reading very large files

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

--
http://www.ceam.es/pausas

        [[alternative HTML version deleted]]

Hi.

General idea:

1. Open your file as a connection, i.e. con <- file(pathname, open="r")

2. Generate a "row to (file offset, row length) map of your text file,
i.e. a numeric vector 'fileOffsets' and 'rowLengths'.  Use readBin()
for this. You build this up as you go by reading the file in chunks
meaning you can handles files of any size.  You can store this lookup
map to file for your future R sessions.

3. Sample a set of rows r = (r1, r2, ..., rR), i.e. rows =
sample(length(fileOffsets)).

4. Look up the file offsets and row lengths for these rows, i.e.
offsets = fileOffsets[rows].  lengths = rowLengths[rows].

5. In case your subset of rows is not ordered, it is wise to order
them first to speed up things.  If order is important, keep track of
the ordering and re-order them at then end.

6. For each row r, use seek(con=con, where=offsets[r]) to jump to the
start of the row.  Use readBin(..., n=lengths[r]) to read the data.

7. Repeat from (3).

/Henrik

On 2/2/07, juli g. pausas <pausas at gmail.com> wrote:

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

--
http://www.ceam.es/pausas

        [[alternative HTML version deleted]]

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

3000 * 450000

input <- file("/tempxx.txt", "r")
sel <- 3000
remaining <- 200000
# get the records numbers to select
recs <- sort(sample(1:remaining, sel))
# compute number to skip on each read; account for the record just read
skip <- diff(c(1, recs)) - 1
# allocate my data
mysel <- vector('character', sel)
system.time({

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

--
http://www.ceam.es/pausas

       [[alternative HTML version deleted]]

Hi.

General idea:

1. Open your file as a connection, i.e. con <- file(pathname, open="r")

2. Generate a "row to (file offset, row length) map of your text file,
i.e. a numeric vector 'fileOffsets' and 'rowLengths'.  Use readBin()
for this. You build this up as you go by reading the file in chunks
meaning you can handles files of any size.  You can store this lookup
map to file for your future R sessions.

3. Sample a set of rows r = (r1, r2, ..., rR), i.e. rows =
sample(length(fileOffsets)).

4. Look up the file offsets and row lengths for these rows, i.e.
offsets = fileOffsets[rows].  lengths = rowLengths[rows].

5. In case your subset of rows is not ordered, it is wise to order
them first to speed up things.  If order is important, keep track of
the ordering and re-order them at then end.

6. For each row r, use seek(con=con, where=offsets[r]) to jump to the
start of the row.  Use readBin(..., n=lengths[r]) to read the data.

7. Repeat from (3).

/Henrik

On 2/2/07, juli g. pausas <pausas at gmail.com> wrote:

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

--
http://www.ceam.es/pausas

        [[alternative HTML version deleted]]

On Fri, 2007-02-02 at 18:40 +0100, juli g. pausas wrote:

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

Juli,

I don't have a file to test this on, so caveat emptor.

The problem with the approach above, is that you are re-reading the
source file, once per line, or 3000 times.  In addition, each read is
likely going through half the file on average to locate the randomly
selected line. Thus, the reality is that you are probably reading on the
order of:

3000 * 450000

[1] 1.35e+09

lines in the file, which of course if going to be quite slow.

In addition, you are also writing to the target file 3000 times.

The basic premise with this approach below, is that you are in effect
creating a sequential file cache in an R object. Reading large chunks of
the source file into the cache. Then randomly selecting rows within the
cache and then writing out the selected rows.

Thus, if you can read 100,000 rows at once, you would have 9 reads of
the source file, and 9 writes of the target file.

The key thing here is to ensure that the offsets within the cache and
the corresponding random row values are properly set.

