hashcache {bit64} R Documentation

## Big caching of hashing, sorting, ordering

### Description

Functions to create cache that accelerates many operations

### Usage

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")


### Arguments

 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 NAs. If we know we don't have NAs, this may speed-up. Note that you risk a crash if there are unexpected NAs with has.na=FALSE 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 hashmap

### Details

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.

### Value

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

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.

### Author(s)

Jens Oehlschlägel <Jens.Oehlschlaegel@truecluster.com>

cache for caching functions and nunique for methods bennefitting from small caches
	x <- as.integer64(sample(c(rep(NA, 9), 1:9), 32, TRUE))