask = FALSE) # remove only cached objects made during runif call## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes
showCache(tmpDir) # only those made during rnorm call## Cache size: ## Total (including Rasters): 804 bytes## Selected objects (not including Rasters): 804 bytes## cacheId tagKey tagValue
## 1: 4fca280cda001fc9 function rnorm
## 2: 4fca280cda001fc9 class numeric
## 3: 4fca280cda001fc9 object.size 1104
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## createdDate
clearCache(tmpDir, ask = FALSE)Example 2: using the “accessed” tag
ranNumsA <- Cache(rnorm, 4, cachePath = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(runif, 4, cachePath = tmpDir, userTags = "objectName:b")
# access it again, from Cache
Sys.sleep(1)
ranNumsA <- Cache(rnorm, 4, cachePath = tmpDir, userTags = "objectName:a")## ...(Object to retrieve (ad0ea27476c50b66.rds))## loaded cached result from previous rnorm call
wholeCache <- showCache(tmpDir)## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes
# keep only items accessed "recently" (i.e., only objectName:a)
onlyRecentlyAccessed <- showCache(tmpDir, userTags = max(wholeCache[tagKey == "accessed"]$tagValue))## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes
# inverse join with 2 data.tables ... using: a[!b]
# i.e., return all of wholeCache that was not recently accessed
# Note: the two different ways to access -- old way with "artifact" will be deprecated
toRemove <- unique(wholeCache[!onlyRecentlyAccessed, on = "cacheId"], by = "cacheId")$cacheId
clearCache(tmpDir, toRemove, ask = FALSE) # remove ones not recently accessed## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes
showCache(tmpDir) # still has more recently accessed## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue
## 1: ad0ea27476c50b66 objectName a
## 2: ad0ea27476c50b66 function rnorm
## 3: ad0ea27476c50b66 class numeric
## 4: ad0ea27476c50b66 object.size 1008
## 5: ad0ea27476c50b66 accessed 2022-12-23 01:52:22
## 6: ad0ea27476c50b66 inCloud FALSE
## 7: ad0ea27476c50b66 resultHash
## 8: ad0ea27476c50b66 elapsedTimeDigest 0.002480984 secs
## 9: ad0ea27476c50b66 elapsedTimeFirstRun 2.503395e-05 secs
## 10: ad0ea27476c50b66 otherFunctions saveRDS
## 11: ad0ea27476c50b66 otherFunctions do.call
## 12: ad0ea27476c50b66 otherFunctions process_file
## 13: ad0ea27476c50b66 otherFunctions process_group
## 14: ad0ea27476c50b66 otherFunctions process_group.block
## 15: ad0ea27476c50b66 otherFunctions call_block
## 16: ad0ea27476c50b66 otherFunctions block_exec
## 17: ad0ea27476c50b66 otherFunctions eng_r
## 18: ad0ea27476c50b66 otherFunctions in_input_dir
## 19: ad0ea27476c50b66 otherFunctions in_dir
## 20: ad0ea27476c50b66 otherFunctions timing_fn
## 21: ad0ea27476c50b66 otherFunctions handle
## 22: ad0ea27476c50b66 preDigest n:7eef4eae85fd9229
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## 24: ad0ea27476c50b66 preDigest sd:853b1797f54b229c
## 25: ad0ea27476c50b66 preDigest .FUN:4f604aa46882b368
## 26: ad0ea27476c50b66 file.size 174
## 27: ad0ea27476c50b66 accessed 2022-12-23 01:52:23
## 28: ad0ea27476c50b66 elapsedTimeLoad 0.002490044 secs
## cacheId tagKey tagValue
## createdDate
## 1: 2022-12-23 01:52:22
## 2: 2022-12-23 01:52:22
## 3: 2022-12-23 01:52:22
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## createdDateExample 3: using keepCache
ranNumsA <- Cache(rnorm, 4, cachePath = tmpDir, userTags = "objectName:a")## ...(Object to retrieve (ad0ea27476c50b66.rds))## loaded cached result from previous rnorm call
ranNumsB <- Cache(runif(4), cachePath = tmpDir, userTags = "objectName:b")
# keep only those cached items from the last 24 hours
oneDay <- 60 * 60 * 24
