Tricks to manage the available memory in an R sess

Question

What tricks do people use to manage the available memory of an interactive R session? I use the functions below [based on postings by Petr Pikal and David Hinds to the r-help list in 2004] to list (and/or sort) the largest objects and to occassionally rm() some of them. But by far the most effective solution was ... to run under 64-bit Linux with ample memory.

Any other nice tricks folks want to share? One per post, please.

# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
    obj.size <- napply(names, object.size)
    obj.dim <- t(napply(names, function(x)
                        as.numeric(dim(x))[1:2]))
    vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
    obj.dim[vec, 1] <- napply(names, length)[vec]
    out <- data.frame(obj.type, obj.size, obj.dim)
    names(out) <- c("Type", "Size", "Rows", "Columns")
    if (!missing(order.by))
        out <- out[order(out[[order.by]], decreasing=decreasing), ]
    if (head)
        out <- head(out, n)
    out
}
# shorthand
lsos <- function(..., n=10) {
    .ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}

Answer 1

Running

for (i in 1:10) 
    gc(reset = T)

from time to time also helps R to free unused but still not released memory.

Answer 2

Ensure you record your work in a reproducible script. From time-to-time, reopen R, then source() your script. You'll clean out anything you're no longer using, and as an added benefit will have tested your code.

Answer 3

I love Dirk's .ls.objects() script but I kept squinting to count characters in the size column. So I did some ugly hacks to make it present with pretty formatting for the size:

.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
    obj.size <- napply(names, object.size)
    obj.prettysize <- sapply(obj.size, function(r) prettyNum(r, big.mark = ",") )
    obj.dim <- t(napply(names, function(x)
                        as.numeric(dim(x))[1:2]))
    vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
    obj.dim[vec, 1] <- napply(names, length)[vec]
    out <- data.frame(obj.type, obj.size,obj.prettysize, obj.dim)
    names(out) <- c("Type", "Size", "PrettySize", "Rows", "Columns")
    if (!missing(order.by))
        out <- out[order(out[[order.by]], decreasing=decreasing), ]
        out <- out[c("Type", "PrettySize", "Rows", "Columns")]
        names(out) <- c("Type", "Size", "Rows", "Columns")
    if (head)
        out <- head(out, n)
    out
}

Answer 4

With only 4GB of RAM (running Windows 10, so make that about 2 or more realistically 1GB) I've had to be real careful with the allocation.

I use data.table almost exclusively.

The 'fread' function allows you to subset information by field names on import; only import the fields that are actually needed to begin with. If you're using base R read, null the spurious columns immediately after import.

As 42- suggests, where ever possible I will then subset within the columns immediately after importing the information.

I frequently rm() objects from the environment as soon as they're no longer needed, e.g. on the next line after using them to subset something else, and call gc().

'fread' and 'fwrite' from data.table can be very fast by comparison with base R reads and writes.

As kpierce8 suggests, I almost always fwrite everything out of the environment and fread it back in, even with thousand / hundreds of thousands of tiny files to get through. This not only keeps the environment 'clean' and keeps the memory allocation low but, possibly due to the severe lack of RAM available, R has a propensity for frequently crashing on my computer; really frequently. Having the information backed up on the drive itself as the code progresses through various stages means I don't have to start right from the beginning if it crashes.

As of 2017, I think the fastest SSDs are running around a few GB per second through the M2 port. I have a really basic 50GB Kingston V300 (550MB/s) SSD that I use as my primary disk (has Windows and R on it). I keep all the bulk information on a cheap 500GB WD platter. I move the data sets to the SSD when I start working on them. This, combined with 'fread'ing and 'fwrite'ing everything has been working out great. I've tried using 'ff' but prefer the former. 4K read/write speeds can create issues with this though; backing up a quarter of a million 1k files (250MBs worth) from the SSD to the platter can take hours. As far as I'm aware, there isn't any R package available yet that can automatically optimise the 'chunkification' process; e.g. look at how much RAM a user has, test the read/write speeds of the RAM / all the drives connected and then suggest an optimal 'chunkification' protocol. This could produce some significant workflow improvements / resource optimisations; e.g. split it to ... MB for the ram -> split it to ... MB for the SSD -> split it to ... MB on the platter -> split it to ... MB on the tape. It could sample data sets beforehand to give it a more realistic gauge stick to work from.

A lot of the problems I've worked on in R involve forming combination and permutation pairs, triples etc, which only makes having limited RAM more of a limitation as they will often at least exponentially expand at some point. This has made me focus a lot of attention on the quality as opposed to quantity of information going into them to begin with, rather than trying to clean it up afterwards, and on the sequence of operations in preparing the information to begin with (starting with the simplest operation and increasing the complexity); e.g. subset, then merge / join, then form combinations / permutations etc.

There do seem to be some benefits to using base R read and write in some instances. For instance, the error detection within 'fread' is so good it can be difficult trying to get really messy information into R to begin with to clean it up. Base R also seems to be a lot easier if you're using Linux. Base R seems to work fine in Linux, Windows 10 uses ~20GB of disc space whereas Ubuntu only needs a few GB, the RAM needed with Ubuntu is slightly lower. But I've noticed large quantities of warnings and errors when installing third party packages in (L)Ubuntu. I wouldn't recommend drifting too far away from (L)Ubuntu or other stock distributions with Linux as you can loose so much overall compatibility it renders the process almost pointless (I think 'unity' is due to be cancelled in Ubuntu as of 2017). I realise this won't go down well with some Linux users but some of the custom distributions are borderline pointless beyond novelty (I've spent years using Linux alone).

Hopefully some of that might help others out.

Answer 5

This adds nothing to the above, but is written in the simple and heavily commented style that I like. It yields a table with the objects ordered in size , but without some of the detail given in the examples above:

#Find the objects       
MemoryObjects = ls()    
#Create an array
MemoryAssessmentTable=array(NA,dim=c(length(MemoryObjects),2))
#Name the columns
colnames(MemoryAssessmentTable)=c("object","bytes")
#Define the first column as the objects
MemoryAssessmentTable[,1]=MemoryObjects
#Define a function to determine size        
MemoryAssessmentFunction=function(x){object.size(get(x))}
#Apply the function to the objects
MemoryAssessmentTable[,2]=t(t(sapply(MemoryAssessmentTable[,1],MemoryAssessmentFunction)))
#Produce a table with the largest objects first
noquote(MemoryAssessmentTable[rev(order(as.numeric(MemoryAssessmentTable[,2]))),])

Answer 6

That's a good trick.

One other suggestion is to use memory efficient objects wherever possible: for instance, use a matrix instead of a data.frame.

This doesn't really address memory management, but one important function that isn't widely known is memory.limit(). You can increase the default using this command, memory.limit(size=2500), where the size is in MB. As Dirk mentioned, you need to be using 64-bit in order to take real advantage of this.