Fast concatenation of data.table columns into one string column

Question

Given an arbitrary list of column names in a data.table, I want to concatenate the contents of those columns into a single string stored in a new column. The column

Answer 1

I don't know how representative the sample data is for your actual data, but in the case of your sampled data you can achieve a substantial performance improvement by only concatenating each unique combination of ConcatCols once instead of multiple times.

That means for the sample data, you'd be looking at ~500k concatenations vs 10 million if you do all the duplicates too.

See the following code and timing example:

system.time({
  setkeyv(DT, ConcatCols)
  DTunique <- unique(DT[, ConcatCols, with=FALSE], by = key(DT))
  DTunique[, State :=  do.call(paste, c(DTunique, sep = ""))]
  DT[DTunique, State := i.State, on = ConcatCols]
})
#       user      system     elapsed 
#      7.448       0.462       4.618 

About half the time is spent on the setkey part. In case your data is already keyed, the time is cut down further to just a bit more than 2 seconds.

setkeyv(DT, ConcatCols)
system.time({
  DTunique <- unique(DT[, ConcatCols, with=FALSE], by = key(DT))
  DTunique[, State :=  do.call(paste, c(DTunique, sep = ""))]
  DT[DTunique, State := i.State, on = ConcatCols]
})
#       user      system     elapsed 
#      2.526       0.280       2.181 

Answer 2

This uses unite from package tidyr. May not be the fastest, but it is probably faster than hand-coded R code.

library(tidyr)
system.time(
  DNew <- DT %>% unite(State, ConcatCols, sep = "", remove = FALSE)
)
# user  system elapsed 
# 14.974   0.183  15.343 

DNew[1:10]
# State   x   y a b c d e f
# 1: foo211621bar foo bar 2 1 1 6 2 1
# 2: foo532735bar foo bar 5 3 2 7 3 5
# 3: foo965776bar foo bar 9 6 5 7 7 6
# 4: foo221284bar foo bar 2 2 1 2 8 4
# 5: foo485976bar foo bar 4 8 5 9 7 6
# 6: foo566778bar foo bar 5 6 6 7 7 8
# 7: foo892636bar foo bar 8 9 2 6 3 6
# 8: foo836672bar foo bar 8 3 6 6 7 2
# 9: foo963926bar foo bar 9 6 3 9 2 6
# 10: foo385216bar foo bar 3 8 5 2 1 6

Answer 3

C to the rescue!

Stealing some code from data.table we can write a C function that works way faster (and could be parallelized to be even faster).

First make sure you have a working C++ toolchain with:

library(inline)

fx <- inline::cfunction( signature(x = "integer", y = "numeric" ) , '
    return ScalarReal( INTEGER(x)[0] * REAL(y)[0] ) ;
' )
fx( 2L, 5 ) #Should return 10

Then this should work (assuming integer-only data, but the code can be extended to other types):

library(inline)
library(data.table)
library(stringi)

header <- "

//Taken from https://github.com/Rdatatable/data.table/blob/master/src/fwrite.c
static inline void reverse(char *upp, char *low)
{
  upp--;
  while (upp>low) {
  char tmp = *upp;
  *upp = *low;
  *low = tmp;
  upp--;
  low++;
  }
}

void writeInt32(int *col, size_t row, char **pch)
{
  char *ch = *pch;
  int x = col[row];
  if (x == INT_MIN) {
  *ch++ = 'N';
  *ch++ = 'A';
  } else {
  if (x<0) { *ch++ = '-'; x=-x; }
  // Avoid log() for speed. Write backwards then reverse when we know how long.
  char *low = ch;
  do { *ch++ = '0'+x%10; x/=10; } while (x>0);
  reverse(ch, low);
  }
  *pch = ch;
}