keepCache(tmpDir, after = Sys.time() - oneDay, ask = FALSE)## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue
## 1: ad0ea27476c50b66 objectName a
## 2: ad0ea27476c50b66 function rnorm
## 3: ad0ea27476c50b66 class numeric
## 4: ad0ea27476c50b66 object.size 1008
## 5: ad0ea27476c50b66 accessed 2022-12-23 01:52:22
## 6: ad0ea27476c50b66 inCloud FALSE
## 7: ad0ea27476c50b66 resultHash
## 8: ad0ea27476c50b66 elapsedTimeDigest 0.002480984 secs
## 9: ad0ea27476c50b66 elapsedTimeFirstRun 2.503395e-05 secs
## 10: ad0ea27476c50b66 otherFunctions saveRDS
## 11: ad0ea27476c50b66 otherFunctions do.call
## 12: ad0ea27476c50b66 otherFunctions process_file
## 13: ad0ea27476c50b66 otherFunctions process_group
## 14: ad0ea27476c50b66 otherFunctions process_group.block
## 15: ad0ea27476c50b66 otherFunctions call_block
## 16: ad0ea27476c50b66 otherFunctions block_exec
## 17: ad0ea27476c50b66 otherFunctions eng_r
## 18: ad0ea27476c50b66 otherFunctions in_input_dir
## 19: ad0ea27476c50b66 otherFunctions in_dir
## 20: ad0ea27476c50b66 otherFunctions timing_fn
## 21: ad0ea27476c50b66 otherFunctions handle
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## 24: ad0ea27476c50b66 preDigest sd:853b1797f54b229c
## 25: ad0ea27476c50b66 preDigest .FUN:4f604aa46882b368
## 26: ad0ea27476c50b66 file.size 174
## 27: ad0ea27476c50b66 accessed 2022-12-23 01:52:23
## 28: ad0ea27476c50b66 elapsedTimeLoad 0.002898216 secs
## 29: ad0ea27476c50b66 accessed 2022-12-23 01:52:23
## 30: deaa37372f85861b objectName b
## 31: deaa37372f85861b function runif
## 32: deaa37372f85861b class numeric
## 33: deaa37372f85861b object.size 1008
## 34: deaa37372f85861b accessed 2022-12-23 01:52:23
## 35: deaa37372f85861b inCloud FALSE
## 36: deaa37372f85861b resultHash
## 37: deaa37372f85861b elapsedTimeDigest 0.002411604 secs
## 38: deaa37372f85861b elapsedTimeFirstRun 2.360344e-05 secs
## 39: deaa37372f85861b otherFunctions saveRDS
## 40: deaa37372f85861b otherFunctions do.call
## 41: deaa37372f85861b otherFunctions process_file
## 42: deaa37372f85861b otherFunctions process_group
## 43: deaa37372f85861b otherFunctions process_group.block
## 44: deaa37372f85861b otherFunctions call_block
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## 54: deaa37372f85861b preDigest .FUN:881ec847b7161f3c
## 55: deaa37372f85861b file.size 167
## cacheId tagKey tagValue
## createdDate
## 1: 2022-12-23 01:52:22
## 2: 2022-12-23 01:52:22
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## createdDate
# Keep all Cache items created with an rnorm() call
keepCache(tmpDir, userTags = "rnorm", ask = FALSE)## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue
## 1: ad0ea27476c50b66 objectName a
## 2: ad0ea27476c50b66 function rnorm
## 3: ad0ea27476c50b66 class numeric
## 4: ad0ea27476c50b66 object.size 1008
## 5: ad0ea27476c50b66 accessed 2022-12-23 01:52:22
## 6: ad0ea27476c50b66 inCloud FALSE
## 7: ad0ea27476c50b66 resultHash
## 8: ad0ea27476c50b66 elapsedTimeDigest 0.002480984 secs
## 9: ad0ea27476c50b66 elapsedTimeFirstRun 2.503395e-05 secs
## 10: ad0ea27476c50b66 otherFunctions saveRDS
## 11: ad0ea27476c50b66 otherFunctions do.call
## 12: ad0ea27476c50b66 otherFunctions process_file
## 13: ad0ea27476c50b66 otherFunctions process_group
## 14: ad0ea27476c50b66 otherFunctions process_group.block
## 15: ad0ea27476c50b66 otherFunctions call_block
## 16: ad0ea27476c50b66 otherFunctions block_exec
## 17: ad0ea27476c50b66 otherFunctions eng_r
## 18: ad0ea27476c50b66 otherFunctions in_input_dir
## 19: ad0ea27476c50b66 otherFunctions in_dir
## 20: ad0ea27476c50b66 otherFunctions timing_fn
## 21: ad0ea27476c50b66 otherFunctions handle
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## 25: ad0ea27476c50b66 preDigest .FUN:4f604aa46882b368
