Python3: Heap memory not getting released when expected

Question

I have a class that has a cache implemented as a dict for numpy arrays, which can occupy GBs of data.

class WorkOperations(object):
    def __init__(self):
        self.data_cache: Dict[str, Dict[str, Tuple[np.ndarray, np.ndarray]]] = {}

    def get_data(key):
        if key not in data_cache:
            add_data(key)
        return self.data_cache[key]

    def add_data(key)
        result = run_heavy_calculation(key)
        self.data_cache[key] = result

I am testing the code with this function -

import gc

def perform_operations()
    work_operations = WorkOperations()

    # input_keys gives a list of keys to process
    for keys in input_keys():
        data = work_operations.get_data(key)
        do_some_operation(data)

    del work_operations

perform_operations()
gc.collect()

The result of run_heavy_calculation is heavy in memory and soon data_cache grows and occupies memory in GBs (which is expected).

But memory does not get released even after perform_operations() is done. I tried adding del work_operations and invoking gc.collect() but that did not help either. I checked memory of the process after several hours, but the memory was still not freed up.

If I don't use caching (data_cache) at all (at the cost of latency), memory never goes high.

I am wondering what is it that is taking memory. I tried running tracemalloc, but it just showed lines occupying memory in KBs. I also took a memory dump with gdb by looking at memory address from process pmap and /proc/<pid>/smaps, but that is really long and even with hexeditor I couldn't figure out much.

I am measuring memory used by the process using top command and looking at RES. I also tried outputting memory in the end from within python process as well with -

import psutils
import gc
import logging

GIGABYTE = 1024.0 * 1024.0 * 1024.0

perform_operations()
gc.collect()

memory_full_info = psutil.Process().memory_full_info()
logging.info(f"process memory after running the process {memory_full_info.uss / GIGABYTE}")

score 0 · Answer 1 · answered Feb 08 '22 at 12:51

Could not reproduce on Ubuntu with this :

import itertools
import time
import os
from typing import Dict, Tuple

import numpy as np
import psutil  # installed with pip


process = psutil.Process(os.getpid())

SIZE = 10**7


def run_heavy_calculation(key):
    array1 = np.zeros(SIZE)
    array2 = np.zeros(SIZE)
    # Linux use "virtual memory", which means that the memory required for the arrays was allocated, but will not
    # be actually deducted until we use them, so we write some 1 into them
    # cf: https://stackoverflow.com/q/29516888/11384184
    for i in range(0, SIZE, 1000):
        array1[i] = 1
        array2[i] = 1
    return {key: (array1, array2)}


class WorkOperations(object):
    def __init__(self):
        self.data_cache: Dict[str, Dict[str, Tuple[np.ndarray, np.ndarray]]] = {}

    def get_data(self, key):
        if key not in self.data_cache:
            self.add_data(key)
        return self.data_cache[key]

    def add_data(self, key):
        result = run_heavy_calculation(key)
        self.data_cache[key] = result


def perform_operations(input_keys):
    work_operations = WorkOperations()

    for key in input_keys():
        data = work_operations.get_data(key)
        time.sleep(0.2)
        print(key, process.memory_info().rss / 10**9)

    del work_operations


perform_operations(lambda: map(str, itertools.product("abcdefgh", "0123456789")))  # dummy keys
print("after operations", process.memory_info().rss / 10**9)
input("pause")

('a', '0') 0.113106944
('a', '1') 0.195014656
('a', '2') 0.276926464
...
('h', '7') 6.421118976
('h', '8') 6.503030784
('h', '9') 6.584942592
after operations 0.031363072
pause

It climbed up to using 6,5 Go of RAM, then returned from the function and all of it was released.

You can add a finalizer (__del__) to the class WorkOperations :

    def __del__(self):
        print("deleted")

I see it printed between the last operation's print and the one after.
Although it does not guarantee that this will always be the case (cf this question), it strongly indicates that everything is working as intended : event without the del, the functions returns (hence the scope is lost), so the reference count for work_operations gets to 0, and then gets GC'ed.

It can be checked with sys.getrefcount too :

    print(sys.getrefcount(work_operations) - 1)  # see https://stackoverflow.com/a/510417/11384184
    del work_operations

which for me print 1.

Please provide a Minimal Reproducible Example and info on your system.

Python3: Heap memory not getting released when expected

1 Answers1