Great question—this is exactly the kind of performance tuning that adds up massively when you’re generating millions of samples or running continuous iterations. Let’s break down your options, including that clever sub-hashmap idea you mentioned, and cover proven techniques to speed up string-keyed dict lookups in Python.

First up: Your sub-hashmap split-by-first-character idea #

Splitting a large dictionary into smaller sub-dictionaries based on key prefixes (like first/second character) makes intuitive sense—after all, it reduces the number of keys each lookup has to check against. But there are important tradeoffs to consider:

When it might help #

This approach can yield gains if your dictionary is huge (10k+ keys) and static (keys don’t change often). If your key distribution is even (e.g., roughly equal number of keys starting with each letter), splitting cuts down the average number of hash comparisons per lookup.

When it’s not worth it #

Python’s built-in dict is already optimized in C—it handles hash collisions efficiently and has decades of engineering behind it. For small dictionaries (hundreds of keys max), the extra logic of checking a sub-dict will actually slow things down, not speed them up. Also, if your keys are skewed (e.g., 70% start with "a"), splitting won’t help much. Plus, maintaining the sub-dict structure (adding/removing keys) adds extra code complexity and runtime cost—so only do this if your dictionary rarely changes.

A quick example implementation (for testing) #

If you want to experiment, here’s a simple class to wrap this logic:

class PrefixSplitDict:
    def __init__(self, original_dict, prefix_length=1):
        self.prefix_length = prefix_length
        self.sub_dicts = {}
        # Populate sub-dictionaries
        for key, value in original_dict.items():
            prefix = key[:prefix_length] if len(key) >= prefix_length else key
            if prefix not in self.sub_dicts:
                self.sub_dicts[prefix] = {}
            self.sub_dicts[prefix][key] = value
    
    def __getitem__(self, key):
        prefix = key[:self.prefix_length] if len(key) >= self.prefix_length else key
        return self.sub_dicts[prefix][key]

Pro tip: Always benchmark this against your original dict using cProfile or timeit—don’t assume it’s faster until you see the numbers!

Proven low-effort optimizations you can use today #

You already know about dict.fromkeys() and shorter keys, so let’s expand on those and add some underrated wins:

1. Cache dictionary values in local variables (huge win for loops) #

This is probably the easiest and most impactful optimization for your use case. When you’re using a dictionary key repeatedly inside a loop, look it up once and store the value in a local variable. Local variable access in Python is way faster than any dictionary lookup.

Bad:

# Repeats the dict lookup every iteration
for _ in range(1_000_000):
    mean = params["normal_mean"]
    std = params["normal_std"]
    # Generate sample using mean/std

Good:

# Look up once, reuse the local variables
mean = params["normal_mean"]
std = params["normal_std"]
for _ in range(1_000_000):
    # Generate sample using mean/std directly

2. Shorten keys (but don’t sacrifice readability) #

Hash time for strings scales with their length, so shorter keys do speed up lookups. But don’t go overboard with cryptic abbreviations—use constants to keep code readable:

Instead of:

params = {"gaussian_distribution_mean": 0.0, "gaussian_distribution_std": 1.0}

Do:

# Define readable constants for short keys
GAUSS_MEAN = "g_mean"
GAUSS_STD = "g_std"

params = {GAUSS_MEAN: 0.0, GAUSS_STD: 1.0}

# Lookup using the constant
mean = params[GAUSS_MEAN]

3. Pre-allocate with dict.fromkeys() (when keys are known) #

You already mentioned this, but it’s worth emphasizing: if you know all keys upfront, dict.fromkeys(known_keys) pre-allocates the exact amount of space needed, avoiding rehashes and collision handling later. This is a tiny win, but it adds up when you’re dealing with millions of lookups.

More advanced optimizations for fixed keys #

If your dictionary keys are fixed (no new keys added after initialization), these methods can deliver even bigger performance gains:

Map strings to integer IDs #

Integer lookups in Python are near-instant (since integers hash to themselves, no complex hashing needed). Here’s how to implement this:

# Step 1: Create a mapping from string keys to unique integers
known_keys = ["mean", "std", "skew", "kurtosis"]
key_to_id = {key: idx for idx, key in enumerate(known_keys)}

# Step 2: Build an integer-keyed dictionary
int_params = {key_to_id[key]: params[key] for key in known_keys}

# Step 3: Lookup using the integer ID (cache the ID if using in a loop!)
current_id = key_to_id["mean"]
for _ in range(1_000_000):
    mean = int_params[current_id]
    # Generate sample...

This cuts out the string hashing step entirely for repeated lookups, which can save a ton of time in tight loops.

Use a class with __slots__ (for small fixed key sets) #

If you only have a handful of keys (e.g., 5-20), replacing the dictionary with a class instance using __slots__ can make access even faster. Class attribute access uses direct memory offsets instead of hashing:

class DistributionParams:
    __slots__ = ["mean", "std", "skew"]  # Fixed "keys"
    def __init__(self, mean, std, skew):
        self.mean = mean
        self.std = std
        self.skew = skew

# Create an instance instead of a dict
params = DistributionParams(0.0, 1.0, 0.1)

# Lookup is just attribute access (faster than dict!)
mean = params.mean

For slightly less boilerplate, you can use dataclasses with slots=True (Python 3.10+):

from dataclasses import dataclass

@dataclass(slots=True)
class DistributionParams:
    mean: float
    std: float
    skew: float

params = DistributionParams(0.0, 1.0, 0.1)
mean = params.mean

Final takeaways #

1. Profile first, optimize second: You’re already doing this with cProfile—great work! Make sure dictionary lookups are actually the bottleneck before investing time in complex optimizations.
2. Start simple: Prioritize local variable caching and shorter keys first—these are low-effort, high-reward wins.
3. Reserve complex optimizations for big, static dictionaries: Split sub-hashmaps and integer ID mapping only make sense if your dictionary is large, static, and lookup is a confirmed bottleneck.
4. Test everything: Always benchmark any custom implementation against the built-in dict—Python’s C-optimized structures beat hand-rolled Python code more often than not.

I hope this helps! Let me know if you want help testing any of these methods with your specific workload.