## 26: ad0ea27476c50b66 file.size 174
## 27: ad0ea27476c50b66 accessed 2022-12-23 01:52:23
## 28: ad0ea27476c50b66 elapsedTimeLoad 0.002898216 secs
## 29: ad0ea27476c50b66 accessed 2022-12-23 01:52:23
## cacheId tagKey tagValue
## createdDate
## 1: 2022-12-23 01:52:22
## 2: 2022-12-23 01:52:22
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## createdDate
# Remove all Cache items that happened within a rnorm() call
clearCache(tmpDir, userTags = "rnorm", ask = FALSE)## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes
showCache(tmpDir) ## empty## Cache size: ## Total (including Rasters): 0 bytes## Selected objects (not including Rasters): 0 bytes## Empty data.table (0 rows and 4 cols): cacheId,tagKey,tagValue,createdDate
# Also, can set a time before caching happens and remove based on this
# --> a useful, simple way to control Cache
ranNumsA <- Cache(rnorm, 4, cachePath = tmpDir, userTags = "objectName:a")
startTime <- Sys.time()
Sys.sleep(1)
ranNumsB <- Cache(rnorm, 5, cachePath = tmpDir, userTags = "objectName:b")
keepCache(tmpDir, after = startTime, ask = FALSE) # keep only those newer than startTime## Cache size: ## Total (including Rasters): 256 bytes## Selected objects (not including Rasters): 256 bytes## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue
## 1: ccacbf62081a42b4 objectName b
## 2: ccacbf62081a42b4 function rnorm
## 3: ccacbf62081a42b4 class numeric
## 4: ccacbf62081a42b4 object.size 1024
## 5: ccacbf62081a42b4 accessed 2022-12-23 01:52:24
## 6: ccacbf62081a42b4 inCloud FALSE
## 7: ccacbf62081a42b4 resultHash
## 8: ccacbf62081a42b4 elapsedTimeDigest 0.002917051 secs
## 9: ccacbf62081a42b4 elapsedTimeFirstRun 2.551079e-05 secs
## 10: ccacbf62081a42b4 otherFunctions saveRDS
## 11: ccacbf62081a42b4 otherFunctions do.call
## 12: ccacbf62081a42b4 otherFunctions process_file
## 13: ccacbf62081a42b4 otherFunctions process_group
## 14: ccacbf62081a42b4 otherFunctions process_group.block
## 15: ccacbf62081a42b4 otherFunctions call_block
## 16: ccacbf62081a42b4 otherFunctions block_exec
## 17: ccacbf62081a42b4 otherFunctions eng_r
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## 26: ccacbf62081a42b4 file.size 181
## cacheId tagKey tagValue
## createdDate
## 1: 2022-12-23 01:52:24
## 2: 2022-12-23 01:52:24
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## createdDate
clearCache(tmpDir, ask = FALSE)Example 4: searching for multiple objects in the cache
# default userTags is "and" matching; for "or" matching use |
ranNumsA <- Cache(runif, 4, cachePath = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(rnorm, 4, cachePath = tmpDir, userTags = "objectName:b")
# show all objects (runif and rnorm in this case)
showCache(tmpDir)## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue
## 1: ad0ea27476c50b66 objectName b
## 2: ad0ea27476c50b66 function rnorm
## 3: ad0ea27476c50b66 class numeric
## 4: ad0ea27476c50b66 object.size 1008
## 5: ad0ea27476c50b66 accessed 2022-12-23 01:52:24
## 6: ad0ea27476c50b66 inCloud FALSE
## 7: ad0ea27476c50b66 resultHash
## 8: ad0ea27476c50b66 elapsedTimeDigest 0.002422094 secs
## 9: ad0ea27476c50b66 elapsedTimeFirstRun 2.574921e-05 secs
## 10: ad0ea27476c50b66 otherFunctions saveRDS
## 11: ad0ea27476c50b66 otherFunctions do.call
## 12: ad0ea27476c50b66 otherFunctions process_file
## 13: ad0ea27476c50b66 otherFunctions process_group
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## 15: ad0ea27476c50b66 otherFunctions call_block
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## 27: deaa37372f85861b objectName a
