I wanted to compare reading lines of string input from stdin using Python and C++ and was shocked to see my C++ code run an order of magnitude slower than the equivalent Python code. Since my C++ is rusty and I’m not yet an expert Pythonista, please tell me if I’m doing something wrong or if I’m misunderstanding something.

(TLDR answer: include the statement: cin.sync_with_stdio(false) or just use fgets instead.

TLDR results: scroll all the way down to the bottom of my question and look at the table.)

C++ code:

#include <iostream>
#include <time.h>

using namespace std;

int main() {
    string input_line;
    long line_count = 0;
    time_t start = time(NULL);
    int sec;
    int lps;

    while (cin) {
        getline(cin, input_line);
        if (!cin.eof())
            line_count++;
    };

    sec = (int) time(NULL) - start;
    cerr << "Read " << line_count << " lines in " << sec << " seconds.";
    if (sec > 0) {
        lps = line_count / sec;
        cerr << " LPS: " << lps << endl;
    } else
        cerr << endl;
    return 0;
}

// Compiled with:
// g++ -O3 -o readline_test_cpp foo.cpp

Python Equivalent:

#!/usr/bin/env python
import time
import sys

count = 0
start = time.time()

for line in  sys.stdin:
    count += 1

delta_sec = int(time.time() - start_time)
if delta_sec >= 0:
    lines_per_sec = int(round(count/delta_sec))
    print("Read {0} lines in {1} seconds. LPS: {2}".format(count, delta_sec,
       lines_per_sec))

Here are my results:

$ cat test_lines | ./readline_test_cpp
Read 5570000 lines in 9 seconds. LPS: 618889

$cat test_lines | ./readline_test.py
Read 5570000 lines in 1 seconds. LPS: 5570000

I should note that I tried this both under Mac OS X v10.6.8 (Snow Leopard) and Linux 2.6.32 (Red Hat Linux 6.2). The former is a MacBook Pro, and the latter is a very beefy server, not that this is too pertinent.

$ for i in {1..5}; do echo "Test run $i at `date`"; echo -n "CPP:"; cat test_lines | ./readline_test_cpp ; echo -n "Python:"; cat test_lines | ./readline_test.py ; done
Test run 1 at Mon Feb 20 21:29:28 EST 2012
CPP:   Read 5570001 lines in 9 seconds. LPS: 618889
Python:Read 5570000 lines in 1 seconds. LPS: 5570000
Test run 2 at Mon Feb 20 21:29:39 EST 2012
CPP:   Read 5570001 lines in 9 seconds. LPS: 618889
Python:Read 5570000 lines in 1 seconds. LPS: 5570000
Test run 3 at Mon Feb 20 21:29:50 EST 2012
CPP:   Read 5570001 lines in 9 seconds. LPS: 618889
Python:Read 5570000 lines in 1 seconds. LPS: 5570000
Test run 4 at Mon Feb 20 21:30:01 EST 2012
CPP:   Read 5570001 lines in 9 seconds. LPS: 618889
Python:Read 5570000 lines in 1 seconds. LPS: 5570000
Test run 5 at Mon Feb 20 21:30:11 EST 2012
CPP:   Read 5570001 lines in 10 seconds. LPS: 557000
Python:Read 5570000 lines in  1 seconds. LPS: 5570000

Tiny benchmark addendum and recap

For completeness, I thought I’d update the read speed for the same file on the same box with the original (synced) C++ code. Again, this is for a 100M line file on a fast disk. Here’s the comparison, with several solutions/approaches:

Implementation      Lines per second
python (default)           3,571,428
cin (default/naive)          819,672
cin (no sync)             12,500,000
fgets                     14,285,714
wc (not fair comparison)  54,644,808

By default, cin is synchronized with stdio, which causes it to avoid any input buffering. If you add this to the top of your main, you should see much better performance:

std::ios_base::sync_with_stdio(false);

Normally, when an input stream is buffered, instead of reading one character at a time, the stream will be read in larger chunks. This reduces the number of system calls, which are typically relatively expensive. However, since the FILE* based stdio and iostreams often have separate implementations and therefore separate buffers, this could lead to a problem if both were used together. For example:

int myvalue1;
cin >> myvalue1;
int myvalue2;
scanf("%d",&myvalue2);

If more input was read by cin than it actually needed, then the second integer value wouldn’t be available for the scanf function, which has its own independent buffer. This would lead to unexpected results.

To avoid this, by default, streams are synchronized with stdio. One common way to achieve this is to have cin read each character one at a time as needed using stdio functions. Unfortunately, this introduces a lot of overhead. For small amounts of input, this isn’t a big problem, but when you are reading millions of lines, the performance penalty is significant.

Fortunately, the library designers decided that you should also be able to disable this feature to get improved performance if you knew what you were doing, so they provided the sync_with_stdio method.

Just out of curiosity I’ve taken a look at what happens under the hood, and I’ve used dtruss/strace on each test.

