Big caching of hashing, sorting, ordering

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
...: passed to 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.

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.

Details

The result of relative expensive operations hashmap(), bit::ramsort(), bit::ramsortorder(), and bit::ramorder() can be stored in a cache in order to avoid multiple excutions. Unless in very specific situations, the recommended method is hashsortorder only.

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.

Examples

  x <- as.integer64(sample(c(rep(NA, 9), 1:9), 32, TRUE))
  sortordercache(x)