How to read a large file line by line

Question

I want to read a file line by line  but without completely loading it in memory   My file is too large to open in memory  and if try to do so I always get out of memory errors   The file size is 1 GB

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The obvious answer wasn t there in all the responses  PHP has a neat streaming delimiter parser available made for exactly that purpose   fp   fopen  quot  path to the file quot    quot r  quot    while    line   stream get line  fp  1024   1024   quot  n quot        false      echo  line    fclose  fp

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You can use an object oriented interface class  for a file - SplFileObject http   php net manual en splfileobject fgets php  PHP 5    5 1 0    lt  php   file   new SplFileObject  file txt        Loop until we reach the end of the file  while    file- gt eof             Echo one line from the file      echo  file- gt fgets          Unset the file to call   destruct    closing the file handle   file   null

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This how I manage with very big file  tested with up to 100G   And it s faster than fgets     block  1024 1024   1MB or counld be any higher than HDD block size 2 if   fh   fopen  file txt    r            left         while   feof  fh       read the file         temp   fread  fh   block             fgetslines   explode   n   temp           fgetslines 0   left  fgetslines 0          if  feof  fh    left   array pop  lines                     foreach   fgetslines as  k   gt   line                 do smth with  line                    fclose  fh

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Using a text file for the example with open  yourFile txt   r   as f      text   f readlines   for line in text      print line    Open your file for reading  r  Read the whole file and save each line into a list  text  Loop through the list printing each line    If you want  for example  to check a specific line for a length greater than 10  work with what you already have available   for line in text      if len line   gt  10          print line

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Use buffering techniques to read the file    filename    test txt    source file   fopen   filename   r    or die  Couldn t open  filename    while   feof  source file          buffer   fread  source file  4096       use a buffer of 4KB      buffer   str replace  old  new  buffer

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One of the popular solutions to this question will have issues with the new line character  It can be fixed pretty easy with a simple str replace    handle   fopen  some file txt    r    if   handle        while    line   fgets  handle       false             line   str replace   n        line             fclose  handle

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this is a possible way of reading a file in python   f   open input file  for line in f      do stuff line  f close     it does not allocate a full list  It iterates over the lines

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If you re opening a big file  you probably want to use Generators alongside fgets   to avoid loading the whole file into memory           return Generator      fileData   function          file   fopen   DIR       file txt    r         if    file          die  file does not exist or cannot be opened         while    line   fgets  file       false            yield  line             fclose  file        Use it like this   foreach   fileData   as  line            line contains current line     This way you can process individual file lines inside the foreach     Note  Generators require    PHP 5 5

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The correct  fully Pythonic way to read a file is the following   with open      as f      for line in f            Do something with  line    The with statement handles opening and closing the file  including if an exception is raised in the inner block  The for line in f treats the file object f as an iterable  which automatically uses buffered I O and memory management so you don t have to worry about large files      There should be one -- and preferably only one -- obvious way to do it

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if   file   fopen  file txt    r          while  feof  file              line   fgets  file             do same stuff with the  line           fclose  file

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To strip newlines   with open file path   rU   as f      for line terminated in f          line   line terminated rstrip   n                 With universal newline support all text file lines will seem to be terminated with   n   whatever the terminators in the file    r     n   or   r n     EDIT - To specify universal newline support    Python 2 on Unix - open file path  mode  rU   - required  thanks  Dave  Python 2 on Windows - open file path  mode  rU   - optional Python 3 - open file path  newline None  - optional   The newline parameter is only supported in Python 3 and defaults to None  The mode parameter defaults to  r  in all cases  The U is deprecated in Python 3  In Python 2 on Windows some other mechanism appears to translate  r n to  n   Docs    open   for Python 2 open   for Python 3   To preserve native line terminators   with open file path   rb   as f      with line native terminated in f                Binary mode can still parse the file into lines with in   Each line will have whatever terminators it has in the file   Thanks to  katrielalex s answer  Python s open   doc  and iPython experiments

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Function to Read with array return  function read file  filename             buffer   array         source file   fopen   filename   r    or die  Couldn t open  filename        while   feof  source file              buffer     fread  source file  4096       use a buffer of 4KB           return  buffer

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Best way to read large file  line by line is to use python enumerate function  with open file name   rU   as read file      for i  row in enumerate read file  1            do something          i in line of that line          row containts all data of that line

