Question

If I have a list(or array, dictionary….) in python that could exceed the available memory address space, (32 bit python) what are the options and there relative speeds? (other than not making a list that large)
The list could exceed the memory but I have no way of knowing before hand. Once it starts exceeding 75% I would like to no longer keep the list in memory (or the new items anyway), is there a way to convert to a file based approach mid-stream?

What are the best (speed in and out) file storage options?

Just need to store a simple list of numbers. no need to random Nth element access, just append/pop type operations.

Answer 1

If your “numbers” are simple-enough ones (signed or unsigned integers of up to 4 bytes each, or floats of 4 or 8 bytes each), I recommend the standard library array module as the best way to keep a few millions of them in memory (the “tip” of your “virtual array”) with a binary file (open for binary R/W) backing the rest of the structure on disk. array.array has very fast fromfile and tofile methods to facilitate the moving of data back and forth.

I.e., basically, assuming for example unsigned-long numbers, something like:

import os

# no more than 100 million items in memory at a time
MAXINMEM = int(1e8)

class bigarray(object):
  def __init__(self):
    self.f = open('afile.dat', 'w+')
    self.a = array.array('L')
  def append(self, n):
    self.a.append(n)
    if len(self.a) > MAXINMEM:
      self.a.tofile(self.f)
      del self.a[:]
  def pop(self):
    if not len(self.a):
      try: self.f.seek(-self.a.itemsize * MAXINMEM, os.SEEK_END)
      except IOError: return self.a.pop()  # ensure normal IndexError &c
      try: self.a.fromfile(self.f, MAXINMEM)
      except EOFError: pass
      self.f.seek(-self.a.itemsize * MAXINMEM, os.SEEK_END)
      self.f.truncate()
    return self.a.pop()

Of course you can add other methods as necessary (e.g. keep track of the overall length, add extend, whatever), but if pop and append are indeed all you need this should serve.