MemoryError with Pickle in Python

I am processing some data and I have stored the results in three dictionaries, and I have saved them to the disk with Pickle. Each dictionary has 500-1000MB.

Now I am loading them with:

import pickle
with open('dict1.txt', "rb") as myFile:
    dict1 = pickle.load(myFile)

However, already at loading the first dictionary I get:

*** set a breakpoint in malloc_error_break to debug
python(3716,0xa08ed1d4) malloc: *** mach_vm_map(size=1048576) failed (error code=3)
*** error: can't allocate region securely
*** set a breakpoint in malloc_error_break to debug
Traceback (most recent call last):
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/", line 858, in load
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/", line 1019, in load_empty_dictionary

How to solve this? My computer has 16GB of RAM so I find it unusual that loading a 800MB dictionary crashes. What I also find unusual is that there were no problems while saving the dictionaries.

Further, in future I plan to process more data resulting in larger dictionaries (3-4GB on the disk), so any advice how to improve the efficiency is appreciated.

Best answer

If your data in the dictionaries are numpy arrays, there are packages (such as joblib and klepto) that make pickling large arrays efficient, as both the klepto and joblib understand how to use minimal state representation for a numpy.array. If you don’t have array data, my suggestion would be to use klepto to store the dictionary entries in several files (instead of a single file) or to a database.

See my answer to a very closely related question, if you are ok with pickling to several files instead of a single file, would like to save/load your data in parallel, or would like to easily experiment with a storage format and backend to see which works best for your case. Also see: for other potential improvements, and here too:

As the links above discuss, you could use klepto — which provides you with the ability to easily store dictionaries to disk or database, using a common API. klepto also enables you to pick a storage format (pickle, json, etc.) –also HDF5 (or a SQL database) is another good option as it allows parallel access. klepto can utilize both specialized pickle formats (like numpy‘s) and compression (if you care about size and not speed of accessing the data).

klepto gives you the option to store the dictionary with “all-in-one” file or “one-entry-per” file, and also can leverage multiprocessing or multithreading — meaning that you can save and load dictionary items to/from the backend in parallel. For examples, see the above links.