3 ms·

Yes another super useful trick you can use with this scope trick is to get the length of a file (or its last line) just do: for i,v in enumerate(open(f)):

• potash 12y ago
  Note that your code still forces python to read the entire file! If all you want is the last line of a file, use the UNIX tail: tail -n 1 $f Tail seeks backwards so it will only read the one line. Of course, this won't give you a line count. EDIT: I haven't tested it but you might be interested in this implementation of tail in python: http://stackoverflow.com/a/136368 http://stackoverflow.com/a/136368
  • acadien 12y ago
    I've done timing tests and it is typically on par with tail & wc. The bulk of the time is wasted reading your file into ram, the time it takes to count the lines is essentially 0. Edit: Of course I misspoke, yes tail is much faster for getting the last line of the file! I meant for getting the line count the loop methods is typically just ~5% slower than wc on sufficiently large files.
• cnvogel 12y ago
  It's a nice trick, but it's not too "blazingly" fast. In [73]: cProfile.run('for i,v in enumerate(open(fn)): pass\nprint(i)') 15496799 406215 function calls in 10.998 seconds If you really don't want to do anything with the content of the file, better not spend time splitting lines, just read in the data in nice, big chunks. def file_block_count_lf(f) : while True : k = f.read(1<<20) if k == '' : break yield k.count('\n') In [77]: cProfile.run('print(sum(file_block_count_lf(open(fn))))') 15496800 10977 function calls in 6.479 seconds On the other hand, I think sum(1 for l in open(fn)) is a little more pythonesque, and can also be written in one single expression, if you must. It's a little slower, though. In [70]: cProfile.run('sum(1 for l in open(fn))') 15903016 function calls in 14.409 seconds Besides cProfile, also "timeit" is a useful python module for this kind of micro benchmarks. (the long output of the profiler is a little more interesting, I've put it in a pastebin http://pastebin.com/WXX93sS8 http://pastebin.com/WXX93sS8 ) EDIT/ADD: There's an awful lot time spent in decoding the characters to unicode strings! def file_block_count_lf(f) : while True : k = f.read(1<<20) if not k : break yield k.count(b'\n') In [48]: cProfile.run("sum(file_block_count_lf(open('more_logs.txt','rb')))") 4768 function calls in 2.969 seconds
  • acadien 12y ago
    When you do your timing tests make sure to do it on different files to make sure you aren't taking advantage of caching your system may be doing. That may explain some of your timing discrepancies. And when I say blazingly fast I mean that these pyscripts are nearly as fast as wc/tail. Edit; I just tested your code and my own and against a set of files of uniform size. time wc -l GIANTFILE1 -> 16.7 seconds cProfile.run("for i,v in enumerate(open("GIANTFILE2")):pass") -> 18.1 seconds cProfile.run("sum(file_block_count_lf(open('GIANTFILE3','rb')))") -> 22.9 seconds
    • cnvogel 12y ago
      Obviously, when comparing different methods of computation with negligible cache/memory footprint, I'd run it on hot caches to maximize the visible effect on the implementation. File here was about 1.5 GB on a 4GB RAM machine, and I repeated each test to have a stable duration for each. Would I have tested, say, different patterns of data access (reading files sequentially, parallel read from multiple disks/partitions), of course I would have made sure that either caches are cleared (/proc/sys/vm/drop_caches), datasets will exceed memory, and actual CPU load of the algorithm is according to the real-world-usage scenario (pure read() throughput is meaningless when CPU is bogged, and L1-3 caches swamped by actual algorithmic work). Re: your additional tests: Interesting, what python-version and size of files are you running it on?
      • acadien 12y ago
        I'm trapped using 2.7! On this machine it looks like I'm running 2.7.8. Files are roughly 1GB each. They're on a relatively slow file system, just a single HD on this machine... no raid, no ssd.