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There is a file   function that returns an array of the lines contained in the file   foreach file  myfile txt   as  line       echo  line    n

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I would strongly recommend not using the default file loading as it is horrendously slow  You should look into the numpy functions and the IOpro functions  e g  numpy loadtxt      http   docs scipy org doc numpy user basics io genfromtxt html  https   store continuum io cshop iopro   Then you can break your pairwise operation into chunks   import numpy as np import math  lines total   n     similarity   np zeros n n  lines per chunk   m n chunks   math ceil float n  m  for i in xrange n chunks       for j in xrange n chunks           chunk i    function of your choice to read lines i lines per chunk to  i 1  lines per chunk          chunk j    function of your choice to read lines j lines per chunk to  j 1  lines per chunk          similarity i lines per chunk  i 1  lines per chunk                     j lines per chunk  j 1  lines per chunk    fast operation chunk i  chunk j     It s almost always much faster to load data in chunks and then do matrix operations on it than to do it element by element

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You can use the fgets   function to read the file line by line    handle   fopen  inputfile txt    r    if   handle        while    line   fgets  handle       false               process the line read             fclose  handle     else          error opening the file

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Be careful with the  while  feof     fgets    stuff  fgets can get an error  returnfing false  and loop forever without reaching the end of file   codaddict was closest to being correct but when your  while fgets  loop ends  check feof  if not true  then you had an error

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Need to frequently read a large file from last position reading    I have created a script used to cut an Apache access log file several times a day  So I needed to set a position cursor on last line parsed during last execution  To this end  I used file seek   and file seek   methods which allows the storage of the cursor in file   My code    ENCODING    utf8  CURRENT FILE DIR   os path dirname os path abspath   file        This file is used to store the last cursor position cursor position   os path join CURRENT FILE DIR   access cursor position log      Log file with new lines log file to cut   os path join CURRENT FILE DIR   access log   cut file   os path join CURRENT FILE DIR   cut access    cut log      Set in from line  from position   0 try      with open cursor position   r   encoding ENCODING  as f          from position   int f read    except Exception as e      pass    We read log file to cut to put new lines in cut file with open log file to cut   r   encoding ENCODING  as f      with open cut file   w   encoding ENCODING  as fw            We set cursor to the last position used  during last run of script          f seek from position          for line in f              fw write   s     line          We save the last position of cursor for next usage     with open cursor position   w   encoding ENCODING  as fw          fw write str f tell

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foreach  new SplFileObject   FILE    as  line        echo  line

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Katrielalex provided the way to open  amp  read one file   However the way your algorithm goes it reads the whole file for each line of the file  That means the overall amount of reading a file - and computing the Levenshtein distance - will be done N N if N is the amount of lines in the file  Since you re concerned about file size and don t want to keep it in memory  I am concerned about the resulting quadratic runtime  Your algorithm is in the O n 2  class of algorithms which often can be improved with specialization   I suspect that you already know the tradeoff of memory versus runtime here  but maybe you would want to investigate if there s an efficient way to compute multiple Levenshtein distances in parallel  If so it would be interesting to share your solution here   How many lines do your files have  and on what kind of machine  mem  amp  cpu power  does your algorithm have to run  and what s the tolerated runtime   Code would look like   with f outer as open input file   r        for line outer in f outer          with f inner as open input file   r                for line inner in f inner                  compute distance line outer  line inner    But the questions are how do you store the distances  matrix   and can you gain an advantage of preparing e g  the outer line for processing  or caching some intermediate results for reuse

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lt  php echo   lt meta charset  utf-8  gt      k  1   f  1   fp   fopen  texttranslate txt    r    while  feof  fp          contents           for  i 1  i lt  1500  i             echo  k   --    fgets  fp     lt br gt    k             contents    fgets  fp             echo   lt hr gt        file put contents  Split new file    f   txt    contents   f        gt