## 28: deaa37372f85861b function runif
## 29: deaa37372f85861b class numeric
## 30: deaa37372f85861b object.size 1008
## 31: deaa37372f85861b accessed 2022-12-23 01:52:24
## 32: deaa37372f85861b inCloud FALSE
## 33: deaa37372f85861b resultHash
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## cacheId tagKey tagValue
## createdDate
## 1: 2022-12-23 01:52:24
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## createdDate
# show objects that are both runif and rnorm
# (i.e., none in this case, because objecs are either or, not both)
showCache(tmpDir, userTags = c("runif", "rnorm")) ## empty## Cache size: ## Total (including Rasters): 0 bytes## Selected objects (not including Rasters): 0 bytes## Empty data.table (0 rows and 4 cols): cacheId,tagKey,tagValue,createdDate
# show objects that are either runif or rnorm ("or" search)
showCache(tmpDir, userTags = "runif|rnorm")## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue
## 1: ad0ea27476c50b66 objectName b
## 2: ad0ea27476c50b66 function rnorm
## 3: ad0ea27476c50b66 class numeric
## 4: ad0ea27476c50b66 object.size 1008
## 5: ad0ea27476c50b66 accessed 2022-12-23 01:52:24
## 6: ad0ea27476c50b66 inCloud FALSE
## 7: ad0ea27476c50b66 resultHash
## 8: ad0ea27476c50b66 elapsedTimeDigest 0.002422094 secs
## 9: ad0ea27476c50b66 elapsedTimeFirstRun 2.574921e-05 secs
## 10: ad0ea27476c50b66 otherFunctions saveRDS
## 11: ad0ea27476c50b66 otherFunctions do.call
## 12: ad0ea27476c50b66 otherFunctions process_file
## 13: ad0ea27476c50b66 otherFunctions process_group
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## 29: deaa37372f85861b class numeric
## 30: deaa37372f85861b object.size 1008
## 31: deaa37372f85861b accessed 2022-12-23 01:52:24
## 32: deaa37372f85861b inCloud FALSE
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## 34: deaa37372f85861b elapsedTimeDigest 0.002568007 secs
## 35: deaa37372f85861b elapsedTimeFirstRun 2.479553e-05 secs
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## cacheId tagKey tagValue
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# keep only objects that are either runif or rnorm ("or" search)
keepCache(tmpDir, userTags = "runif|rnorm", ask = FALSE)## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue
## 1: ad0ea27476c50b66 objectName b
## 2: ad0ea27476c50b66 function rnorm
## 3: ad0ea27476c50b66 class numeric
## 4: ad0ea27476c50b66 object.size 1008
## 5: ad0ea27476c50b66 accessed 2022-12-23 01:52:24
## 6: ad0ea27476c50b66 inCloud FALSE
## 7: ad0ea27476c50b66 resultHash
## 8: ad0ea27476c50b66 elapsedTimeDigest 0.002422094 secs
## 9: ad0ea27476c50b66 elapsedTimeFirstRun 2.574921e-05 secs
## 10: ad0ea27476c50b66 otherFunctions saveRDS
## 11: ad0ea27476c50b66 otherFunctions do.call
## 12: ad0ea27476c50b66 otherFunctions process_file
## 13: ad0ea27476c50b66 otherFunctions process_group
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## 26: ad0ea27476c50b66 file.size 174
## 27: deaa37372f85861b objectName a
## 28: deaa37372f85861b function runif
## 29: deaa37372f85861b class numeric
## 30: deaa37372f85861b object.size 1008
## 31: deaa37372f85861b accessed 2022-12-23 01:52:24
## 32: deaa37372f85861b inCloud FALSE
## 33: deaa37372f85861b resultHash
## 34: deaa37372f85861b elapsedTimeDigest 0.002568007 secs
## 35: deaa37372f85861b elapsedTimeFirstRun 2.479553e-05 secs
## 36: deaa37372f85861b otherFunctions saveRDS
## 37: deaa37372f85861b otherFunctions do.call
## 38: deaa37372f85861b otherFunctions process_file
## 39: deaa37372f85861b otherFunctions process_group
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## cacheId tagKey tagValue
