CircularDict
CircularDict is a Python dict
that acts as a Circular Buffer. This dictionary maintains a controlled size, limited either by a specified number of items (maxlen) or total size in bytes (maxsize_bytes). Upon reaching the defined limit, CircularDict automatically removes the oldest entries, maintaining memory usage under control.
Built upon Python's OrderedDict
, CircularDict inherits all standard dictionary usage and operations, augmented by the capability of memory management. It's particularly useful in scenarios such as caching, where limiting memory consumption is crucial. The class combines dictionary and circular-queue behaviors, providing an efficient and scalable solution for various use cases.
Installation
To install CircularDict simply run:
pip install circular-dict
Usage
Working with CircularDict is as simple as using a standard Python dict
, with additional parameters maxlen
or maxsize_bytes
on the initialization to control the buffer size. You can use one of them or both.
from circular_dict import CircularDict
my_dict = CircularDict(maxlen=3, maxsize_bytes=4*1024*1024)
Example with maxlen
You can use maxlen
to define the maximum amount of items that the dictionary can store. It is useful for defining fixed size buffers.
from circular_dict import CircularDict
my_buffer = CircularDict(maxlen=3)
my_buffer['item1'] = 'value1'
my_buffer['item2'] = 'value2'
my_buffer['item3'] = 'value3'
print(f"When filling it: {circ_dict}")
my_buffer['item4'] = 'value4'
print(f"After adding an element beyond maxlen: {circ_dict}")
Output:
When filling it: {'item1': 'value1', 'item2': 'value2', 'item3': 'value3'}
After adding an element beyond maxlen: {'item2': 'value2', 'item3': 'value3', 'item4': 'value4'}
Example with maxsize_bytes
You can use maxsize_bytes
to define the maximum amount of memory that the dict
can store. It is particularly beneficial when defining caches, to prevent memory overflows.
from circular_dict import CircularDict
import numpy as np
import sys
my_buffer = CircularDict(maxsize_bytes=100*1024)
my_buffer['item1'] = np.zeros((10, 1024), dtype=np.int32)
my_buffer['item2'] = np.ones((10, 1024), dtype=np.int32)
print(f"{len(my_buffer)} Elements {tuple(my_buffer.keys())}. Dict size: {my_buffer.current_size/1024} Kb")
my_buffer['item3'] = np.ones((8, 1024), dtype=np.int32)
print(f"{len(my_buffer)} Elements {tuple(my_buffer.keys())}. Dict size: {my_buffer.current_size/1024} Kb")
too_big_array = np.ones((40, 1024), dtype=np.int32)
try:
my_buffer['item4'] = too_big_array
except MemoryError:
print(f"Cannot add an element with {sys.getsizeof(too_big_array)/1024}Kb in a dict with"\
f"maxsize_bytes of {my_buffer.maxsize_bytes/1024} Kb. Current elements {tuple(my_buffer.keys())}")
Output
2 Elements ('item1', 'item2'). Dict size: 80.35 Kb
2 Elements ('item2', 'item3'). Dict size: 72.35 Kb
Cannot add an element with 160.12Kb in a dict with maxsize_bytes of 100.0 Kb. Current elements ('item2', 'item3')
Please remember that the maxsize_bytes
parameter considers the total memory footprint, including the sizes of keys and values. If you try to add an item that exceeds the maxsize_bytes
, a MemoryError
will be raised.