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# MemoCache

A small, fixed-size cache with retention management, e.g. for use with memoization.

This library does not use the standard library so it is compatible with `#[no_std]` crates.

## Introduction

Sometimes it can be beneficial to speedup pure function calls by using [memoization](https://en.wikipedia.org/wiki/Memoization).

The cache storage required for implementing such a speedup often is an associative container (i.e. a key/value store).
Programming language standard libraries provide such containers, often implemented as a [hash table](https://en.wikipedia.org/wiki/Hash_table) or a [red-black tree](https://en.wikipedia.org/wiki/Red%E2%80%93black_tree).
These implementations are fine for performance, but do not actually cover all use cases because of the lack of retention management

Suppose your input data covers the whole space that can be represented by a 64-bit integer.
There probably is some (generally non-uniform) probability distribution with which the input values arrive, but it's statistically possible that over time _all_ possible values pass by.
Any cache without retention management will then grow to potentially enormous dimensions in memory which is undesirable, especially in memory-constrained environments.

The cache implemented in this library uses a FIFO-style sequential data storage with fixed size, pre-allocated memory.
When the cache is full, the oldest item is evicted.

## Example usage

Suppose we have a pure method `calculate` on a type `Process` (without memoization):

```rs
struct Process { /* ... */ }

impl Process {
    fn calculate(&self, input: u64) -> f64 {
        // ..do expensive calculation on `input`..
    }
}
```

Each single call to this function results in the resource costs of the calculation.
We can add memoization to this function in two different ways:

- Using `MemoCache::get_or_insert_with` (or `get_or_try_insert_with`),
- Using `MemoCache::get` and `MemoCache::insert`.

For each of the following examples: each call to `calculate` will first check if the input value is already in the cache.
If so: use the cached value, otherwise update the cache with a new, calculated value.

The cache is fixed-size, so if it is full, the oldest key/value pair will be evicted, and memory usage is constant.

See the `examples/` directory for more example code.

### Example A: `get_or_insert_with`

```rs
struct Process {
    cache: MemoCache<u64, f64, 32>,
}

impl Process {
    fn calculate(&mut self, input: u64) -> f64 {
        *self.cache.get_or_insert_with(&input, |&i| /* ..do calculation on `input`.. */)
    }
}
```

For fallible insert functions, there's `get_or_try_insert_with` that returns a `Result`.

### Example B: `get` and `insert`

```rs
struct Process {
    cache: MemoCache<u64, f64, 32>,
}

impl Process {
    fn calculate(&mut self, input: u64) -> f64 {
        if let Some(result) = self.cache.get(&input) {
            *result // Use cached value.
        } else {
            let result = /* ..do calculation on `input`.. */;
            self.cache.insert(input, result);
            result
        }
    }
}
```

## Performance notes

The use of a simple sequential data storage does have performance impact, especially for key lookup.
That's why this cache will only be beneficial performance-wise when used with a relatively small size, up to about 128 elements.

The performance is very much input data-dependent.
If the cache capacity is greater than or equal to the input data set size, performance is optimal (and `MemoCache` outperforms a `HashTable`).
However, if the input data set size is greater than the cache size, elements will be purged from the cache leading to cache misses and cache churn.
In this scenario, the fixed size of the cache, and/or the retention management aspect of `MemoCache` must weigh against the loss in performance over a `HashTable`.
Always analyze your input data and perform measurements to select the cache size / type you use.

The current implementation of the cache is focused on simplicity, making it outperform a `HashTable` under the right circumstances.

Run the included benchmarks using [criterion](https://crates.io/crates/criterion) by invoking: `cargo bench`

## Implementation details

This cache stores its key/value pairs in a fixed-size array.
A slot in this array represents a key/value and is either empty or occupied.
A cursor pointing to an array slot keeps track of the next slot to be overwritten.

Movement of the cursor is linear and incremental always pointing to the next empty slot, or the oldest slot.
When the cursor is at the end of the array it wraps around to the beginning, so any next insert will overwrite an already existing slot.

The implementation of the cache makes no assumptions whatsoever about the input data probability distribution, keeping the cache clean and simple.

## TODO

- Currently, the implementation focuses on simplicity and makes no assumptions about the data arrival probability distribution. However, this could potentially be very beneficial. Investigate cache performance improvements (e.g. start [here](https://en.wikipedia.org/wiki/Cache_replacement_policies)).
- Perhaps add cursor motion policies based on estimated input data probability distributions (e.g. in the current implementation an often-seen input value will still be overwritten by cursor movement).
- More detailed benchmarks w.r.t. insert / lookup performance.

## License

Licensed under either of [Apache License, Version 2.0](LICENSE-APACHE) or [MIT license](LICENSE-MIT) at your option.