## createdDate
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## createdDate
clearCache(tmpDir, ask = FALSE)Example 5: using caching to speed up rerunning expensive computations
ras <- raster(extent(0, 5, 0, 5), res = 1,
vals = sample(1:5, replace = TRUE, size = 25),
crs = "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84")
# A slow operation, like GIS operation
notCached <- suppressWarnings(
# project raster generates warnings when run non-interactively
projectRaster(ras, crs = crs(ras), res = 5, cachePath = tmpDir)
)
cached <- suppressWarnings(
# project raster generates warnings when run non-interactively
# using quote works also
Cache(projectRaster, ras, crs = crs(ras), res = 5, cachePath = tmpDir)
)
# second time is much faster
reRun <- suppressWarnings(
# project raster generates warnings when run non-interactively
Cache(projectRaster, ras, crs = crs(ras), res = 5, cachePath = tmpDir)
)## ...(Object to retrieve (adf774b9e0ccfe2d.rds))## loaded cached result from previous projectRaster call
# recovered cached version is same as non-cached version
all.equal(notCached, reRun) ## TRUE## [1] TRUENested Caching
Nested caching, which is when Caching of a function occurs inside an outer function, which is itself cached. This is a critical element to working within a reproducible work flow. It is not enough during development to cache flat code chunks, as there will be many levels of “slow” functions. Ideally, at all points in a development cycle, it should be possible to get to any line of code starting from the very initial steps, running through everything up to that point, in less than a few seconds. If the workflow can be kept very fast like this, then there is a guarantee that it will work at any point.
##########################
## Nested Caching
# Make 2 functions
inner <- function(mean) {
d <- 1
Cache(rnorm, n = 3, mean = mean)
}
outer <- function(n) {
Cache(inner, 0.1, cachePath = tmpdir2)
}
# make 2 different cache paths
tmpdir1 <- file.path(tempdir(), "first")
tmpdir2 <- file.path(tempdir(), "second")
# Run the Cache ... notOlderThan propagates to all 3 Cache calls,
# but cachePath is tmpdir1 in top level Cache and all nested
# Cache calls, unless individually overridden ... here inner
# uses tmpdir2 repository
Cache(outer, n = 2, cachePath = tmpdir1, notOlderThan = Sys.time())## [1] -0.8330596 1.1995926 -0.5233390
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:dffc8e3bf8c23b6e"
## attr(,"call")
## [1] ""
showCache(tmpdir1) # 2 function calls## Cache size: ## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue
## 1: dffc8e3bf8c23b6e function outer
## 2: dffc8e3bf8c23b6e class numeric
## 3: dffc8e3bf8c23b6e object.size 1008
## 4: dffc8e3bf8c23b6e accessed 2022-12-23 01:52:25
## 5: dffc8e3bf8c23b6e inCloud FALSE
## 6: dffc8e3bf8c23b6e resultHash
## 7: dffc8e3bf8c23b6e elapsedTimeDigest 0.002435923 secs
## 8: dffc8e3bf8c23b6e elapsedTimeFirstRun 0.03944659 secs
## 9: dffc8e3bf8c23b6e otherFunctions saveRDS
## 10: dffc8e3bf8c23b6e otherFunctions do.call
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## 24: efa1ccee79a31d4c function rnorm
## 25: efa1ccee79a31d4c class numeric
## 26: efa1ccee79a31d4c object.size 1008
## 27: efa1ccee79a31d4c accessed 2022-12-23 01:52:25
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## 33: efa1ccee79a31d4c otherFunctions do.call
## 34: efa1ccee79a31d4c otherFunctions process_file
## 35: efa1ccee79a31d4c otherFunctions process_group
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## cacheId tagKey tagValue
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showCache(tmpdir2) # 1 function call## Cache size: ## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue