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Two memory efficient ways in ranked order  first is best  -  use of with - supported from python 2 5 and above use of yield if you really want to have control over how much to read  1  use of with with is the nice and efficient pythonic way to read large files  advantages - 1  file object is automatically closed after exiting from with execution block  2  exception handling inside the with block  3  memory for loop iterates through the f file object line by line  internally it does buffered IO  to optimized on costly IO operations  and memory management  with open  quot x txt quot   as f      for line in f          do something with data  2  use of yield Sometimes one might want more fine-grained control over how much to read in each iteration  In that case use iter  amp  yield  Note with this method one explicitly needs close the file at the end  def readInChunks fileObj  chunkSize 2048        quot  quot  quot      Lazy function to read a file piece by piece      Default chunk size  2kB        quot  quot  quot      while True          data   fileObj read chunkSize          if not data              break         yield data  f   open  bigFile   for chunk in readInChunks f       do something chunk  f close     Pitfalls and for the sake of completeness - below methods are not as good or not as elegant for reading large files but please read to get rounded understanding  In Python  the most common way to read lines from a file is to do the following  for line in open  myfile   r   readlines        do something line   When this is done  however  the readlines   function  same applies for read   function  loads the entire file into memory  then iterates over it  A slightly better approach  the first mentioned two methods above are the best  for large files is to use the fileinput module  as follows  import fileinput  for line in fileinput input   myfile         do something line   the fileinput input   call reads lines sequentially  but doesn t keep them in memory after they ve been read or even simply so this  since file in python is iterable  References  Python with statement

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From the python documentation for fileinput input        This iterates over the lines of all files listed in sys argv 1    defaulting to sys stdin if the list is empty   further  the definition of the function is   fileinput FileInput  files   inplace   backup   mode   openhook         reading between the lines  this tells me that files can be a list so you could have something like   for each line in fileinput input  input file  input file      do something each line    See here for more information

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SplFileObject is useful when it comes to dealing with large files   function parse file  filename        try            file   new SplFileObject  filename         catch  LogicException  exception            die  SplFileObject      exception- gt getMessage               while   file- gt valid               line    file- gt fgets              do something with  line              don t forget to free the file handle       file   null