//end of copied code 

"



 worker_fun <- inline::cfunction( signature(x = "list", preallocated_target = "character", columns = "integer", start_row = "integer", end_row = "integer"), includes = header , "
  const size_t _start_row = INTEGER(start_row)[0] - 1;
  const size_t _end_row = INTEGER(end_row)[0];

  const int max_out_len = 256 * 256; //max length of the final string
  char buffer[max_out_len];
  const size_t num_elements = _end_row - _start_row;
  const size_t num_columns = LENGTH(columns);
  const int * _columns = INTEGER(columns);

  for(size_t i = _start_row; i < _end_row; ++i) {
    char *buf_pos = buffer;
    for(size_t c = 0; c < num_columns; ++c) {
      if(c > 0) {
        buf_pos[0] = ',';
        ++buf_pos;
      }
      writeInt32(INTEGER(VECTOR_ELT(x, _columns[c] - 1)), i, &buf_pos);
    }
    SET_STRING_ELT(preallocated_target,i, mkCharLen(buffer, buf_pos - buffer));
  }
return preallocated_target;
" )

#Test with the same data

RowCount <- 5e6
DT <- data.table(x = "foo",
                 y = "bar",
                 a = sample.int(9, RowCount, TRUE),
                 b = sample.int(9, RowCount, TRUE),
                 c = sample.int(9, RowCount, TRUE),
                 d = sample.int(9, RowCount, TRUE),
                 e = sample.int(9, RowCount, TRUE),
                 f = sample.int(9, RowCount, TRUE))

## Generate an expression to paste an arbitrary list of columns together
ConcatCols <- list("a","b","c","d","e","f")
## Do it 3x as many times
ConcatCols <- c(ConcatCols,ConcatCols,ConcatCols)


ptm <- proc.time()
preallocated_target <- character(RowCount)
column_indices <- sapply(ConcatCols, FUN = function(x) { which(colnames(DT) == x )})
x <- worker_fun(DT, preallocated_target, column_indices, as.integer(1), as.integer(RowCount))
DT[, State := preallocated_target]
proc.time() - ptm

While your (integer only) example runs in about 20s on my PC, this runs in ~5s and can be easily parallelized.

Some things to note:

• The code is not production ready - a lot of sanity checks should be made on the function inputs (especially checking if all columns are the same length, checking column types, preallocated_target size etc.)
• The function puts its output into a preallocated character vector, this is non-standard and ugly (R usually does not have pass-by-reference semantics) but allows for parallelization (see below).
• The last two parameters are start and end rows to be processed, once again, this is for paralellization
• The function accepts column indices not column names. All columns have to be of type integer.
• Except for the input data.table and preallocated_target the inputs have to be integers
• Compilation time for the function is not included (as you should compile it beforehand - maybe even make a package)

Parallelization

EDIT: The approach below would actually fail due to the way clusterExport and R string storage work. Paralellization thus probably needs to be done in C as well, similarly to the way it is achieved in data.table.

Since you cannot pass inline-compiled functions across R processes, paralellization requires some more work. To be able to use the above function in parallel, you either need to compile it separately with R compiler and use dyn.load OR wrap it in a package OR use a forking backend for parallel (I don't have one, forking works only on UNIX).

Running in parallel would then look something like (not tested):

no_cores <- detectCores()

# Initiate cluster
cl <- makeCluster(no_cores)

#Preallocated target and prepare params
num_elements <- length(DT[[1]])
preallocated_target <- character(num_elements)
block_size <- 4096 #No of rows processed at once. Adjust for best performance
column_indices <- sapply(ConcatCols, FUN = function(x) { which(colnames(DT) == x )})

num_blocks <- ceiling(num_elements / block_size)

clusterExport(cl, 
   c("DT","preallocated_target","column_indices","num_elements", "block_size"))
clusterEvalQ(cl, <CODE TO LOAD THE NATIVE FUNCTION HERE>)

parLapply(cl, 1:num_blocks ,
          function(block_id)
          {
            throw_away <- 
              worker_fun(DT, preallocated_target, columns, 
              (block_id - 1) * block_size + 1, min(num_elements, block_id * block_size - 1))
            return(NULL)
          })



stopCluster(cl)