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## createdDate
# userTags get appended
# all items have the outer tag propagate, plus inner ones only have inner ones
clearCache(tmpdir1, ask = FALSE)
outerTag <- "outerTag"
innerTag <- "innerTag"
inner <- function(mean) {
d <- 1
Cache(rnorm, n = 3, mean = mean, notOlderThan = Sys.time() - 1e5, userTags = innerTag)
}
outer <- function(n) {
Cache(inner, 0.1)
}
aa <- Cache(outer, n = 2, cachePath = tmpdir1, userTags = outerTag)
showCache(tmpdir1) # rnorm function has outerTag and innerTag, inner and outer only have outerTag## Cache size: ## Total (including Rasters): 756 bytes## Selected objects (not including Rasters): 756 bytes## cacheId tagKey tagValue
## 1: 88a34e1d033329e5 outerTag outerTag
## 2: 88a34e1d033329e5 function outer
## 3: 88a34e1d033329e5 class numeric
## 4: 88a34e1d033329e5 object.size 1008
## 5: 88a34e1d033329e5 accessed 2022-12-23 01:52:25
## 6: 88a34e1d033329e5 inCloud FALSE
## 7: 88a34e1d033329e5 resultHash
## 8: 88a34e1d033329e5 elapsedTimeDigest 0.002161264 secs
## 9: 88a34e1d033329e5 elapsedTimeFirstRun 0.03096986 secs
## 10: 88a34e1d033329e5 otherFunctions saveRDS
## 11: 88a34e1d033329e5 otherFunctions do.call
## 12: 88a34e1d033329e5 otherFunctions process_file
## 13: 88a34e1d033329e5 otherFunctions process_group
## 14: 88a34e1d033329e5 otherFunctions process_group.block
## 15: 88a34e1d033329e5 otherFunctions call_block
## 16: 88a34e1d033329e5 otherFunctions block_exec
## 17: 88a34e1d033329e5 otherFunctions eng_r
## 18: 88a34e1d033329e5 otherFunctions in_input_dir
## 19: 88a34e1d033329e5 otherFunctions in_dir
## 20: 88a34e1d033329e5 otherFunctions timing_fn
## 21: 88a34e1d033329e5 otherFunctions handle
## 22: 88a34e1d033329e5 preDigest n:82dc709f2b91918a
## 23: 88a34e1d033329e5 preDigest .FUN:5f06fb5fbffe9e3b
## 24: 88a34e1d033329e5 file.size 165
## 25: b06af03d5a73dc7d outerTag outerTag
## 26: b06af03d5a73dc7d function inner
## 27: b06af03d5a73dc7d class numeric
## 28: b06af03d5a73dc7d object.size 1008
## 29: b06af03d5a73dc7d accessed 2022-12-23 01:52:25
## 30: b06af03d5a73dc7d inCloud FALSE
## 31: b06af03d5a73dc7d resultHash
## 32: b06af03d5a73dc7d elapsedTimeDigest 0.002165794 secs
## 33: b06af03d5a73dc7d elapsedTimeFirstRun 0.0160501 secs
## 34: b06af03d5a73dc7d otherFunctions saveRDS
## 35: b06af03d5a73dc7d otherFunctions do.call
## 36: b06af03d5a73dc7d otherFunctions process_file
## 37: b06af03d5a73dc7d otherFunctions process_group
## 38: b06af03d5a73dc7d otherFunctions process_group.block
## 39: b06af03d5a73dc7d otherFunctions call_block
## 40: b06af03d5a73dc7d otherFunctions block_exec
## 41: b06af03d5a73dc7d otherFunctions eng_r
## 42: b06af03d5a73dc7d otherFunctions in_input_dir
## 43: b06af03d5a73dc7d otherFunctions in_dir
## 44: b06af03d5a73dc7d otherFunctions timing_fn
## 45: b06af03d5a73dc7d otherFunctions handle
## 46: b06af03d5a73dc7d otherFunctions outer
## 47: b06af03d5a73dc7d preDigest mean:22413394efd9f6a3
## 48: b06af03d5a73dc7d preDigest .FUN:7ad10bc1ae528d8c
## 49: b06af03d5a73dc7d file.size 165
## 50: efa1ccee79a31d4c innerTag innerTag
## 51: efa1ccee79a31d4c outerTag outerTag
## 52: efa1ccee79a31d4c function rnorm
## 53: efa1ccee79a31d4c class numeric
## 54: efa1ccee79a31d4c object.size 1008
## 55: efa1ccee79a31d4c accessed 2022-12-23 01:52:25
## 56: efa1ccee79a31d4c inCloud FALSE
## 57: efa1ccee79a31d4c resultHash
## 58: efa1ccee79a31d4c elapsedTimeDigest 0.002499819 secs
## 59: efa1ccee79a31d4c elapsedTimeFirstRun 2.479553e-05 secs
## 60: efa1ccee79a31d4c otherFunctions saveRDS
## 61: efa1ccee79a31d4c otherFunctions do.call
## 62: efa1ccee79a31d4c