Big caching of hashing, sorting, ordering
Functions to create cache that accelerates many operations
hashcache(x, nunique=NULL, ...) sortcache(x, has.na = NULL) sortordercache(x, has.na = NULL, stable = NULL) ordercache(x, has.na = NULL, stable = NULL, optimize = "time")
x |
an atomic vector (note that currently only integer64 is supported) |
nunique |
giving correct number of unique elements can help reducing the size of the hashmap |
has.na |
boolean scalar defining whether the input vector might contain |
stable |
boolean scalar defining whether stable sorting is needed. Allowing non-stable may speed-up. |
optimize |
by default ramsort optimizes for 'time' which requires more RAM, set to 'memory' to minimize RAM requirements and sacrifice speed |
... |
passed to |
The result of relative expensive operations hashmap, ramsort, ramsortorder and ramorder can be stored in a cache in order to avoid multiple excutions. Unless in very specific situations, the recommended method is hashsortorder only.
x with a cache that contains the result of the expensive operations, possible together with small derived information (such as nunique.integer64) and previously cached results.
Note that we consider storing the big results from sorting and/or ordering as a relevant side-effect, and therefore storing them in the cache should require a conscious decision of the user.
Jens Oehlschlägel <Jens.Oehlschlaegel@truecluster.com>
x <- as.integer64(sample(c(rep(NA, 9), 1:9), 32, TRUE)) sortordercache(x)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.