Here's the code:

# Generate the random values
sel <- sample(1:900000, 3000)

# Set up a sequence for the cache chunks
# Presume you can read 100,000 rows at once
Cuts <- seq(0, 900000, 100000)

# Loop over the length of Cuts, less 1
for (i in seq(along = Cuts[-1]))
{
  # Get a 100,000 row chunk, skipping rows
  # as appropriate for each subsequent chunk
  Chunk <- scan("myfile", what = character(), sep = "\n", 
                 skip = Cuts[i], nlines = 100000)

  # set up a row sequence for the current 
  # chunk
  Rows <- (Cuts[i] + 1):(Cuts[i + 1])

  # Are any of the random values in the 
  # current chunk?
  Chunk.Sel <- sel[which(sel %in% Rows)]

  # If so, get them 
  if (length(Chunk.Sel) > 0)
  {
    Write.Rows <- Chunk[sel - Cuts[i]]

    # Now write them out
    write(Write.Rows, "myfile_short", append = TRUE)
  }
}


As noted, I have not tested this, so there may yet be additional ways to
save time with file seeks, etc.

On Fri, 2007-02-02 at 12:32 -0600, Marc Schwartz wrote:

Juli,

I don't have a file to test this on, so caveat emptor.

The problem with the approach above, is that you are re-reading the
source file, once per line, or 3000 times.  In addition, each read is
likely going through half the file on average to locate the randomly
selected line. Thus, the reality is that you are probably reading on the
order of:

3000 * 450000

[1] 1.35e+09

lines in the file, which of course if going to be quite slow.

In addition, you are also writing to the target file 3000 times.

The basic premise with this approach below, is that you are in effect
creating a sequential file cache in an R object. Reading large chunks of
the source file into the cache. Then randomly selecting rows within the
cache and then writing out the selected rows.

Thus, if you can read 100,000 rows at once, you would have 9 reads of
the source file, and 9 writes of the target file.

The key thing here is to ensure that the offsets within the cache and
the corresponding random row values are properly set.

Here's the code:

# Generate the random values
sel <- sample(1:900000, 3000)

# Set up a sequence for the cache chunks
# Presume you can read 100,000 rows at once
Cuts <- seq(0, 900000, 100000)

# Loop over the length of Cuts, less 1
for (i in seq(along = Cuts[-1]))
{
  # Get a 100,000 row chunk, skipping rows
  # as appropriate for each subsequent chunk
  Chunk <- scan("myfile", what = character(), sep = "\n", 
                 skip = Cuts[i], nlines = 100000)

  # set up a row sequence for the current 
  # chunk
  Rows <- (Cuts[i] + 1):(Cuts[i + 1])

  # Are any of the random values in the 
  # current chunk?
  Chunk.Sel <- sel[which(sel %in% Rows)]

  # If so, get them 
  if (length(Chunk.Sel) > 0)
  {
    Write.Rows <- Chunk[sel - Cuts[i]]

Quick typo correction:

The last line above should be:

      Write.Rows <- Chunk[sel - Cuts[i], ]

    # Now write them out
    write(Write.Rows, "myfile_short", append = TRUE)
  }
}

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
 trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
 write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

--
 http://www.ceam.es/pausas

Thank so much for your help and comments.
The approach proposed by Jim Holtman was the simplest and fastest. The
approach by Marc Schwartz also worked (after a very small
modification).

It is clear that a good knowledge of R save a lot of time!! I've been
able to do in few minutes a process that was only 1/4th done after 25
h!

Many thanks

Juli

R.version

On Sat, 2007-02-03 at 19:06 +0100, juli g. pausas wrote:

Thank so much for your help and comments.
The approach proposed by Jim Holtman was the simplest and fastest. The
approach by Marc Schwartz also worked (after a very small
modification).

It is clear that a good knowledge of R save a lot of time!! I've been
able to do in few minutes a process that was only 1/4th done after 25
h!

Many thanks

Juli

Juli,

Just out of curiosity, what change did you make?

Also, what were the running times for the solutions?

Regards,

Marc