C++

./a.out < in
Saw 6512403 lines in 8 seconds.  Crunch speed: 814050

syscalls sudo dtruss -c ./a.out < in

CALL                                        COUNT
__mac_syscall                                   1
<snip>
open                                            6
pread                                           8
mprotect                                       17
mmap                                           22
stat64                                         30
read_nocancel                               25958

Python

./a.py < in
Read 6512402 lines in 1 seconds. LPS: 6512402

syscalls sudo dtruss -c ./a.py < in

CALL                                        COUNT
__mac_syscall                                   1
<snip>
open                                            5
pread                                           8
mprotect                                       17
mmap                                           21
stat64                                         29

I’m a few years behind here, but:

In ‘Edit 4/5/6’ of the original post, you are using the construction:

$ /usr/bin/time cat big_file | program_to_benchmark

This is wrong in a couple of different ways:

1. You’re actually timing the execution of `cat`, not your benchmark. The ‘user’ and ‘sys’ CPU usage displayed by `time` are those of `cat`, not your benchmarked program. Even worse, the ‘real’ time is also not necessarily accurate. Depending on the implementation of `cat` and of pipelines in your local OS, it is possible that `cat` writes a final giant buffer and exits long before the reader process finishes its work.

2. Use of `cat` is unnecessary and in fact counterproductive; you’re adding moving parts. If you were on a sufficiently old system (i.e. with a single CPU and — in certain generations of computers — I/O faster than CPU) — the mere fact that `cat` was running could substantially color the results. You are also subject to whatever input and output buffering and other processing `cat` may do. (This would likely earn you a ‘Useless Use Of Cat’ award if I were Randal Schwartz.

A better construction would be:

$ /usr/bin/time program_to_benchmark < big_file

In this statement it is the shell which opens big_file, passing it to your program (well, actually to `time` which then executes your program as a subprocess) as an already-open file descriptor. 100% of the file reading is strictly the responsibility of the program you’re trying to benchmark. This gets you a real reading of its performance without spurious complications.

I will mention two possible, but actually wrong, ‘fixes’ which could also be considered (but I ‘number’ them differently as these are not things which were wrong in the original post):

A. You could ‘fix’ this by timing only your program:

$ cat big_file | /usr/bin/time program_to_benchmark

B. or by timing the entire pipeline:

$ /usr/bin/time sh -c 'cat big_file | program_to_benchmark'

These are wrong for the same reasons as #2: they’re still using `cat` unnecessarily. I mention them for a few reasons:

• they’re more ‘natural’ for people who aren’t entirely comfortable with the I/O redirection facilities of the POSIX shell

• there may be cases where `cat` is needed (e.g.: the file to be read requires some sort of privilege to access, and you do not want to grant that privilege to the program to be benchmarked: `sudo cat /dev/sda | /usr/bin/time my_compression_test –no-output`)

• in practice, on modern machines, the added `cat` in the pipeline is probably of no real consequence

But I say that last thing with some hesitation. If we examine the last result in ‘Edit 5’ —

$ /usr/bin/time cat temp_big_file | wc -l
0.01user 1.34system 0:01.83elapsed 74%CPU ...

— this claims that `cat` consumed 74% of the CPU during the test; and indeed 1.34/1.83 is approximately 74%. Perhaps a run of:

$ /usr/bin/time wc -l < temp_big_file

would have taken only the remaining .49 seconds! Probably not: `cat` here had to pay for the read() system calls (or equivalent) which transferred the file from ‘disk’ (actually buffer cache), as well as the pipe writes to deliver them to `wc`. The correct test would still have had to do those read() calls; only the write-to-pipe and read-from-pipe calls would have been saved, and those should be pretty cheap.

Still, I predict you would be able to measure the difference between `cat file | wc -l` and `wc -l < file` and find a noticeable (2-digit percentage) difference. Each of the slower tests will have paid a similar penalty in absolute time; which would however amount to a smaller fraction of its larger total time.

In fact I did some quick tests with a 1.5 gigabyte file of garbage, on a Linux 3.13 (Ubuntu 14.04) system, obtaining these results (these are actually ‘best of 3’ results; after priming the cache, of course):

$ time wc -l < /tmp/junk
real 0.280s user 0.156s sys 0.124s (total cpu 0.280s)
$ time cat /tmp/junk | wc -l
real 0.407s user 0.157s sys 0.618s (total cpu 0.775s)
$ time sh -c 'cat /tmp/junk | wc -l'
real 0.411s user 0.118s sys 0.660s (total cpu 0.778s)

Notice that the two pipeline results claim to have taken more CPU time (user+sys) than real wall-clock time. This is because I’m using the shell (bash)’s built-in ‘time’ command, which is cognizant of the pipeline; and I’m on a multi-core machine where separate processes in a pipeline can use separate cores, accumulating CPU time faster than realtime. Using /usr/bin/time I see smaller CPU time than realtime — showing that it can only time the single pipeline element passed to it on its command line. Also, the shell’s output gives milliseconds while /usr/bin/time only gives hundredths of a second.

