In this lesson, you will learn how to implement type-aware memoization to cache results of generic functions while respecting Rust’s strict ownership and trait requirements.
The Challenge of Generic Memoization
When we write a generic method—for instance, one that performs an expensive calculation on a type T—we often want to avoid recomputing the same output for the same input. However, because T can be any type, we cannot simply use a global HashMap<T, V>, as T might not be hashable or even known at compile time.
To cache generic results, we must constrain T with the std::hash::Hash and Eq traits. Without these bounds, the compiler has no way to look up your input in a collection.
Implementation Pattern
The most effective strategy for generic methods is to wrap the logic in a struct that manages a HashMap internally. By using Default or OnceLock, we can create a thread-safe cache that persists across calls.
use std::collections::HashMap;
use std::hash::Hash;
pub struct Memoizer<K, V> {
cache: HashMap<K, V>,
}
impl<K: Eq + Hash, V: Clone> Memoizer<K, V> {
pub fn get_or_compute<F>(&mut self, key: K, f: F) -> V
where
F: FnOnce(&K) -> V
{
self.cache.entry(key).or_insert_with_key(f).clone()
}
}
In this example, the generic method get_or_compute takes a closure F. This allows the cache to remain agnostic of the specific "expensive" logic while ensuring that the key type K satisfies the requirements for HashMap storage.
Why This Matters
If you try to cache results for a generic type without these bounds, the compiler will rightfully stop you. By explicitly requiring K: Eq + Hash, you transform a "black box" generic method into one that interacts safely with memory-resident storage. This pattern is particularly useful when your generic function involves complex transformations—like string parsing or heavy mathematical operations—where the input set is smaller than the total number of possible executions.
Key Takeaway: To memoize generic methods, you must strictly bound your generic keys with Eq and Hash traits and store them in a persistent data structure, such as a HashMap, to ensure predictable lookup and type safety.