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Some context up front as to where I am coming from  Code snippets are at the end   When I can  I prefer to use an open source tool like H2O to do super high performance parallel CSV file reads  but this tool is limited in feature set  I end up writing a lot of code to create data science pipelines before feeding to H2O cluster for the supervised learning proper   I have been reading files like 8GB HIGGS dataset from UCI repo and even 40GB CSV files for data science purposes significantly faster by adding lots of parallelism with the multiprocessing library s pool object and map function  For example clustering with nearest neighbor searches and also DBSCAN and Markov clustering algorithms requires some parallel programming finesse to bypass some seriously challenging memory and wall clock time problems    I usually like to break the file row-wise into parts using gnu tools first and then glob-filemask them all to find and read them in parallel in the python program  I use something like 1000  partial files commonly  Doing these tricks helps immensely with processing speed and memory limits     The pandas dataframe read csv is single threaded so you can do these tricks to make pandas quite faster by running a map   for parallel execution   You can use htop to see that with plain old sequential pandas dataframe read csv  100  cpu on just one core is the actual bottleneck in pd read csv  not the disk at all   I should add I m using an SSD on fast video card bus  not a spinning HD on SATA6 bus  plus 16 CPU cores   Also  another technique that I discovered works great in some applications is parallel CSV file reads all within one giant file  starting each worker at different offset into the file  rather than pre-splitting one big file into many part files  Use python s file seek   and tell   in each parallel worker to read the big text file in strips  at different byte offset start-byte and end-byte locations in the big file  all at the same time concurrently  You can do a regex findall on the bytes  and return the count of linefeeds  This is a partial sum   Finally sum up the partial sums to get the global sum when the map function returns after the workers finished   Following is some example benchmarks using the parallel byte offset trick   I use 2 files  HIGGS csv is 8 GB  It is from the UCI machine learning repository   all bin  csv is 40 4 GB and is from my current project  I use 2 programs  GNU wc program which comes with Linux  and the pure python fastread py program which I developed   HP-Z820  mnt fastssd fast file reader  ls -l  mnt fastssd nzv HIGGS csv -rw-rw-r-- 1 8035497980 Jan 24 16 00  mnt fastssd nzv HIGGS csv  HP-Z820  mnt fastssd  ls -l all bin csv -rw-rw-r-- 1 40412077758 Feb  2 09 00 all bin csv  ga ga-HP-Z820  mnt fastssd  time python fastread py --fileName  all bin csv  --numProcesses 32 --balanceFactor 2 2367496  real    0m8 920s user    1m30 056s sys 2m38 744s  In  1   40412077758    8 92 Out 1   4530501990 807175   That   s some 4 5 GB s  or 45 Gb s  file slurping speed   That ain   t no spinning hard disk  my friend  That   s actually a Samsung Pro 950 SSD    Below is the speed benchmark for the same file being line-counted by gnu wc  a pure C compiled program   What is cool is you can see my pure python program essentially matched the speed of the gnu wc compiled C program in this case   Python is interpreted but C is compiled  so this is a pretty interesting feat of speed  I think you would agree   Of course  wc really needs to be changed to a parallel program  and then it would really beat the socks off my python program  But as it stands today  gnu wc is just a sequential program   You do what you can  and python can do parallel today  Cython compiling might be able to help me  for some other time   Also memory mapped files was not explored yet   HP-Z820  mnt fastssd  time wc -l all bin csv 2367496 all bin csv  real    0m8 807s user    0m1 168s sys 0m7 636s   HP-Z820  mnt fastssd fast file reader  time python fastread py --fileName  HIGGS csv  --numProcesses 16 --balanceFactor 2 11000000  real    0m2 257s user    0m12 088s sys 0m20 512s  HP-Z820  mnt fastssd fast file reader  time wc -l HIGGS csv 11000000 HIGGS csv  real    0m1 820s user    0m0 364s sys 0m1 456s   Conclusion  The speed is good for a pure python program compared to a C program  However  it   s not good enough to use the pure python program over the C program  at least for linecounting purpose  Generally the technique can be used for other file processing  so this python code is still good   Question  Does compiling the regex just one time and passing it to all workers will improve speed  Answer  Regex pre-compiling does NOT help in this application  I suppose the reason is that the overhead of process serialization and creation for all the workers is dominating   One more thing   Does parallel CSV file reading even help   Is the disk the bottleneck  or is it the CPU   Many so-called top-rated answers on stackoverflow contain the common dev wisdom that you only need one thread to read a file  best you can do  they say  Are they sure  though   Let   s find out   HP-Z820  mnt fastssd fast file reader  time python fastread py --fileName  HIGGS csv  --numProcesses 16 --balanceFactor 2 11000000  real    0m2 256s user    0m10 696s sys 0m19 952s  HP-Z820  mnt fastssd fast file reader  time python fastread py --fileName  HIGGS csv  --numProcesses 1 --balanceFactor 1 11000000  real    0m17 380s user    0m11 124s sys 0m6 272s   Oh yes  yes it does  Parallel file reading works quite well   Well there you go   Ps  In case some of you wanted to know  what if the balanceFactor was 2 when using a single worker process  Well  it   s horrible   HP-Z820  mnt fastssd fast file reader  time python fastread py --fileName  HIGGS csv  --numProcesses 1 --balanceFactor 2 11000000  real    1m37 077s user    0m12 432s sys 1m24 700s   Key parts of the fastread py python program   fileBytes   stat fileName  st size    Read quickly from OS how many bytes are in a text file startByte  endByte   PartitionDataToWorkers workers numProcesses  items fileBytes  balanceFactor balanceFactor  p   Pool numProcesses  partialSum   p starmap ReadFileSegment  zip startByte  endByte  repeat fileName      startByte is already a list  fileName is made into a same-length list of duplicates values  globalSum   sum partialSum  print globalSum    def ReadFileSegment startByte  endByte  fileName  searchChar   n       counts number of searchChar appearing in the byte range     with open fileName   r   as f          f seek startByte-1     seek is initially at byte 0 and then moves forward the specified amount  so seek 5  points at the 6th byte          bytes   f read endByte - startByte   1          cnt   len re findall searchChar  bytes     findall with implicit compiling runs just as fast here as re compile once   re finditer many times      return cnt   The def for PartitionDataToWorkers is just ordinary sequential code  I left it out in case someone else wants to get some practice on what parallel programming is like  I gave away for free the harder parts  the tested and working parallel code  for your learning benefit   Thanks to   The open-source H2O project  by Arno and Cliff and the H2O staff for their great software and instructional videos  which have provided me the inspiration for this pure python high performance parallel byte offset reader as shown above   H2O does parallel file reading using java  is callable by python and R programs  and is crazy fast  faster than anything on the planet at reading big CSV files

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