What does killed mean when a processing of a huge CSV with Python which suddenly stops

Question

I have a Python script that imports a large CSV file and then counts the number of occurrences of each word in the file  then exports the counts to another CSV file   But what is happening is that once that counting part is finished and the exporting begins it says Killed in the terminal   I don t think this is a memory problem  if it was I assume I would be getting a memory error and not Killed    Could it be that the process is taking too long  If so  is there a way to extend the time-out period so I can avoid this   Here is the code   csv field size limit sys maxsize      counter        with open   home alex Documents version2 cooccur list csv   rb   as file name          reader csv reader file name          for row in reader              if len row  gt 1                  pair row 0      row 1                  if pair in counter                      counter pair   1                 else                      counter pair  1     print  finished counting      writer   csv writer open   home alex Documents version2 dict csv    wb        for key  value in counter items            writer writerow  key  value     And the Killed happens after finished counting has printed  and the full message is    killed  program exited with code  137

User · Answer

I doubt anything is killing the process just because it takes a long time   Killed generically means something from the outside terminated the process  but probably not in this case hitting Ctrl-C since that would cause Python to exit on a KeyboardInterrupt exception   Also  in Python you would get MemoryError exception if that was the problem   What might be happening is you re hitting a bug in Python or standard library code that causes a crash of the process

User · Answer

There are two storage areas involved  the stack and the heap The stack is where the current state of a method call is kept  ie local variables and references   and the heap is where objects are stored  recursion and memory  I gues there are too many keys in the counter dict that will consume too much memory of the heap region  so the Python runtime will raise a OutOfMemory exception   To save it  don t create a giant object  e g  the counter   1 StackOverflow  a program that create too many local variables   Python 2 7 9  default  Mar  1 2015  12 57 24    GCC 4 9 2  on linux2 Type  help    copyright    credits  or  license  for more information   gt  gt  gt  f   open  stack overflow py   w    gt  gt  gt  f write  def foo    n    gt  gt  gt  for x in xrange 10000000         f write   tx d    d n     x  x         gt  gt  gt  f write  foo      gt  gt  gt  f close    gt  gt  gt  execfile  stack overflow py   Killed   2 OutOfMemory  a program that creats a giant dict includes too many keys    gt  gt  gt  f   open  out of memory py   w    gt  gt  gt  f write  def foo    n    gt  gt  gt  f write   tcounter      n    gt  gt  gt  for x in xrange 10000000         f write  counter  d     d n     x  x         gt  gt  gt  f write  foo   n    gt  gt  gt  f close    gt  gt  gt  execfile  out of memory py   Killed     References   7  Memory   Stack vs Heap recursion and memory

User · Answer

I just had the same happen on me when I tried to run a python script from a shared folder in VirtualBox within the new Ubuntu 20 04 LTS  Python bailed with Killed while loading my own personal library  When I moved the folder to a local directory  the issue went away  It appears that the Killed stop happened during the initial imports of my library as I got messages of missing libraries once I moved the folder over   The issue went away after I restarted my computer   Therefore  people may want to try moving the program to a local directory if its over a share of some kind or it could be a transient problem that just requires a reboot of the OS

User · Answer

Exit code 137  128 9  indicates that your program exited due to receiving signal 9  which is SIGKILL  This also explains the killed message  The question is  why did you receive that signal   The most likely reason is probably that your process crossed some limit in the amount of system resources that you are allowed to use  Depending on your OS and configuration  this could mean you had too many open files  used too much filesytem space or something else  The most likely is that your program was using too much memory  Rather than risking things breaking when memory allocations started failing  the system sent a kill signal to the process that was using too much memory   As I commented earlier  one reason you might hit a memory limit after printing finished counting is that your call to counter items   in your final loop allocates a list that contains all the keys and values from your dictionary  If your dictionary had a lot of data  this might be a very big list  A possible solution would be to use counter iteritems   which is a generator  Rather than returning all the items in a list  it lets you iterate over them with much less memory usage   So  I d suggest trying this  as your final loop   for key  value in counter iteritems        writer writerow  key  value     Note that in Python 3  items returns a  dictionary view  object which does not have the same overhead as Python 2 s version  It replaces iteritems  so if you later upgrade Python versions  you ll end up changing the loop back to the way it was

User · Answer

Most likely  you ran out of memory  so the Kernel killed your process   Have you heard about OOM Killer    Here s a log from a script that I developed for processing a huge set of data from CSV files   Mar 12 18 20 38 server com kernel   63802 396693  Out of memory  Kill process 12216  python3  score 915 or sacrifice child Mar 12 18 20 38 server com kernel   63802 402542  Killed process 12216  python3  total-vm 9695784kB  anon-rss 7623168kB  file-rss 4kB  shmem-rss 0kB Mar 12 18 20 38 server com kernel   63803 002121  oom reaper  reaped process 12216  python3   now anon-rss 0kB  file-rss 0kB  shmem-rss 0kB   It was taken from  var log syslog   Basically       PID 12216 elected as a victim  due to its use of  9Gb of total-vm   so oom killer reaped it    Here s a article about OOM behavior

[python] What does 'killed' mean when a processing of a huge CSV with Python, which suddenly stops?

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