Crates.io | fchashmap |
lib.rs | fchashmap |
version | 0.1.3 |
source | src |
created_at | 2021-05-16 16:36:19.23795 |
updated_at | 2021-05-16 16:48:13.123054 |
description | A fixed capacity no_std hashmap |
homepage | |
repository | https://github.com/Simsys/fchashmap |
max_upload_size | |
id | 398245 |
size | 48,240 |
A fixed capacity no_std hashmap.
A Hashmap is a data structure that implements an associative array, a structure that can map keys to values. Inserting, deleting and searching of entries is fast. This size limited hashmap is intended for small systems and does not require a dynamic heap allocator and can be used on the stack. The basis of this implementation is the so-called Robin Hood hashing, which was originally developed by Pedro Celis. In these two publications from Emmanuel Goossaert (1, 2) the functionality is explained very nicely.
The realization of the hashmap is based on the Robin Hood hashing algorithm. This method
is simple and robust with reasonable performance. However, the fixed capacity implementation
has some limitations:
use fchashmap::FcHashMap;
use hash32_derive::Hash32;
use hash32::Hash;
#[derive(Debug)]
struct Reading {
temperature: f32,
humidy: f32,
}
#[derive(Copy, Clone, Debug, PartialEq, Eq, Hash32)]
struct DeviceId([u8; 8]);
impl DeviceId {
fn new(input: &[u8; 8]) -> Self {
let mut id = [0_u8; 8];
id.copy_from_slice(input);
Self(id)
}
}
let mut fc_hash_map = FcHashMap::<DeviceId, Reading, 128>::new();
let dev1 = DeviceId::new(b"12345678");
let dev2 = DeviceId::new(b"12345679");
let dev3 = DeviceId::new(b"12345680");
fc_hash_map.insert(dev1, Reading { temperature: 23.1, humidy: 76.3 }).unwrap();
fc_hash_map.insert(dev2, Reading { temperature: 22.7, humidy: 55.5 }).unwrap();
let reading = fc_hash_map.get(&dev1).unwrap();
assert_eq!(reading.temperature, 23.1);
assert_eq!(reading.humidy, 76.3);
let reading = fc_hash_map.get(&dev2).unwrap();
assert_eq!(reading.temperature, 22.7);
assert_eq!(reading.humidy, 55.5);
assert!(fc_hash_map.get(&dev3).is_none());
The following diagram shows the timing behavior on a Cortex M4f system (STM32F3) at 72 MHz. It can be seen that the performance of the hashmap decreases significantly from a fill margin of about 80%.
In a project I use the crate ArrayVec because of missing functionality in Heapless::Vec. Since I needed additionally a HashMap I had to find out that there was no suitable stand alone HashMap, which gets along without memory allocation and is no_std compatible. Since I am learning Rust anyway, I decided to write my own hashmap.
For the realization of the hashmap I started from the above mentioned papers. For the implementation I got many ideas from the Japarics Heapless::FnvIndexMap. I found that this HashMap also uses Robin Hood hashing and I ended up with almost the same solution. Anyway, FcHashMap is unfortunately almost 200 bytes bigger but still about 10% faster than FnvIndexMap. Which realization is better to understand and maintain, please let everyone decide for himself. Many thanks to the authors of FnvHashMap for the many useful inspirations.
Licensed under either of Apache License, Version 2.0 or MIT license at your option.