1 Reproducible 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.
1.1 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.
1.2 Function-level caching
Any function can be cached 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: splibrary(reproducible)
tmpDir <- file.path(tempdir(), "reproducible_examples", "Cache")
checkPath(tmpDir, create = TRUE)## [1] "C:/Users/EMCINT~1.L-V/AppData/Local/Temp/RtmpI5BCs4/reproducible_examples/Cache"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 +ellps=WGS84"
newCRS <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
# No Cache
system.time(map1 <- projectRaster(ras, crs = newCRS))## user system elapsed
## 1.86 0.23 2.11# 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(map1 <- Cache(projectRaster, ras, crs = newCRS, cacheRepo = tmpDir,
notOlderThan = Sys.time()))## user system elapsed
## 2.70 0.55 3.41# vastly faster the second time
system.time(map2 <- Cache(projectRaster, ras, crs = newCRS, cacheRepo = tmpDir))## ...(Object to retrieve (8e07ebc7bbe758ad.rds) is large: 6.5 Mb)## loading cached result from previous projectRaster call, adding to memoised copy## user system elapsed
## 0.28 0.06 0.37# may be faster the third time because of memoise; but this example is too simple to show
system.time(map3 <- Cache(projectRaster, ras, crs = newCRS, cacheRepo = tmpDir))## ...(Object to retrieve (8e07ebc7bbe758ad.rds) is large: 6.5 Mb)## loading memoised result from previous projectRaster call.## user system elapsed
## 0.26 0.05 0.33## [1] TRUE## [1] TRUE1.3 Caching examples
1.3.1 Basic use
##
## Attaching package: 'magrittr'## The following object is masked from 'package:raster':
##
## extracttry(clearCache(tmpDir, ask = FALSE), silent = TRUE) # just to make sure it is clear
ranNumsA <- Cache(rnorm, 10, 16, cacheRepo = tmpDir)
# All same
ranNumsB <- Cache(rnorm, 10, 16, cacheRepo = tmpDir) # recovers cached copy## loading cached result from previous rnorm call.## loading cached result from previous 'rnorm' pipe sequence call.## loading cached result from previous rnorm call.1.5 Example 2: using the “accessed” tag
ranNumsA <- Cache(rnorm, 4, cacheRepo = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(runif, 4, cacheRepo = tmpDir, userTags = "objectName:b")
# access it again, from Cache
Sys.sleep(1)
ranNumsA <- Cache(rnorm, 4, cacheRepo = tmpDir, userTags = "objectName:a")## loading cached result from previous rnorm call.## 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## Cache size:## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue createdDate
## 1: f7bee22047b8d0c1 objectName a 2020-02-19 17:02:23
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## 20: f7bee22047b8d0c1 accessed 2020-02-19 17:02:24 2020-02-19 17:02:241.6 Example 3: using keepCache
## loading cached result from previous rnorm call.ranNumsB <- Cache(runif, 4, cacheRepo = 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 createdDate
## 1: f7bee22047b8d0c1 objectName a 2020-02-19 17:02:23
## 2: f7bee22047b8d0c1 function rnorm 2020-02-19 17:02:23
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## cacheId tagKey tagValue 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## cacheId tagKey tagValue createdDate
## 1: f7bee22047b8d0c1 objectName a 2020-02-19 17:02:23
## 2: f7bee22047b8d0c1 function rnorm 2020-02-19 17:02:23
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## cacheId tagKey tagValue 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## 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, cacheRepo = tmpDir, userTags = "objectName:a")
startTime <- Sys.time()
Sys.sleep(1)
ranNumsB <- Cache(rnorm, 5, cacheRepo = 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## cacheId tagKey tagValue createdDate
## 1: 142b4176fe87d51d objectName b 2020-02-19 17:02:26
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## 19: 142b4176fe87d51d file.size 185 2020-02-19 17:02:261.7 Example 4: searching for multiple objects in the cache
# default userTags is "and" matching; for "or" matching use |
ranNumsA <- Cache(runif, 4, cacheRepo = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(rnorm, 4, cacheRepo = 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 createdDate
