When building a web service, it is desirable to save commonly requested products in a cache directory to avoid time wasted reproducing them unnecessarily. Because the cache has finite disk space allocated to it, the cache should be routinely purged of old or outdated files to make room new ones. The manageCache() utility function simplifies this process.
Lets first make a cache directory and put some data products in it.
# Create a cache directory
CACHE_DIR <- file.path(tempdir(), 'cache')
if ( file.exists(CACHE_DIR) == FALSE ) {
dir.create(CACHE_DIR)
}
# Add a few files to the cache
write.csv(matrix(1,400,500), file=file.path(CACHE_DIR,'m1.csv'))
Sys.sleep(1) # wait a bit between each to give them different mtimes
write.csv(matrix(2,400,500), file=file.path(CACHE_DIR,'m2.csv'))
Sys.sleep(1)
write.csv(matrix(3,400,500), file=file.path(CACHE_DIR,'m3.csv'))
Sys.sleep(1)
write.csv(matrix(4,400,500), file=file.path(CACHE_DIR,'m4.csv'))We can look in our new cache directory and see the four files we just added. The directory contains about 1.5 MB of data.
cachedFiles <- list.files(CACHE_DIR, full.names = TRUE)
infoDF <- file.info(cachedFiles)
cacheSize = (sum(infoDF$size) / 1e6) # in MB
print(list.files(CACHE_DIR))
#> [1] "m1.csv" "m2.csv" "m3.csv" "m4.csv"
sprintf("Cache size = %s MB", cacheSize)
#> [1] "Cache size = 1.622748 MB"In order to simulate file requests, lets read two of them to update their access time.
# Access two of the files, updating their atime
invisible( read.csv(file.path(CACHE_DIR, 'm1.csv')) )
invisible( read.csv(file.path(CACHE_DIR, 'm2.csv')) )Now, lets use manageCache() to get our cache down to 1 MB.
# Use manageCache() to get cache to 1 MB
library(MazamaCoreUtils)
#> Loading required package: futile.logger
manageCache(CACHE_DIR, extensions = 'csv', maxCacheSize = 1)When we check our cache again, we will see that the two files with the oldest access times are gone and the cache size is now under 1 MB.
# Check cache contents and total size again
cachedFiles <- list.files(CACHE_DIR, full.names = TRUE)
infoDF <- file.info(cachedFiles)
cacheSize = (sum(infoDF$size) / 1e6) # in MB
print(list.files(CACHE_DIR))
#> [1] "m1.csv" "m2.csv"
sprintf("Cache size = %s MB", cacheSize)
#> [1] "Cache size = 0.811374 MB"Web services that provide access to real-time data often generate products that have an expiration date. Files older than a specific number of days or hours should be removed from the cache because they no longer represent the current status.
Removing stale files can also help to keep the cache much smaller than the absolute maximum cache size, enhancing overall performance.
Stale files – files that haven’t been modified in a while – can be removed regardless of cache size with the maxFileAge parameter. When this is set, files with an mtime older than maxFileAge will be removed before any test of the maxCacheSize. Fractional days are allowed.
You can remove standard products in the cache that haven’t been modified in the last 3 hours with:
manageCache(CACHE_DIR, maxFileAge = 3/24)In the case of a product cache, the most typical behavior will be to sort files based on last access time. The manageCache() function uses sortBy = "atime" as the default. It is also possible to sort based on modification time mtime or change time ctime.
The use case scenario for sortBy = "mtime" might involve files that are considered stale if the contents aren’t updated.
A use case scenario for sortBy = "ctime" is not clear.