Fastest Matlab file reading?
Solution 1:
Problem statement
This is a common struggle, and there is nothing like a test to answer. Here are my assumptions:
A well formatted ASCII file, containing two columns of numbers. No headers, no inconsistent lines etc.
The method must scale to reading files that are too large to be contained in memory, (although my patience is limited, so my test file is only 500,000 lines).
The actual operation (what the OP calls "do stuff with nums") must be performed one row at a time, cannot be vectorized.
Discussion
With that in mind, the answers and comments seem to be encouraging efficiency in three areas:
- reading the file in larger batches
- performing the string to number conversion more efficiently (either via batching, or using better functions)
- making the actual processing more efficient (which I have ruled out via rule 3, above).
Results
I put together a quick script to test out the ingestion speed (and consistency of result) of 6 variations on these themes. The results are:
- Initial code. 68.23 sec. 582582 check
- Using sscanf, once per line. 27.20 sec. 582582 check
- Using fscanf in large batches. 8.93 sec. 582582 check
- Using textscan in large batches. 8.79 sec. 582582 check
- Reading large batches into memory, then sscanf. 8.15 sec. 582582 check
- Using java single line file reader and sscanf on single lines. 63.56 sec. 582582 check
- Using java single item token scanner. 81.19 sec. 582582 check
- Fully batched operations (non-compliant). 1.02 sec. 508680 check (violates rule 3)
Summary
More than half of the original time (68 -> 27 sec) was consumed with inefficiencies in the str2num call, which can be removed by switching the sscanf.
About another 2/3 of the remaining time (27 -> 8 sec) can be reduced by using larger batches for both file reading and string to number conversions.
If we are willing to violate rule number three in the original post, another 7/8 of the time can be reduced by switching to a fully numeric processing. However, some algorithms do not lend themselves to this, so we leave it alone. (Not the "check" value does not match for the last entry.)
Finally, in direct contradiction a previous edit of mine within this response, no savings are available by switching the the available cached Java, single line readers. In fact that solution is 2 -- 3 times slower than the comparable single line result using native readers. (63 vs. 27 seconds).
Sample code for all of the solutions described above are included below.
Sample code
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Create a test file
cd(tempdir);
fName = 'demo_file.txt';
fid = fopen(fName,'w');
for ixLoop = 1:5
d = randi(1e6, 1e5,2);
fprintf(fid, '%d, %d \n',d);
end
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Initial code
CHECK = 0;
tic;
fid = fopen('demo_file.txt');
tline = fgetl(fid);
while ischar(tline)
nums = str2num(tline);
CHECK = round((CHECK + mean(nums) ) /2);
tline = fgetl(fid);
end
fclose(fid);
t = toc;
fprintf(1,'Initial code. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Using sscanf, once per line
CHECK = 0;
tic;
fid = fopen('demo_file.txt');
tline = fgetl(fid);
while ischar(tline)
nums = sscanf(tline,'%d, %d');
CHECK = round((CHECK + mean(nums) ) /2);
tline = fgetl(fid);
end
fclose(fid);
t = toc;
fprintf(1,'Using sscanf, once per line. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Using fscanf in large batches
CHECK = 0;
tic;
bufferSize = 1e4;
fid = fopen('demo_file.txt');
scannedData = reshape(fscanf(fid, '%d, %d', bufferSize),2,[])' ;
while ~isempty(scannedData)
for ix = 1:size(scannedData,1)
nums = scannedData(ix,:);
CHECK = round((CHECK + mean(nums) ) /2);
end
scannedData = reshape(fscanf(fid, '%d, %d', bufferSize),2,[])' ;
end
fclose(fid);
t = toc;
fprintf(1,'Using fscanf in large batches. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Using textscan in large batches
CHECK = 0;
tic;
bufferSize = 1e4;
fid = fopen('demo_file.txt');
scannedData = textscan(fid, '%d, %d \n', bufferSize) ;
while ~isempty(scannedData{1})
for ix = 1:size(scannedData{1},1)
nums = [scannedData{1}(ix) scannedData{2}(ix)];
CHECK = round((CHECK + mean(nums) ) /2);
end
scannedData = textscan(fid, '%d, %d \n', bufferSize) ;
end
fclose(fid);
t = toc;
fprintf(1,'Using textscan in large batches. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Reading in large batches into memory, incrementing to end-of-line, sscanf
CHECK = 0;
tic;
fid = fopen('demo_file.txt');
bufferSize = 1e4;
eol = sprintf('\n');
dataBatch = fread(fid,bufferSize,'uint8=>char')';
dataIncrement = fread(fid,1,'uint8=>char');
while ~isempty(dataIncrement) && (dataIncrement(end) ~= eol) && ~feof(fid)
dataIncrement(end+1) = fread(fid,1,'uint8=>char'); %This can be slightly optimized