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## cacheId tagKey tagValue 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 createdDate
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## cacheId tagKey tagValue createdDate# 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 createdDate
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## cacheId tagKey tagValue createdDate1.8 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, cacheRepo = tmpDir)
)
cached <- suppressWarnings(
# project raster generates warnings when run non-interactively
# using quote works also
Cache(projectRaster, ras, crs = crs(ras), res = 5, cacheRepo = tmpDir)
)
# second time is much faster
reRun <- suppressWarnings(
# project raster generates warnings when run non-interactively
Cache(projectRaster, ras, crs = crs(ras), res = 5, cacheRepo = tmpDir)
)## loading cached result from previous projectRaster call.## [1] TRUE1.9 Nested 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 that 1 second. 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, cacheRepo = 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 cacheRepo is tmpdir1 in top level Cache and all nested
# Cache calls, unless individually overridden ... here inner
# uses tmpdir2 repository
Cache(outer, n = 2, cacheRepo = tmpdir1, notOlderThan = Sys.time())## [1] 0.6824337 1.2243838 -0.7529345
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:a7af5367c13aba8f"
## attr(,"call")
## [1] ""## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## cacheId tagKey tagValue createdDate
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## cacheId tagKey tagValue createdDate## Cache size:## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## cacheId tagKey tagValue createdDate
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## 18: 33ceb4fb525fd08f file.size 166 2020-02-19 17:02:28# userTags get appended
# all items have the outer tag propagate, plus inner ones only have inner ones
clearCache(tmpdir1, ask = FALSE)
outerTag <- "outerTag"
innerTag <- "innerTag"
inner <- function(mean) {
d <- 1
Cache(rnorm, n = 3, mean = mean, notOlderThan = Sys.time() - 1e5, userTags = innerTag)
}
outer <- function(n) {
Cache(inner, 0.1)
}
aa <- Cache(outer, n = 2, cacheRepo = tmpdir1, userTags = outerTag)
showCache(tmpdir1) # rnorm function has outerTag and innerTag, inner and outer only have outerTag## Cache size:## Total (including Rasters): 756 bytes## Selected objects (not including Rasters): 756 bytes## cacheId tagKey tagValue createdDate
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## cacheId tagKey tagValue createdDate1.10 cacheId
Sometimes, it is not absolutely desirable to maintain the work flow intact because changes that are irrelevant to the analysis, such as changing messages sent to a user, may be changed, without a desire to rerun functions. The cacheId argument is for this. Once a piece of code is run, then the cacheId can be manually extracted (it is reported at the end of a Cache call) and manually placed in the code, passed in as, say, cacheId = "ad184ce64541972b50afd8e7b75f821b".
## [1] -0.6264538
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:7072c305d8c69df0"
## attr(,"call")
## [1] ""# manually look at output attribute which shows cacheId: 7072c305d8c69df0
Cache(rnorm, 1, cacheRepo = tmpdir1, cacheId = "7072c305d8c69df0") # same value## cacheId is same as calculated hash## loading cached result from previous rnorm call.## [1] -0.6264538
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] FALSE
##
## attr(,"tags")
## [1] "cacheId:7072c305d8c69df0"
## attr(,"call")
## [1] ""# override even with different inputs:
Cache(rnorm, 2, cacheRepo = tmpdir1, cacheId = "7072c305d8c69df0")## cacheId is not same as calculated hash. Manually searching for cacheId:7072c305d8c69df0
## loading cached result from previous rnorm call.## [1] -0.6264538
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] FALSE
##
## attr(,"tags")
## [1] "cacheId:7072c305d8c69df0"
## attr(,"call")
## [1] ""1.11 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): 3.3 Kb## Selected objects (not including Rasters): 3.3 Kb