Can cython optimize python dictionary memory and lookup speed as well?

Question

I have a class which primarily contains the three dicts:

class KB(object):

  def __init__(self):

    # key:str value: list of str
    linear_patterns = defaultdict(list)

    # key:str value: list of str        
    nonlinear_patterns = defaultdict(list)

    # key: str value: dict
    pattern_meta_info = {}
    ...
    self.__initialize()

def __initialize(self):
    # the 3 dicts are populated 
    ...

The size of the 3 dicts are below:

linear_patterns: 100,000
non_linear_patterns: 900,000
pattern_meta_info: 700,000

After the program is run and done, it takes about 15 seconds to release the memory. When I reduces the number of the dict sizes above by loading less data in initialization, the memory release is faster, so I judge it's due to these dict sizes that cause memory release slower. The total program takes about 8G memory. Also, after the dicts are built, all operations are lookup, no modifications.

Is there a way to use cython to optimize the 3 data structures above, especially in terms of memory usage? Is there a similar cython dictionary that can replaces the python dicts?