end
data = [dataBatch dataIncrement];
while ~isempty(data)
scannedData = reshape(sscanf(data,'%d, %d'),2,[])';
for ix = 1:size(scannedData,1)
nums = scannedData(ix,:);
CHECK = round((CHECK + mean(nums) ) /2);
end
dataBatch = fread(fid,bufferSize,'uint8=>char')';
dataIncrement = fread(fid,1,'uint8=>char');
while ~isempty(dataIncrement) && (dataIncrement(end) ~= eol) && ~feof(fid)
dataIncrement(end+1) = fread(fid,1,'uint8=>char');%This can be slightly optimized
end
data = [dataBatch dataIncrement];
end
fclose(fid);
t = toc;
fprintf(1,'Reading large batches into memory, then sscanf. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Using Java single line readers + sscanf
CHECK = 0;
tic;
bufferSize = 1e4;
reader = java.io.LineNumberReader(java.io.FileReader('demo_file.txt'),bufferSize );
tline = char(reader.readLine());
while ~isempty(tline)
nums = sscanf(tline,'%d, %d');
CHECK = round((CHECK + mean(nums) ) /2);
tline = char(reader.readLine());
end
reader.close();
t = toc;
fprintf(1,'Using java single line file reader and sscanf on single lines. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Using Java scanner for file reading and string conversion
CHECK = 0;
tic;
jFile = java.io.File('demo_file.txt');
scanner = java.util.Scanner(jFile);
scanner.useDelimiter('[\s\,\n\r]+');
while scanner.hasNextInt()
nums = [scanner.nextInt() scanner.nextInt()];
CHECK = round((CHECK + mean(nums) ) /2);
end
scanner.close();
t = toc;
fprintf(1,'Using java single item token scanner. %3.2f sec. %d check \n', t, CHECK);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Reading in large batches into memory, vectorized operations (non-compliant solution)
CHECK = 0;
tic;
fid = fopen('demo_file.txt');
bufferSize = 1e4;
eol = sprintf('\n');
dataBatch = fread(fid,bufferSize,'uint8=>char')';
dataIncrement = fread(fid,1,'uint8=>char');
while ~isempty(dataIncrement) && (dataIncrement(end) ~= eol) && ~feof(fid)
dataIncrement(end+1) = fread(fid,1,'uint8=>char'); %This can be slightly optimized
end
data = [dataBatch dataIncrement];
while ~isempty(data)
scannedData = reshape(sscanf(data,'%d, %d'),2,[])';
CHECK = round((CHECK + mean(scannedData(:)) ) /2);
dataBatch = fread(fid,bufferSize,'uint8=>char')';
dataIncrement = fread(fid,1,'uint8=>char');
while ~isempty(dataIncrement) && (dataIncrement(end) ~= eol) && ~feof(fid)
dataIncrement(end+1) = fread(fid,1,'uint8=>char');%This can be slightly optimized
end
data = [dataBatch dataIncrement];
end
fclose(fid);
t = toc;
fprintf(1,'Fully batched operations. %3.2f sec. %d check \n', t, CHECK);
(original answer)
To expand on the point made by Ben ... your bottleneck will always be file I/O if you are reading these files line by line.
I understand that sometimes you cannot fit a whole file into memory. I typically read in a large batch of characters (1e5, 1e6 or thereabouts, depending on the memory of your system). Then I either read additional single characters (or back off single characters) to get a round number of lines, and then run your string parsing (e.g. sscanf).
Then if you want you can process the resulting large matrix one row at a time, before repeating the process until you read the end of the file.
It's a little bit tedious, but not that hard. I typically see 90% plus improvement in speed over single line readers.
(terrible idea using Java batched line readers removed in shame)
Solution 2:
I have had good results (speedwise) using memmapfile(). This minimises the amount of memory data copying, and makes use of the kernel's IO buffering. You need enough free address space (though not actual free memory) to map the entire file, and enough free memory to hold the output variable (obviously!)
The example code below reads a text file into a two-column matrix data of int32 type.
fname = 'file.txt';
fstats = dir(fname);
% Map the file as one long character string
m = memmapfile(fname, 'Format', {'uint8' [ 1 fstats.bytes] 'asUint8'});
textdata = char(m.Data(1).asUint8);
% Use textscan() to parse the string and convert to an int32 matrix
data = textscan(textdata, '%d %d', 'CollectOutput', 1);
data = data{:};
% Tidy up!
clear('m')
You may need to fiddle with the parameters to textscan() to get exactly what you want - see the online docs.
Solution 3:
Even if you can't fit the whole file in memory, you should read a large batch using the matrix read functions.
Maybe you can even use vector operations for some of the data processing, which would speed things along further.
Solution 4:
I have found that MATLAB reads csv files significantly faster than text files, so if it's possible to convert your text file to csv using some other software, it may significantly speed up Matlab's operations.