ddR (version 0.1.1)

dmapply: Distributed version of mapply. Similar to R's 'mapply', it allows a multivariate function, FUN, to be applied to several inputs. Unlike standard mapply, it always returns a distributed object.

Description

Though dmapply is modeled after mapply, there are several important differences, as evident in the parameters described below.

Usage

dmapply(FUN, ..., MoreArgs = list(), output.type = c("dlist", "dframe",
  "darray", "sparse_darray"), nparts = NULL, combine = c("default", "c",
  "rbind", "cbind"))

Arguments

FUN

function to apply, found via 'match.fun'.

...

arguments to vectorize over (vectors or lists of strictly positive length, or all of zero length). These may also be distributed objects, such as dlists, darrays, and dframes.

MoreArgs

a list of other arguments to 'FUN'.

output.type

the output type of the distributed object. The default value of "dlist" means that the result of dmapply will be stored in a distributed list. "darray" will make dmapply return a darray, just as "dframe" will make it return a dframe. "sparse_darray" resul

nparts

a 1d or 2d numeric value to specify how the output should be partitioned. dlists only have one-dimensional partitioning, whereas darrays and dframes have two (representing the number partitions across the vertical and horizontal dimensions).

combine

for dframes and darrays, it specifies how the results of dmapply are combined within each partition (if each partition contains more than one result). If "rbind", the results are stitched using rbind; if "cbind", cbind is used. If the value is "c", the re

Value

• A dlist, darray, or dframe (depending on the value of output.type), with number of partitions equal to nparts

References

Prasad, S., Fard, A., Gupta, V., Martinez, J., LeFevre, J., Xu, V., Hsu, M., Roy, I. Large scale predictive analytics in Vertica: Fast data transfer, distributed model creation and in-database prediction. _Sigmod 2015_, 1657-1668.

Venkataraman, S., Bodzsar, E., Roy, I., AuYoung, A., and Schreiber, R. (2013) Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices. _EuroSys 2013_, 197-210.

Homepage: https://github.com/vertica/ddR

Examples

## A dlist created by adding two input vectors
a <- dmapply(function(x,y) x+y, 1:5, 2:6, nparts=3)
collect(a)

##Create a darray with 4 partitions. Partitions are stitched in 2x2 fashion,
# meaning the overall dims of the darray will be 4x4.
b <- dmapply(function(x) matrix(x,2,2), 1:4,output.type="darray",combine="rbind",nparts=c(2,2))
collect(b,1) #First partition
collect(b)