otherFunctions process_file
## 63: efa1ccee79a31d4c otherFunctions process_group
## 64: efa1ccee79a31d4c otherFunctions process_group.block
## 65: efa1ccee79a31d4c otherFunctions call_block
## 66: efa1ccee79a31d4c otherFunctions block_exec
## 67: efa1ccee79a31d4c otherFunctions eng_r
## 68: efa1ccee79a31d4c otherFunctions in_input_dir
## 69: efa1ccee79a31d4c otherFunctions in_dir
## 70: efa1ccee79a31d4c otherFunctions timing_fn
## 71: efa1ccee79a31d4c otherFunctions handle
## 72: efa1ccee79a31d4c otherFunctions outer
## 73: efa1ccee79a31d4c otherFunctions inner
## 74: efa1ccee79a31d4c preDigest n:7f12988bd88a0fb8
## 75: efa1ccee79a31d4c preDigest mean:22413394efd9f6a3
## 76: efa1ccee79a31d4c preDigest sd:853b1797f54b229c
## 77: efa1ccee79a31d4c preDigest .FUN:4f604aa46882b368
## 78: efa1ccee79a31d4c file.size 165
## cacheId tagKey tagValue
## createdDate
## 1: 2022-12-23 01:52:25
## 2: 2022-12-23 01:52:25
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## createdDatecacheId
Sometimes, it is not absolutely desirable to maintain the work flow
intact because changes that are irrelevant to the analysis, such as
changing messages sent to a user, may be changed, without a desire to
rerun functions. The cacheId argument is for this. Once a
piece of code is run, then the cacheId can be manually
extracted (it is reported at the end of a Cache call) and manually
placed in the code, passed in as, say,
cacheId = "ad184ce64541972b50afd8e7b75f821b".
## [1] -0.6264538
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:422bae4ed2f770cc"
## attr(,"call")
## [1] ""
# manually look at output attribute which shows cacheId: 7072c305d8c69df0
Cache(rnorm, 1, cachePath = tmpdir1, cacheId = "7072c305d8c69df0") # same value## cacheId is not same as calculated hash. Manually searching for cacheId:7072c305d8c69df0## ...(Object to retrieve (422bae4ed2f770cc.rds))## loaded cached result from previous rnorm call## [1] -0.6264538
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] FALSE
##
## attr(,"tags")
## [1] "cacheId:422bae4ed2f770cc"
## attr(,"call")
## [1] ""
# override even with different inputs:
Cache(rnorm, 2, cachePath = tmpdir1, cacheId = "7072c305d8c69df0")## cacheId is not same as calculated hash. Manually searching for cacheId:7072c305d8c69df0## [1] 0.7383247 0.5757814
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:7072c305d8c69df0"
## attr(,"call")
## [1] ""Working with the Cache manually
Since the cache is simply a DBI data table (of an SQLite
database by default). In addition, there are several helpers in the
reproducible package, including showCache,
keepCache and clearCache that may be useful.
Also, one can access cached items manually (rather than simply rerunning
the same Cache function again).
# As of reproducible version 1.0, there is a new backend directly using DBI
mapHash <- unique(showCache(tmpDir, userTags = "projectRaster")$cacheId)## Cache size: ## Total (including Rasters): 3.4 Kb## Selected objects (not including Rasters): 3.4 Kb
map <- loadFromCache(mapHash[1], cachePath = tmpDir)
plot(map)
Alternative database backends
By default, caching relies on a sqlite database for it’s backend.
While this works in many situations, there are some important
limitations of using sqlite for caching, including 1) speed; 2)
concurrent transactions; 3) sharing database across machines or
projects. Fortunately, Cache makes use of DBI
package and thus supports several database backends, including mysql and
postgresql.
See https://github.com/PredictiveEcology/SpaDES/wiki/Using-alternate-database-backends-for-Cache for further information on configuring these additional backends.