So at the efficiency level of `wc -l`, the `cat` makes a huge difference: 409 / 283 = 1.453 or 45.3% more realtime, and 775 / 280 = 2.768, or a whopping 177% more CPU used! On my random it-was-there-at-the-time test box.

I should add that there is at least one other significant difference between these styles of testing, and I can’t say whether it is a benefit or fault; you have to decide this yourself:

When you run `cat big_file | /usr/bin/time my_program`, your program is receiving input from a pipe, at precisely the pace sent by `cat`, and in chunks no larger than written by `cat`.

When you run `/usr/bin/time my_program < big_file`, your program receives an open file descriptor to the actual file. Your program — or in many cases the I/O libraries of the language in which it was written — may take different actions when presented with a file descriptor referencing a regular file. It may use mmap(2) to map the input file into its address space, instead of using explicit read(2) system calls. These differences could have a far larger effect on your benchmark results than the small cost of running the `cat` binary.

Of course it is an interesting benchmark result if the same program performs significantly differently between the two cases. It shows that, indeed, the program or its I/O libraries are doing something interesting, like using mmap(). So in practice it might be good to run the benchmarks both ways; perhaps discounting the `cat` result by some small factor to “forgive” the cost of running `cat` itself.

I reproduced the original result on my computer using g++ on a Mac.

Adding the following statements to the C++ version just before the while loop brings it inline with the Python version:

std::ios_base::sync_with_stdio(false);
char buffer[1048576];
std::cin.rdbuf()->pubsetbuf(buffer, sizeof(buffer));

sync_with_stdio improved speed to 2 seconds, and setting a larger buffer brought it down to 1 second.

getline, stream operators, scanf, can be convenient if you don’t care about file loading time or if you are loading small text files. But, if the performance is something you care about, you should really just buffer the entire file into memory (assuming it will fit).

Here’s an example:

//open file in binary mode
std::fstream file( filename, std::ios::in|::std::ios::binary );
if( !file ) return NULL;

//read the size...
file.seekg(0, std::ios::end);
size_t length = (size_t)file.tellg();
file.seekg(0, std::ios::beg);

//read into memory buffer, then close it.
char *filebuf = new char[length+1];
file.read(filebuf, length);
filebuf[length] = '\0'; //make it null-terminated
file.close();

If you want, you can wrap a stream around that buffer for more convenient access like this:

std::istrstream header(&filebuf[0], length);

Also, if you are in control of the file, consider using a flat binary data format instead of text. It’s more reliable to read and write because you don’t have to deal with all the ambiguities of whitespace. It’s also smaller and much faster to parse.

The following code was faster for me than the other code posted here so far: (Visual Studio 2013, 64-bit, 500 MB file with line length uniformly in [0, 1000)).

const int buffer_size = 500 * 1024;  // Too large/small buffer is not good.
std::vector<char> buffer(buffer_size);
int size;
while ((size = fread(buffer.data(), sizeof(char), buffer_size, stdin)) > 0) {
    line_count += count_if(buffer.begin(), buffer.begin() + size, [](char ch) { return ch == '\n'; });
}

It beats all my Python attempts by more than a factor 2.

By the way, the reason the line count for the C++ version is one greater than the count for the Python version is that the eof flag only gets set when an attempt is made to read beyond eof. So the correct loop would be:

while (cin) {
    getline(cin, input_line);

    if (!cin.eof())
        line_count++;
};

In your second example (with scanf()) reason why this is still slower might be because scanf(“%s”) parses string and looks for any space char (space, tab, newline).

Also, yes, CPython does some caching to avoid harddisk reads.

A first element of an answer: <iostream> is slow. Damn slow. I get a huge performance boost with scanf as in the below, but it is still two times slower than Python.

#include <iostream>
#include <time.h>
#include <cstdio>

using namespace std;

int main() {
    char buffer[10000];
    long line_count = 0;
    time_t start = time(NULL);
    int sec;
    int lps;

    int read = 1;
    while(read > 0) {
        read = scanf("%s", buffer);
        line_count++;
    };
    sec = (int) time(NULL) - start;
    line_count--;
    cerr << "Saw " << line_count << " lines in " << sec << " seconds." ;
    if (sec > 0) {
        lps = line_count / sec;
        cerr << "  Crunch speed: " << lps << endl;
    } 
    else
        cerr << endl;
    return 0;
}

Well, I see that in your second solution you switched from cin to scanf, which was the first suggestion I was going to make you (cin is sloooooooooooow). Now, if you switch from scanf to fgets, you would see another boost in performance: fgets is the fastest C++ function for string input.

BTW, didn’t know about that sync thing, nice. But you should still try fgets.