RealizationSink objects

Description

Get or set the realization backend for the current session with getRealizationBackend or setRealizationBackend.

Advanced users: Create a RealizationSink object with the backend-agnostic RealizationSink() constructor. Use this object to write an array-like object block by block to disk (or to memory).

Usage

supportedRealizationBackends()
getRealizationBackend()
setRealizationBackend(BACKEND=NULL)

RealizationSink(dim, dimnames=NULL, type="double")

Arguments

Details

The realization backend controls where/how realization happens e.g. as an ordinary array if set to NULL, as an RleArray object if set to "RleArray", or as an HDF5Array object if set to "HDF5Array".

Value

supportedRealizationBackends: A data frame with 1 row per supported realization backend.

getRealizationBackend: NULL or a single string specifying the name of the realization backend currently in use.

RealizationSink: A RealizationSink object for the current realization backend.

See Also

• write_block.

• blockGrid to define grids to use in the context of block processing of array-like objects.

• DelayedArray objects.

• RleArray objects.

• HDF5Array objects in the HDF5Array package.

• HDF5-dump-management in the HDF5Array package to control the location and physical properties of automatically created HDF5 datasets.

• array objects in base R.

Examples

## ---------------------------------------------------------------------
## A. supportedRealizationBackends() AND FAMILY
## ---------------------------------------------------------------------
supportedRealizationBackends()
getRealizationBackend()  # backend is set to NULL

setRealizationBackend("HDF5Array")
supportedRealizationBackends()
getRealizationBackend()  # backend is set to "HDF5Array"

## ---------------------------------------------------------------------
## B. A SIMPLE (AND VERY ARTIFICIAL) RealizationSink() EXAMPLE
## ---------------------------------------------------------------------
getHDF5DumpChunkLength()
setHDF5DumpChunkLength(500L)
getHDF5DumpChunkShape()

sink <- RealizationSink(c(120L, 50L))
dim(sink)
chunkdim(sink)

grid <- blockGrid(sink, block.length=600)
for (bid in seq_along(grid)) {
    viewport <- grid[[bid]]
    block <- 101 * bid + runif(length(viewport))
    dim(block) <- dim(viewport)
    write_block(sink, viewport, block)
}

## Always close the RealizationSink object when you are done writing to
## it and before coercing it to DelayedArray:
close(sink)
A <- as(sink, "DelayedArray")
A

## ---------------------------------------------------------------------
## C. AN ADVANCED EXAMPLE OF USER-IMPLEMENTED BLOCK PROCESSING USING
##    colGrid() AND A REALIZATION SINK
## ---------------------------------------------------------------------
## Say we have 2 matrices with the same number of columns. Each column
## represents a biological sample:
library(HDF5Array)
R <- as(matrix(runif(75000), ncol=1000), "HDF5Array")   # 75 rows
G <- as(matrix(runif(250000), ncol=1000), "HDF5Array")  # 250 rows

## Say we want to compute the matrix U obtained by applying the same
## binary functions FUN() to all samples i.e. U is defined as:
##
##   U[ , j] <- FUN(R[ , j], G[ , j]) for 1 <= j <= 1000
##
## Note that FUN() should return a vector of constant length, say 200,
## so U will be a 200x1000 matrix. A naive implementation would be:
##
##   pFUN <- function(r, g) {
##       stopifnot(ncol(r) == ncol(g))  # sanity check
##       sapply(seq_len(ncol(r)), function(j) FUN(r[ , j], g[ , j]))
##   }
##
## But because U is going to be too big to fit in memory, we can't
## just do pFUN(R, G). So we want to compute U block by block and
## write the blocks to disk as we go. The blocks will be made of full
## columns. Also since we need to walk on 2 matrices at the same time
## (R and G), we can't use blockApply() or blockReduce() so we'll use
## a "for" loop.

## Before we get to the "for" loop, we need 4 things:

## 1) Two grids of blocks, one on R and one on G. The blocks in the
##    2 grids must contain the same number of columns. We arbitrarily
##    choose to use blocks of 150 columns:
R_grid <- colGrid(R, ncol=150)
G_grid <- colGrid(G, ncol=150)

## 2) The function pFUN(). It will take 2 blocks as input, 1 from R
##    and 1 from G, apply FUN() to all the samples in the blocks,
##    and return a matrix with one columns per sample:
pFUN <- function(r, g) {
    stopifnot(ncol(r) == ncol(g))  # sanity check
    ## Return a matrix with 200 rows with random values. Completely
    ## artificial sorry. A realistic example would actually need to
    ## apply the same binary function to r[ ,j] and g[ , j] for
    ## 1 <= j <= ncol(r).
    matrix(runif(200 * ncol(r)), nrow=200)
}

## 3) A RealizationSink object where to write the matrices returned
##    by pFUN() as we go. Note that instead of creating a realization
##    sink by calling a backend-specific sink constructor (e.g.
##    HDF5Array:::HDF5RealizationSink), we use the backend-agnostic
##    constructor RealizationSink() and set the current realization
##    backend to HDF5:

setRealizationBackend("HDF5Array")
U_sink <- RealizationSink(c(200L, 1000L))

## 4) Finally, we create a grid on U_sink with blocks that contain the
##    same number of columns as the corresponding blocks in R and G:

U_grid <- colGrid(U_sink, ncol=150)

## Note that the 3 grids should have the same number of blocks:
stopifnot(length(U_grid) == length(R_grid))
stopifnot(length(U_grid) == length(G_grid))

## Now we can procede. We use a "for" loop where we walk on R and G at
## the same time, block by block, apply pFUN(), and write the output
## of pFUN() to U_sink:
for (bid in seq_along(U_grid)) {
    R_block <- read_block(R, R_grid[[bid]])
    G_block <- read_block(G, G_grid[[bid]])
    U_block <- pFUN(R_block, G_block)
    write_block(U_sink, U_grid[[bid]], U_block)
}

close(U_sink)
U <- as(U_sink, "DelayedArray")

## A note about parallelization: even though concurrent block reading
## from the same object is supported, concurrent writing to a sink is
## not supported yet. So the above code cannot be parallelized at the
## moment.